diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..5e15c0ca --- /dev/null +++ b/.env.example @@ -0,0 +1,36 @@ +# Paper2Code .env file example +# Copy this file to .env and modify as needed + +# IMPORTANT: When using LiteLLM, ensure you have both litellm AND any provider-specific +# dependencies installed. For AWS Bedrock, this includes boto3. + +# Uncomment ONE of the following provider configurations: + +# 1. AWS Bedrock Configuration +AWS_REGION=us-west-2 +BEDROCK_MODEL=anthropic.claude-3-sonnet-20240229-v1:0 +DISABLE_PROMPT_CACHING=0 # Set to 1 to disable caching +AWS_SHARED_CREDENTIALS_FILE=~/.aws/credentials +AWS_CONFIG_FILE=~/.aws/config +# NOTE: For AWS Bedrock, you must: +# 1. Have boto3 installed (pip install boto3) +# 2. Have valid AWS credentials configured +# 3. Have appropriate permissions to use the specified model + +# 2. OpenAI +# OPENAI_API_KEY=your_api_key_here +# OPENAI_MODEL=o3-mini # Default if not specified + +# 3. Direct Anthropic API (not Bedrock) +# ANTHROPIC_API_KEY=your_api_key_here +# ANTHROPIC_MODEL=claude-3-sonnet-20240229 # Default if not specified + + +# Paper-specific settings (these should be set via command line args or script) +# PAPER_NAME=Transformer +# PDF_PATH=./examples/Transformer.pdf +# PDF_JSON_PATH=./examples/Transformer.json +# PDF_JSON_CLEANED_PATH=./examples/Transformer_cleaned.json +# PDF_LATEX_PATH=./examples/Transformer_cleaned.tex +# OUTPUT_DIR=./outputs/Transformer +# OUTPUT_REPO_DIR=./outputs/Transformer_repo \ No newline at end of file diff --git a/README.md b/README.md index 58ce892e..1256d5e4 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,55 @@ cd scripts bash run_llm.sh ``` +### Using Other LLM Providers with LiteLLM +- PaperCoder now supports any LLM provider available through [LiteLLM](https://github.com/BerriAI/litellm) +- Configure your model settings in a `.env` file in the project root directory (see `.env.example`) +- Supports standard LiteLLM provider syntax including: + - AWS Bedrock (`bedrock/model-name`) - requires boto3 + - OpenAI (`openai/model-name`) - uses o3-mini by default + - Anthropic (`anthropic/model-name`) - direct API access + +#### LiteLLM Provider Configurations +Choose ONE of the following provider configurations in your .env file: + +##### 1. AWS Bedrock +``` +AWS_REGION= +BEDROCK_MODEL= # e.g., anthropic.claude-3-sonnet-20240229-v1:0 +DISABLE_PROMPT_CACHING=0 +AWS_SHARED_CREDENTIALS_FILE=~/.aws/credentials +AWS_CONFIG_FILE=~/.aws/config +``` + +##### 2. OpenAI +``` +OPENAI_API_KEY= +OPENAI_MODEL=o3-mini # Default if not specified +``` + +##### 3. Anthropic Direct API +``` +ANTHROPIC_API_KEY= +ANTHROPIC_MODEL=claude-3-sonnet-20240229 # Default if not specified +``` + +```bash +# Install LiteLLM +pip install litellm + +# For provider-specific dependencies: +# - AWS Bedrock requires boto3 +pip install boto3 + +# Copy and modify the example .env file +cp .env.example .env +# Edit the .env file with your provider configuration + +# Run the scripts - they will use LiteLLM if configured or fall back to vLLM +cd scripts +bash run_llm.sh +``` + ### Output Folder Structure (Only Important Files) ```bash outputs @@ -65,11 +114,14 @@ outputs - For OpenAI API: `openai` - For open-source models: `vllm` - If you encounter any issues installing vLLM, please refer to the [official vLLM repository](https://github.com/vllm-project/vllm). + - For other LLM providers (like AWS Bedrock): `litellm` + - Check the [LiteLLM documentation](https://github.com/BerriAI/litellm) for supported models and configurations. ```bash pip install openai -pip install vllm +pip install vllm +pip install litellm ``` - Or, if you prefer, you can install all dependencies using `pip`: @@ -132,8 +184,9 @@ bash run_latex.sh ``` -#### Using Open Source Models with vLLM -- The default model is `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`. +#### Using Open Source Models with vLLM or LiteLLM +- The default model is `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct` (using vLLM) +- For LiteLLM integration, create a `.env` file in the project root with your provider configuration ```bash # Using the PDF-based JSON format of the paper @@ -147,6 +200,23 @@ cd scripts bash run_latex_llm.sh ``` +```bash +# Example .env configuration (AWS Bedrock with Claude) +AWS_REGION=eu-north-1 +BEDROCK_MODEL=anthropic.claude-3-sonnet-20240229-v1:0 +DISABLE_PROMPT_CACHING=0 +AWS_SHARED_CREDENTIALS_FILE=~/.aws/credentials +AWS_CONFIG_FILE=~/.aws/config + +# Or for OpenAI +# OPENAI_API_KEY=your-api-key +# OPENAI_MODEL=o3-mini + +# Or for Anthropic Direct API +# ANTHROPIC_API_KEY=your-api-key +# ANTHROPIC_MODEL=claude-3-sonnet-20240229 +``` + --- ## 📦 Paper2Code Benchmark Datasets diff --git a/codes/1_planning_llm.py b/codes/1_planning_llm.py index 1d2d3cc7..d1b44646 100644 --- a/codes/1_planning_llm.py +++ b/codes/1_planning_llm.py @@ -3,8 +3,11 @@ import os import sys from utils import print_response -from transformers import AutoTokenizer -from vllm import LLM, SamplingParams +from load_dotenv import load_env_config +from litellm_utils import get_llm_client, run_inference + +# Load environment variables +load_env_config() parser = argparse.ArgumentParser() @@ -221,37 +224,17 @@ }] -model_name = args.model_name -tokenizer = AutoTokenizer.from_pretrained(model_name) - - -if "Qwen" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True, - rope_scaling={"factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn"}) - sampling_params = SamplingParams(temperature=temperature, max_tokens=131072) - -elif "deepseek" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True) - sampling_params = SamplingParams(temperature=temperature, max_tokens=128000, stop_token_ids=[tokenizer.eos_token_id]) - +# Initialize LLM client (either LiteLLM from environment or vLLM from args) +client, tokenizer, is_litellm, sampling_params = get_llm_client( + model_name=model_name, + tp_size=tp_size, + max_model_len=max_model_len, + temperature=temperature +) def run_llm(msg): - # vllm - prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in [msg]] - - outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) - - completion = [output.outputs[0].text for output in outputs] - - return completion[0] + """Run inference using either LiteLLM or vLLM based on configuration""" + return run_inference(client, tokenizer, is_litellm, sampling_params, msg) responses = [] trajectories = [] diff --git a/codes/2_analyzing_llm.py b/codes/2_analyzing_llm.py index 31123730..82b6e1ca 100644 --- a/codes/2_analyzing_llm.py +++ b/codes/2_analyzing_llm.py @@ -4,11 +4,14 @@ from utils import extract_planning, content_to_json, print_response import copy import sys -from transformers import AutoTokenizer -from vllm import LLM, SamplingParams +from load_dotenv import load_env_config +from litellm_utils import get_llm_client, run_inference import argparse +# Load environment variables +load_env_config() + parser = argparse.ArgumentParser() parser.add_argument('--paper_name',type=str) @@ -146,36 +149,17 @@ def get_write_msg(todo_file_name, todo_file_desc): -model_name = args.model_name -tokenizer = AutoTokenizer.from_pretrained(model_name) - - -if "Qwen" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True, - rope_scaling={"factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn"}) - sampling_params = SamplingParams(temperature=temperature, max_tokens=131072) - -elif "deepseek" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True) - sampling_params = SamplingParams(temperature=temperature, max_tokens=128000, stop_token_ids=[tokenizer.eos_token_id]) +# Initialize LLM client (either LiteLLM from environment or vLLM from args) +client, tokenizer, is_litellm, sampling_params = get_llm_client( + model_name=model_name, + tp_size=tp_size, + max_model_len=max_model_len, + temperature=temperature +) def run_llm(msg): - # vllm - prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in [msg]] - - outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) - - completion = [output.outputs[0].text for output in outputs] - - return completion[0] + """Run inference using either LiteLLM or vLLM based on configuration""" + return run_inference(client, tokenizer, is_litellm, sampling_params, msg) artifact_output_dir=f'{output_dir}/analyzing_artifacts' os.makedirs(artifact_output_dir, exist_ok=True) diff --git a/codes/3_coding_llm.py b/codes/3_coding_llm.py index e523fd69..55875b39 100644 --- a/codes/3_coding_llm.py +++ b/codes/3_coding_llm.py @@ -4,11 +4,14 @@ import sys import copy from utils import extract_planning, content_to_json, extract_code_from_content,extract_code_from_content2, print_response, print_log_cost, load_accumulated_cost, save_accumulated_cost -from transformers import AutoTokenizer -from vllm import LLM, SamplingParams +from load_dotenv import load_env_config +from litellm_utils import get_llm_client, run_inference import argparse +# Load environment variables +load_env_config() + parser = argparse.ArgumentParser() parser.add_argument('--paper_name',type=str) @@ -154,37 +157,17 @@ def get_write_msg(todo_file_name, detailed_logic_analysis, done_file_lst): ## Code: {todo_file_name}"""}] return write_msg -model_name = args.model_name -tokenizer = AutoTokenizer.from_pretrained(model_name) - - -if "Qwen" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True, - rope_scaling={"factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn"}) - sampling_params = SamplingParams(temperature=temperature, max_tokens=131072) - -elif "deepseek" in model_name: - llm = LLM(model=model_name, - tensor_parallel_size=tp_size, - max_model_len=max_model_len, - gpu_memory_utilization=0.95, - trust_remote_code=True, enforce_eager=True) - sampling_params = SamplingParams(temperature=temperature, max_tokens=128000, stop_token_ids=[tokenizer.eos_token_id]) - +# Initialize LLM client (either LiteLLM from environment or vLLM from args) +client, tokenizer, is_litellm, sampling_params = get_llm_client( + model_name=model_name, + tp_size=tp_size, + max_model_len=max_model_len, + temperature=temperature +) def run_llm(msg): - # vllm - prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in [msg]] - - outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) - - completion = [output.outputs[0].text for output in outputs] - - return completion[0] + """Run inference using either LiteLLM or vLLM based on configuration""" + return run_inference(client, tokenizer, is_litellm, sampling_params, msg) # testing for checking diff --git a/codes/litellm_utils.py b/codes/litellm_utils.py new file mode 100644 index 00000000..758beb72 --- /dev/null +++ b/codes/litellm_utils.py @@ -0,0 +1,172 @@ +import os +import litellm +from typing import List, Dict, Any, Optional, Union +from transformers import AutoTokenizer +from load_dotenv import load_env_config + +def initialize_litellm(): + """ + Initialize LiteLLM with configuration from environment variables. + Must be called after load_env_config(). + """ + # Load environment variables if not already loaded + load_env_config() + + try: + # Check for different provider configurations in environment + if os.environ.get("AWS_REGION_NAME") or (os.environ.get("AWS_REGION") and os.environ.get("BEDROCK_MODEL")): + # AWS Bedrock configuration detected + try: + import boto3 + except ImportError: + print("WARNING: boto3 is required for AWS Bedrock but is not installed.") + print("Please install it with 'pip install boto3'") + return None + + # Set region from either standard variable or our custom one + if os.environ.get("AWS_REGION") and not os.environ.get("AWS_REGION_NAME"): + os.environ["AWS_REGION_NAME"] = os.environ.get("AWS_REGION") + + # Get model name + model_name = os.environ.get("BEDROCK_MODEL", "anthropic.claude-3-sonnet-20240229-v1:0") + bedrock_model = f"bedrock/{model_name}" + + # Set AWS credentials paths if provided + if os.environ.get("AWS_SHARED_CREDENTIALS_FILE"): + os.environ["AWS_SHARED_CREDENTIALS_FILE"] = os.path.expanduser(os.environ.get("AWS_SHARED_CREDENTIALS_FILE")) + + if os.environ.get("AWS_CONFIG_FILE"): + os.environ["AWS_CONFIG_FILE"] = os.path.expanduser(os.environ.get("AWS_CONFIG_FILE")) + + # Configure caching + if os.environ.get("DISABLE_PROMPT_CACHING") == "1": + litellm.cache = None + + print(f"LiteLLM configured to use AWS Bedrock with model: {bedrock_model}") + return bedrock_model + + elif os.environ.get("OPENAI_API_KEY"): + # OpenAI configuration detected + model_name = os.environ.get("OPENAI_MODEL", "o3-mini") + openai_model = f"openai/{model_name}" + print(f"LiteLLM configured to use OpenAI with model: {openai_model}") + return openai_model + + elif os.environ.get("ANTHROPIC_API_KEY"): + # Anthropic configuration detected + model_name = os.environ.get("ANTHROPIC_MODEL", "claude-3-sonnet-20240229") + anthropic_model = f"anthropic/{model_name}" + print(f"LiteLLM configured to use Anthropic with model: {anthropic_model}") + return anthropic_model + + else: + # No LiteLLM provider configuration detected + print("Using model specified via command line args (no LiteLLM configuration detected)") + return None + except Exception as e: + print(f"WARNING: Failed to initialize LiteLLM: {str(e)}") + print("Falling back to vLLM implementation") + return None + +def get_llm_client(model_name: str, tp_size: int = 2, max_model_len: int = 128000, temperature: float = 1.0): + """ + Get an LLM client based on the environment configuration. + + Args: + model_name: The model to use (may be overridden by env vars) + tp_size: tensor parallel size for vLLM + max_model_len: maximum model length for vLLM + temperature: sampling temperature + + Returns: + A tuple of (client, tokenizer, is_litellm, sampling_params) + """ + # Try to initialize LiteLLM first + litellm_model = initialize_litellm() + + # If LiteLLM is configured, use it + if litellm_model: + tokenizer = None # LiteLLM handles tokenization + return litellm_model, tokenizer, True, {"temperature": temperature} + + # Otherwise use vLLM implementation + try: + from vllm import LLM, SamplingParams + + tokenizer = AutoTokenizer.from_pretrained(model_name) + + if "Qwen" in model_name: + llm = LLM(model=model_name, + tensor_parallel_size=tp_size, + max_model_len=max_model_len, + gpu_memory_utilization=0.95, + trust_remote_code=True, enforce_eager=True, + rope_scaling={"factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn"}) + sampling_params = SamplingParams(temperature=temperature, max_tokens=131072) + + elif "deepseek" in model_name: + llm = LLM(model=model_name, + tensor_parallel_size=tp_size, + max_model_len=max_model_len, + gpu_memory_utilization=0.95, + trust_remote_code=True, enforce_eager=True) + sampling_params = SamplingParams(temperature=temperature, max_tokens=128000, stop_token_ids=[tokenizer.eos_token_id]) + + else: + # Generic configuration for other models + llm = LLM(model=model_name, + tensor_parallel_size=tp_size, + max_model_len=max_model_len, + gpu_memory_utilization=0.95, + trust_remote_code=True) + sampling_params = SamplingParams(temperature=temperature, max_tokens=128000) + + return llm, tokenizer, False, sampling_params + except ImportError: + raise ImportError("vLLM is not installed. Please install it with 'pip install vllm' to use this mode") + except Exception as e: + raise RuntimeError(f"Failed to initialize vLLM: {str(e)}") + +def run_inference(client, tokenizer, is_litellm, sampling_params, messages): + """ + Run inference using either LiteLLM or vLLM. + + Args: + client: The LLM client (either LiteLLM model name or vLLM instance) + tokenizer: Tokenizer for vLLM (None for LiteLLM) + is_litellm: Whether we're using LiteLLM + sampling_params: Parameters for sampling + messages: The messages to process + + Returns: + Generated completion text + """ + if is_litellm: + # Use LiteLLM for inference with exponential backoff retry (prevents stopping with service bottlenecks) + import time + max_retries = 10 + retry_delay = 1 # start with 1 second + + for attempt in range(max_retries): + try: + response = litellm.completion( + model=client, + messages=messages, + temperature=sampling_params.get("temperature", 1.0) + ) + return response.choices[0].message.content + except Exception as e: + print(f"Attempt {attempt+1}/{max_retries} failed: {str(e)}") + if attempt < max_retries - 1: + print(f"Retrying in {retry_delay} seconds...") + time.sleep(retry_delay) + retry_delay *= 2 # Double the wait time + else: + print(f"All {max_retries} attempts failed") + raise RuntimeError(f"LiteLLM failed after {max_retries} attempts: {str(e)}") + else: + # Use vLLM for inference + prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True)] + outputs = client.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) + completion = [output.outputs[0].text for output in outputs] + return completion[0] \ No newline at end of file diff --git a/codes/load_dotenv.py b/codes/load_dotenv.py new file mode 100644 index 00000000..4f344704 --- /dev/null +++ b/codes/load_dotenv.py @@ -0,0 +1,60 @@ +import os +from dotenv import load_dotenv +import sys + +def load_env_config(): + """ + Load environment variables from .env file. + This function should be imported and called at the beginning of each script + that needs access to environment variables. + + Returns: + bool: True if .env file was loaded successfully, False otherwise + """ + # Get the project root directory (assumes this file is in the codes/ directory) + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) + + # Path to the .env file + env_path = os.path.join(project_root, '.env') + + # Load environment variables from .env file + if os.path.exists(env_path): + load_dotenv(env_path) + print(f"Loaded environment variables from {env_path}") + return True + else: + print(f"Warning: .env file not found at {env_path}", file=sys.stderr) + return False + +if __name__ == "__main__": + # If run directly, just load the environment variables + load_env_config() + + # Print all environment variables (for debugging) + env_vars = [ + "OPENAI_API_KEY", + "GPT_VERSION", + "PAPER_NAME", + "PDF_PATH", + "PDF_JSON_PATH", + "PDF_JSON_CLEANED_PATH", + "PDF_LATEX_PATH", + "OUTPUT_DIR", + "OUTPUT_REPO_DIR" + ] + + # LiteLLM environment variables + litellm_vars = [ + "AWS_REGION", + "ANTHROPIC_MODEL", + "DISABLE_PROMPT_CACHING", + "AWS_SHARED_CREDENTIALS_FILE", + "AWS_CONFIG_FILE" + ] + + for var in env_vars + litellm_vars: + # Mask API keys for security + if var in ["OPENAI_API_KEY"] and os.environ.get(var): + print(f"{var}=***********{os.environ.get(var)[-4:]}") + else: + print(f"{var}={os.environ.get(var, 'Not set')}") \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/config.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/config.py_simple_analysis.txt new file mode 100644 index 00000000..b1ec74d2 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/config.py_simple_analysis.txt @@ -0,0 +1,107 @@ +# Logic Analysis: config.py + +## Overview +The `config.py` module will define a `Config` class responsible for managing all hyperparameters and configuration settings for the Transformer model implementation. This class needs to be designed to load and provide access to the configuration parameters specified in the `config.yaml` file, supporting both 'base' and 'big' model variants as described in the paper. + +## Requirements Analysis + +The `Config` class must: +1. Load configuration from the YAML file +2. Support different model sizes ('base' and 'big') +3. Provide organized access to different parameter groups (model architecture, training, inference, data) +4. Include getter methods for common parameter groups +5. Handle device selection (CPU/GPU) + +## Key Parameters from the Paper + +### Model Architecture Parameters +- **Base Model**: + - d_model = 512 (embedding dimension) + - n_heads = 8 (attention heads) + - num_encoder_layers = num_decoder_layers = 6 + - dim_feedforward = 2048 + - dropout = 0.1 + - Weight sharing between embedding and output projection + +- **Big Model**: + - d_model = 1024 + - n_heads = 16 + - num_encoder_layers = num_decoder_layers = 6 + - dim_feedforward = 4096 + - dropout = 0.3 (for EN-DE) or 0.1 (for EN-FR) + - Weight sharing between embedding and output projection + +### Training Parameters +- Adam optimizer with β1 = 0.9, β2 = 0.98, ε = 10^-9 +- Learning rate schedule with warmup_steps = 4000 +- Label smoothing ϵ = 0.1 +- Batch size ~25000 source and target tokens +- Training steps: 100,000 for base model, 300,000 for big model +- Checkpoint averaging: 5 for base, 20 for big (saved at 10-min intervals) + +### Inference Parameters +- Beam size = 4 +- Length penalty α = 0.6 +- Max output length = input length + 50 + +### Data Parameters +- EN-DE: BPE vocabulary of ~37,000 tokens +- EN-FR: Word-piece vocabulary of 32,000 tokens + +## Class Design + +The `Config` class should: +1. Initialize with model size ('base' or 'big') +2. Load the YAML configuration file +3. Set attributes based on the configuration and model size +4. Provide methods for accessing grouped parameters + +## Implementation Logic + +1. **Constructor**: + - Accept model_size parameter ('base' or 'big') + - Accept optional config_path parameter (default to a standard location) + - Load the YAML file + - Set attributes based on model size + - Initialize device (CPU/GPU) + +2. **YAML Loading**: + - Use PyYAML to load the configuration + - Handle file not found errors gracefully + - Validate that the required sections exist in the config + +3. **Parameter Grouping Methods**: + - `get_model_params()`: Return architecture parameters (d_model, n_heads, etc.) + - `get_training_params()`: Return training parameters (optimizer settings, learning rate, etc.) + - `get_inference_params()`: Return inference parameters (beam size, length penalty, etc.) + - `get_data_params()`: Return data processing parameters (dataset paths, vocabulary sizes, etc.) + +4. **Special Handling**: + - Language pair selection (EN-DE vs EN-FR) might affect some parameters + - Device selection based on CUDA availability + - Provide default values for any missing parameters in the config file + +## Error Handling and Validation + +The class should: +1. Validate model_size is either 'base' or 'big' +2. Validate the existence of the config file +3. Handle missing parameters with sensible defaults where appropriate +4. Provide clear error messages for required parameters that are missing + +## Interface with Other Modules + +The `Config` class will be imported by: +1. `model.py` - To access model architecture parameters +2. `train.py` - To access training parameters +3. `evaluate.py` - To access inference parameters +4. `data_processing.py` - To access data parameters +5. `main.py` - To initialize the configuration + +## Data Flow + +1. `main.py` creates a Config instance +2. The Config instance is passed to DataProcessor, TransformerModel, Trainer, and Evaluator +3. Each module accesses the relevant parameters via the getter methods + +This design ensures a centralized configuration management system that supports the reproduction of both the base and big Transformer models as described in the paper while providing flexibility for experimentation and adaptation to available resources. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/data_processing.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/data_processing.py_simple_analysis.txt new file mode 100644 index 00000000..cee1a829 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/data_processing.py_simple_analysis.txt @@ -0,0 +1,126 @@ +# Logic Analysis for data_processing.py + +## Overview + +The `data_processing.py` file is responsible for handling all data-related operations for the Transformer model implementation. This includes downloading datasets, tokenization, vocabulary building, batch creation, and mask generation. The implementation must align with the paper "Attention Is All You Need" which specifies using byte-pair encoding (BPE) for English-German translation and word-piece encoding for English-French translation. + +## Key Requirements + +1. Process the WMT14 dataset for English-German (~4.5M sentence pairs) and English-French (~36M sentence pairs) +2. Implement tokenization with BPE (37K vocab) for EN-DE and word-piece (32K vocab) for EN-FR +3. Create batches containing approximately 25,000 tokens (target batch size) +4. Handle masking for padding and autoregressive behavior +5. Provide an interface compatible with PyTorch's DataLoader + +## Data Processor Class Design + +### `DataProcessor` Class + +#### `__init__(config: Config)` +- Initialize using configuration from `config.yaml` via the `Config` class +- Store parameters like vocabulary sizes, maximum sequence length, tokenization method (BPE vs word-piece) +- Initialize tokenizers (sentencepiece for BPE/word-piece) +- Set up device for tensor operations +- Store special token indices (PAD, BOS, EOS, UNK) + +#### `load_data(dataset_path: str) -> Tuple[DataLoader]` +- Check if dataset exists locally, if not, download and extract +- Handle paths for WMT14 EN-DE or EN-FR datasets based on config +- Load train/val/test splits according to paper (newstest2013 for validation, newstest2014 for testing) +- Process raw text files into source-target sentence pairs +- Apply tokenization to all datasets +- Build vocabularies from training data if not already created +- Create data loaders with batching +- Return tuple of (train_loader, val_loader, test_loader) + +#### `build_vocab(train_data: List) -> Tuple[Vocab]` +- Process training data to build vocabularies for source and target languages +- Use sentencepiece to train BPE model for EN-DE (37K tokens) or word-piece model for EN-FR (32K tokens) +- Include special tokens: PAD (padding), BOS (beginning of sequence), EOS (end of sequence), UNK (unknown) +- Save vocabulary to disk for reuse +- Return tuple of (source_vocab, target_vocab) + +#### `tokenize(text: str) -> List[str]` +- Basic tokenization of text (e.g., splitting by whitespace, handling punctuation) +- This is a pre-processing step before applying BPE/word-piece +- Return list of tokens + +#### `apply_bpe(tokens: List[str]) -> List[str]` / `apply_wordpiece(tokens: List[str]) -> List[str]` +- Apply byte-pair encoding or word-piece encoding to tokenized text +- Use sentencepiece library for consistent implementation +- Convert tokens to subword units according to trained BPE/word-piece model +- Return list of subword tokens + +#### `batch_data(data: List) -> Iterator` +- Group sentences of similar lengths to minimize padding waste +- Create batches with target size of ~25,000 tokens as mentioned in the paper +- Convert tokenized text to tensor indices using vocabulary +- Add BOS/EOS tokens appropriately +- Pad sequences to equal length within each batch +- Return iterator over batched data + +#### `create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]` +- Create padding mask for source sequence (to mask padding tokens in encoder self-attention) +- Create combined padding + subsequent mask for target sequence: + - Padding mask: to mask padding tokens in decoder self-attention + - Subsequent mask: to prevent attending to future positions in decoder self-attention +- These masks are essential for the Transformer's attention mechanisms +- Return tuple of (src_mask, tgt_mask) + +## Technical Considerations + +1. **Data Loading Efficiency**: + - Processing WMT14 datasets (especially EN-FR with 36M pairs) requires efficient streaming + - Need to handle large files without loading entire dataset into memory + +2. **Tokenization Pipeline**: + - The paper uses BPE for EN-DE and word-piece for EN-FR + - Both can be implemented using sentencepiece library + - Need to save/load tokenizer models for consistent application across train/val/test sets + +3. **Batch Creation Logic**: + - The paper mentions batches containing ~25,000 source and ~25,000 target tokens + - This requires dynamic batch sizing based on sequence lengths + - Sentences should be grouped by approximate length to minimize padding + +4. **Mask Creation**: + - Source mask: binary mask where 1s indicate non-padding positions + - Target mask: combination of padding mask and subsequent (causal/triangular) mask + - These masks should be properly shaped for the attention mechanism + +5. **Vocabulary Management**: + - Shared source-target vocabulary for both language pairs (37K for EN-DE, 32K for EN-FR) + - Need to handle special tokens (PAD, BOS, EOS, UNK) consistently + +6. **Data Caching**: + - For efficiency, preprocessed datasets should be cached to disk + - Vocabulary and tokenizer models should be saved for reuse + +7. **Integration with Training Loop**: + - Ensure data processing output is compatible with model input requirements + - Provide proper formatting for source/target sequences and attention masks + +## Integration with Other Modules + +- The `Config` class will provide configuration parameters like vocabulary sizes, maximum sequence length, etc. +- The `utils.py` module will provide masking functions that are used in `create_masks()` +- The produced data loaders will be consumed by the `Trainer` and `Evaluator` classes +- The vocabulary objects will be used by the `TransformerModel` class for embedding layers + +## Dependencies + +- `torch`: For tensor operations and DataLoader creation +- `torchtext`: For vocabulary and dataset utilities +- `sentencepiece`: For BPE and word-piece tokenization +- Custom `utils.py`: For mask creation functions +- Custom `config.py`: For configuration parameters + +## Alignment with Paper + +The implementation must adhere to the following specifications from the paper: +- Use of BPE with 37K shared vocabulary for EN-DE translation +- Use of word-piece with 32K vocabulary for EN-FR translation +- Batch size of ~25,000 source and ~25,000 target tokens +- Proper mask creation for the Transformer's attention mechanism + +This data processing module is critical for accurate reproduction as improper data preprocessing can significantly impact the model's performance and ability to reach the BLEU scores reported in the paper. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/evaluate.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/evaluate.py_simple_analysis.txt new file mode 100644 index 00000000..3b5f1b3d --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/evaluate.py_simple_analysis.txt @@ -0,0 +1,151 @@ +# Logic Analysis for evaluate.py + +## Purpose and Scope +This module is responsible for evaluating a trained Transformer model, computing performance metrics, and handling inference tasks like translation. The primary functionality revolves around: + +1. Model inference using beam search for translation +2. Computing BLEU scores to evaluate translation quality +3. Averaging model checkpoints for improved performance +4. Providing utilities for single sentence translation + +## Class Structure: Evaluator + +### Class Initialization +```python +def __init__(config: Config, model: TransformerModel, data_processor: DataProcessor) +``` + +The initialization should: +1. Store the transformer model instance +2. Store the data processor for tokenization and vocabulary access +3. Configure beam search parameters from config: + - `beam_size` (default: 4 from config.yaml) + - `length_penalty` (default: 0.6 from config.yaml) + - `max_length_factor` (default: 50 from config.yaml) +4. Set the device (CPU or GPU) to match the model +5. Save special token indices (start_token, end_token, pad_token) from the data processor vocabularies + +### Method: evaluate(test_data: DataLoader) -> dict + +This method evaluates the model on a test dataset and computes metrics: + +Logic flow: +1. Set the model to evaluation mode +2. Initialize lists to store references and hypotheses +3. Iterate through test_data batches: + - For each batch, get source and target tensors + - Create necessary masks for attention + - Generate translations using beam search + - Convert token IDs to text for both references and hypotheses + - Store the results +4. Calculate BLEU score using the stored references and hypotheses +5. Return a dictionary containing metrics: + - BLEU score + - Number of examples evaluated + - Additional metrics if needed (e.g., average translation length) + +The implementation needs to handle: +- Batch processing of data with attention masks +- Converting tensor outputs from beam search to readable text +- Maintaining proper references and hypotheses lists for BLEU calculation + +### Method: translate_sentence(sentence: str) -> str + +This method translates a single sentence from source language to target language: + +Logic flow: +1. Tokenize and preprocess the input sentence using the data processor +2. Convert tokens to tensor and add batch dimension +3. Create source mask for attention +4. Use beam search to generate translation +5. Convert output token IDs back to text +6. Return the translated sentence as a string + +This method should: +- Handle unknown tokens in the input +- Apply proper preprocessing (BPE or wordpiece) through the data processor +- Remove any special tokens from the output (like EOS tokens) +- Return a clean, readable translation + +### Method: compute_bleu(references: List[str], hypotheses: List[str]) -> float + +Logic flow: +1. Format references and hypotheses for sacrebleu: + - References should be a list of lists (multiple references per translation) + - Hypotheses should be a flat list +2. Use sacrebleu to compute the BLEU score +3. Return the corpus BLEU score as a float + +Implementation details: +- Use sacrebleu's corpus_bleu function for BLEU calculation +- Format references correctly even if there's only one reference per translation +- Return the score value (not the object) for easy reporting + +### Method: average_checkpoints(paths: List[str]) -> None + +This method implements checkpoint averaging for improved inference, which was mentioned in the paper: + +Logic flow: +1. Load the first checkpoint to get the model structure +2. Initialize a dictionary to store the sum of all parameter tensors +3. For each checkpoint path: + - Load the checkpoint state dictionary + - Add each parameter tensor to the running sum +4. Divide all summed parameters by the number of checkpoints +5. Load the averaged parameters into the model + +Implementation details: +- Handle different model structures if necessary +- Maintain proper device placement for tensors +- This method should modify the model in-place + +## Beam Search Implementation + +While the main beam search is implemented in the TransformerModel class (as specified in the design), the evaluator will need to properly use it: + +1. Determine max_len based on input length + max_length_factor (50 by default) +2. Call model.beam_search with proper parameters: + - Source tensor + - Max length + - Start token index + - Beam size + - Length penalty alpha +3. Process the beam search output to get the best translation + +## Constraints and Dependencies + +1. **Model State**: The model must be in evaluation mode during inference (no dropout) +2. **Data Processing**: Must use the same data processor used during training for consistent tokenization +3. **Config Parameters**: Must respect configuration settings for beam size and length penalty +4. **BLEU Calculation**: Must use sacrebleu for consistent BLEU calculation as in literature +5. **Checkpoint Format**: Needs to handle PyTorch state dictionaries properly + +## Integration with Other Components + +1. **Model Integration**: The evaluator needs to interface with the TransformerModel's beam_search method +2. **Data Processor Integration**: Must use the data processor for consistent tokenization and vocabulary handling +3. **Utils Integration**: Uses the average_checkpoints utility for model averaging + +## Error Handling Considerations + +1. Handle cases where model produces no valid translation (e.g., all outputs are invalid or shorter than minimum length) +2. Handle tensor device mismatches gracefully +3. Provide informative error messages for missing checkpoints or invalid paths +4. Handle cases where input sentences might be too long for the model's position encoding limit + +## Performance Considerations + +1. Batch processing for faster evaluation on test sets +2. Proper device management (GPU/CPU) for tensor operations +3. Progress tracking with tqdm for long evaluations + +## Specific Requirements from Paper + +1. Use beam search with beam size = 4 +2. Apply length penalty with alpha = 0.6 +3. Set maximum output length to input_length + 50 +4. For base models, average last 5 checkpoints +5. For big models, average last 20 checkpoints +6. Terminate beam search early when possible (when EOS token is generated) + +This implementation aligns with the approach described in the Transformer paper, particularly the evaluation methods mentioned in Section 6.1, paragraph 3. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/main.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/main.py_simple_analysis.txt new file mode 100644 index 00000000..3657cc16 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/main.py_simple_analysis.txt @@ -0,0 +1,124 @@ +# Logic Analysis for main.py + +## Overview +This module serves as the entry point for the Transformer model implementation based on the "Attention Is All You Need" paper. It will provide functions to train the model, evaluate its performance, and translate individual sentences. The script will use command-line arguments to determine which operation to perform and with what configuration. + +## Key Components and Flow + +### 1. Command-Line Argument Parsing +- Will use `argparse` to define and parse command-line arguments +- Need to support different modes of operation: + - `train`: Train a new model + - `evaluate`: Evaluate a trained model on test data + - `translate`: Translate a single sentence using a trained model +- Arguments will include: + - `mode`: Operation mode (train/evaluate/translate) + - `--config_path`: Path to the YAML configuration file + - `--model_path`: Path to saved model checkpoint (for evaluate/translate) + - `--model_size`: "base" or "big" model variant + - `--language_pair`: "en-de" or "en-fr" + - `--sentence`: Input sentence for translation (for translate mode) + - `--output_dir`: Directory to save model checkpoints and logs + +### 2. Configuration Loading +- Will load configuration from the specified YAML file (or use default path) +- Need to initialize the `Config` class with the appropriate model size +- Configuration will dictate model architecture, training parameters, and inference settings +- Must handle the case where config file is not found + +### 3. Device Selection +- Need to detect CUDA availability and set device accordingly +- Should print a message indicating whether using CPU or GPU +- Will need to move model and data to the selected device + +### 4. `train_model` Function +- Takes an optional config path as parameter +- Workflow: + 1. Load configuration + 2. Initialize data processor and load training/validation data + 3. Build vocabulary from training data + 4. Create Transformer model with appropriate parameters + 5. Initialize Trainer with model and data processor + 6. Execute training loop for specified number of steps/epochs + 7. Save final model and evaluate on validation set + 8. Return trained model + +### 5. `evaluate_model` Function +- Takes model path and optional config path as parameters +- Workflow: + 1. Load configuration + 2. Initialize data processor and load test data + 3. Load model from checkpoint + 4. Initialize Evaluator with model and data processor + 5. Perform model weights averaging if specified in config + 6. Run evaluation on test set + 7. Print results (BLEU score) + +### 6. `translate` Function +- Takes model path, input sentence, and optional config path +- Workflow: + 1. Load configuration + 2. Initialize data processor + 3. Load model from checkpoint + 4. Initialize Evaluator + 5. Preprocess input sentence (tokenize, apply BPE/wordpiece) + 6. Use beam search to generate translation + 7. Post-process and return translated sentence + +### 7. `main` Function +- Parse command-line arguments +- Call appropriate function based on the selected mode +- Handle exceptions and provide useful error messages +- Set random seeds for reproducibility + +## Integration with Other Modules + +### Data Processing Integration +- Will use `DataProcessor` from `data_processing.py` to: + - Load and preprocess datasets + - Build vocabularies + - Create data loaders with appropriate batching + +### Model Integration +- Will use `TransformerModel` from `model.py` to: + - Create the model architecture according to configuration + - Load pretrained model weights for evaluation/translation + +### Training Integration +- Will use `Trainer` from `train.py` to: + - Execute training loop + - Implement learning rate scheduling + - Apply regularization techniques (dropout, label smoothing) + - Save checkpoints + +### Evaluation Integration +- Will use `Evaluator` from `evaluate.py` to: + - Perform beam search decoding + - Calculate BLEU scores + - Average model weights from checkpoints + +## Error Handling and Validation +- Need to validate command-line arguments +- Check existence of model checkpoint for evaluate/translate modes +- Validate config file format +- Handle cases where CUDA is requested but not available +- Set appropriate fallbacks for missing parameters + +## Logging and Progress Reporting +- Will use appropriate logging mechanism +- Report on training progress (loss, learning rate, examples per second) +- Display evaluation metrics +- Show beam search progress/results during translation + +## Performance Considerations +- Must handle large datasets efficiently +- Should detect available hardware and adapt batch sizes accordingly +- Needs to handle memory constraints, especially for the "big" model +- Should provide estimates of training time based on hardware + +## Reproducibility +- Will set random seeds for PyTorch, NumPy, and Python's random module +- Should store configuration used for training alongside model checkpoints +- Will track BLEU scores using standardized evaluation metrics + +This logic analysis provides a comprehensive overview of how `main.py` will be structured and how it will interact with other modules in the system to implement the Transformer model as described in the "Attention Is All You Need" paper. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/model.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/model.py_simple_analysis.txt new file mode 100644 index 00000000..8be1d261 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/model.py_simple_analysis.txt @@ -0,0 +1,185 @@ +# Logic Analysis for model.py + +## Overview +The `model.py` file will implement the core Transformer architecture as described in the paper "Attention Is All You Need". This module is responsible for defining the complete model structure including positional encoding, multi-head attention, feed-forward networks, encoder and decoder layers, and the full Transformer model. + +## Positional Encoding + +### Class: `PositionalEncoding` +This class implements the positional encoding mechanism described in Section 3.5 of the paper. + +#### Logic Flow: +1. Initialize with model dimension, dropout rate, and maximum sequence length +2. Create a position encoding tensor of shape (max_len, d_model) using sine and cosine functions +3. For even indices (2i), use sine function: `PE(pos, 2i) = sin(pos / 10000^(2i/d_model))` +4. For odd indices (2i+1), use cosine function: `PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))` +5. Store this tensor as a buffer (not a parameter) in the module +6. In the forward pass: + - Take input embeddings x of shape (batch_size, seq_len, d_model) + - Add positional encodings (sliced to match sequence length) + - Apply dropout + - Return the result + +## Multi-Head Attention + +### Class: `MultiHeadAttention` +This class implements the multi-head attention mechanism described in Section 3.2.2 of the paper. + +#### Logic Flow: +1. Initialize with model dimension, number of heads, and dropout rate +2. Calculate dimensions for each head: d_k = d_v = d_model / n_heads +3. Create linear projections for queries, keys, values, and output: + - W_q: d_model → d_model (will be reshaped per head) + - W_k: d_model → d_model (will be reshaped per head) + - W_v: d_model → d_model (will be reshaped per head) + - W_o: d_model → d_model +4. Implement `attention` method (scaled dot-product attention): + - Compute dot products of queries and keys: QK^T + - Scale by 1/√d_k + - Apply mask to set masked positions to -∞ (or a very large negative value) + - Apply softmax to get attention weights + - Compute weighted sum of values + - Return attention output and attention weights +5. In the forward pass: + - Project queries, keys, values using respective projections + - Reshape to separate batch_size, seq_len, n_heads, d_k dimensions + - Transpose to get shape (batch_size, n_heads, seq_len, d_k) + - Apply scaled dot-product attention to get attention outputs + - Transpose and reshape back to original dimensions + - Apply final output projection W_o + - Apply dropout + - Return attention output + +## Position-wise Feed-Forward Networks + +### Class: `PositionwiseFeedforward` +Implements the position-wise feed-forward network described in Section 3.3 of the paper. + +#### Logic Flow: +1. Initialize with model dimension, feed-forward dimension, and dropout rate +2. Create two linear transformations: + - linear1: d_model → d_ff + - linear2: d_ff → d_model +3. In the forward pass: + - Apply first linear transformation to input + - Apply ReLU activation + - Apply dropout + - Apply second linear transformation + - Apply dropout again + - Return the result + +## Encoder Layer + +### Class: `EncoderLayer` +Implements a single encoder layer as described in Section 3.1 of the paper. + +#### Logic Flow: +1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate +2. Create components: + - self_attn: MultiHeadAttention for self-attention + - feed_forward: PositionwiseFeedforward + - norm1, norm2: Layer normalization modules +3. In the forward pass: + - Apply self-attention with residual connection and layer normalization: + - attn_output = self_attn(x, x, x, mask) + - x = norm1(x + dropout(attn_output)) + - Apply feed-forward network with residual connection and layer normalization: + - ff_output = feed_forward(x) + - x = norm2(x + dropout(ff_output)) + - Return the result + +## Decoder Layer + +### Class: `DecoderLayer` +Implements a single decoder layer as described in Section 3.1 of the paper. + +#### Logic Flow: +1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate +2. Create components: + - self_attn: MultiHeadAttention for masked self-attention + - cross_attn: MultiHeadAttention for encoder-decoder attention + - feed_forward: PositionwiseFeedforward + - norm1, norm2, norm3: Layer normalization modules +3. In the forward pass: + - Apply masked self-attention with residual connection and layer normalization: + - attn_output = self_attn(x, x, x, tgt_mask) + - x = norm1(x + dropout(attn_output)) + - Apply encoder-decoder attention with residual connection and layer normalization: + - attn_output = cross_attn(x, memory, memory, src_mask) + - x = norm2(x + dropout(attn_output)) + - Apply feed-forward network with residual connection and layer normalization: + - ff_output = feed_forward(x) + - x = norm3(x + dropout(ff_output)) + - Return the result + +## Transformer Model + +### Class: `TransformerModel` +Implements the complete Transformer model as described in Section 3 of the paper. + +#### Logic Flow: +1. Initialize with configuration, source vocabulary size, and target vocabulary size +2. Create components: + - src_embed: Source embedding + positional encoding + - tgt_embed: Target embedding + positional encoding + - encoder: Stack of N encoder layers + - decoder: Stack of N decoder layers + - generator: Linear + softmax to convert decoder output to probabilities + - Apply weight sharing between embedding layers and output projection if specified +3. Implement `encode` method: + - Apply source embedding to inputs + - Pass through encoder layers with source mask + - Return encoder output (memory) +4. Implement `decode` method: + - Apply target embedding to inputs + - Pass through decoder layers with memory, source mask, and target mask + - Return decoder output +5. Implement `forward` method: + - Call `encode` to get memory + - Call `decode` with memory, source mask, target input, and target mask + - Return decoder output +6. Implement `beam_search` method: + - Encode source sequence + - Initialize beams with start symbol + - For each decoding step: + - Expand all current beams + - Calculate scores for all expanded beams + - Select top-k beams based on scores + - Check for completed sequences (reached EOS or max length) + - Apply length penalty to final scores + - Return best translation + +## Key Implementation Considerations + +1. **Masking**: + - In encoder self-attention: mask for padding tokens + - In decoder self-attention: mask for padding tokens and future positions + - In encoder-decoder attention: mask for padding in source sequence + +2. **Weight Sharing**: + - Share weights between embedding layers and output projection as mentioned in Section 3.4 of the paper + - Multiply embedding weights by √d_model + +3. **Scaled Dot-Product Attention**: + - Scale the dot products by 1/√d_k to prevent small gradients in softmax + +4. **Multi-Head Attention Implementation**: + - Efficient reshaping and transposition operations to handle multiple heads in parallel + - Proper reshaping back to original dimensions after attention + +5. **Beam Search**: + - Efficient implementation to handle expanding and pruning beams + - Length normalization as described in the paper (`(5+len)^a / (5+1)^a`) + +6. **Parameter Initialization**: + - Initialize parameters to ensure proper gradient flow through the network + +## Dependencies +- `torch`: For tensor operations and neural network modules +- `math`: For mathematical functions (sqrt, sin, cos) +- `copy`: For deep copying objects during beam search +- `numpy`: For numerical operations +- `config`: For accessing model configuration parameters +- `utils`: For mask creation functions + +This logic analysis provides a comprehensive breakdown of how each component of the Transformer model should be implemented according to the paper. The design follows the paper's specifications closely, ensuring accurate reproduction of the architecture. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/train.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/train.py_simple_analysis.txt new file mode 100644 index 00000000..1e624db4 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/train.py_simple_analysis.txt @@ -0,0 +1,235 @@ +# Logic Analysis: train.py + +## Overview +The `train.py` file will handle the training and validation of the Transformer model according to the specifications in the paper "Attention Is All You Need". This module will implement the Trainer class that encapsulates all functionalities needed for model training, including learning rate scheduling, optimization, evaluation, and checkpoint management. + +## Core Functionality Requirements + +### 1. Initialization and Setup +The `Trainer` class needs to: +- Initialize with a model, config object, and data processor +- Set up the Adam optimizer with parameters specified in the config (β₁ = 0.9, β₂ = 0.98, ε = 10⁻⁹) +- Configure learning rate scheduling according to the formula in Section 5.3 of the paper +- Set up loss function with label smoothing (ε_ls = 0.1) +- Initialize TensorBoard for logging training metrics + +### 2. Learning Rate Schedule +The paper describes a specific learning rate schedule in Section 5.3: +``` +lrate = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5)) +``` +Where: +- `d_model` is the model dimension (512 for base, 1024 for big model) +- `step_num` is the current training step +- `warmup_steps` is 4000 as specified in the paper and config + +This schedule increases the learning rate linearly for the first `warmup_steps` training steps, and decreases it thereafter proportionally to the inverse square root of the step number. + +### 3. Training Loop +The main training loop should: +- Iterate through epochs +- For each epoch, iterate through batches of the training data +- Forward pass: Run model on input data with appropriate masks +- Apply label smoothing to loss calculation +- Backward pass: Compute gradients +- Apply gradient clipping if necessary (not explicitly mentioned in paper but common practice) +- Update model parameters with optimizer +- Adjust learning rate according to schedule +- Periodically validate on validation set +- Save checkpoints at regular intervals (every 10 minutes as per config) +- Log metrics to TensorBoard + +### 4. Validation +The validation loop should: +- Set model to evaluation mode +- Iterate through validation data without computing gradients +- Calculate validation loss and metrics +- Return validation metrics for monitoring + +### 5. Checkpointing +Checkpoint management should: +- Save model state, optimizer state, current epoch, and other training metadata +- Load checkpoints to resume training +- Support checkpoint averaging for evaluation (used in the paper) + +## Implementation Details + +### Class: Trainer + +#### Method: __init__ +```python +def __init__(self, config, model, data_processor): + """ + Initialize the trainer with model, config, and data processor. + + Args: + config: Configuration object with model and training parameters + model: The Transformer model instance + data_processor: Data processor for creating masks and handling data + """ +``` +- Store config, model, and data processor +- Set device (GPU/CPU) +- Initialize Adam optimizer with parameters from config +- Set up learning rate scheduler (from utils) +- Initialize criterion with label smoothing support +- Set up TensorBoard writer +- Initialize training step counter and best validation metrics + +#### Method: train +```python +def train(self, train_data, val_data, epochs): + """ + Main training loop + + Args: + train_data: Training data loader + val_data: Validation data loader + epochs: Number of training epochs + """ +``` +- Loop through specified number of epochs +- For each epoch, call train_epoch +- After each epoch, validate on validation data +- Track best validation performance +- Save checkpoints +- Log metrics to TensorBoard + +#### Method: train_epoch +```python +def train_epoch(self, train_data): + """ + Train for one epoch + + Args: + train_data: Training data loader + + Returns: + float: Average training loss for the epoch + """ +``` +- Set model to training mode +- Initialize metrics tracking +- Loop through batches with a progress bar +- For each batch: + - Move data to device + - Create source and target masks + - Forward pass through model + - Calculate loss with label smoothing + - Backward pass + - Apply gradient clipping + - Step optimizer + - Adjust learning rate + - Update metrics +- Return average metrics for the epoch + +#### Method: validate +```python +def validate(self, val_data): + """ + Validate the model + + Args: + val_data: Validation data loader + + Returns: + float: Validation loss + """ +``` +- Set model to evaluation mode +- Initialize metrics tracking +- Loop through validation data with progress bar +- No gradient calculation +- Forward pass and loss calculation +- Return validation metrics + +#### Method: save_checkpoint +```python +def save_checkpoint(self, path): + """ + Save model checkpoint + + Args: + path: Path to save the checkpoint + """ +``` +- Save model state dict +- Save optimizer state dict +- Save current epoch and step +- Save best validation metrics +- Use utility function from utils.py + +#### Method: load_checkpoint +```python +def load_checkpoint(self, path): + """ + Load model checkpoint + + Args: + path: Path to the checkpoint + """ +``` +- Load checkpoint using utility function +- Set model state dict +- Set optimizer state dict +- Set current epoch and step +- Set best validation metrics + +#### Method: adjust_learning_rate +```python +def adjust_learning_rate(self, step): + """ + Adjust learning rate according to the schedule in the paper + + Args: + step: Current training step + """ +``` +- Calculate new learning rate using the formula from the paper +- Set learning rate in optimizer + +## Integration Points + +1. **With model.py**: + - Uses the TransformerModel for forward passes + - Creates masks for the model using data_processor + +2. **With config.py**: + - Uses training parameters (learning rate, warmup steps, etc.) + - Uses model parameters (d_model for learning rate scaling) + +3. **With utils.py**: + - Uses label_smoothed_nll_loss for loss calculation + - Uses get_lr_scheduler for learning rate scheduling + - Uses save_checkpoint and load_checkpoint for checkpoint management + +4. **With data_processing.py**: + - Uses data_processor to create masks for the model + - Processes batched data from dataloaders + +## Critical Considerations + +1. **Batch Processing**: The paper mentions batching by approximate sequence length with ~25000 tokens per batch. The training loop needs to handle variable batch sizes. + +2. **Learning Rate Schedule**: Correctly implementing the learning rate schedule is critical for training stability and convergence. + +3. **Regularization**: + - Apply dropout as specified in the model + - Use label smoothing with ε_ls = 0.1 + +4. **Training Time**: + - Base model: ~12 hours on 8 P100 GPUs (100,000 steps) + - Big model: ~3.5 days on 8 P100 GPUs (300,000 steps) + - Implementation needs to adapt to available resources + +5. **Checkpointing**: + - Save checkpoints every 10 minutes as per config + - For evaluation, base models use the average of the last 5 checkpoints + - For big models, use the average of the last 20 checkpoints + +6. **Performance Monitoring**: + - Track training and validation loss + - Monitor learning rate changes + - Log to TensorBoard for visualization + +By implementing these components according to the paper specifications and configuration settings, the `train.py` module will provide a complete training pipeline for the Transformer model. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/utils.py_simple_analysis.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/utils.py_simple_analysis.txt new file mode 100644 index 00000000..f64c651e --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/analyzing_artifacts/utils.py_simple_analysis.txt @@ -0,0 +1,165 @@ +# Logic Analysis for utils.py + +## Function: create_subsequent_mask(size) + +**Purpose**: +Creates a triangular (look-ahead) mask for the decoder's self-attention mechanism to prevent positions from attending to subsequent positions, ensuring causality in the decoding process. + +**Implementation Logic**: +1. Create a square matrix of size `size` × `size` +2. Fill the upper triangle with `float('-inf')` or a large negative number to effectively zero out those positions after softmax +3. Fill the lower triangle (including diagonal) with zeros, allowing attention to current and previous positions +4. This creates a binary mask where positions can only attend to previous positions and themselves + +**Mathematical Foundation**: +- The mask must conform to the attention formula in the paper: Attention(Q,K,V) = softmax(QK^T/√d_k)V +- When we add the mask to QK^T before the softmax operation, values of `-inf` will become zero after softmax, effectively blocking attention to future positions + +**Usage Context**: +- Used during training and inference in the decoder's self-attention mechanism +- Critical for maintaining the autoregressive property of the model +- Will be combined with padding masks when necessary + +## Function: create_padding_mask(seq, pad_idx) + +**Purpose**: +Creates a mask to hide padding tokens in the input sequences, ensuring the model doesn't attend to padding positions. + +**Implementation Logic**: +1. Create a binary mask where positions equal to `pad_idx` are marked as 1 (to be masked) +2. All other positions are marked as 0 (to be attended to) +3. Reshape the mask to be compatible with the attention mechanism's expected dimensions [batch_size, 1, 1, seq_len] +4. The mask will be added to the scaled dot product (before softmax) as large negative values + +**Mathematical Foundation**: +- In the attention mechanism, this mask effectively zeroes out the contribution of padding tokens +- Must be compatible with the attention shapes for broadcasting + +**Usage Context**: +- Applied in both encoder self-attention and decoder cross-attention +- Ensures that the model's predictions are not influenced by padding tokens +- Will be combined with sequence masks in the decoder + +## Function: label_smoothed_nll_loss(pred, target, epsilon) + +**Purpose**: +Implements label smoothing regularization technique mentioned in the paper (section 5.4) to prevent the model from becoming overconfident and improve generalization. + +**Implementation Logic**: +1. Create a distribution with `epsilon/K` probability mass for all K vocabulary items +2. Assign a probability of `1-epsilon` to the correct label +3. Calculate the KL-divergence between the predicted distribution and this smoothed distribution +4. Ignore loss calculations on padding tokens + +**Mathematical Foundation**: +- Standard cross-entropy loss: H(q,p) = -∑q(k)log(p(k)) +- Label smoothing modifies the target distribution q from a one-hot distribution to: + q'(k) = (1-ε)δ(k,y) + ε/K + where δ(k,y) is 1 when k=y (the correct class) and 0 otherwise +- As mentioned in the paper, this "hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score" + +**Usage Context**: +- Used during training only +- The paper uses ε_ls = 0.1 + +## Function: get_lr_scheduler(optimizer, d_model, warmup_steps) + +**Purpose**: +Creates a learning rate scheduler that implements the formula from the paper (section 5.3): lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5)) + +**Implementation Logic**: +1. Define a function that calculates learning rate based on step count +2. Initial phase: Learning rate increases linearly for first `warmup_steps` steps +3. Later phase: Learning rate decreases proportionally to the inverse square root of step number +4. Scale by factor of d_model^(-0.5) +5. Apply this calculated learning rate to the optimizer + +**Mathematical Foundation**: +- Formula from paper: lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5)) +- The initial linear warmup prevents early training instability +- The square root decay helps maintain stable training for extended periods + +**Usage Context**: +- Applied during training after each step +- As per paper, warmup_steps = 4000 +- Critical for the model's convergence + +## Function: save_checkpoint(model, optimizer, epoch, path) + +**Purpose**: +Saves the model's parameters, optimizer state, and training progress to a file for later resumption of training or evaluation. + +**Implementation Logic**: +1. Create a dictionary containing: + - Model state dictionary (parameters) + - Optimizer state dictionary + - Current epoch + - Any other training metadata needed +2. Use torch.save to serialize this dictionary to disk +3. Handle potential IO errors + +**Technical Considerations**: +- Need to save both model and optimizer states to properly resume training +- Should be compatible with model averaging functionality +- Should be efficient with large models + +**Usage Context**: +- Called periodically during training (every 10 minutes as per paper) +- Used for creating checkpoint ensembles and resuming training + +## Function: load_checkpoint(path, model, optimizer=None) + +**Purpose**: +Loads a previously saved model checkpoint for continued training or evaluation. + +**Implementation Logic**: +1. Load the checkpoint dictionary from disk using torch.load +2. Apply the saved parameters to the model +3. If optimizer is provided and we're resuming training, restore optimizer state +4. Return any additional metadata from the checkpoint (epoch, etc.) +5. Handle potential IO errors and file not found scenarios + +**Technical Considerations**: +- Must handle cases where optimizer is not provided (evaluation mode) +- Should verify compatibility of loaded weights with current model architecture +- Handle device mapping (CPU/GPU) gracefully + +**Usage Context**: +- Used when resuming training +- Used when loading models for evaluation +- Used when performing checkpoint averaging + +## Function: average_checkpoints(paths, model) + +**Purpose**: +Implements checkpoint averaging as mentioned in the paper (section 6.1), which averages parameters from the last N checkpoints for improved performance. + +**Implementation Logic**: +1. Load all specified checkpoint files +2. Extract model parameters from each checkpoint +3. Calculate the average value for each parameter across all checkpoints +4. Update the provided model with these averaged parameters +5. Leave optimizer state untouched (averaging is only for evaluation) + +**Mathematical Foundation**: +- Simple arithmetic mean of each parameter across checkpoints +- Averaging parameters often improves generalization compared to using a single checkpoint + +**Usage Context**: +- Used for evaluation only, not during training +- Paper specifies averaging: + - Last 5 checkpoints for base models + - Last 20 checkpoints for big models +- Checkpoints written at 10-minute intervals + +**Implementation Note**: +- Need to handle potentially large memory usage when loading multiple checkpoints +- Consider implementing a streaming average to reduce peak memory requirements + +## Summary + +The `utils.py` file provides essential utility functions that support the core Transformer implementation, closely following the specifications from the "Attention Is All You Need" paper. The masking functions are critical for the attention mechanism's proper operation, ensuring autoregressive behavior in the decoder and proper handling of padded sequences. + +The label smoothing implementation helps improve generalization as specified in section 5.4 of the paper. The learning rate scheduler precisely implements the formula described in section 5.3, which is vital for stable training. + +The checkpoint management functions implement the model averaging technique described in section 6.1, which substantially improves final model performance. According to the paper, the base models use an average of the last 5 checkpoints, while big models average the last 20 checkpoints. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/config.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/config.py_coding.txt new file mode 100644 index 00000000..006d0822 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/config.py_coding.txt @@ -0,0 +1,204 @@ +I'll implement the `config.py` file according to the provided requirements, design, and configuration in `config.yaml`. + +## Code: config.py +```python +""" +Configuration module for the Transformer model implementation. +Contains the Config class to store all hyperparameters and configuration settings. +""" +import os +import yaml +from typing import Dict, Any, Optional + + +class Config: + """ + Configuration class for the Transformer model. + Stores parameters for model architecture, training, and evaluation. + """ + def __init__(self, model_size: str = 'base', config_path: Optional[str] = None): + """ + Initialize configuration based on model size ('base' or 'big') + + Args: + model_size: Model size, either 'base' or 'big' + config_path: Path to config.yaml file (optional) + """ + self.model_size = model_size + if model_size not in ['base', 'big']: + raise ValueError(f"Model size must be 'base' or 'big', got {model_size}") + + # Load configuration from YAML file + if config_path is None: + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml') + + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + # Load model architecture parameters + model_config = config['model'][model_size] + self.d_model = model_config['d_model'] + self.n_heads = model_config['n_heads'] + self.num_encoder_layers = model_config['num_encoder_layers'] + self.num_decoder_layers = model_config['num_decoder_layers'] + self.dim_feedforward = model_config['dim_feedforward'] + self.dropout = model_config['dropout'] + self.max_seq_length = model_config['max_seq_length'] + self.share_weights = model_config['share_weights'] + + # Derived parameters + self.d_k = self.d_model // self.n_heads # Dimension of key per head + self.d_v = self.d_model // self.n_heads # Dimension of value per head + + # Load training parameters + self.optimizer = config['training']['optimizer'] + self.beta1 = config['training']['beta1'] + self.beta2 = config['training']['beta2'] + self.epsilon = config['training']['epsilon'] + self.warmup_steps = config['training']['warmup_steps'] + self.label_smoothing = config['training']['label_smoothing'] + self.batch_tokens = config['training']['batch_tokens'] + + if self.model_size == 'base': + self.total_steps = config['training']['base_steps'] + self.average_checkpoints = config['training']['average_checkpoints']['base'] + else: + self.total_steps = config['training']['big_steps'] + self.average_checkpoints = config['training']['average_checkpoints']['big'] + + self.checkpoint_interval = config['training']['checkpoint_interval'] + + # Load inference parameters + self.beam_size = config['inference']['beam_size'] + self.length_penalty = config['inference']['length_penalty'] + self.max_length_factor = config['inference']['max_length_factor'] + + # Load data parameters (default to English-German) + self.data_config = config['data']['en_de'] + + # Set device (will be determined at runtime) + self.device = 'cuda' if self._is_cuda_available() else 'cpu' + + # Learning rate setup - depends on d_model + self.learning_rate = None # Will be calculated dynamically using scheduler + + def get_model_params(self) -> Dict[str, Any]: + """ + Return a dictionary of model architecture parameters + + Returns: + Dictionary containing model architecture parameters + """ + return { + 'd_model': self.d_model, + 'n_heads': self.n_heads, + 'num_encoder_layers': self.num_encoder_layers, + 'num_decoder_layers': self.num_decoder_layers, + 'dim_feedforward': self.dim_feedforward, + 'dropout': self.dropout, + 'd_k': self.d_k, + 'd_v': self.d_v, + 'max_seq_length': self.max_seq_length, + 'share_weights': self.share_weights + } + + def get_training_params(self) -> Dict[str, Any]: + """ + Return a dictionary of training parameters + + Returns: + Dictionary containing training parameters + """ + return { + 'optimizer': self.optimizer, + 'beta1': self.beta1, + 'beta2': self.beta2, + 'epsilon': self.epsilon, + 'warmup_steps': self.warmup_steps, + 'label_smoothing': self.label_smoothing, + 'batch_tokens': self.batch_tokens, + 'total_steps': self.total_steps, + 'checkpoint_interval': self.checkpoint_interval, + 'average_checkpoints': self.average_checkpoints, + 'device': self.device + } + + def get_inference_params(self) -> Dict[str, Any]: + """ + Return a dictionary of inference parameters + + Returns: + Dictionary containing inference parameters + """ + return { + 'beam_size': self.beam_size, + 'length_penalty': self.length_penalty, + 'max_length_factor': self.max_length_factor, + 'device': self.device + } + + def get_data_params(self) -> Dict[str, Any]: + """ + Return a dictionary of data parameters + + Returns: + Dictionary containing data parameters + """ + return self.data_config + + def set_language_pair(self, language_pair: str) -> None: + """ + Set the language pair for data configuration + + Args: + language_pair: Language pair code ('en_de' or 'en_fr') + """ + if language_pair not in ['en_de', 'en_fr']: + raise ValueError(f"Language pair must be 'en_de' or 'en_fr', got {language_pair}") + + # Load configuration from YAML file + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml') + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + self.data_config = config['data'][language_pair] + + # Update dropout for EN-FR if using big model + if language_pair == 'en_fr' and self.model_size == 'big': + self.dropout = 0.1 # As mentioned in the paper for EN-FR big model + + def _is_cuda_available(self) -> bool: + """ + Check if CUDA is available for GPU acceleration + + Returns: + Boolean indicating CUDA availability + """ + try: + import torch + return torch.cuda.is_available() + except ImportError: + return False + + +# For testing +if __name__ == "__main__": + # Create configurations for base and big models + base_config = Config(model_size='base') + big_config = Config(model_size='big') + + # Print some configuration values + print("Base model parameters:") + print(f"d_model: {base_config.d_model}") + print(f"n_heads: {base_config.n_heads}") + print(f"dropout: {base_config.dropout}") + + print("\nBig model parameters:") + print(f"d_model: {big_config.d_model}") + print(f"n_heads: {big_config.n_heads}") + print(f"dropout: {big_config.dropout}") + + # Change language pair and check dropout for big model + big_config.set_language_pair('en_fr') + print(f"\nBig model dropout after setting language to EN-FR: {big_config.dropout}") +``` \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/data_processing.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/data_processing.py_coding.txt new file mode 100644 index 00000000..aa44d924 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/data_processing.py_coding.txt @@ -0,0 +1,545 @@ +I'll implement the data_processing.py file based on the design specifications, leveraging the configuration and utility functions we already have. + +## Code: data_processing.py +```python +""" +Data processing module for the Transformer model implementation. +Handles data loading, preprocessing, tokenization, and batching. +""" + +import os +import math +import numpy as np +import torch +import torch.utils.data as data +from torch.nn.utils.rnn import pad_sequence +from torchtext.data import Dataset, Example, Field, Iterator +from torchtext.vocab import Vocab, build_vocab_from_iterator +from torchtext.datasets import WMT14 +from torchtext.data.utils import get_tokenizer +import sentencepiece as spm +from typing import List, Tuple, Dict, Iterator as IterType, Union, Optional, Callable + +# Import from project modules +from config import Config +from utils import create_masks, create_padding_mask, create_subsequent_mask + + +class DataProcessor: + """ + Handles data loading, preprocessing, tokenization, and batching. + """ + def __init__(self, config: Config): + """ + Initialize with configuration. + + Args: + config: Configuration object + """ + self.config = config + self.data_config = config.get_data_params() + self.model_config = config.get_model_params() + self.device = config.device + self.max_seq_length = self.model_config['max_seq_length'] + + # Get language pair info + self.source_lang = self.data_config['source_lang'] + self.target_lang = self.data_config['target_lang'] + + # Set vocabulary size and tokenization method + self.vocab_size = self.data_config['vocab_size'] + self.tokenization = self.data_config['tokenization'] + + # Special tokens + self.PAD_IDX = 0 + self.BOS_IDX = 1 + self.EOS_IDX = 2 + self.UNK_IDX = 3 + + # Initialize vocabularies as None (to be built) + self.src_vocab = None + self.tgt_vocab = None + + # Initialize tokenizers + self._init_tokenizers() + + def _init_tokenizers(self) -> None: + """ + Initialize tokenizers based on configuration. + """ + # Base tokenizer function (word-level tokenization) + self.base_tokenizer = get_tokenizer('spacy', language=f'{self.source_lang}_core_web_sm') + + # Initialize BPE or WordPiece tokenizers if needed + if self.tokenization == 'bpe': + self._init_bpe_tokenizer() + elif self.tokenization == 'wordpiece': + self._init_wordpiece_tokenizer() + + def _init_bpe_tokenizer(self) -> None: + """ + Initialize byte-pair encoding tokenizer. + Will train the model if it doesn't exist or load a pre-trained model. + """ + # Create directory for tokenizer models if it doesn't exist + os.makedirs('tokenizers', exist_ok=True) + + # Define model path + model_prefix = f'tokenizers/bpe_{self.source_lang}_{self.target_lang}' + model_path = f'{model_prefix}.model' + + # Check if model exists, otherwise train it (when datasets are loaded) + self.bpe_model_path = model_path + self.bpe_model_prefix = model_prefix + + # We'll train or load the model later when data is available + + def _init_wordpiece_tokenizer(self) -> None: + """ + Initialize WordPiece tokenizer. + Will train the model if it doesn't exist or load a pre-trained model. + """ + # Create directory for tokenizer models if it doesn't exist + os.makedirs('tokenizers', exist_ok=True) + + # Define model path + model_prefix = f'tokenizers/wp_{self.source_lang}_{self.target_lang}' + model_path = f'{model_prefix}.model' + + # Check if model exists, otherwise train it (when datasets are loaded) + self.wp_model_path = model_path + self.wp_model_prefix = model_prefix + + # We'll train or load the model later when data is available + + def load_data(self, dataset_path: Optional[str] = None) -> Tuple[data.DataLoader, data.DataLoader, data.DataLoader]: + """ + Load and prepare train/val/test data. + + Args: + dataset_path: Path to dataset (optional, will use default if not provided) + + Returns: + Tuple of (train_dataloader, val_dataloader, test_dataloader) + """ + print(f"Loading {self.source_lang}-{self.target_lang} dataset...") + + # Create dataset paths + if dataset_path is None: + dataset_path = '.data' + + # Define dataset splits based on config + train_split = self.data_config['train'] + valid_split = self.data_config['valid'] + test_split = self.data_config['test'] + + # Create Fields for source and target + src_field = Field( + tokenize=self.tokenize, + init_token='', + eos_token='', + pad_token='', + unk_token='', + lower=True, + batch_first=True + ) + + tgt_field = Field( + tokenize=self.tokenize, + init_token='', + eos_token='', + pad_token='', + unk_token='', + lower=True, + batch_first=True + ) + + # Specify fields for torchtext dataset + fields = [(self.source_lang, src_field), (self.target_lang, tgt_field)] + + # Load datasets + train_data, valid_data, test_data = WMT14.splits( + exts=(f'.{self.source_lang}', f'.{self.target_lang}'), + fields=fields, + root=dataset_path, + filter_pred=lambda x: len(vars(x)[self.source_lang]) <= self.max_seq_length and + len(vars(x)[self.target_lang]) <= self.max_seq_length + ) + + print(f"Number of training examples: {len(train_data)}") + print(f"Number of validation examples: {len(valid_data)}") + print(f"Number of testing examples: {len(test_data)}") + + # Build vocabularies from training data + if self.tokenization in ['bpe', 'wordpiece']: + # For subword tokenization, we need to train the tokenizer first + if self.tokenization == 'bpe': + self._train_bpe_tokenizer(train_data) + else: + self._train_wordpiece_tokenizer(train_data) + + # Apply subword tokenization to the datasets + train_data = self._apply_subword_tokenization(train_data) + valid_data = self._apply_subword_tokenization(valid_data) + test_data = self._apply_subword_tokenization(test_data) + + # Build vocabularies + self.build_vocab(train_data, src_field, tgt_field) + + # Create bucketed iterators to efficiently batch sequences of similar lengths + train_iterator, valid_iterator, test_iterator = self.batch_data(train_data, valid_data, test_data) + + # Convert iterators to PyTorch DataLoader format + train_dataloader = self._convert_to_dataloader(train_iterator) + valid_dataloader = self._convert_to_dataloader(valid_iterator) + test_dataloader = self._convert_to_dataloader(test_iterator) + + return train_dataloader, valid_dataloader, test_dataloader + + def _train_bpe_tokenizer(self, train_data: Dataset) -> None: + """ + Train byte-pair encoding tokenizer on training data. + + Args: + train_data: Training dataset + """ + # Check if model already exists + if os.path.exists(self.bpe_model_path): + print(f"Loading existing BPE model from {self.bpe_model_path}") + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpe_model_path) + return + + # Create corpus file for training + corpus_file = f'{self.bpe_model_prefix}.corpus' + with open(corpus_file, 'w', encoding='utf-8') as f: + # Write source sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.source_lang]) + '\n') + + # Write target sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.target_lang]) + '\n') + + # Train SentencePiece model + print(f"Training BPE tokenizer with vocabulary size {self.vocab_size}") + spm.SentencePieceTrainer.train( + f'--input={corpus_file} ' + f'--model_prefix={self.bpe_model_prefix} ' + f'--vocab_size={self.vocab_size} ' + f'--character_coverage=0.9995 ' + f'--model_type=bpe ' + f'--pad_id=0 --bos_id=1 --eos_id=2 --unk_id=3 ' + f'--user_defined_symbols=,,,' + ) + + # Load trained model + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpe_model_path) + + # Remove corpus file + os.remove(corpus_file) + + def _train_wordpiece_tokenizer(self, train_data: Dataset) -> None: + """ + Train WordPiece tokenizer on training data. + + Args: + train_data: Training dataset + """ + # Check if model already exists + if os.path.exists(self.wp_model_path): + print(f"Loading existing WordPiece model from {self.wp_model_path}") + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.wp_model_path) + return + + # Create corpus file for training + corpus_file = f'{self.wp_model_prefix}.corpus' + with open(corpus_file, 'w', encoding='utf-8') as f: + # Write source sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.source_lang]) + '\n') + + # Write target sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.target_lang]) + '\n') + + # Train SentencePiece model with WordPiece + print(f"Training WordPiece tokenizer with vocabulary size {self.vocab_size}") + spm.SentencePieceTrainer.train( + f'--input={corpus_file} ' + f'--model_prefix={self.wp_model_prefix} ' + f'--vocab_size={self.vocab_size} ' + f'--character_coverage=0.9995 ' + f'--model_type=word ' + f'--pad_id=0 --bos_id=1 --eos_id=2 --unk_id=3 ' + f'--user_defined_symbols=,,,' + ) + + # Load trained model + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.wp_model_path) + + # Remove corpus file + os.remove(corpus_file) + + def _apply_subword_tokenization(self, dataset: Dataset) -> Dataset: + """ + Apply subword tokenization to a dataset. + + Args: + dataset: Dataset to tokenize + + Returns: + Tokenized dataset + """ + # Create a new dataset with subword-tokenized examples + examples = [] + for example in dataset.examples: + src_text = ' '.join(vars(example)[self.source_lang]) + tgt_text = ' '.join(vars(example)[self.target_lang]) + + # Apply subword tokenization + src_tokens = self.sp.encode(src_text, out_type=str) + tgt_tokens = self.sp.encode(tgt_text, out_type=str) + + # Create a new example with tokenized text + new_example = Example() + setattr(new_example, self.source_lang, src_tokens) + setattr(new_example, self.target_lang, tgt_tokens) + examples.append(new_example) + + # Create new dataset with tokenized examples + return Dataset(examples, dataset.fields) + + def build_vocab(self, train_data: Dataset, src_field: Field, tgt_field: Field) -> Tuple[Vocab, Vocab]: + """ + Build source and target vocabularies. + + Args: + train_data: Training dataset + src_field: Source field + tgt_field: Target field + + Returns: + Tuple of (source vocabulary, target vocabulary) + """ + if self.tokenization in ['bpe', 'wordpiece']: + # For subword tokenization, use the vocabulary from SentencePiece + sp_vocab = {self.sp.id_to_piece(i): i for i in range(self.sp.get_piece_size())} + src_field.vocab = Vocab(sp_vocab, specials=[]) + tgt_field.vocab = Vocab(sp_vocab, specials=[]) + else: + # For word-level tokenization, build vocabulary from training data + src_field.build_vocab(train_data, max_size=self.vocab_size) + tgt_field.build_vocab(train_data, max_size=self.vocab_size) + + # Store vocabularies + self.src_vocab = src_field.vocab + self.tgt_vocab = tgt_field.vocab + + print(f"Source vocabulary size: {len(self.src_vocab)}") + print(f"Target vocabulary size: {len(self.tgt_vocab)}") + + return self.src_vocab, self.tgt_vocab + + def tokenize(self, text: str) -> List[str]: + """ + Tokenize text. + + Args: + text: Text to tokenize + + Returns: + List of tokens + """ + # Use base tokenizer for initial tokenization + tokens = self.base_tokenizer(text) + return tokens + + def apply_bpe(self, tokens: List[str]) -> List[str]: + """ + Apply byte-pair encoding to tokens. + + Args: + tokens: List of tokens + + Returns: + List of BPE tokens + """ + # Join tokens and apply BPE + text = ' '.join(tokens) + bpe_tokens = self.sp.encode(text, out_type=str) + return bpe_tokens + + def batch_data(self, train_data: Dataset, valid_data: Dataset, test_data: Dataset) -> Tuple[Iterator, Iterator, Iterator]: + """ + Create batches of similar lengths. + + Args: + train_data: Training dataset + valid_data: Validation dataset + test_data: Test dataset + + Returns: + Tuple of (train iterator, validation iterator, test iterator) + """ + # Calculate batch size based on target tokens per batch + # We'll do dynamic batching in the bucket iterator + batch_size = self.config.batch_tokens // self.max_seq_length + batch_size = max(1, batch_size) # Ensure at least 1 + + # Create BucketIterator for batching similar-length sequences + train_iterator, valid_iterator, test_iterator = Iterator.splits( + (train_data, valid_data, test_data), + batch_size=batch_size, + sort_key=lambda x: len(getattr(x, self.source_lang)), + sort_within_batch=True, + device=self.device + ) + + return train_iterator, valid_iterator, test_iterator + + def _convert_to_dataloader(self, iterator: Iterator) -> data.DataLoader: + """ + Convert torchtext iterator to PyTorch DataLoader. + + Args: + iterator: torchtext iterator + + Returns: + PyTorch DataLoader + """ + # Create a dataset that yields batches from the iterator + dataset = _IteratorDataset(iterator, self.source_lang, self.target_lang) + + # Create a DataLoader with the dataset + return data.DataLoader( + dataset, + batch_size=None, # Batching is already done by the iterator + collate_fn=None # No need for collation + ) + + def create_masks(self, src: torch.Tensor, tgt: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Create attention masks for the transformer model. + + Args: + src: Source tensor of shape (batch_size, src_len) + tgt: Target tensor of shape (batch_size, tgt_len) (optional, for training) + + Returns: + Tuple of (source mask, target mask) where target mask is None if tgt is None + """ + return create_masks(src, tgt, self.PAD_IDX) + + def encode_sentence(self, sentence: str, is_source: bool = True) -> torch.Tensor: + """ + Encode a sentence to tensor with vocabulary indices. + + Args: + sentence: Sentence to encode + is_source: Whether this is a source sentence (or target) + + Returns: + Tensor with vocabulary indices + """ + # Tokenize the sentence + tokens = self.tokenize(sentence) + + # Apply subword tokenization if needed + if self.tokenization in ['bpe', 'wordpiece']: + tokens = self.sp.encode(' '.join(tokens), out_type=str) + + # Get vocabulary (source or target) + vocab = self.src_vocab if is_source else self.tgt_vocab + + # Convert tokens to indices + indices = [vocab.stoi.get(token, self.UNK_IDX) for token in tokens] + + # Add BOS and EOS tokens + indices = [self.BOS_IDX] + indices + [self.EOS_IDX] + + # Convert to tensor + return torch.tensor(indices, dtype=torch.long).unsqueeze(0) + + def decode_sentence(self, indices: torch.Tensor, is_source: bool = False) -> str: + """ + Decode indices to sentence. + + Args: + indices: Tensor with vocabulary indices + is_source: Whether this is a source sentence (or target) + + Returns: + Decoded sentence + """ + # Get vocabulary (source or target) + vocab = self.src_vocab if is_source else self.tgt_vocab + + # Convert indices to tokens, skipping special tokens + tokens = [] + for idx in indices: + if idx == self.EOS_IDX: + break + if idx != self.BOS_IDX and idx != self.PAD_IDX: + tokens.append(vocab.itos[idx]) + + # For subword tokenization, join with spaces and then remove separators + if self.tokenization in ['bpe', 'wordpiece']: + return self.sp.decode(tokens) + else: + # For word-level tokenization, join with spaces + return ' '.join(tokens) + + +class _IteratorDataset(data.Dataset): + """ + Dataset adapter for torchtext iterator to PyTorch DataLoader. + """ + def __init__(self, iterator: Iterator, src_field: str, tgt_field: str): + """ + Initialize with iterator and field names. + + Args: + iterator: torchtext iterator + src_field: Source field name + tgt_field: Target field name + """ + self.iterator = iterator + self.src_field = src_field + self.tgt_field = tgt_field + self._iterator = iter(iterator) + self.length = len(iterator) + + def __len__(self) -> int: + """Get dataset length.""" + return self.length + + def __iter__(self) -> IterType: + """Reset and return iterator.""" + self._iterator = iter(self.iterator) + return self + + def __next__(self) -> Tuple[torch.Tensor, torch.Tensor]: + """Get next batch.""" + batch = next(self._iterator) + src = getattr(batch, self.src_field) + tgt = getattr(batch, self.tgt_field) + return src, tgt + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: + """Get item is not meaningful for iterator, but needed for DataLoader.""" + raise NotImplementedError("This dataset does not support random access") +``` + +This implementation follows the design specified for the DataProcessor class, handling data loading, tokenization, vocabulary building, and batch creation. Key features include: + +1. Support for both byte-pair encoding (BPE) and WordPiece tokenization as specified in the paper +2. Dynamic batching of similar sequence lengths to maximize efficiency +3. Integration with torchtext for dataset loading and preprocessing +4. Mask creation for transformer self-attention mechanisms +5. Methods for encoding and decoding sentences for translation + +The code uses SentencePiece for subword tokenization, with configuration based on the specified parameters in the config.yaml file. It handles the complete data processing pipeline from raw text to batched tensors ready for the transformer model. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/evaluate.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/evaluate.py_coding.txt new file mode 100644 index 00000000..c357ec6d --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/evaluate.py_coding.txt @@ -0,0 +1,409 @@ +I'll implement the `evaluate.py` file according to the specifications in the task, following the design from "Data structures and interfaces" and using configurations from the provided config.yaml file. This file will handle model evaluation and inference for the Transformer model. + +## Code: evaluate.py +```python +""" +Evaluation module for the Transformer model implementation. +Handles evaluation and inference for the trained model. +""" + +import os +import time +import math +from typing import Optional, Tuple, Dict, List, Any, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +import sacrebleu +from tqdm import tqdm + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from utils import average_checkpoints, create_masks + + +class Evaluator: + """ + Handles evaluation and inference for the trained Transformer model. + """ + def __init__( + self, + config: Config, + model: TransformerModel, + data_processor: DataProcessor + ): + """ + Initialize evaluator. + + Args: + config: Configuration object + model: TransformerModel instance + data_processor: DataProcessor instance + """ + self.config = config + self.model = model + self.data_processor = data_processor + + # Get inference parameters + inference_params = config.get_inference_params() + self.device = inference_params['device'] + self.beam_size = inference_params['beam_size'] + self.length_penalty = inference_params['length_penalty'] + self.max_length_factor = inference_params['max_length_factor'] + + # Move model to device + self.model.to(self.device) + + # Set model to evaluation mode + self.model.eval() + + def evaluate(self, test_data: DataLoader) -> Dict[str, float]: + """ + Evaluate model on test data. + + Args: + test_data: Test data loader + + Returns: + Dictionary containing evaluation metrics (e.g., BLEU score) + """ + self.model.eval() + + # Lists to store references and hypotheses + references = [] + hypotheses = [] + + print("Evaluating model...") + + # Create progress bar + pbar = tqdm(test_data, desc="Evaluating") + + with torch.no_grad(): + for src, tgt in pbar: + # Move data to device + src = src.to(self.device) + + # Get batch size and max possible length + batch_size = src.size(0) + max_len = min(src.size(1) + self.max_length_factor, + self.config.model_config['max_seq_length']) + + # Generate translations using beam search + generated = self.model.beam_search( + src, + max_len, + start_symbol=self.data_processor.BOS_IDX, + end_symbol=self.data_processor.EOS_IDX + ) + + # Convert tensors to sentences + for i in range(batch_size): + # Get reference sentence (target) + ref_sentence = self.data_processor.decode_sentence(tgt[i], is_source=False) + references.append(ref_sentence) + + # Get hypothesis sentence (generated) + hyp_sentence = self.data_processor.decode_sentence(generated[i], is_source=False) + hypotheses.append(hyp_sentence) + + # Calculate BLEU score + bleu_score = self.compute_bleu(references, hypotheses) + + print(f"BLEU score: {bleu_score}") + + # Return metrics + return { + 'bleu': bleu_score, + 'num_samples': len(references) + } + + def translate_sentence(self, sentence: str) -> str: + """ + Translate a single sentence. + + Args: + sentence: Input sentence in source language + + Returns: + Translated sentence in target language + """ + self.model.eval() + + # Encode the sentence + src_tensor = self.data_processor.encode_sentence(sentence, is_source=True).to(self.device) + + # Calculate maximum output length + max_len = min(src_tensor.size(1) + self.max_length_factor, + self.config.model_config['max_seq_length']) + + # Generate translation + with torch.no_grad(): + generated = self.model.beam_search( + src_tensor, + max_len, + start_symbol=self.data_processor.BOS_IDX, + end_symbol=self.data_processor.EOS_IDX + ) + + # Decode the generated tensor to text + translation = self.data_processor.decode_sentence(generated[0], is_source=False) + + return translation + + def compute_bleu(self, references: List[str], hypotheses: List[str]) -> float: + """ + Compute BLEU score using sacrebleu. + + Args: + references: List of reference sentences + hypotheses: List of hypothesis sentences + + Returns: + BLEU score + """ + # Convert single references to list of lists format required by sacrebleu + references_list = [[ref] for ref in references] + + # Calculate corpus BLEU score + bleu = sacrebleu.corpus_bleu(hypotheses, references_list) + + # Return the score as a float + return bleu.score + + def average_checkpoints(self, paths: List[str]) -> None: + """ + Average model weights from multiple checkpoints as described in the paper. + + Args: + paths: List of paths to checkpoints + """ + if not paths: + print("No checkpoint paths provided for averaging.") + return + + print(f"Averaging {len(paths)} checkpoints...") + average_checkpoints(paths, self.model) + print("Checkpoint averaging complete.") + + def find_latest_checkpoints( + self, + checkpoint_dir: str, + num_checkpoints: int + ) -> List[str]: + """ + Find the latest checkpoints in a directory. + + Args: + checkpoint_dir: Directory containing checkpoints + num_checkpoints: Number of latest checkpoints to find + + Returns: + List of paths to latest checkpoints + """ + # List all checkpoint files + checkpoint_files = [ + os.path.join(checkpoint_dir, f) for f in os.listdir(checkpoint_dir) + if f.startswith('checkpoint_') and f.endswith('.pt') + ] + + # Sort by modification time (newest first) + checkpoint_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + + # Return the specified number of checkpoints + return checkpoint_files[:num_checkpoints] + + def generate_translations( + self, + src_sentences: List[str], + output_file: Optional[str] = None + ) -> List[str]: + """ + Generate translations for a list of source sentences. + + Args: + src_sentences: List of source language sentences + output_file: Path to write translations to (optional) + + Returns: + List of translated sentences + """ + self.model.eval() + translations = [] + + # Create progress bar + pbar = tqdm(src_sentences, desc="Generating translations") + + for sentence in pbar: + translation = self.translate_sentence(sentence) + translations.append(translation) + + # Write translations to file if specified + if output_file: + with open(output_file, 'w', encoding='utf-8') as f: + for trans in translations: + f.write(trans + '\n') + print(f"Translations written to {output_file}") + + return translations + + def evaluate_from_checkpoint(self, checkpoint_path: str, test_data: DataLoader) -> Dict[str, float]: + """ + Load a checkpoint and evaluate the model. + + Args: + checkpoint_path: Path to checkpoint + test_data: Test data loader + + Returns: + Dictionary containing evaluation metrics + """ + # Load checkpoint + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + + # Load model state + self.model.load_state_dict(checkpoint['model']) + + # Move model to device + self.model.to(self.device) + + # Set model to evaluation mode + self.model.eval() + + print(f"Loaded checkpoint from {checkpoint_path}") + + # Evaluate model + return self.evaluate(test_data) + + def evaluate_averaged_model( + self, + checkpoint_dir: str, + test_data: DataLoader, + num_checkpoints: Optional[int] = None + ) -> Dict[str, float]: + """ + Average checkpoints and evaluate the resulting model. + + Args: + checkpoint_dir: Directory containing checkpoints + test_data: Test data loader + num_checkpoints: Number of checkpoints to average (if None, use config value) + + Returns: + Dictionary containing evaluation metrics + """ + # Get number of checkpoints to average from config if not specified + if num_checkpoints is None: + num_checkpoints = self.config.average_checkpoints + + # Find the latest checkpoints + checkpoint_paths = self.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + + if not checkpoint_paths: + raise ValueError(f"No checkpoints found in {checkpoint_dir}") + + # Average the checkpoints + self.average_checkpoints(checkpoint_paths) + + # Evaluate the averaged model + return self.evaluate(test_data) + + def compute_perplexity(self, data_loader: DataLoader) -> float: + """ + Compute perplexity on a dataset. + + Args: + data_loader: Data loader for evaluation + + Returns: + Perplexity score + """ + self.model.eval() + total_loss = 0 + total_tokens = 0 + + with torch.no_grad(): + for src, tgt in tqdm(data_loader, desc="Computing perplexity"): + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss + loss = F.cross_entropy( + logits, + tgt_output, + ignore_index=self.data_processor.PAD_IDX, + reduction='sum' + ) + + # Update statistics + total_loss += loss.item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Calculate perplexity + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + perplexity = math.exp(avg_loss) + + print(f"Perplexity: {perplexity:.2f}") + + return perplexity + + +if __name__ == "__main__": + # Simple test for the evaluator + print("Testing Evaluator class...") + + # Load configuration + config = Config(model_size='base') + + # Create dummy data processor + data_processor = DataProcessor(config) + + # Create dummy model + model = TransformerModel( + config, + src_vocab_size=1000, # Dummy value + tgt_vocab_size=1000 # Dummy value + ) + + # Create evaluator + evaluator = Evaluator(config, model, data_processor) + + print("Evaluator initialized successfully!") + print(f"Beam size: {evaluator.beam_size}") + print(f"Length penalty: {evaluator.length_penalty}") + print(f"Device: {evaluator.device}") +``` + +The implementation includes: + +1. The Evaluator class that manages model evaluation and inference processes +2. Methods for evaluating translation quality using BLEU scores +3. Support for beam search decoding as described in the paper +4. Checkpoint averaging functionality to reproduce the paper's approach +5. Functions for translating individual sentences and generating translations for multiple sentences +6. Utility methods for finding and loading checkpoints +7. Additional evaluation metrics like perplexity calculation +8. Functions for evaluating either single checkpoints or averaged models + +The code strictly follows the design specified in the "Data structures and interfaces" section and uses configuration values from the config.yaml file. It properly integrates with the other components (Config, TransformerModel, DataProcessor) to create a complete evaluation system for the Transformer model. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/main.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/main.py_coding.txt new file mode 100644 index 00000000..21c569bf --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/main.py_coding.txt @@ -0,0 +1,455 @@ +I'll follow the provided instructions and implement the `main.py` file for the Transformer model implementation based on the "Attention Is All You Need" paper. This file will serve as the entry point for running training and evaluation. + +## Code: main.py +```python +""" +Main module for the Transformer model implementation. +Entry point for running training and evaluation of the model. +""" + +import os +import argparse +import torch +import time +from typing import Optional, List, Tuple + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from train import Trainer +from evaluate import Evaluator +from utils import average_checkpoints + + +def train_model( + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + checkpoint_dir: str = "checkpoints", + resume_checkpoint: Optional[str] = None, + epochs: Optional[int] = None +) -> None: + """ + Train the transformer model. + + Args: + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to train on ('en_de' or 'en_fr') + config_path: Path to configuration file + checkpoint_dir: Directory to save checkpoints + resume_checkpoint: Path to checkpoint to resume training from + epochs: Number of epochs to train (if None, will use steps from config) + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Create directory for checkpoints + os.makedirs(checkpoint_dir, exist_ok=True) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load data + print(f"Loading {language_pair} dataset...") + train_data, val_data, test_data = data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Count model parameters + param_count = sum(p.numel() for p in model.parameters()) + print(f"Model has {param_count:,} parameters") + + # Initialize trainer + trainer = Trainer(config, model, data_processor) + + # Resume from checkpoint if specified + if resume_checkpoint: + print(f"Resuming from checkpoint: {resume_checkpoint}") + trainer.load_checkpoint(resume_checkpoint) + + # Train model + print("Starting training...") + trainer.train(train_data, val_data, epochs=epochs, checkpoint_dir=checkpoint_dir) + + print("Training complete!") + + +def evaluate_model( + model_path: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + is_averaged: bool = False, + checkpoint_dir: Optional[str] = None, + num_checkpoints: Optional[int] = None, + output_file: Optional[str] = None +) -> float: + """ + Evaluate the trained model. + + Args: + model_path: Path to trained model + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to evaluate on ('en_de' or 'en_fr') + config_path: Path to configuration file + is_averaged: Whether to average checkpoints + checkpoint_dir: Directory containing checkpoints (for averaging) + num_checkpoints: Number of checkpoints to average + output_file: Path to write translations to + + Returns: + BLEU score + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load data + print(f"Loading {language_pair} test dataset...") + _, _, test_data = data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + if is_averaged and checkpoint_dir: + # Average checkpoints + if num_checkpoints is None: + # Use default from config + num_checkpoints = config.average_checkpoints + + print(f"Averaging {num_checkpoints} checkpoints from {checkpoint_dir}...") + checkpoint_paths = evaluator.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + evaluator.average_checkpoints(checkpoint_paths) + else: + # Load single model + print(f"Loading model from {model_path}...") + evaluator.evaluate_from_checkpoint(model_path, test_data) + + # Evaluate model + print("Evaluating model...") + eval_results = evaluator.evaluate(test_data) + + # Print results + bleu_score = eval_results['bleu'] + print(f"BLEU score: {bleu_score:.2f}") + + # Generate translations for test set and save to file if specified + if output_file: + print(f"Generating translations and saving to {output_file}...") + # Extract source sentences from test data + src_sentences = [] + for batch in test_data: + src, _ = batch + for i in range(src.size(0)): + src_sentence = data_processor.decode_sentence(src[i], is_source=True) + src_sentences.append(src_sentence) + + # Generate translations + evaluator.generate_translations(src_sentences, output_file) + + return bleu_score + + +def translate( + model_path: str, + sentence: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None +) -> str: + """ + Translate a single sentence. + + Args: + model_path: Path to trained model + sentence: Sentence to translate + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to translate ('en_de' or 'en_fr') + config_path: Path to configuration file + + Returns: + Translated sentence + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load vocabularies + print("Building vocabularies...") + # Need to load some data to build vocabularies + data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Load model + print(f"Loading model from {model_path}...") + checkpoint = torch.load(model_path, map_location=torch.device('cpu')) + model.load_state_dict(checkpoint['model']) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + # Translate sentence + print("Translating sentence...") + translation = evaluator.translate_sentence(sentence) + + return translation + + +def average_model_checkpoints( + checkpoint_dir: str, + output_path: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + num_checkpoints: Optional[int] = None +) -> None: + """ + Average multiple model checkpoints and save the result. + + Args: + checkpoint_dir: Directory containing checkpoints + output_path: Path to save the averaged model + model_size: Size of the model ('base' or 'big') + language_pair: Language pair ('en_de' or 'en_fr') + config_path: Path to configuration file + num_checkpoints: Number of checkpoints to average + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor (needed for vocabulary sizes) + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load vocabularies + print("Building vocabularies...") + data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + # If num_checkpoints is not specified, use from config + if num_checkpoints is None: + num_checkpoints = config.average_checkpoints + + # Find the latest checkpoints + checkpoint_paths = evaluator.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + + if not checkpoint_paths: + print(f"No checkpoints found in {checkpoint_dir}") + return + + # Average checkpoints + print(f"Averaging {len(checkpoint_paths)} checkpoints...") + evaluator.average_checkpoints(checkpoint_paths) + + # Save averaged model + print(f"Saving averaged model to {output_path}...") + # Create a simple checkpoint structure + checkpoint = { + 'model': model.state_dict(), + 'epoch': 0, # Not relevant for averaged model + 'step': 0, # Not relevant for averaged model + 'loss': 0.0 # Not relevant for averaged model + } + + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + # Save the model + torch.save(checkpoint, output_path) + + print(f"Averaged model saved to {output_path}") + + +def main() -> None: + """ + Parse command line arguments and run the appropriate function. + """ + parser = argparse.ArgumentParser(description='Train and evaluate Transformer models') + + # Create subparsers for different commands + subparsers = parser.add_subparsers(dest='command', help='Command to run') + + # Train command + train_parser = subparsers.add_parser('train', help='Train a model') + train_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + train_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to train on') + train_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + train_parser.add_argument('--checkpoint-dir', type=str, default='checkpoints', + help='Directory to save checkpoints') + train_parser.add_argument('--resume', type=str, default=None, + help='Path to checkpoint to resume training from') + train_parser.add_argument('--epochs', type=int, default=None, + help='Number of epochs to train (if None, will use steps from config)') + + # Evaluate command + eval_parser = subparsers.add_parser('evaluate', help='Evaluate a model') + eval_parser.add_argument('model_path', type=str, + help='Path to trained model') + eval_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + eval_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to evaluate on') + eval_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + eval_parser.add_argument('--average', action='store_true', + help='Average checkpoints before evaluation') + eval_parser.add_argument('--checkpoint-dir', type=str, default=None, + help='Directory containing checkpoints (for averaging)') + eval_parser.add_argument('--num-checkpoints', type=int, default=None, + help='Number of checkpoints to average') + eval_parser.add_argument('--output-file', type=str, default=None, + help='Path to write translations to') + + # Translate command + translate_parser = subparsers.add_parser('translate', help='Translate a sentence') + translate_parser.add_argument('model_path', type=str, + help='Path to trained model') + translate_parser.add_argument('sentence', type=str, + help='Sentence to translate') + translate_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + translate_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to translate') + translate_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + + # Average command + average_parser = subparsers.add_parser('average', help='Average model checkpoints') + average_parser.add_argument('checkpoint_dir', type=str, + help='Directory containing checkpoints') + average_parser.add_argument('output_path', type=str, + help='Path to save the averaged model') + average_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + average_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair') + average_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + average_parser.add_argument('--num-checkpoints', type=int, default=None, + help='Number of checkpoints to average') + + # Parse arguments + args = parser.parse_args() + + # Check if a command was specified + if args.command is None: + parser.print_help() + return + + # Run the appropriate function + if args.command == 'train': + train_model( + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + checkpoint_dir=args.checkpoint_dir, + resume_checkpoint=args.resume, + epochs=args.epochs + ) + elif args.command == 'evaluate': + evaluate_model( + model_path=args.model_path, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + is_averaged=args.average, + checkpoint_dir=args.checkpoint_dir, + num_checkpoints=args.num_checkpoints, + output_file=args.output_file + ) + elif args.command == 'translate': + translation = translate( + model_path=args.model_path, + sentence=args.sentence, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path + ) + print(f"Translation: {translation}") + elif args.command == 'average': + average_model_checkpoints( + checkpoint_dir=args.checkpoint_dir, + output_path=args.output_path, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + num_checkpoints=args.num_checkpoints + ) + + +if __name__ == "__main__": + # Record start time + start_time = time.time() + + # Run main function + main() + + # Print elapsed time + elapsed_time = time.time() - start_time + print(f"Elapsed time: {elapsed_time:.2f} seconds") +``` + +This implementation of `main.py` provides the entry point for running the Transformer model implementation. It follows the design specified in the task and provides command-line interfaces for training, evaluation, translation, and checkpoint averaging. The code respects the configuration values from `config.yaml` and interfaces with the other modules as described in the design. + +The file includes the following main functions: +1. `train_model()`: For training the transformer model +2. `evaluate_model()`: For evaluating a trained model on test data +3. `translate()`: For translating a single sentence +4. `average_model_checkpoints()`: For averaging multiple model checkpoints +5. `main()`: Entry point that parses command-line arguments and calls the appropriate function + +The implementation ensures that all the parameters align with those described in the "Attention Is All You Need" paper, such as the model sizes ('base' and 'big'), beam search parameters, and checkpoint averaging functionality. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/model.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/model.py_coding.txt new file mode 100644 index 00000000..842e1249 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/model.py_coding.txt @@ -0,0 +1,782 @@ +I'll implement the `model.py` file according to the provided design and paper specifications, ensuring it follows the Transformer architecture as described in "Attention Is All You Need". + +## Code: model.py +```python +""" +Model module for the Transformer architecture implementation. +Implements the Transformer model as described in 'Attention Is All You Need' paper. + +This module contains the full Transformer architecture with encoder and decoder stacks, +multi-head attention mechanisms, position-wise feed-forward networks, and positional encoding. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +import copy +import numpy as np +from typing import Optional, Tuple, List + +# Import from project modules +from config import Config +from utils import clones, create_padding_mask, create_subsequent_mask + + +class PositionalEncoding(nn.Module): + """ + Implement the positional encoding as described in section 3.5 of the paper. + Adds positional information to the token embeddings. + """ + def __init__(self, d_model: int, dropout: float, max_len: int = 5000): + """ + Initialize positional encoding. + + Args: + d_model: Dimension of the model + dropout: Dropout rate + max_len: Maximum sequence length + """ + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + # Create a matrix of shape (max_len, d_model) + pe = torch.zeros(max_len, d_model) + + # Create a vector of shape (max_len) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) + + # Create a vector of shape (d_model) + div_term = torch.exp( + torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model) + ) + + # Apply sine to even indices and cosine to odd indices + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + # Add batch dimension and transpose to shape (1, max_len, d_model) + pe = pe.unsqueeze(0) + + # Register buffer (not a parameter, but part of the module) + self.register_buffer('pe', pe) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Add positional encoding to the input embeddings. + + Args: + x: Input embeddings of shape (batch_size, seq_len, d_model) + + Returns: + Output with positional encoding added + """ + # Add positional encoding to the input + x = x + self.pe[:, :x.size(1)] + + # Apply dropout + return self.dropout(x) + + +class MultiHeadAttention(nn.Module): + """ + Implement the multi-head attention mechanism as described in section 3.2.2 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, dropout: float): + """ + Initialize multi-head attention. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + dropout: Dropout rate + """ + super(MultiHeadAttention, self).__init__() + + # Ensure d_model is divisible by n_heads + assert d_model % n_heads == 0, "d_model must be divisible by n_heads" + + # Store parameters + self.d_model = d_model + self.n_heads = n_heads + self.d_k = d_model // n_heads # Dimension of key per head + self.d_v = d_model // n_heads # Dimension of value per head + + # Linear projections + self.W_q = nn.Linear(d_model, d_model) # Query projection + self.W_k = nn.Linear(d_model, d_model) # Key projection + self.W_v = nn.Linear(d_model, d_model) # Value projection + self.W_o = nn.Linear(d_model, d_model) # Output projection + + # Dropout + self.dropout = nn.Dropout(p=dropout) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Apply multi-head attention. + + Args: + query: Query tensor of shape (batch_size, query_len, d_model) + key: Key tensor of shape (batch_size, key_len, d_model) + value: Value tensor of shape (batch_size, value_len, d_model) + mask: Optional mask tensor of shape (batch_size, 1, 1, key_len) or (batch_size, 1, query_len, key_len) + + Returns: + Output tensor of shape (batch_size, query_len, d_model) + """ + batch_size = query.size(0) + + # Linear projections and split into h heads + # Shape: (batch_size, seq_len, n_heads, d_k) -> (batch_size, n_heads, seq_len, d_k) + q = self.W_q(query).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2) + k = self.W_k(key).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2) + v = self.W_v(value).view(batch_size, -1, self.n_heads, self.d_v).transpose(1, 2) + + # Apply attention + scores, attn = self.attention(q, k, v, mask) + + # Concatenate heads and apply final linear projection + # Shape: (batch_size, n_heads, seq_len, d_v) -> (batch_size, seq_len, d_model) + concat = scores.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model) + output = self.W_o(concat) + + return output + + def attention( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Compute scaled dot-product attention as described in section 3.2.1 of the paper. + + Args: + query: Query tensor of shape (batch_size, n_heads, query_len, d_k) + key: Key tensor of shape (batch_size, n_heads, key_len, d_k) + value: Value tensor of shape (batch_size, n_heads, value_len, d_v) + mask: Optional mask tensor of shape (batch_size, 1, 1, key_len) or (batch_size, 1, query_len, key_len) + + Returns: + Tuple of (output tensor of shape (batch_size, n_heads, query_len, d_v), + attention weights of shape (batch_size, n_heads, query_len, key_len)) + """ + # Get dimensions + d_k = query.size(-1) + + # Compute scaled dot product + # Shape: (batch_size, n_heads, query_len, key_len) + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) + + # Apply mask if provided + if mask is not None: + scores = scores.masked_fill(mask == 1, -1e9) + + # Apply softmax to get attention weights + attn = F.softmax(scores, dim=-1) + + # Apply dropout to attention weights + attn = self.dropout(attn) + + # Apply attention weights to values + # Shape: (batch_size, n_heads, query_len, d_v) + output = torch.matmul(attn, value) + + return output, attn + + +class PositionwiseFeedforward(nn.Module): + """ + Implement the position-wise feed-forward network as described in section 3.3 of the paper. + """ + def __init__(self, d_model: int, d_ff: int, dropout: float): + """ + Initialize position-wise feed-forward network. + + Args: + d_model: Dimension of the model + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(PositionwiseFeedforward, self).__init__() + + # Two linear transformations with a ReLU activation in between + self.linear1 = nn.Linear(d_model, d_ff) + self.linear2 = nn.Linear(d_ff, d_model) + + # Dropout + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Apply position-wise feed-forward network. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # First linear transformation with ReLU activation + x = F.relu(self.linear1(x)) + + # Apply dropout + x = self.dropout(x) + + # Second linear transformation + x = self.linear2(x) + + return x + + +class LayerNorm(nn.Module): + """ + Layer normalization module as described in the paper. + """ + def __init__(self, features: int, eps: float = 1e-6): + """ + Initialize layer normalization. + + Args: + features: Number of features + eps: Small constant for numerical stability + """ + super(LayerNorm, self).__init__() + + # Learnable parameters + self.a_2 = nn.Parameter(torch.ones(features)) + self.b_2 = nn.Parameter(torch.zeros(features)) + + # Epsilon for numerical stability + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Apply layer normalization. + + Args: + x: Input tensor of shape (batch_size, seq_len, features) + + Returns: + Normalized tensor of shape (batch_size, seq_len, features) + """ + # Calculate mean and standard deviation + mean = x.mean(-1, keepdim=True) + std = x.std(-1, keepdim=True) + + # Normalize + x = (x - mean) / (std + self.eps) + + # Scale and shift + return self.a_2 * x + self.b_2 + + +class EncoderLayer(nn.Module): + """ + Single layer of the encoder as described in section 3.1 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float): + """ + Initialize encoder layer. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(EncoderLayer, self).__init__() + + # Multi-head self-attention + self.self_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Position-wise feed-forward network + self.feed_forward = PositionwiseFeedforward(d_model, d_ff, dropout) + + # Layer normalization + self.norm1 = LayerNorm(d_model) + self.norm2 = LayerNorm(d_model) + + # Dropout + self.dropout = dropout + self.dropout_layer = nn.Dropout(p=dropout) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + """ + Process input through encoder layer. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + mask: Mask tensor of shape (batch_size, 1, 1, seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Self-attention with residual connection and layer normalization + # (Apply layer norm before sublayer as per paper section 5.4) + attn_output = self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), mask) + x = x + self.dropout_layer(attn_output) + + # Position-wise feed-forward with residual connection and layer normalization + ff_output = self.feed_forward(self.norm2(x)) + x = x + self.dropout_layer(ff_output) + + return x + + +class DecoderLayer(nn.Module): + """ + Single layer of the decoder as described in section 3.1 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float): + """ + Initialize decoder layer. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(DecoderLayer, self).__init__() + + # Multi-head self-attention + self.self_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Multi-head cross-attention + self.cross_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Position-wise feed-forward network + self.feed_forward = PositionwiseFeedforward(d_model, d_ff, dropout) + + # Layer normalization + self.norm1 = LayerNorm(d_model) + self.norm2 = LayerNorm(d_model) + self.norm3 = LayerNorm(d_model) + + # Dropout + self.dropout = dropout + self.dropout_layer = nn.Dropout(p=dropout) + + def forward( + self, + x: torch.Tensor, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through decoder layer. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Self-attention with residual connection and layer normalization + # (Apply layer norm before sublayer as per paper section 5.4) + attn_output = self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), tgt_mask) + x = x + self.dropout_layer(attn_output) + + # Cross-attention with residual connection and layer normalization + attn_output = self.cross_attn(self.norm2(x), memory, memory, src_mask) + x = x + self.dropout_layer(attn_output) + + # Position-wise feed-forward with residual connection and layer normalization + ff_output = self.feed_forward(self.norm3(x)) + x = x + self.dropout_layer(ff_output) + + return x + + +class Encoder(nn.Module): + """ + Encoder stack as described in section 3.1 of the paper. + """ + def __init__(self, layer: EncoderLayer, N: int): + """ + Initialize encoder stack. + + Args: + layer: Encoder layer to be stacked + N: Number of layers + """ + super(Encoder, self).__init__() + + # Create a stack of N identical encoder layers + self.layers = clones(layer, N) + + # Layer normalization for the final output + self.norm = LayerNorm(layer.self_attn.d_model) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + """ + Process input through encoder stack. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + mask: Mask tensor of shape (batch_size, 1, 1, seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Pass through each encoder layer + for layer in self.layers: + x = layer(x, mask) + + # Apply final layer normalization + return self.norm(x) + + +class Decoder(nn.Module): + """ + Decoder stack as described in section 3.1 of the paper. + """ + def __init__(self, layer: DecoderLayer, N: int): + """ + Initialize decoder stack. + + Args: + layer: Decoder layer to be stacked + N: Number of layers + """ + super(Decoder, self).__init__() + + # Create a stack of N identical decoder layers + self.layers = clones(layer, N) + + # Layer normalization for the final output + self.norm = LayerNorm(layer.self_attn.d_model) + + def forward( + self, + x: torch.Tensor, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through decoder stack. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Pass through each decoder layer + for layer in self.layers: + x = layer(x, memory, src_mask, tgt_mask) + + # Apply final layer normalization + return self.norm(x) + + +class TransformerModel(nn.Module): + """ + Full Transformer model as described in the paper. + """ + def __init__(self, config: Config, src_vocab_size: int, tgt_vocab_size: int): + """ + Initialize Transformer model. + + Args: + config: Configuration object + src_vocab_size: Size of source vocabulary + tgt_vocab_size: Size of target vocabulary + """ + super(TransformerModel, self).__init__() + + # Get model parameters from config + model_params = config.get_model_params() + d_model = model_params['d_model'] + n_heads = model_params['n_heads'] + num_encoder_layers = model_params['num_encoder_layers'] + num_decoder_layers = model_params['num_decoder_layers'] + d_ff = model_params['dim_feedforward'] + dropout = model_params['dropout'] + max_seq_length = model_params['max_seq_length'] + share_weights = model_params['share_weights'] + + # Create embedding layers + self.src_embed = nn.Embedding(src_vocab_size, d_model) + self.tgt_embed = nn.Embedding(tgt_vocab_size, d_model) + + # Scale embeddings by sqrt(d_model) as mentioned in the paper + self.src_scale = math.sqrt(d_model) + self.tgt_scale = math.sqrt(d_model) + + # Positional encoding + self.pos_encoding = PositionalEncoding(d_model, dropout, max_seq_length) + + # Create encoder and decoder components + encoder_layer = EncoderLayer(d_model, n_heads, d_ff, dropout) + decoder_layer = DecoderLayer(d_model, n_heads, d_ff, dropout) + + self.encoder = Encoder(encoder_layer, num_encoder_layers) + self.decoder = Decoder(decoder_layer, num_decoder_layers) + + # Output projection + self.generator = nn.Linear(d_model, tgt_vocab_size) + + # Weight sharing between embedding layers and output projection + if share_weights: + self.generator.weight = self.tgt_embed.weight + + # Initialize parameters + self._init_parameters() + + def _init_parameters(self): + """ + Initialize model parameters. + """ + # Initialize parameters with Xavier uniform distribution + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward( + self, + src: torch.Tensor, + tgt: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through the full Transformer model. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, tgt_seq_len, tgt_vocab_size) + """ + # Encode source sequence + memory = self.encode(src, src_mask) + + # Decode target sequence + output = self.decode(memory, src_mask, tgt, tgt_mask) + + # Generate final output logits + return self.generator(output) + + def encode(self, src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor: + """ + Encode source sequence. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + + Returns: + Memory tensor of shape (batch_size, src_seq_len, d_model) + """ + # Apply input embedding, scaling, and positional encoding + src_embedded = self.pos_encoding(self.src_embed(src) * self.src_scale) + + # Encode + return self.encoder(src_embedded, src_mask) + + def decode( + self, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Decode target sequence. + + Args: + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Decoded output tensor of shape (batch_size, tgt_seq_len, d_model) + """ + # Apply target embedding, scaling, and positional encoding + tgt_embedded = self.pos_encoding(self.tgt_embed(tgt) * self.tgt_scale) + + # Decode + return self.decoder(tgt_embedded, memory, src_mask, tgt_mask) + + def create_masks(self, src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Create masks for transformer model. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + + Returns: + Tuple of (source mask, target mask) + """ + # Determine padding index (assuming 0) + pad_idx = 0 + + # Create source padding mask + src_mask = create_padding_mask(src, pad_idx) + + # Create target mask (combining padding and subsequent masks) + tgt_padding_mask = create_padding_mask(tgt, pad_idx) + tgt_subsequent_mask = create_subsequent_mask(tgt.size(1)).to(tgt.device) + + # Combine masks (broadcasting to the right shape) + tgt_mask = tgt_padding_mask | tgt_subsequent_mask + + return src_mask, tgt_mask + + def beam_search(self, src: torch.Tensor, max_len: int, start_symbol: int, end_symbol: int = 2) -> torch.Tensor: + """ + Perform beam search decoding for inference. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + max_len: Maximum length of output sequence + start_symbol: Start symbol index + end_symbol: End symbol index (default: 2) + + Returns: + Generated output sequence tensor of shape (batch_size, seq_len) + """ + # Get device from source tensor + device = src.device + + # Get beam size from config (default to 4 if not in config) + beam_size = 4 # Will be overridden by actual config value + length_penalty = 0.6 # Will be overridden by actual config value + + # Try to get beam size and length penalty from global config + try: + from config import Config + cfg = Config() + inference_params = cfg.get_inference_params() + beam_size = inference_params.get('beam_size', beam_size) + length_penalty = inference_params.get('length_penalty', length_penalty) + except: + pass + + # Get batch size and encode the source sequence + batch_size = src.size(0) + src_mask = create_padding_mask(src, 0) + memory = self.encode(src, src_mask) + + # We'll process each batch item separately + generated_sequences = [] + + for batch_idx in range(batch_size): + # Take the memory for this batch item and repeat it for beam size + batch_memory = memory[batch_idx:batch_idx+1].repeat(beam_size, 1, 1) + batch_src_mask = src_mask[batch_idx:batch_idx+1].repeat(beam_size, 1, 1, 1) + + # Initialize beams with start symbol + beams = torch.full((beam_size, 1), start_symbol, dtype=torch.long, device=device) + + # Initialize beam scores + beam_scores = torch.zeros(beam_size, device=device) + + # Flag to indicate which beams are completed + completed_beams = torch.zeros(beam_size, dtype=torch.bool, device=device) + + # Generate tokens one by one + for step in range(max_len - 1): + # Don't decode further for completed beams + if completed_beams.all(): + break + + # Create mask for current beams + beam_tgt_mask = create_subsequent_mask(beams.size(1)).to(device) + + # Decode one step for all beams + decoder_output = self.decode(batch_memory, batch_src_mask, beams, beam_tgt_mask) + + # Get next token predictions (last position) + logits = self.generator(decoder_output[:, -1]) + + # Convert to log probabilities + log_probs = F.log_softmax(logits, dim=-1) + + # For completed beams, only the end_symbol should be considered + for b in range(beam_size): + if completed_beams[b]: + log_probs[b, :] = float('-inf') + log_probs[b, end_symbol] = 0.0 + + # Calculate scores for next tokens based on current beam scores + vocab_size = log_probs.size(1) + next_scores = beam_scores.unsqueeze(1) + log_probs + + # For first step, consider only single beam + if step == 0: + # For first step, treat log_probs for first beam only + next_scores = next_scores[0].unsqueeze(0) + + # Flatten scores to select top-k + flat_next_scores = next_scores.view(-1) + + # Select top-k scores and convert to beam indices and token indices + if step == 0: + # For first step, select top-k from the first beam only + top_scores, top_indices = flat_next_scores.topk(beam_size, dim=0) + beam_indices = torch.zeros(beam_size, dtype=torch.long, device=device) + token_indices = top_indices + else: + # For subsequent steps, select top-k from all beams + top_scores, top_indices = flat_next_scores.topk(beam_size, dim=0) + beam_indices = top_indices // vocab_size + token_indices = top_indices % vocab_size + + # Update beam scores + beam_scores = top_scores + + # Create new beams by appending selected tokens to selected beams + new_beams = [] + for i in range(beam_size): + beam_idx = beam_indices[i] + token_idx = token_indices[i] + + # Update completed beams flag + if token_idx == end_symbol: + completed_beams[i] = True + + # Create new beam by appending token to selected beam + new_beam = torch.cat([beams[beam_idx], token_idx.unsqueeze(0)], dim=0).unsqueeze(0) + new_beams.append(new_beam) + + # Concatenate new beams + beams = torch.cat(new_beams, dim=0) + + # Apply length penalty + beam_lengths = torch.sum(beams != 0, dim=1).float() + normalized_scores = beam_scores / ((5 + beam_lengths) ** length_penalty / (5 + 1) ** length_penalty) + + # Select the best beam + best_beam_idx = normalized_scores.argmax() + best_beam = beams[best_beam_idx].unsqueeze(0) + + # Add to generated sequences + generated_sequences.append(best_beam) + + # Concatenate all batch results + return torch.cat(generated_sequences, dim=0) + +``` \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/train.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/train.py_coding.txt new file mode 100644 index 00000000..bc6d633e --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/train.py_coding.txt @@ -0,0 +1,385 @@ +## train.py + +```python +""" +Training module for the Transformer model implementation. +Handles the training and validation loops for the Transformer model. +""" + +import os +import time +import math +from typing import Optional, Tuple, Dict, List, Any + +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from utils import ( + label_smoothed_nll_loss, + get_lr_scheduler, + save_checkpoint, + load_checkpoint, + create_masks +) + + +class Trainer: + """ + Handles the training and validation loops for the Transformer model. + """ + def __init__( + self, + config: Config, + model: TransformerModel, + data_processor: DataProcessor + ): + """ + Initialize trainer. + + Args: + config: Configuration object + model: TransformerModel instance + data_processor: DataProcessor instance + """ + self.config = config + self.model = model + self.data_processor = data_processor + + # Get training parameters + training_params = config.get_training_params() + self.device = training_params['device'] + self.warmup_steps = training_params['warmup_steps'] + self.label_smoothing = training_params['label_smoothing'] + self.total_steps = training_params['total_steps'] + self.checkpoint_interval = training_params['checkpoint_interval'] + + # Move model to device + self.model.to(self.device) + + # Initialize optimizer with betas and epsilon as specified in the paper + self.optimizer = optim.Adam( + model.parameters(), + lr=0.0, # Will be set by scheduler + betas=(training_params['beta1'], training_params['beta2']), + eps=training_params['epsilon'] + ) + + # Initialize learning rate scheduler + self.lr_scheduler = get_lr_scheduler( + self.optimizer, + config.get_model_params()['d_model'], + self.warmup_steps + ) + + # Initialize tensorboard writer + self.writer = SummaryWriter(log_dir="logs") + + # Track training statistics + self.step = 0 + self.epoch = 0 + self.best_val_loss = float('inf') + + def train( + self, + train_data: DataLoader, + val_data: DataLoader, + epochs: Optional[int] = None, + checkpoint_dir: str = "checkpoints" + ) -> None: + """ + Main training loop. + + Args: + train_data: Training data loader + val_data: Validation data loader + epochs: Number of epochs to train (if None, will use total_steps) + checkpoint_dir: Directory to save checkpoints + """ + # Create checkpoint directory if it doesn't exist + os.makedirs(checkpoint_dir, exist_ok=True) + + # Start time for checkpoint saving + last_checkpoint_time = time.time() + + print(f"Starting training on device: {self.device}") + print(f"Model size: {self.config.model_size}") + + # Main training loop + while True: + self.epoch += 1 + print(f"\nEpoch {self.epoch}") + + # Train for one epoch + train_loss = self.train_epoch(train_data) + + # Evaluate on validation data + val_loss = self.validate(val_data) + + # Log losses + self.writer.add_scalar('Loss/train', train_loss, self.epoch) + self.writer.add_scalar('Loss/val', val_loss, self.epoch) + + print(f"Epoch {self.epoch}: Train loss = {train_loss:.4f}, Val loss = {val_loss:.4f}") + + # Save best model + if val_loss < self.best_val_loss: + self.best_val_loss = val_loss + self.save_checkpoint(os.path.join(checkpoint_dir, "best_model.pt")) + print(f"New best model saved with validation loss: {val_loss:.4f}") + + # Save checkpoint periodically + current_time = time.time() + if current_time - last_checkpoint_time > self.checkpoint_interval * 60: # Convert to seconds + checkpoint_path = os.path.join(checkpoint_dir, f"checkpoint_epoch{self.epoch}_step{self.step}.pt") + self.save_checkpoint(checkpoint_path) + last_checkpoint_time = current_time + + # Check stopping conditions + if self.step >= self.total_steps: + print(f"Reached {self.total_steps} steps. Training complete.") + # Save final model + self.save_checkpoint(os.path.join(checkpoint_dir, "final_model.pt")) + break + + if epochs is not None and self.epoch >= epochs: + print(f"Reached {epochs} epochs. Training complete.") + # Save final model + self.save_checkpoint(os.path.join(checkpoint_dir, "final_model.pt")) + break + + def train_epoch(self, train_data: DataLoader) -> float: + """ + Train for one epoch. + + Args: + train_data: Training data loader + + Returns: + Average loss for the epoch + """ + self.model.train() + total_loss = 0 + total_tokens = 0 + start_time = time.time() + + # Create progress bar + pbar = tqdm(train_data, desc=f"Training epoch {self.epoch}") + + for i, (src, tgt) in enumerate(pbar): + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + # Input: w1 w2 w3 + # Target: w1 w2 w3 + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss with label smoothing + loss, nll_loss = label_smoothed_nll_loss( + logits, + tgt_output, + self.label_smoothing, + ignore_index=self.data_processor.PAD_IDX + ) + + # Backward pass and optimization + self.optimizer.zero_grad() + loss.backward() + + # Clip gradients as mentioned in paper (not explicitly stated value, using common default) + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + + # Adjust learning rate according to schedule + lr = self.lr_scheduler(self.step) + + # Update parameters + self.optimizer.step() + + # Update statistics + self.step += 1 + total_loss += nll_loss.item() * tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Update progress bar + pbar.set_postfix({ + 'loss': f"{loss.item():.4f}", + 'lr': f"{lr:.7f}", + 'step': self.step + }) + + # Log to tensorboard + self.writer.add_scalar('Loss/train_step', loss.item(), self.step) + self.writer.add_scalar('Learning rate', lr, self.step) + + # Check if total steps reached + if self.step >= self.total_steps: + break + + # Calculate average loss + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + + # Calculate training time + elapsed_time = time.time() - start_time + tokens_per_sec = total_tokens / elapsed_time if elapsed_time > 0 else 0 + + print(f"Epoch {self.epoch} completed in {elapsed_time:.2f} seconds") + print(f"Training throughput: {tokens_per_sec:.2f} tokens/sec") + + return avg_loss + + def validate(self, val_data: DataLoader) -> float: + """ + Validate model. + + Args: + val_data: Validation data loader + + Returns: + Validation loss + """ + self.model.eval() + total_loss = 0 + total_tokens = 0 + + # Create progress bar + pbar = tqdm(val_data, desc=f"Validating epoch {self.epoch}") + + with torch.no_grad(): + for src, tgt in pbar: + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss with label smoothing + _, nll_loss = label_smoothed_nll_loss( + logits, + tgt_output, + self.label_smoothing, + ignore_index=self.data_processor.PAD_IDX + ) + + # Update statistics + total_loss += nll_loss.item() * tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Calculate average loss + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + + return avg_loss + + def save_checkpoint(self, path: str) -> None: + """ + Save model checkpoint. + + Args: + path: Path to save checkpoint + """ + save_checkpoint( + self.model, + self.optimizer, + self.epoch, + self.step, + self.best_val_loss, + path + ) + + def load_checkpoint(self, path: str) -> Dict[str, Any]: + """ + Load model checkpoint. + + Args: + path: Path to load checkpoint from + + Returns: + Dictionary with checkpoint metadata + """ + metadata = load_checkpoint(path, self.model, self.optimizer) + + # Update trainer state + self.epoch = metadata.get('epoch', 0) + self.step = metadata.get('step', 0) + self.best_val_loss = metadata.get('loss', float('inf')) + + print(f"Loaded checkpoint from {path}") + print(f"Epoch: {self.epoch}, Step: {self.step}, Best val loss: {self.best_val_loss:.4f}") + + return metadata + + def adjust_learning_rate(self, step: int) -> float: + """ + Adjust learning rate according to schedule. + + Args: + step: Current step number + + Returns: + New learning rate + """ + return self.lr_scheduler(step) + + +if __name__ == "__main__": + # Simple test for the trainer + print("Testing Trainer class...") + + # Load configuration + config = Config(model_size='base') + + # Create dummy data processor + data_processor = DataProcessor(config) + + # Create dummy model + model = TransformerModel( + config, + src_vocab_size=1000, # Dummy value + tgt_vocab_size=1000 # Dummy value + ) + + # Create trainer + trainer = Trainer(config, model, data_processor) + + print("Trainer initialized successfully!") + print(f"Model will be trained for {trainer.total_steps} steps with {trainer.warmup_steps} warmup steps") + print(f"Label smoothing: {trainer.label_smoothing}") + print(f"Device: {trainer.device}") +``` \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/utils.py_coding.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/utils.py_coding.txt new file mode 100644 index 00000000..26956547 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/coding_artifacts/utils.py_coding.txt @@ -0,0 +1,415 @@ +Analyzing the requirements for `utils.py` based on the paper, design, and task information. + +According to the design, utils.py should contain utility functions for the Transformer implementation, including: +- Mask creation for self-attention +- Implementation of label smoothing loss +- Learning rate scheduling +- Checkpoint management +- Model weight averaging + +I'll now implement these functions based on the paper's specifications and the provided configuration. + +## Code: utils.py + +```python +""" +Utility functions for the Transformer model implementation. + +This module contains helper functions used across the project: +- Attention masking functions +- Label smoothing loss +- Learning rate scheduler +- Checkpoint management +- Model weight averaging +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import os +import math +import copy +from typing import Dict, List, Optional, Callable, Tuple + + +def create_subsequent_mask(size: int) -> torch.Tensor: + """ + Create a mask for decoder self-attention to prevent attending to future positions. + + Args: + size: Size of the square mask + + Returns: + Mask tensor of shape (1, size, size) with zeros for allowed positions and -inf for masked positions + """ + # Create upper triangular matrix with ones (including diagonal) + mask = torch.triu(torch.ones(1, size, size), diagonal=1) + # Convert to boolean mask where True means position should be masked + return mask == 1 + + +def create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: + """ + Create a mask to hide padding tokens in the input sequence. + + Args: + seq: Sequence tensor of shape (batch_size, seq_len) + pad_idx: Index used for padding + + Returns: + Mask tensor of shape (batch_size, 1, 1, seq_len) with zeros for tokens and ones for padding + """ + # Create mask where 1 indicates a padding token + mask = (seq == pad_idx).unsqueeze(1).unsqueeze(2) + return mask + + +def label_smoothed_nll_loss( + pred: torch.Tensor, + target: torch.Tensor, + epsilon: float, + ignore_index: int = -100 +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate cross entropy loss with label smoothing. + + Args: + pred: Predicted logits of shape (batch_size, seq_len, vocab_size) + target: Target indices of shape (batch_size, seq_len) + epsilon: Label smoothing factor + ignore_index: Index to ignore in the loss calculation (e.g., padding) + + Returns: + Tuple of (smoothed loss, regular nll loss for logging purposes) + """ + # Get vocab size from predictions + vocab_size = pred.size(-1) + + # Calculate standard NLL loss for logging + nll_loss = F.nll_loss( + F.log_softmax(pred, dim=-1).view(-1, vocab_size), + target.view(-1), + ignore_index=ignore_index, + reduction='sum' + ) + + # If no label smoothing, return standard loss + if epsilon == 0: + return nll_loss, nll_loss + + # Create smoothed target distribution: + # - (1-epsilon) for correct class + # - epsilon/(vocab_size-1) for all other classes + + # Create a mask for non-ignored positions + non_pad_mask = target != ignore_index + num_non_pad_tokens = non_pad_mask.sum().item() + + # Apply log_softmax to get log probabilities + log_probs = F.log_softmax(pred, dim=-1) + + # Reshape to (N, vocab_size) + log_probs = log_probs.view(-1, vocab_size) + target_flat = target.view(-1) + + # Calculate smoothed loss: + # - Loss with correct targets weighted by (1-epsilon) + # - Loss with uniform distribution weighted by epsilon + smooth_loss = -log_probs.sum(dim=-1) # Sum over vocab (uniform part) + smooth_loss = smooth_loss.masked_select(non_pad_mask.view(-1)).sum() # Sum over non-pad tokens + smooth_loss = smooth_loss * (epsilon / vocab_size) + + # Add the non-smoothed component + correct_log_probs = log_probs.gather(dim=-1, index=target_flat.unsqueeze(1)).squeeze(1) + correct_log_probs = correct_log_probs.masked_select(non_pad_mask.view(-1)) + smooth_loss -= correct_log_probs.sum() * (epsilon / vocab_size) # Subtract from uniform part + smooth_loss += (1 - epsilon) * (-correct_log_probs.sum()) # Add weighted correct part + + # Return both smoothed loss and original NLL loss (for logging) + return smooth_loss / num_non_pad_tokens, nll_loss / num_non_pad_tokens + + +def get_lr_scheduler( + optimizer: torch.optim.Optimizer, + d_model: int, + warmup_steps: int +) -> Callable[[int], float]: + """ + Create a learning rate scheduler as described in the paper: + lr = d_model^(-0.5) * min(step^(-0.5), step * warmup_steps^(-1.5)) + + Args: + optimizer: Optimizer to schedule + d_model: Model dimension + warmup_steps: Number of warmup steps + + Returns: + Function that updates learning rate based on step number + """ + # Define the learning rate schedule function + def lr_lambda(step: int) -> float: + """Calculate learning rate multiplier based on step.""" + # Protect against division by zero + step = max(1, step) + + # Apply formula from paper: lrate = d_model^(-0.5) * min(step^(-0.5), step * warmup_steps^(-1.5)) + factor = d_model ** (-0.5) + arg1 = step ** (-0.5) + arg2 = step * (warmup_steps ** (-1.5)) + + return factor * min(arg1, arg2) + + # Create a function to update optimizer learning rate based on step + def update_lr(step: int) -> None: + """Update optimizer learning rate based on step number.""" + lr = lr_lambda(step) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + return lr + + return update_lr + + +def save_checkpoint( + model: nn.Module, + optimizer: torch.optim.Optimizer, + epoch: int, + step: int, + loss: float, + path: str +) -> None: + """ + Save model checkpoint. + + Args: + model: Model to save + optimizer: Optimizer to save + epoch: Current epoch number + step: Current step number + loss: Current loss value + path: Path to save checkpoint + """ + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(path), exist_ok=True) + + # Save checkpoint with model state, optimizer state, and metadata + checkpoint = { + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + 'step': step, + 'loss': loss + } + + torch.save(checkpoint, path) + print(f"Checkpoint saved to {path}") + + +def load_checkpoint( + path: str, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None +) -> Dict: + """ + Load model checkpoint. + + Args: + path: Path to load checkpoint from + model: Model to load weights into + optimizer: Optimizer to load state into (optional) + + Returns: + Dictionary containing checkpoint metadata (epoch, step, loss) + """ + # Check if checkpoint file exists + if not os.path.isfile(path): + raise FileNotFoundError(f"Checkpoint file not found: {path}") + + # Load checkpoint + checkpoint = torch.load(path, map_location=torch.device('cpu')) + + # Load model state + model.load_state_dict(checkpoint['model']) + + # Load optimizer state if provided + if optimizer is not None and 'optimizer' in checkpoint: + optimizer.load_state_dict(checkpoint['optimizer']) + + # Return metadata + return { + 'epoch': checkpoint.get('epoch', 0), + 'step': checkpoint.get('step', 0), + 'loss': checkpoint.get('loss', float('inf')) + } + + +def average_checkpoints(paths: List[str], model: nn.Module) -> None: + """ + Average weights from multiple model checkpoints as described in the paper. + + Args: + paths: List of paths to checkpoints + model: Model to load averaged weights into + """ + # Ensure there are checkpoints to average + if not paths: + raise ValueError("No checkpoint paths provided for averaging") + + # Initialize dictionary to store sum of parameters + averaged_params = {} + + # Load and sum parameters from each checkpoint + for i, path in enumerate(paths): + # Load checkpoint + checkpoint = torch.load(path, map_location=torch.device('cpu')) + model_params = checkpoint['model'] + + # Initialize parameter sum or add to it + if i == 0: + for name, param in model_params.items(): + averaged_params[name] = param.clone() + else: + for name, param in model_params.items(): + averaged_params[name] += param + + # Average parameters by dividing by number of checkpoints + for name, param in averaged_params.items(): + averaged_params[name] = param / len(paths) + + # Load averaged parameters into model + model.load_state_dict(averaged_params) + print(f"Loaded averaged weights from {len(paths)} checkpoints") + + +def greedy_decode( + model: nn.Module, + src: torch.Tensor, + src_mask: torch.Tensor, + max_len: int, + start_symbol: int, + end_symbol: int +) -> torch.Tensor: + """ + Greedy decoding for inference (used for ablation studies). + + Args: + model: Transformer model + src: Source tensor of shape (batch_size, src_len) + src_mask: Source mask tensor + max_len: Maximum output sequence length + start_symbol: Start symbol index + end_symbol: End symbol index + + Returns: + Generated output sequence tensor of shape (batch_size, seq_len) + """ + # Get device from source tensor + device = src.device + + # Get batch size + batch_size = src.size(0) + + # Encode the source sequence + memory = model.encode(src, src_mask) + + # Initialize decoder input with start symbol for each batch item + ys = torch.ones(batch_size, 1).fill_(start_symbol).long().to(device) + + # Generate output sequence + for i in range(max_len - 1): + # Create target mask for current output + tgt_mask = create_subsequent_mask(ys.size(1)).to(device) + + # Decode one step + out = model.decode(memory, src_mask, ys, tgt_mask) + + # Get next token prediction (last position) + prob = model.generator(out[:, -1]) + + # Select most likely next token + _, next_word = torch.max(prob, dim=1) + + # Add predicted token to output + next_word = next_word.unsqueeze(1) + ys = torch.cat([ys, next_word], dim=1) + + # Stop if all sequences have end symbol + if (next_word == end_symbol).all(): + break + + return ys + + +def create_masks( + src: torch.Tensor, + tgt: Optional[torch.Tensor], + pad_idx: int +) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Create masks for transformer model. + + Args: + src: Source tensor of shape (batch_size, src_len) + tgt: Target tensor of shape (batch_size, tgt_len) or None for inference + pad_idx: Padding token index + + Returns: + Tuple of (src_mask, tgt_mask) where tgt_mask is None if tgt is None + """ + # Create source padding mask + src_mask = create_padding_mask(src, pad_idx) + + # Create target mask if target is provided + tgt_mask = None + if tgt is not None: + # Create padding mask + tgt_padding_mask = create_padding_mask(tgt, pad_idx) + + # Create subsequent mask + tgt_subsequent_mask = create_subsequent_mask(tgt.size(1)).to(tgt.device) + + # Combine padding and subsequent masks + tgt_mask = tgt_padding_mask | tgt_subsequent_mask + + return src_mask, tgt_mask + + +def clones(module: nn.Module, N: int) -> nn.ModuleList: + """ + Produce N identical layers. + Used for creating multiple identical encoder/decoder layers. + + Args: + module: Module to clone + N: Number of clones to make + + Returns: + ModuleList containing N clones of the module + """ + return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) +``` + +This implementation of `utils.py` provides all the utility functions needed for the Transformer model according to the design specifications. Key features include: + +1. **Masking functions**: + - `create_subsequent_mask`: Creates causal mask for decoder self-attention + - `create_padding_mask`: Creates mask for padding tokens + - `create_masks`: Helper function to create both source and target masks + +2. **Loss function**: + - `label_smoothed_nll_loss`: Implements label smoothing as described in the paper + +3. **Learning rate scheduler**: + - `get_lr_scheduler`: Implements the custom learning rate schedule from the paper with warmup + +4. **Checkpoint management**: + - `save_checkpoint`: Saves model and optimizer states + - `load_checkpoint`: Loads model and optimizer states + - `average_checkpoints`: Averages multiple checkpoints as described in the paper + +5. **Decoding helpers**: + - `greedy_decode`: Simple greedy decoding function (for testing or ablation studies) + - `clones`: Utility to create multiple identical layers (used for encoder/decoder) + +All functions are properly typed and documented for clarity and reusability. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_response.json new file mode 100644 index 00000000..04f30571 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis: config.py\n\n## Overview\nThe `config.py` module will define a `Config` class responsible for managing all hyperparameters and configuration settings for the Transformer model implementation. This class needs to be designed to load and provide access to the configuration parameters specified in the `config.yaml` file, supporting both 'base' and 'big' model variants as described in the paper.\n\n## Requirements Analysis\n\nThe `Config` class must:\n1. Load configuration from the YAML file\n2. Support different model sizes ('base' and 'big')\n3. Provide organized access to different parameter groups (model architecture, training, inference, data)\n4. Include getter methods for common parameter groups\n5. Handle device selection (CPU/GPU)\n\n## Key Parameters from the Paper\n\n### Model Architecture Parameters\n- **Base Model**:\n - d_model = 512 (embedding dimension)\n - n_heads = 8 (attention heads)\n - num_encoder_layers = num_decoder_layers = 6\n - dim_feedforward = 2048\n - dropout = 0.1\n - Weight sharing between embedding and output projection\n\n- **Big Model**:\n - d_model = 1024\n - n_heads = 16\n - num_encoder_layers = num_decoder_layers = 6\n - dim_feedforward = 4096\n - dropout = 0.3 (for EN-DE) or 0.1 (for EN-FR)\n - Weight sharing between embedding and output projection\n\n### Training Parameters\n- Adam optimizer with \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule with warmup_steps = 4000\n- Label smoothing \u03f5 = 0.1\n- Batch size ~25000 source and target tokens\n- Training steps: 100,000 for base model, 300,000 for big model\n- Checkpoint averaging: 5 for base, 20 for big (saved at 10-min intervals)\n\n### Inference Parameters\n- Beam size = 4\n- Length penalty \u03b1 = 0.6\n- Max output length = input length + 50\n\n### Data Parameters\n- EN-DE: BPE vocabulary of ~37,000 tokens\n- EN-FR: Word-piece vocabulary of 32,000 tokens\n\n## Class Design\n\nThe `Config` class should:\n1. Initialize with model size ('base' or 'big')\n2. Load the YAML configuration file\n3. Set attributes based on the configuration and model size\n4. Provide methods for accessing grouped parameters\n\n## Implementation Logic\n\n1. **Constructor**:\n - Accept model_size parameter ('base' or 'big')\n - Accept optional config_path parameter (default to a standard location)\n - Load the YAML file\n - Set attributes based on model size\n - Initialize device (CPU/GPU)\n\n2. **YAML Loading**:\n - Use PyYAML to load the configuration\n - Handle file not found errors gracefully\n - Validate that the required sections exist in the config\n\n3. **Parameter Grouping Methods**:\n - `get_model_params()`: Return architecture parameters (d_model, n_heads, etc.)\n - `get_training_params()`: Return training parameters (optimizer settings, learning rate, etc.)\n - `get_inference_params()`: Return inference parameters (beam size, length penalty, etc.)\n - `get_data_params()`: Return data processing parameters (dataset paths, vocabulary sizes, etc.)\n\n4. **Special Handling**:\n - Language pair selection (EN-DE vs EN-FR) might affect some parameters\n - Device selection based on CUDA availability\n - Provide default values for any missing parameters in the config file\n\n## Error Handling and Validation\n\nThe class should:\n1. Validate model_size is either 'base' or 'big'\n2. Validate the existence of the config file\n3. Handle missing parameters with sensible defaults where appropriate\n4. Provide clear error messages for required parameters that are missing\n\n## Interface with Other Modules\n\nThe `Config` class will be imported by:\n1. `model.py` - To access model architecture parameters\n2. `train.py` - To access training parameters\n3. `evaluate.py` - To access inference parameters\n4. `data_processing.py` - To access data parameters\n5. `main.py` - To initialize the configuration\n\n## Data Flow\n\n1. `main.py` creates a Config instance\n2. The Config instance is passed to DataProcessor, TransformerModel, Trainer, and Evaluator\n3. Each module accesses the relevant parameters via the getter methods\n\nThis design ensures a centralized configuration management system that supports the reproduction of both the base and big Transformer models as described in the paper while providing flexibility for experimentation and adaptation to available resources."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..c40ab578 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/config.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'config.py', which is intended for 'Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\n\nClass:\n- Config: Stores configuration for model architecture, training, and evaluation\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\n - get_model_params(): Returns dictionary of model architecture parameters\n - get_training_params(): Returns dictionary of training parameters\n\nThe config will define:\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\n- Training parameters (learning rate, warmup steps, etc.)\n- Evaluation parameters (beam size, length penalty, etc.)\n- Data processing parameters (max sequence length, batch size, etc.)\n\nNo external dependencies besides standard Python libraries.'.\n\n-----\n\n## Logic Analysis: config.py"}, {"role": "assistant", "content": "# Logic Analysis: config.py\n\n## Overview\nThe `config.py` module will define a `Config` class responsible for managing all hyperparameters and configuration settings for the Transformer model implementation. This class needs to be designed to load and provide access to the configuration parameters specified in the `config.yaml` file, supporting both 'base' and 'big' model variants as described in the paper.\n\n## Requirements Analysis\n\nThe `Config` class must:\n1. Load configuration from the YAML file\n2. Support different model sizes ('base' and 'big')\n3. Provide organized access to different parameter groups (model architecture, training, inference, data)\n4. Include getter methods for common parameter groups\n5. Handle device selection (CPU/GPU)\n\n## Key Parameters from the Paper\n\n### Model Architecture Parameters\n- **Base Model**:\n - d_model = 512 (embedding dimension)\n - n_heads = 8 (attention heads)\n - num_encoder_layers = num_decoder_layers = 6\n - dim_feedforward = 2048\n - dropout = 0.1\n - Weight sharing between embedding and output projection\n\n- **Big Model**:\n - d_model = 1024\n - n_heads = 16\n - num_encoder_layers = num_decoder_layers = 6\n - dim_feedforward = 4096\n - dropout = 0.3 (for EN-DE) or 0.1 (for EN-FR)\n - Weight sharing between embedding and output projection\n\n### Training Parameters\n- Adam optimizer with \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule with warmup_steps = 4000\n- Label smoothing \u03f5 = 0.1\n- Batch size ~25000 source and target tokens\n- Training steps: 100,000 for base model, 300,000 for big model\n- Checkpoint averaging: 5 for base, 20 for big (saved at 10-min intervals)\n\n### Inference Parameters\n- Beam size = 4\n- Length penalty \u03b1 = 0.6\n- Max output length = input length + 50\n\n### Data Parameters\n- EN-DE: BPE vocabulary of ~37,000 tokens\n- EN-FR: Word-piece vocabulary of 32,000 tokens\n\n## Class Design\n\nThe `Config` class should:\n1. Initialize with model size ('base' or 'big')\n2. Load the YAML configuration file\n3. Set attributes based on the configuration and model size\n4. Provide methods for accessing grouped parameters\n\n## Implementation Logic\n\n1. **Constructor**:\n - Accept model_size parameter ('base' or 'big')\n - Accept optional config_path parameter (default to a standard location)\n - Load the YAML file\n - Set attributes based on model size\n - Initialize device (CPU/GPU)\n\n2. **YAML Loading**:\n - Use PyYAML to load the configuration\n - Handle file not found errors gracefully\n - Validate that the required sections exist in the config\n\n3. **Parameter Grouping Methods**:\n - `get_model_params()`: Return architecture parameters (d_model, n_heads, etc.)\n - `get_training_params()`: Return training parameters (optimizer settings, learning rate, etc.)\n - `get_inference_params()`: Return inference parameters (beam size, length penalty, etc.)\n - `get_data_params()`: Return data processing parameters (dataset paths, vocabulary sizes, etc.)\n\n4. **Special Handling**:\n - Language pair selection (EN-DE vs EN-FR) might affect some parameters\n - Device selection based on CUDA availability\n - Provide default values for any missing parameters in the config file\n\n## Error Handling and Validation\n\nThe class should:\n1. Validate model_size is either 'base' or 'big'\n2. Validate the existence of the config file\n3. Handle missing parameters with sensible defaults where appropriate\n4. Provide clear error messages for required parameters that are missing\n\n## Interface with Other Modules\n\nThe `Config` class will be imported by:\n1. `model.py` - To access model architecture parameters\n2. `train.py` - To access training parameters\n3. `evaluate.py` - To access inference parameters\n4. `data_processing.py` - To access data parameters\n5. `main.py` - To initialize the configuration\n\n## Data Flow\n\n1. `main.py` creates a Config instance\n2. The Config instance is passed to DataProcessor, TransformerModel, Trainer, and Evaluator\n3. Each module accesses the relevant parameters via the getter methods\n\nThis design ensures a centralized configuration management system that supports the reproduction of both the base and big Transformer models as described in the paper while providing flexibility for experimentation and adaptation to available resources."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_response.json new file mode 100644 index 00000000..99223bfb --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis for data_processing.py\n\n## Overview\n\nThe `data_processing.py` file is responsible for handling all data-related operations for the Transformer model implementation. This includes downloading datasets, tokenization, vocabulary building, batch creation, and mask generation. The implementation must align with the paper \"Attention Is All You Need\" which specifies using byte-pair encoding (BPE) for English-German translation and word-piece encoding for English-French translation.\n\n## Key Requirements\n\n1. Process the WMT14 dataset for English-German (~4.5M sentence pairs) and English-French (~36M sentence pairs)\n2. Implement tokenization with BPE (37K vocab) for EN-DE and word-piece (32K vocab) for EN-FR\n3. Create batches containing approximately 25,000 tokens (target batch size)\n4. Handle masking for padding and autoregressive behavior\n5. Provide an interface compatible with PyTorch's DataLoader\n\n## Data Processor Class Design\n\n### `DataProcessor` Class\n\n#### `__init__(config: Config)`\n- Initialize using configuration from `config.yaml` via the `Config` class\n- Store parameters like vocabulary sizes, maximum sequence length, tokenization method (BPE vs word-piece)\n- Initialize tokenizers (sentencepiece for BPE/word-piece)\n- Set up device for tensor operations\n- Store special token indices (PAD, BOS, EOS, UNK)\n\n#### `load_data(dataset_path: str) -> Tuple[DataLoader]`\n- Check if dataset exists locally, if not, download and extract\n- Handle paths for WMT14 EN-DE or EN-FR datasets based on config\n- Load train/val/test splits according to paper (newstest2013 for validation, newstest2014 for testing)\n- Process raw text files into source-target sentence pairs\n- Apply tokenization to all datasets\n- Build vocabularies from training data if not already created\n- Create data loaders with batching\n- Return tuple of (train_loader, val_loader, test_loader)\n\n#### `build_vocab(train_data: List) -> Tuple[Vocab]`\n- Process training data to build vocabularies for source and target languages\n- Use sentencepiece to train BPE model for EN-DE (37K tokens) or word-piece model for EN-FR (32K tokens)\n- Include special tokens: PAD (padding), BOS (beginning of sequence), EOS (end of sequence), UNK (unknown)\n- Save vocabulary to disk for reuse\n- Return tuple of (source_vocab, target_vocab)\n\n#### `tokenize(text: str) -> List[str]`\n- Basic tokenization of text (e.g., splitting by whitespace, handling punctuation)\n- This is a pre-processing step before applying BPE/word-piece\n- Return list of tokens\n\n#### `apply_bpe(tokens: List[str]) -> List[str]` / `apply_wordpiece(tokens: List[str]) -> List[str]`\n- Apply byte-pair encoding or word-piece encoding to tokenized text\n- Use sentencepiece library for consistent implementation\n- Convert tokens to subword units according to trained BPE/word-piece model\n- Return list of subword tokens\n\n#### `batch_data(data: List) -> Iterator`\n- Group sentences of similar lengths to minimize padding waste\n- Create batches with target size of ~25,000 tokens as mentioned in the paper\n- Convert tokenized text to tensor indices using vocabulary\n- Add BOS/EOS tokens appropriately\n- Pad sequences to equal length within each batch\n- Return iterator over batched data\n\n#### `create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]`\n- Create padding mask for source sequence (to mask padding tokens in encoder self-attention)\n- Create combined padding + subsequent mask for target sequence:\n - Padding mask: to mask padding tokens in decoder self-attention\n - Subsequent mask: to prevent attending to future positions in decoder self-attention\n- These masks are essential for the Transformer's attention mechanisms\n- Return tuple of (src_mask, tgt_mask)\n\n## Technical Considerations\n\n1. **Data Loading Efficiency**:\n - Processing WMT14 datasets (especially EN-FR with 36M pairs) requires efficient streaming\n - Need to handle large files without loading entire dataset into memory\n\n2. **Tokenization Pipeline**:\n - The paper uses BPE for EN-DE and word-piece for EN-FR\n - Both can be implemented using sentencepiece library\n - Need to save/load tokenizer models for consistent application across train/val/test sets\n\n3. **Batch Creation Logic**:\n - The paper mentions batches containing ~25,000 source and ~25,000 target tokens\n - This requires dynamic batch sizing based on sequence lengths\n - Sentences should be grouped by approximate length to minimize padding\n\n4. **Mask Creation**:\n - Source mask: binary mask where 1s indicate non-padding positions\n - Target mask: combination of padding mask and subsequent (causal/triangular) mask\n - These masks should be properly shaped for the attention mechanism\n\n5. **Vocabulary Management**:\n - Shared source-target vocabulary for both language pairs (37K for EN-DE, 32K for EN-FR)\n - Need to handle special tokens (PAD, BOS, EOS, UNK) consistently\n\n6. **Data Caching**:\n - For efficiency, preprocessed datasets should be cached to disk\n - Vocabulary and tokenizer models should be saved for reuse\n\n7. **Integration with Training Loop**:\n - Ensure data processing output is compatible with model input requirements\n - Provide proper formatting for source/target sequences and attention masks\n\n## Integration with Other Modules\n\n- The `Config` class will provide configuration parameters like vocabulary sizes, maximum sequence length, etc.\n- The `utils.py` module will provide masking functions that are used in `create_masks()`\n- The produced data loaders will be consumed by the `Trainer` and `Evaluator` classes\n- The vocabulary objects will be used by the `TransformerModel` class for embedding layers\n\n## Dependencies\n\n- `torch`: For tensor operations and DataLoader creation\n- `torchtext`: For vocabulary and dataset utilities\n- `sentencepiece`: For BPE and word-piece tokenization\n- Custom `utils.py`: For mask creation functions\n- Custom `config.py`: For configuration parameters\n\n## Alignment with Paper\n\nThe implementation must adhere to the following specifications from the paper:\n- Use of BPE with 37K shared vocabulary for EN-DE translation\n- Use of word-piece with 32K vocabulary for EN-FR translation\n- Batch size of ~25,000 source and ~25,000 target tokens\n- Proper mask creation for the Transformer's attention mechanism\n\nThis data processing module is critical for accurate reproduction as improper data preprocessing can significantly impact the model's performance and ability to reach the BLEU scores reported in the paper."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..a54187c5 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/data_processing.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'data_processing.py', which is intended for 'Handles data loading, preprocessing, tokenization, and batching.\n\nClass:\n- DataProcessor:\n - __init__(config: Config): Initialize with configuration\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\n - tokenize(text: str) -> List[str]: Tokenize text\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\n\nThis module will handle:\n- Downloading and extracting WMT14 datasets if not available\n- Tokenization using byte-pair encoding or word-piece encoding\n- Building vocabularies\n- Creating batches of similar sequence lengths\n- Creating attention masks for the transformer model\n\nImports:\n- torch\n- torchtext\n- sentencepiece\n- utils (create_padding_mask, create_subsequent_mask)\n- config (Config)'.\n\n-----\n\n## Logic Analysis: data_processing.py"}, {"role": "assistant", "content": "# Logic Analysis for data_processing.py\n\n## Overview\n\nThe `data_processing.py` file is responsible for handling all data-related operations for the Transformer model implementation. This includes downloading datasets, tokenization, vocabulary building, batch creation, and mask generation. The implementation must align with the paper \"Attention Is All You Need\" which specifies using byte-pair encoding (BPE) for English-German translation and word-piece encoding for English-French translation.\n\n## Key Requirements\n\n1. Process the WMT14 dataset for English-German (~4.5M sentence pairs) and English-French (~36M sentence pairs)\n2. Implement tokenization with BPE (37K vocab) for EN-DE and word-piece (32K vocab) for EN-FR\n3. Create batches containing approximately 25,000 tokens (target batch size)\n4. Handle masking for padding and autoregressive behavior\n5. Provide an interface compatible with PyTorch's DataLoader\n\n## Data Processor Class Design\n\n### `DataProcessor` Class\n\n#### `__init__(config: Config)`\n- Initialize using configuration from `config.yaml` via the `Config` class\n- Store parameters like vocabulary sizes, maximum sequence length, tokenization method (BPE vs word-piece)\n- Initialize tokenizers (sentencepiece for BPE/word-piece)\n- Set up device for tensor operations\n- Store special token indices (PAD, BOS, EOS, UNK)\n\n#### `load_data(dataset_path: str) -> Tuple[DataLoader]`\n- Check if dataset exists locally, if not, download and extract\n- Handle paths for WMT14 EN-DE or EN-FR datasets based on config\n- Load train/val/test splits according to paper (newstest2013 for validation, newstest2014 for testing)\n- Process raw text files into source-target sentence pairs\n- Apply tokenization to all datasets\n- Build vocabularies from training data if not already created\n- Create data loaders with batching\n- Return tuple of (train_loader, val_loader, test_loader)\n\n#### `build_vocab(train_data: List) -> Tuple[Vocab]`\n- Process training data to build vocabularies for source and target languages\n- Use sentencepiece to train BPE model for EN-DE (37K tokens) or word-piece model for EN-FR (32K tokens)\n- Include special tokens: PAD (padding), BOS (beginning of sequence), EOS (end of sequence), UNK (unknown)\n- Save vocabulary to disk for reuse\n- Return tuple of (source_vocab, target_vocab)\n\n#### `tokenize(text: str) -> List[str]`\n- Basic tokenization of text (e.g., splitting by whitespace, handling punctuation)\n- This is a pre-processing step before applying BPE/word-piece\n- Return list of tokens\n\n#### `apply_bpe(tokens: List[str]) -> List[str]` / `apply_wordpiece(tokens: List[str]) -> List[str]`\n- Apply byte-pair encoding or word-piece encoding to tokenized text\n- Use sentencepiece library for consistent implementation\n- Convert tokens to subword units according to trained BPE/word-piece model\n- Return list of subword tokens\n\n#### `batch_data(data: List) -> Iterator`\n- Group sentences of similar lengths to minimize padding waste\n- Create batches with target size of ~25,000 tokens as mentioned in the paper\n- Convert tokenized text to tensor indices using vocabulary\n- Add BOS/EOS tokens appropriately\n- Pad sequences to equal length within each batch\n- Return iterator over batched data\n\n#### `create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]`\n- Create padding mask for source sequence (to mask padding tokens in encoder self-attention)\n- Create combined padding + subsequent mask for target sequence:\n - Padding mask: to mask padding tokens in decoder self-attention\n - Subsequent mask: to prevent attending to future positions in decoder self-attention\n- These masks are essential for the Transformer's attention mechanisms\n- Return tuple of (src_mask, tgt_mask)\n\n## Technical Considerations\n\n1. **Data Loading Efficiency**:\n - Processing WMT14 datasets (especially EN-FR with 36M pairs) requires efficient streaming\n - Need to handle large files without loading entire dataset into memory\n\n2. **Tokenization Pipeline**:\n - The paper uses BPE for EN-DE and word-piece for EN-FR\n - Both can be implemented using sentencepiece library\n - Need to save/load tokenizer models for consistent application across train/val/test sets\n\n3. **Batch Creation Logic**:\n - The paper mentions batches containing ~25,000 source and ~25,000 target tokens\n - This requires dynamic batch sizing based on sequence lengths\n - Sentences should be grouped by approximate length to minimize padding\n\n4. **Mask Creation**:\n - Source mask: binary mask where 1s indicate non-padding positions\n - Target mask: combination of padding mask and subsequent (causal/triangular) mask\n - These masks should be properly shaped for the attention mechanism\n\n5. **Vocabulary Management**:\n - Shared source-target vocabulary for both language pairs (37K for EN-DE, 32K for EN-FR)\n - Need to handle special tokens (PAD, BOS, EOS, UNK) consistently\n\n6. **Data Caching**:\n - For efficiency, preprocessed datasets should be cached to disk\n - Vocabulary and tokenizer models should be saved for reuse\n\n7. **Integration with Training Loop**:\n - Ensure data processing output is compatible with model input requirements\n - Provide proper formatting for source/target sequences and attention masks\n\n## Integration with Other Modules\n\n- The `Config` class will provide configuration parameters like vocabulary sizes, maximum sequence length, etc.\n- The `utils.py` module will provide masking functions that are used in `create_masks()`\n- The produced data loaders will be consumed by the `Trainer` and `Evaluator` classes\n- The vocabulary objects will be used by the `TransformerModel` class for embedding layers\n\n## Dependencies\n\n- `torch`: For tensor operations and DataLoader creation\n- `torchtext`: For vocabulary and dataset utilities\n- `sentencepiece`: For BPE and word-piece tokenization\n- Custom `utils.py`: For mask creation functions\n- Custom `config.py`: For configuration parameters\n\n## Alignment with Paper\n\nThe implementation must adhere to the following specifications from the paper:\n- Use of BPE with 37K shared vocabulary for EN-DE translation\n- Use of word-piece with 32K vocabulary for EN-FR translation\n- Batch size of ~25,000 source and ~25,000 target tokens\n- Proper mask creation for the Transformer's attention mechanism\n\nThis data processing module is critical for accurate reproduction as improper data preprocessing can significantly impact the model's performance and ability to reach the BLEU scores reported in the paper."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_response.json new file mode 100644 index 00000000..9a489e9d --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis for evaluate.py\n\n## Purpose and Scope\nThis module is responsible for evaluating a trained Transformer model, computing performance metrics, and handling inference tasks like translation. The primary functionality revolves around:\n\n1. Model inference using beam search for translation\n2. Computing BLEU scores to evaluate translation quality\n3. Averaging model checkpoints for improved performance\n4. Providing utilities for single sentence translation\n\n## Class Structure: Evaluator\n\n### Class Initialization\n```python\ndef __init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n```\n\nThe initialization should:\n1. Store the transformer model instance\n2. Store the data processor for tokenization and vocabulary access\n3. Configure beam search parameters from config:\n - `beam_size` (default: 4 from config.yaml)\n - `length_penalty` (default: 0.6 from config.yaml)\n - `max_length_factor` (default: 50 from config.yaml)\n4. Set the device (CPU or GPU) to match the model\n5. Save special token indices (start_token, end_token, pad_token) from the data processor vocabularies\n\n### Method: evaluate(test_data: DataLoader) -> dict\n\nThis method evaluates the model on a test dataset and computes metrics:\n\nLogic flow:\n1. Set the model to evaluation mode\n2. Initialize lists to store references and hypotheses\n3. Iterate through test_data batches:\n - For each batch, get source and target tensors\n - Create necessary masks for attention\n - Generate translations using beam search\n - Convert token IDs to text for both references and hypotheses\n - Store the results\n4. Calculate BLEU score using the stored references and hypotheses\n5. Return a dictionary containing metrics:\n - BLEU score\n - Number of examples evaluated\n - Additional metrics if needed (e.g., average translation length)\n\nThe implementation needs to handle:\n- Batch processing of data with attention masks\n- Converting tensor outputs from beam search to readable text\n- Maintaining proper references and hypotheses lists for BLEU calculation\n\n### Method: translate_sentence(sentence: str) -> str\n\nThis method translates a single sentence from source language to target language:\n\nLogic flow:\n1. Tokenize and preprocess the input sentence using the data processor\n2. Convert tokens to tensor and add batch dimension\n3. Create source mask for attention\n4. Use beam search to generate translation\n5. Convert output token IDs back to text\n6. Return the translated sentence as a string\n\nThis method should:\n- Handle unknown tokens in the input\n- Apply proper preprocessing (BPE or wordpiece) through the data processor\n- Remove any special tokens from the output (like EOS tokens)\n- Return a clean, readable translation\n\n### Method: compute_bleu(references: List[str], hypotheses: List[str]) -> float\n\nLogic flow:\n1. Format references and hypotheses for sacrebleu:\n - References should be a list of lists (multiple references per translation)\n - Hypotheses should be a flat list\n2. Use sacrebleu to compute the BLEU score\n3. Return the corpus BLEU score as a float\n\nImplementation details:\n- Use sacrebleu's corpus_bleu function for BLEU calculation\n- Format references correctly even if there's only one reference per translation\n- Return the score value (not the object) for easy reporting\n\n### Method: average_checkpoints(paths: List[str]) -> None\n\nThis method implements checkpoint averaging for improved inference, which was mentioned in the paper:\n\nLogic flow:\n1. Load the first checkpoint to get the model structure\n2. Initialize a dictionary to store the sum of all parameter tensors\n3. For each checkpoint path:\n - Load the checkpoint state dictionary\n - Add each parameter tensor to the running sum\n4. Divide all summed parameters by the number of checkpoints\n5. Load the averaged parameters into the model\n\nImplementation details:\n- Handle different model structures if necessary\n- Maintain proper device placement for tensors\n- This method should modify the model in-place\n\n## Beam Search Implementation\n\nWhile the main beam search is implemented in the TransformerModel class (as specified in the design), the evaluator will need to properly use it:\n\n1. Determine max_len based on input length + max_length_factor (50 by default)\n2. Call model.beam_search with proper parameters:\n - Source tensor\n - Max length\n - Start token index\n - Beam size\n - Length penalty alpha\n3. Process the beam search output to get the best translation\n\n## Constraints and Dependencies\n\n1. **Model State**: The model must be in evaluation mode during inference (no dropout)\n2. **Data Processing**: Must use the same data processor used during training for consistent tokenization\n3. **Config Parameters**: Must respect configuration settings for beam size and length penalty\n4. **BLEU Calculation**: Must use sacrebleu for consistent BLEU calculation as in literature\n5. **Checkpoint Format**: Needs to handle PyTorch state dictionaries properly\n\n## Integration with Other Components\n\n1. **Model Integration**: The evaluator needs to interface with the TransformerModel's beam_search method\n2. **Data Processor Integration**: Must use the data processor for consistent tokenization and vocabulary handling\n3. **Utils Integration**: Uses the average_checkpoints utility for model averaging\n\n## Error Handling Considerations\n\n1. Handle cases where model produces no valid translation (e.g., all outputs are invalid or shorter than minimum length)\n2. Handle tensor device mismatches gracefully\n3. Provide informative error messages for missing checkpoints or invalid paths\n4. Handle cases where input sentences might be too long for the model's position encoding limit\n\n## Performance Considerations\n\n1. Batch processing for faster evaluation on test sets\n2. Proper device management (GPU/CPU) for tensor operations\n3. Progress tracking with tqdm for long evaluations\n\n## Specific Requirements from Paper\n\n1. Use beam search with beam size = 4\n2. Apply length penalty with alpha = 0.6\n3. Set maximum output length to input_length + 50\n4. For base models, average last 5 checkpoints\n5. For big models, average last 20 checkpoints\n6. Terminate beam search early when possible (when EOS token is generated)\n\nThis implementation aligns with the approach described in the Transformer paper, particularly the evaluation methods mentioned in Section 6.1, paragraph 3."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..217148a4 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/evaluate.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'evaluate.py', which is intended for 'Handles evaluation and inference for the trained model.\n\nClass:\n- Evaluator:\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\n - translate_sentence(sentence: str) -> str: Translate a single sentence\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\n\nImplements:\n- Beam search for decoding\n- BLEU score calculation using sacrebleu\n- Model averaging for evaluation\n- Length penalty during beam search\n\nImports:\n- torch\n- tqdm\n- sacrebleu\n- model (TransformerModel)\n- config (Config)\n- data_processing (DataProcessor)\n- utils (average_checkpoints)'.\n\n-----\n\n## Logic Analysis: evaluate.py"}, {"role": "assistant", "content": "# Logic Analysis for evaluate.py\n\n## Purpose and Scope\nThis module is responsible for evaluating a trained Transformer model, computing performance metrics, and handling inference tasks like translation. The primary functionality revolves around:\n\n1. Model inference using beam search for translation\n2. Computing BLEU scores to evaluate translation quality\n3. Averaging model checkpoints for improved performance\n4. Providing utilities for single sentence translation\n\n## Class Structure: Evaluator\n\n### Class Initialization\n```python\ndef __init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n```\n\nThe initialization should:\n1. Store the transformer model instance\n2. Store the data processor for tokenization and vocabulary access\n3. Configure beam search parameters from config:\n - `beam_size` (default: 4 from config.yaml)\n - `length_penalty` (default: 0.6 from config.yaml)\n - `max_length_factor` (default: 50 from config.yaml)\n4. Set the device (CPU or GPU) to match the model\n5. Save special token indices (start_token, end_token, pad_token) from the data processor vocabularies\n\n### Method: evaluate(test_data: DataLoader) -> dict\n\nThis method evaluates the model on a test dataset and computes metrics:\n\nLogic flow:\n1. Set the model to evaluation mode\n2. Initialize lists to store references and hypotheses\n3. Iterate through test_data batches:\n - For each batch, get source and target tensors\n - Create necessary masks for attention\n - Generate translations using beam search\n - Convert token IDs to text for both references and hypotheses\n - Store the results\n4. Calculate BLEU score using the stored references and hypotheses\n5. Return a dictionary containing metrics:\n - BLEU score\n - Number of examples evaluated\n - Additional metrics if needed (e.g., average translation length)\n\nThe implementation needs to handle:\n- Batch processing of data with attention masks\n- Converting tensor outputs from beam search to readable text\n- Maintaining proper references and hypotheses lists for BLEU calculation\n\n### Method: translate_sentence(sentence: str) -> str\n\nThis method translates a single sentence from source language to target language:\n\nLogic flow:\n1. Tokenize and preprocess the input sentence using the data processor\n2. Convert tokens to tensor and add batch dimension\n3. Create source mask for attention\n4. Use beam search to generate translation\n5. Convert output token IDs back to text\n6. Return the translated sentence as a string\n\nThis method should:\n- Handle unknown tokens in the input\n- Apply proper preprocessing (BPE or wordpiece) through the data processor\n- Remove any special tokens from the output (like EOS tokens)\n- Return a clean, readable translation\n\n### Method: compute_bleu(references: List[str], hypotheses: List[str]) -> float\n\nLogic flow:\n1. Format references and hypotheses for sacrebleu:\n - References should be a list of lists (multiple references per translation)\n - Hypotheses should be a flat list\n2. Use sacrebleu to compute the BLEU score\n3. Return the corpus BLEU score as a float\n\nImplementation details:\n- Use sacrebleu's corpus_bleu function for BLEU calculation\n- Format references correctly even if there's only one reference per translation\n- Return the score value (not the object) for easy reporting\n\n### Method: average_checkpoints(paths: List[str]) -> None\n\nThis method implements checkpoint averaging for improved inference, which was mentioned in the paper:\n\nLogic flow:\n1. Load the first checkpoint to get the model structure\n2. Initialize a dictionary to store the sum of all parameter tensors\n3. For each checkpoint path:\n - Load the checkpoint state dictionary\n - Add each parameter tensor to the running sum\n4. Divide all summed parameters by the number of checkpoints\n5. Load the averaged parameters into the model\n\nImplementation details:\n- Handle different model structures if necessary\n- Maintain proper device placement for tensors\n- This method should modify the model in-place\n\n## Beam Search Implementation\n\nWhile the main beam search is implemented in the TransformerModel class (as specified in the design), the evaluator will need to properly use it:\n\n1. Determine max_len based on input length + max_length_factor (50 by default)\n2. Call model.beam_search with proper parameters:\n - Source tensor\n - Max length\n - Start token index\n - Beam size\n - Length penalty alpha\n3. Process the beam search output to get the best translation\n\n## Constraints and Dependencies\n\n1. **Model State**: The model must be in evaluation mode during inference (no dropout)\n2. **Data Processing**: Must use the same data processor used during training for consistent tokenization\n3. **Config Parameters**: Must respect configuration settings for beam size and length penalty\n4. **BLEU Calculation**: Must use sacrebleu for consistent BLEU calculation as in literature\n5. **Checkpoint Format**: Needs to handle PyTorch state dictionaries properly\n\n## Integration with Other Components\n\n1. **Model Integration**: The evaluator needs to interface with the TransformerModel's beam_search method\n2. **Data Processor Integration**: Must use the data processor for consistent tokenization and vocabulary handling\n3. **Utils Integration**: Uses the average_checkpoints utility for model averaging\n\n## Error Handling Considerations\n\n1. Handle cases where model produces no valid translation (e.g., all outputs are invalid or shorter than minimum length)\n2. Handle tensor device mismatches gracefully\n3. Provide informative error messages for missing checkpoints or invalid paths\n4. Handle cases where input sentences might be too long for the model's position encoding limit\n\n## Performance Considerations\n\n1. Batch processing for faster evaluation on test sets\n2. Proper device management (GPU/CPU) for tensor operations\n3. Progress tracking with tqdm for long evaluations\n\n## Specific Requirements from Paper\n\n1. Use beam search with beam size = 4\n2. Apply length penalty with alpha = 0.6\n3. Set maximum output length to input_length + 50\n4. For base models, average last 5 checkpoints\n5. For big models, average last 20 checkpoints\n6. Terminate beam search early when possible (when EOS token is generated)\n\nThis implementation aligns with the approach described in the Transformer paper, particularly the evaluation methods mentioned in Section 6.1, paragraph 3."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_response.json new file mode 100644 index 00000000..f1692f7a --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis for main.py\n\n## Overview\nThis module serves as the entry point for the Transformer model implementation based on the \"Attention Is All You Need\" paper. It will provide functions to train the model, evaluate its performance, and translate individual sentences. The script will use command-line arguments to determine which operation to perform and with what configuration.\n\n## Key Components and Flow\n\n### 1. Command-Line Argument Parsing\n- Will use `argparse` to define and parse command-line arguments\n- Need to support different modes of operation:\n - `train`: Train a new model\n - `evaluate`: Evaluate a trained model on test data\n - `translate`: Translate a single sentence using a trained model\n- Arguments will include:\n - `mode`: Operation mode (train/evaluate/translate)\n - `--config_path`: Path to the YAML configuration file\n - `--model_path`: Path to saved model checkpoint (for evaluate/translate)\n - `--model_size`: \"base\" or \"big\" model variant\n - `--language_pair`: \"en-de\" or \"en-fr\" \n - `--sentence`: Input sentence for translation (for translate mode)\n - `--output_dir`: Directory to save model checkpoints and logs\n\n### 2. Configuration Loading\n- Will load configuration from the specified YAML file (or use default path)\n- Need to initialize the `Config` class with the appropriate model size\n- Configuration will dictate model architecture, training parameters, and inference settings\n- Must handle the case where config file is not found\n\n### 3. Device Selection\n- Need to detect CUDA availability and set device accordingly\n- Should print a message indicating whether using CPU or GPU\n- Will need to move model and data to the selected device\n\n### 4. `train_model` Function\n- Takes an optional config path as parameter\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor and load training/validation data\n 3. Build vocabulary from training data\n 4. Create Transformer model with appropriate parameters\n 5. Initialize Trainer with model and data processor\n 6. Execute training loop for specified number of steps/epochs\n 7. Save final model and evaluate on validation set\n 8. Return trained model\n\n### 5. `evaluate_model` Function\n- Takes model path and optional config path as parameters\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor and load test data\n 3. Load model from checkpoint\n 4. Initialize Evaluator with model and data processor\n 5. Perform model weights averaging if specified in config\n 6. Run evaluation on test set\n 7. Print results (BLEU score)\n\n### 6. `translate` Function\n- Takes model path, input sentence, and optional config path\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor\n 3. Load model from checkpoint\n 4. Initialize Evaluator\n 5. Preprocess input sentence (tokenize, apply BPE/wordpiece)\n 6. Use beam search to generate translation\n 7. Post-process and return translated sentence\n\n### 7. `main` Function\n- Parse command-line arguments\n- Call appropriate function based on the selected mode\n- Handle exceptions and provide useful error messages\n- Set random seeds for reproducibility\n\n## Integration with Other Modules\n\n### Data Processing Integration\n- Will use `DataProcessor` from `data_processing.py` to:\n - Load and preprocess datasets\n - Build vocabularies\n - Create data loaders with appropriate batching\n\n### Model Integration\n- Will use `TransformerModel` from `model.py` to:\n - Create the model architecture according to configuration\n - Load pretrained model weights for evaluation/translation\n\n### Training Integration\n- Will use `Trainer` from `train.py` to:\n - Execute training loop\n - Implement learning rate scheduling\n - Apply regularization techniques (dropout, label smoothing)\n - Save checkpoints\n\n### Evaluation Integration\n- Will use `Evaluator` from `evaluate.py` to:\n - Perform beam search decoding\n - Calculate BLEU scores\n - Average model weights from checkpoints\n\n## Error Handling and Validation\n- Need to validate command-line arguments\n- Check existence of model checkpoint for evaluate/translate modes\n- Validate config file format\n- Handle cases where CUDA is requested but not available\n- Set appropriate fallbacks for missing parameters\n\n## Logging and Progress Reporting\n- Will use appropriate logging mechanism\n- Report on training progress (loss, learning rate, examples per second)\n- Display evaluation metrics\n- Show beam search progress/results during translation\n\n## Performance Considerations\n- Must handle large datasets efficiently\n- Should detect available hardware and adapt batch sizes accordingly\n- Needs to handle memory constraints, especially for the \"big\" model\n- Should provide estimates of training time based on hardware\n\n## Reproducibility\n- Will set random seeds for PyTorch, NumPy, and Python's random module\n- Should store configuration used for training alongside model checkpoints\n- Will track BLEU scores using standardized evaluation metrics\n\nThis logic analysis provides a comprehensive overview of how `main.py` will be structured and how it will interact with other modules in the system to implement the Transformer model as described in the \"Attention Is All You Need\" paper."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..0a8e48e9 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/main.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'main.py', which is intended for 'Entry point for running training and evaluation.\n\nFunctions:\n- train_model(config_path: str = None) -> None: Train the transformer model\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\n- main(): Parse command line arguments and run appropriate function\n\nThis script will:\n- Parse command-line arguments\n- Initialize configuration\n- Set up data processing\n- Create and train model or load pre-trained model\n- Evaluate model performance\n- Allow for single sentence translation\n\nImports:\n- torch\n- argparse\n- os\n- config (Config)\n- model (TransformerModel)\n- data_processing (DataProcessor)\n- train (Trainer)\n- evaluate (Evaluator)'.\n\n-----\n\n## Logic Analysis: main.py"}, {"role": "assistant", "content": "# Logic Analysis for main.py\n\n## Overview\nThis module serves as the entry point for the Transformer model implementation based on the \"Attention Is All You Need\" paper. It will provide functions to train the model, evaluate its performance, and translate individual sentences. The script will use command-line arguments to determine which operation to perform and with what configuration.\n\n## Key Components and Flow\n\n### 1. Command-Line Argument Parsing\n- Will use `argparse` to define and parse command-line arguments\n- Need to support different modes of operation:\n - `train`: Train a new model\n - `evaluate`: Evaluate a trained model on test data\n - `translate`: Translate a single sentence using a trained model\n- Arguments will include:\n - `mode`: Operation mode (train/evaluate/translate)\n - `--config_path`: Path to the YAML configuration file\n - `--model_path`: Path to saved model checkpoint (for evaluate/translate)\n - `--model_size`: \"base\" or \"big\" model variant\n - `--language_pair`: \"en-de\" or \"en-fr\" \n - `--sentence`: Input sentence for translation (for translate mode)\n - `--output_dir`: Directory to save model checkpoints and logs\n\n### 2. Configuration Loading\n- Will load configuration from the specified YAML file (or use default path)\n- Need to initialize the `Config` class with the appropriate model size\n- Configuration will dictate model architecture, training parameters, and inference settings\n- Must handle the case where config file is not found\n\n### 3. Device Selection\n- Need to detect CUDA availability and set device accordingly\n- Should print a message indicating whether using CPU or GPU\n- Will need to move model and data to the selected device\n\n### 4. `train_model` Function\n- Takes an optional config path as parameter\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor and load training/validation data\n 3. Build vocabulary from training data\n 4. Create Transformer model with appropriate parameters\n 5. Initialize Trainer with model and data processor\n 6. Execute training loop for specified number of steps/epochs\n 7. Save final model and evaluate on validation set\n 8. Return trained model\n\n### 5. `evaluate_model` Function\n- Takes model path and optional config path as parameters\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor and load test data\n 3. Load model from checkpoint\n 4. Initialize Evaluator with model and data processor\n 5. Perform model weights averaging if specified in config\n 6. Run evaluation on test set\n 7. Print results (BLEU score)\n\n### 6. `translate` Function\n- Takes model path, input sentence, and optional config path\n- Workflow:\n 1. Load configuration\n 2. Initialize data processor\n 3. Load model from checkpoint\n 4. Initialize Evaluator\n 5. Preprocess input sentence (tokenize, apply BPE/wordpiece)\n 6. Use beam search to generate translation\n 7. Post-process and return translated sentence\n\n### 7. `main` Function\n- Parse command-line arguments\n- Call appropriate function based on the selected mode\n- Handle exceptions and provide useful error messages\n- Set random seeds for reproducibility\n\n## Integration with Other Modules\n\n### Data Processing Integration\n- Will use `DataProcessor` from `data_processing.py` to:\n - Load and preprocess datasets\n - Build vocabularies\n - Create data loaders with appropriate batching\n\n### Model Integration\n- Will use `TransformerModel` from `model.py` to:\n - Create the model architecture according to configuration\n - Load pretrained model weights for evaluation/translation\n\n### Training Integration\n- Will use `Trainer` from `train.py` to:\n - Execute training loop\n - Implement learning rate scheduling\n - Apply regularization techniques (dropout, label smoothing)\n - Save checkpoints\n\n### Evaluation Integration\n- Will use `Evaluator` from `evaluate.py` to:\n - Perform beam search decoding\n - Calculate BLEU scores\n - Average model weights from checkpoints\n\n## Error Handling and Validation\n- Need to validate command-line arguments\n- Check existence of model checkpoint for evaluate/translate modes\n- Validate config file format\n- Handle cases where CUDA is requested but not available\n- Set appropriate fallbacks for missing parameters\n\n## Logging and Progress Reporting\n- Will use appropriate logging mechanism\n- Report on training progress (loss, learning rate, examples per second)\n- Display evaluation metrics\n- Show beam search progress/results during translation\n\n## Performance Considerations\n- Must handle large datasets efficiently\n- Should detect available hardware and adapt batch sizes accordingly\n- Needs to handle memory constraints, especially for the \"big\" model\n- Should provide estimates of training time based on hardware\n\n## Reproducibility\n- Will set random seeds for PyTorch, NumPy, and Python's random module\n- Should store configuration used for training alongside model checkpoints\n- Will track BLEU scores using standardized evaluation metrics\n\nThis logic analysis provides a comprehensive overview of how `main.py` will be structured and how it will interact with other modules in the system to implement the Transformer model as described in the \"Attention Is All You Need\" paper."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_response.json new file mode 100644 index 00000000..5444c3d5 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis for model.py\n\n## Overview\nThe `model.py` file will implement the core Transformer architecture as described in the paper \"Attention Is All You Need\". This module is responsible for defining the complete model structure including positional encoding, multi-head attention, feed-forward networks, encoder and decoder layers, and the full Transformer model.\n\n## Positional Encoding\n\n### Class: `PositionalEncoding`\nThis class implements the positional encoding mechanism described in Section 3.5 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, dropout rate, and maximum sequence length\n2. Create a position encoding tensor of shape (max_len, d_model) using sine and cosine functions\n3. For even indices (2i), use sine function: `PE(pos, 2i) = sin(pos / 10000^(2i/d_model))`\n4. For odd indices (2i+1), use cosine function: `PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))`\n5. Store this tensor as a buffer (not a parameter) in the module\n6. In the forward pass:\n - Take input embeddings x of shape (batch_size, seq_len, d_model)\n - Add positional encodings (sliced to match sequence length)\n - Apply dropout\n - Return the result\n\n## Multi-Head Attention\n\n### Class: `MultiHeadAttention`\nThis class implements the multi-head attention mechanism described in Section 3.2.2 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, and dropout rate\n2. Calculate dimensions for each head: d_k = d_v = d_model / n_heads\n3. Create linear projections for queries, keys, values, and output:\n - W_q: d_model \u2192 d_model (will be reshaped per head)\n - W_k: d_model \u2192 d_model (will be reshaped per head)\n - W_v: d_model \u2192 d_model (will be reshaped per head)\n - W_o: d_model \u2192 d_model\n4. Implement `attention` method (scaled dot-product attention):\n - Compute dot products of queries and keys: QK^T\n - Scale by 1/\u221ad_k\n - Apply mask to set masked positions to -\u221e (or a very large negative value)\n - Apply softmax to get attention weights\n - Compute weighted sum of values\n - Return attention output and attention weights\n5. In the forward pass:\n - Project queries, keys, values using respective projections\n - Reshape to separate batch_size, seq_len, n_heads, d_k dimensions\n - Transpose to get shape (batch_size, n_heads, seq_len, d_k)\n - Apply scaled dot-product attention to get attention outputs\n - Transpose and reshape back to original dimensions\n - Apply final output projection W_o\n - Apply dropout\n - Return attention output\n\n## Position-wise Feed-Forward Networks\n\n### Class: `PositionwiseFeedforward`\nImplements the position-wise feed-forward network described in Section 3.3 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, feed-forward dimension, and dropout rate\n2. Create two linear transformations:\n - linear1: d_model \u2192 d_ff\n - linear2: d_ff \u2192 d_model\n3. In the forward pass:\n - Apply first linear transformation to input\n - Apply ReLU activation\n - Apply dropout\n - Apply second linear transformation\n - Apply dropout again\n - Return the result\n\n## Encoder Layer\n\n### Class: `EncoderLayer`\nImplements a single encoder layer as described in Section 3.1 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate\n2. Create components:\n - self_attn: MultiHeadAttention for self-attention\n - feed_forward: PositionwiseFeedforward\n - norm1, norm2: Layer normalization modules\n3. In the forward pass:\n - Apply self-attention with residual connection and layer normalization:\n - attn_output = self_attn(x, x, x, mask)\n - x = norm1(x + dropout(attn_output))\n - Apply feed-forward network with residual connection and layer normalization:\n - ff_output = feed_forward(x)\n - x = norm2(x + dropout(ff_output))\n - Return the result\n\n## Decoder Layer\n\n### Class: `DecoderLayer`\nImplements a single decoder layer as described in Section 3.1 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate\n2. Create components:\n - self_attn: MultiHeadAttention for masked self-attention\n - cross_attn: MultiHeadAttention for encoder-decoder attention\n - feed_forward: PositionwiseFeedforward\n - norm1, norm2, norm3: Layer normalization modules\n3. In the forward pass:\n - Apply masked self-attention with residual connection and layer normalization:\n - attn_output = self_attn(x, x, x, tgt_mask)\n - x = norm1(x + dropout(attn_output))\n - Apply encoder-decoder attention with residual connection and layer normalization:\n - attn_output = cross_attn(x, memory, memory, src_mask)\n - x = norm2(x + dropout(attn_output))\n - Apply feed-forward network with residual connection and layer normalization:\n - ff_output = feed_forward(x)\n - x = norm3(x + dropout(ff_output))\n - Return the result\n\n## Transformer Model\n\n### Class: `TransformerModel`\nImplements the complete Transformer model as described in Section 3 of the paper.\n\n#### Logic Flow:\n1. Initialize with configuration, source vocabulary size, and target vocabulary size\n2. Create components:\n - src_embed: Source embedding + positional encoding\n - tgt_embed: Target embedding + positional encoding\n - encoder: Stack of N encoder layers\n - decoder: Stack of N decoder layers\n - generator: Linear + softmax to convert decoder output to probabilities\n - Apply weight sharing between embedding layers and output projection if specified\n3. Implement `encode` method:\n - Apply source embedding to inputs\n - Pass through encoder layers with source mask\n - Return encoder output (memory)\n4. Implement `decode` method:\n - Apply target embedding to inputs\n - Pass through decoder layers with memory, source mask, and target mask\n - Return decoder output\n5. Implement `forward` method:\n - Call `encode` to get memory\n - Call `decode` with memory, source mask, target input, and target mask\n - Return decoder output\n6. Implement `beam_search` method:\n - Encode source sequence\n - Initialize beams with start symbol\n - For each decoding step:\n - Expand all current beams\n - Calculate scores for all expanded beams\n - Select top-k beams based on scores\n - Check for completed sequences (reached EOS or max length)\n - Apply length penalty to final scores\n - Return best translation\n\n## Key Implementation Considerations\n\n1. **Masking**:\n - In encoder self-attention: mask for padding tokens\n - In decoder self-attention: mask for padding tokens and future positions\n - In encoder-decoder attention: mask for padding in source sequence\n\n2. **Weight Sharing**:\n - Share weights between embedding layers and output projection as mentioned in Section 3.4 of the paper\n - Multiply embedding weights by \u221ad_model\n\n3. **Scaled Dot-Product Attention**:\n - Scale the dot products by 1/\u221ad_k to prevent small gradients in softmax\n\n4. **Multi-Head Attention Implementation**:\n - Efficient reshaping and transposition operations to handle multiple heads in parallel\n - Proper reshaping back to original dimensions after attention\n\n5. **Beam Search**:\n - Efficient implementation to handle expanding and pruning beams\n - Length normalization as described in the paper (`(5+len)^a / (5+1)^a`)\n\n6. **Parameter Initialization**:\n - Initialize parameters to ensure proper gradient flow through the network\n\n## Dependencies\n- `torch`: For tensor operations and neural network modules\n- `math`: For mathematical functions (sqrt, sin, cos)\n- `copy`: For deep copying objects during beam search\n- `numpy`: For numerical operations\n- `config`: For accessing model configuration parameters\n- `utils`: For mask creation functions\n\nThis logic analysis provides a comprehensive breakdown of how each component of the Transformer model should be implemented according to the paper. The design follows the paper's specifications closely, ensuring accurate reproduction of the architecture."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..7c44a536 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/model.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'model.py', which is intended for 'Implements the Transformer architecture as described in the paper.\n\nClasses:\n- PositionalEncoding: Adds positional information to embeddings\n - __init__(d_model: int, dropout: float, max_len: int)\n - forward(x: torch.Tensor) -> torch.Tensor\n\n- MultiHeadAttention: Implements multi-head attention mechanism\n - __init__(d_model: int, n_heads: int, dropout: float)\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\n\n- PositionwiseFeedforward: Implements position-wise feed-forward network\n - __init__(d_model: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor) -> torch.Tensor\n\n- EncoderLayer: Single layer of the encoder\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\n\n- DecoderLayer: Single layer of the decoder\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n\n- TransformerModel: Full transformer model\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\n\nImports:\n- torch\n- math\n- copy\n- numpy\n- config (Config)\n- utils (for mask creation)'.\n\n-----\n\n## Logic Analysis: model.py"}, {"role": "assistant", "content": "# Logic Analysis for model.py\n\n## Overview\nThe `model.py` file will implement the core Transformer architecture as described in the paper \"Attention Is All You Need\". This module is responsible for defining the complete model structure including positional encoding, multi-head attention, feed-forward networks, encoder and decoder layers, and the full Transformer model.\n\n## Positional Encoding\n\n### Class: `PositionalEncoding`\nThis class implements the positional encoding mechanism described in Section 3.5 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, dropout rate, and maximum sequence length\n2. Create a position encoding tensor of shape (max_len, d_model) using sine and cosine functions\n3. For even indices (2i), use sine function: `PE(pos, 2i) = sin(pos / 10000^(2i/d_model))`\n4. For odd indices (2i+1), use cosine function: `PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))`\n5. Store this tensor as a buffer (not a parameter) in the module\n6. In the forward pass:\n - Take input embeddings x of shape (batch_size, seq_len, d_model)\n - Add positional encodings (sliced to match sequence length)\n - Apply dropout\n - Return the result\n\n## Multi-Head Attention\n\n### Class: `MultiHeadAttention`\nThis class implements the multi-head attention mechanism described in Section 3.2.2 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, and dropout rate\n2. Calculate dimensions for each head: d_k = d_v = d_model / n_heads\n3. Create linear projections for queries, keys, values, and output:\n - W_q: d_model \u2192 d_model (will be reshaped per head)\n - W_k: d_model \u2192 d_model (will be reshaped per head)\n - W_v: d_model \u2192 d_model (will be reshaped per head)\n - W_o: d_model \u2192 d_model\n4. Implement `attention` method (scaled dot-product attention):\n - Compute dot products of queries and keys: QK^T\n - Scale by 1/\u221ad_k\n - Apply mask to set masked positions to -\u221e (or a very large negative value)\n - Apply softmax to get attention weights\n - Compute weighted sum of values\n - Return attention output and attention weights\n5. In the forward pass:\n - Project queries, keys, values using respective projections\n - Reshape to separate batch_size, seq_len, n_heads, d_k dimensions\n - Transpose to get shape (batch_size, n_heads, seq_len, d_k)\n - Apply scaled dot-product attention to get attention outputs\n - Transpose and reshape back to original dimensions\n - Apply final output projection W_o\n - Apply dropout\n - Return attention output\n\n## Position-wise Feed-Forward Networks\n\n### Class: `PositionwiseFeedforward`\nImplements the position-wise feed-forward network described in Section 3.3 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, feed-forward dimension, and dropout rate\n2. Create two linear transformations:\n - linear1: d_model \u2192 d_ff\n - linear2: d_ff \u2192 d_model\n3. In the forward pass:\n - Apply first linear transformation to input\n - Apply ReLU activation\n - Apply dropout\n - Apply second linear transformation\n - Apply dropout again\n - Return the result\n\n## Encoder Layer\n\n### Class: `EncoderLayer`\nImplements a single encoder layer as described in Section 3.1 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate\n2. Create components:\n - self_attn: MultiHeadAttention for self-attention\n - feed_forward: PositionwiseFeedforward\n - norm1, norm2: Layer normalization modules\n3. In the forward pass:\n - Apply self-attention with residual connection and layer normalization:\n - attn_output = self_attn(x, x, x, mask)\n - x = norm1(x + dropout(attn_output))\n - Apply feed-forward network with residual connection and layer normalization:\n - ff_output = feed_forward(x)\n - x = norm2(x + dropout(ff_output))\n - Return the result\n\n## Decoder Layer\n\n### Class: `DecoderLayer`\nImplements a single decoder layer as described in Section 3.1 of the paper.\n\n#### Logic Flow:\n1. Initialize with model dimension, number of heads, feed-forward dimension, and dropout rate\n2. Create components:\n - self_attn: MultiHeadAttention for masked self-attention\n - cross_attn: MultiHeadAttention for encoder-decoder attention\n - feed_forward: PositionwiseFeedforward\n - norm1, norm2, norm3: Layer normalization modules\n3. In the forward pass:\n - Apply masked self-attention with residual connection and layer normalization:\n - attn_output = self_attn(x, x, x, tgt_mask)\n - x = norm1(x + dropout(attn_output))\n - Apply encoder-decoder attention with residual connection and layer normalization:\n - attn_output = cross_attn(x, memory, memory, src_mask)\n - x = norm2(x + dropout(attn_output))\n - Apply feed-forward network with residual connection and layer normalization:\n - ff_output = feed_forward(x)\n - x = norm3(x + dropout(ff_output))\n - Return the result\n\n## Transformer Model\n\n### Class: `TransformerModel`\nImplements the complete Transformer model as described in Section 3 of the paper.\n\n#### Logic Flow:\n1. Initialize with configuration, source vocabulary size, and target vocabulary size\n2. Create components:\n - src_embed: Source embedding + positional encoding\n - tgt_embed: Target embedding + positional encoding\n - encoder: Stack of N encoder layers\n - decoder: Stack of N decoder layers\n - generator: Linear + softmax to convert decoder output to probabilities\n - Apply weight sharing between embedding layers and output projection if specified\n3. Implement `encode` method:\n - Apply source embedding to inputs\n - Pass through encoder layers with source mask\n - Return encoder output (memory)\n4. Implement `decode` method:\n - Apply target embedding to inputs\n - Pass through decoder layers with memory, source mask, and target mask\n - Return decoder output\n5. Implement `forward` method:\n - Call `encode` to get memory\n - Call `decode` with memory, source mask, target input, and target mask\n - Return decoder output\n6. Implement `beam_search` method:\n - Encode source sequence\n - Initialize beams with start symbol\n - For each decoding step:\n - Expand all current beams\n - Calculate scores for all expanded beams\n - Select top-k beams based on scores\n - Check for completed sequences (reached EOS or max length)\n - Apply length penalty to final scores\n - Return best translation\n\n## Key Implementation Considerations\n\n1. **Masking**:\n - In encoder self-attention: mask for padding tokens\n - In decoder self-attention: mask for padding tokens and future positions\n - In encoder-decoder attention: mask for padding in source sequence\n\n2. **Weight Sharing**:\n - Share weights between embedding layers and output projection as mentioned in Section 3.4 of the paper\n - Multiply embedding weights by \u221ad_model\n\n3. **Scaled Dot-Product Attention**:\n - Scale the dot products by 1/\u221ad_k to prevent small gradients in softmax\n\n4. **Multi-Head Attention Implementation**:\n - Efficient reshaping and transposition operations to handle multiple heads in parallel\n - Proper reshaping back to original dimensions after attention\n\n5. **Beam Search**:\n - Efficient implementation to handle expanding and pruning beams\n - Length normalization as described in the paper (`(5+len)^a / (5+1)^a`)\n\n6. **Parameter Initialization**:\n - Initialize parameters to ensure proper gradient flow through the network\n\n## Dependencies\n- `torch`: For tensor operations and neural network modules\n- `math`: For mathematical functions (sqrt, sin, cos)\n- `copy`: For deep copying objects during beam search\n- `numpy`: For numerical operations\n- `config`: For accessing model configuration parameters\n- `utils`: For mask creation functions\n\nThis logic analysis provides a comprehensive breakdown of how each component of the Transformer model should be implemented according to the paper. The design follows the paper's specifications closely, ensuring accurate reproduction of the architecture."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.1_overall_plan.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.1_overall_plan.txt new file mode 100644 index 00000000..01927480 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.1_overall_plan.txt @@ -0,0 +1,255 @@ +# Reproduction Plan for "Attention Is All You Need" (Transformer Paper) + +## Overview + +This plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions. + +## Part 1: Transformer Architecture Implementation + +### 1. Model Architecture + +The Transformer follows an encoder-decoder architecture with the following specifications: + +#### 1.1 Overall Architecture +- **Encoder**: Stack of N=6 identical layers +- **Decoder**: Stack of N=6 identical layers +- **Output**: Linear layer + softmax for next-token prediction +- **Dimension**: All sub-layers output dimension d_model = 512 + +#### 1.2 Encoder Components +Each encoder layer has: +- Multi-head self-attention sub-layer +- Position-wise fully connected feed-forward network +- Residual connections around each sub-layer + layer normalization + +#### 1.3 Decoder Components +Each decoder layer has: +- Masked multi-head self-attention sub-layer +- Multi-head attention over encoder output +- Position-wise fully connected feed-forward network +- Residual connections around each sub-layer + layer normalization +- Masking to prevent positions from attending to subsequent positions + +### 2. Key Components + +#### 2.1 Attention Mechanism + +**Scaled Dot-Product Attention**: +- Formula: Attention(Q,K,V) = softmax(QK^T/√d_k)V +- Inputs: Queries and keys of dimension d_k, values of dimension d_v +- Scaling factor: 1/√d_k to prevent small gradients in softmax + +**Multi-Head Attention**: +- Perform attention function h times in parallel +- Parameters: + - Number of heads h = 8 + - Dimension per head d_k = d_v = d_model/h = 64 +- Projection matrices: + - W_Q^i ∈ R^(d_model×d_k) + - W_K^i ∈ R^(d_model×d_k) + - W_V^i ∈ R^(d_model×d_v) + - W_O ∈ R^(hd_v×d_model) + +#### 2.2 Position-wise Feed-Forward Networks +- Two linear transformations with ReLU activation: + - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2 +- Parameters: + - Input and output dimensionality: d_model = 512 + - Inner layer dimensionality: d_ff = 2048 + +#### 2.3 Embeddings and Positional Encoding +- Token embeddings: Learned embeddings of dimension d_model = 512 +- Positional encodings: Using sine and cosine functions + - PE(pos,2i) = sin(pos/10000^(2i/d_model)) + - PE(pos,2i+1) = cos(pos/10000^(2i/d_model)) +- Position embeddings added to token embeddings +- Embedding weights multiplied by √d_model +- Weight sharing between embedding layers and pre-softmax linear transformation + +#### 2.4 Regularization Techniques +- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1 +- Embedding dropout: Applied to sums of embeddings and positional encodings +- Label smoothing: ϵ_ls = 0.1 + +## Part 2: Training and Evaluation + +### 1. Datasets + +#### 1.1 Machine Translation +- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs) + - Evaluation on newstest2014 + - Development set: newstest2013 +- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs) + - Evaluation on newstest2014 + +#### 1.2 Preprocessing +- Tokenization using byte-pair encoding (BPE) + - EN-DE: Shared vocabulary of ~37,000 tokens + - EN-FR: Word-piece vocabulary of 32,000 tokens + +### 2. Training Configuration + +#### 2.1 Base Model Hyperparameters +- N = 6 layers for both encoder and decoder +- d_model = 512 +- d_ff = 2048 +- h = 8 attention heads +- d_k = d_v = 64 +- P_drop = 0.1 +- ϵ_ls = 0.1 (label smoothing) + +#### 2.2 Big Model Hyperparameters +- N = 6 layers +- d_model = 1024 +- d_ff = 4096 +- h = 16 attention heads +- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR) + +#### 2.3 Training Settings +- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch +- Adam optimizer: β1 = 0.9, β2 = 0.98, ε = 10^-9 +- Learning rate schedule: + - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5)) + - warmup_steps = 4000 +- Training steps: + - Base model: 100,000 steps (~12 hours on 8 P100 GPUs) + - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs) + +### 3. Inference and Evaluation + +#### 3.1 Decoding Strategy +- Beam search with beam size = 4 +- Length penalty α = 0.6 +- Maximum output length = input length + 50, with early termination + +#### 3.2 Model Averaging +- Base models: Average last 5 checkpoints (saved at 10-minute intervals) +- Big models: Average last 20 checkpoints + +#### 3.3 Evaluation Metrics +- BLEU score for machine translation +- Expected scores: + - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big) + - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big) + +## Part 3: Implementation Plan Breakdown + +### Phase 1: Basic Implementation + +1. **Set up project structure and dependencies** + - PyTorch/TensorFlow + - Data processing libraries + - Evaluation tools (SACREBLEU for BLEU scoring) + +2. **Implement core Transformer components** + - Scaled dot-product attention + - Multi-head attention + - Position-wise feed-forward networks + - Positional encoding + - Embedding layers with weight sharing + +3. **Assemble encoder and decoder** + - Encoder layer with self-attention and feed-forward + - Decoder layer with masked self-attention, cross-attention, and feed-forward + - Full encoder and decoder with N=6 layers + - Attention masking for decoder + +4. **Implement full Transformer model** + - Connect encoder and decoder + - Add final linear layer and softmax + - Implement label smoothing + +### Phase 2: Training Infrastructure + +1. **Data preprocessing pipeline** + - Download WMT 2014 datasets (EN-DE, EN-FR) + - Implement BPE/word-piece tokenization + - Create batching logic (grouping by similar sequence length) + +2. **Training loop** + - Implement Adam optimizer with custom learning rate schedule + - Set up gradient clipping and optimization + - Implement dropout regularization + - Configure logging and checkpointing + +3. **Model initialization** + - Initialize model parameters according to paper specifications + - Weight sharing between embedding layers and pre-softmax projection + +### Phase 3: Inference and Evaluation + +1. **Beam search implementation** + - Implement beam search with configurable beam size + - Add length penalty + - Handle early termination + +2. **Checkpoint averaging** + - Implement logic to average model weights from checkpoints + +3. **Evaluation pipeline** + - Set up BLEU score computation + - Compare results with paper benchmarks + +### Phase 4: Experimentation and Validation + +1. **Base model training** + - Train on EN-DE dataset with base configuration + - Validate on newstest2013 + - Test on newstest2014 + +2. **Big model training** (if resources permit) + - Train with larger configuration + - Validate and test as above + +3. **Ablation studies** (optional but recommended) + - Vary number of attention heads + - Test different key/value dimensions + - Experiment with model size variations + +## Additional Implementation Details + +### Key Nuances to Consider + +1. **Attention Implementation Details**: + - Ensure proper masking in decoder self-attention + - Implement attention scaling correctly (1/√d_k) + - Handle padding tokens in attention calculations + +2. **Positional Encoding**: + - Implement sine/cosine functions properly + - Add to token embeddings before each encoder/decoder stack + +3. **Training Stability**: + - Monitor loss for instability + - Implement learning rate warmup carefully + - Consider gradient clipping if not mentioned in paper + +4. **Efficiency Considerations**: + - Implement attention batching efficiently + - Memory management for large model variants + - Parallelize training if possible + +### Potential Challenges + +1. **Computational Resources**: + - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available + - Consider using mixed precision training if memory is limited + +2. **Hyperparameter Sensitivity**: + - Pay special attention to learning rate schedule + - Label smoothing implementation details + +3. **Dataset Preparation**: + - Ensure correct BPE implementation + - Handle large dataset sizes efficiently + +## Final Verification + +To validate successful reproduction: + +1. Train base model on EN-DE WMT 2014 +2. Evaluate on newstest2014 using beam search +3. Compare BLEU scores (target: ~27.3 for base model) +4. If resources permit, train and evaluate big model (target: ~28.4 BLEU) + +This plan should provide all the necessary details to implement the Transformer architecture as described in "Attention Is All You Need" and reproduce the paper's main results. \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.2_arch_design.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.2_arch_design.txt new file mode 100644 index 00000000..f6d6fa43 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.2_arch_design.txt @@ -0,0 +1,26 @@ +---------------------------------------- +[Implementation approach] +We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations. + +---------------------------------------- +[File list] +- main.py +- config.py +- model.py +- data_processing.py +- train.py +- evaluate.py +- utils.py + +---------------------------------------- +[Data structures and interfaces] +classDiagramclass Config {+int d_model+int n_heads+int num_encoder_layers+int num_decoder_layers+int dim_feedforward+float dropout+int max_seq_length+str device+float learning_rate+int warmup_steps+float label_smoothing+int beam_size+float length_penalty+__init__(model_size: str)+get_model_params() -> dict+get_training_params() -> dict}class TransformerModel {+torch.nn.Module encoder+torch.nn.Module decoder+torch.nn.Module src_embed+torch.nn.Module tgt_embed+torch.nn.Module generator+__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)+create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]+forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor+encode(src: Tensor, src_mask: Tensor) -> Tensor+decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor+beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor}class PositionalEncoding {+torch.nn.Module dropout+Tensor pe+__init__(d_model: int, dropout: float, max_len: int)+forward(x: Tensor) -> Tensor}class MultiHeadAttention {+int d_model+int n_heads+int d_k+int d_v+torch.nn.Module W_q+torch.nn.Module W_k+torch.nn.Module W_v+torch.nn.Module W_o+__init__(d_model: int, n_heads: int, dropout: float)+forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor-attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]}class PositionwiseFeedforward {+torch.nn.Module linear1+torch.nn.Module linear2+torch.nn.Module dropout+__init__(d_model: int, d_ff: int, dropout: float)+forward(x: Tensor) -> Tensor}class EncoderLayer {+torch.nn.Module self_attn+torch.nn.Module feed_forward+torch.nn.Module norm1+torch.nn.Module norm2+float dropout+__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)+forward(x: Tensor, mask: Tensor) -> Tensor}class DecoderLayer {+torch.nn.Module self_attn+torch.nn.Module cross_attn+torch.nn.Module feed_forward+torch.nn.Module norm1+torch.nn.Module norm2+torch.nn.Module norm3+float dropout+__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)+forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor}class DataProcessor {+torchtext.vocab.Vocab src_vocab+torchtext.vocab.Vocab tgt_vocab+int max_seq_length+str device+__init__(config: Config)+load_data(dataset_path: str) -> Tuple[DataLoader]+build_vocab(train_data: List) -> Tuple[Vocab]+tokenize(text: str) -> List[str]+apply_bpe(tokens: List[str]) -> List[str]+batch_data(data: List) -> Iterator+create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]}class Trainer {+TransformerModel model+torch.optim.Optimizer optimizer+torch.nn.Module criterion+DataProcessor data_processor+str device+int warmup_steps+__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)+train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None+train_epoch(train_data: DataLoader) -> float+validate(val_data: DataLoader) -> float+save_checkpoint(path: str) -> None+load_checkpoint(path: str) -> None-adjust_learning_rate(step: int) -> None}class Evaluator {+TransformerModel model+DataProcessor data_processor+int beam_size+float length_penalty+__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)+evaluate(test_data: DataLoader) -> dict+translate_sentence(sentence: str) -> str+compute_bleu(references: List[str], hypotheses: List[str]) -> float+average_checkpoints(paths: List[str]) -> None}TransformerModel --> MultiHeadAttentionTransformerModel --> PositionalEncodingTransformerModel --> EncoderLayerTransformerModel --> DecoderLayerEncoderLayer --> MultiHeadAttentionEncoderLayer --> PositionwiseFeedforwardDecoderLayer --> MultiHeadAttentionDecoderLayer --> PositionwiseFeedforwardTrainer --> TransformerModelTrainer --> DataProcessorEvaluator --> TransformerModelEvaluator --> DataProcessor + +---------------------------------------- +[Program call flow] +sequenceDiagramparticipant Main as main.pyparticipant Cfg as Configparticipant DP as DataProcessorparticipant TM as TransformerModelparticipant TR as Trainerparticipant EV as EvaluatorMain->>Cfg: config = Config('base')Main->>DP: data_processor = DataProcessor(config)Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')DP-->>Main: return dataloadersMain->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))TM->>TM: init encoder/decoder layersTM->>PositionalEncoding: create positional encodingPositionalEncoding-->>TM: return moduleTM->>MultiHeadAttention: create attention modulesMultiHeadAttention-->>TM: return modulesTM-->>Main: return initialized modelMain->>TR: trainer = Trainer(config, model, data_processor)Main->>TR: trainer.train(train_data, val_data, epochs=100)loop For each epochTR->>TR: train_epoch(train_data)loop For each batchTR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)DP-->>TR: return masksTR->>TM: output = model(src, tgt, src_mask, tgt_mask)TM->>TM: encode(src, src_mask)TM->>TM: decode(memory, src_mask, tgt, tgt_mask)TM-->>TR: return outputTR->>TR: calculate loss with label smoothingTR->>TR: backward() and optimizer.step()TR->>TR: adjust_learning_rate(step)endTR->>TR: validate(val_data)TR->>TR: save_checkpoint('checkpoint.pt')endTR-->>Main: training completeMain->>TR: trainer.save_checkpoint('final_model.pt')Main->>EV: evaluator = Evaluator(config, model, data_processor)Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])Main->>EV: results = evaluator.evaluate(test_data)loop For each test batchEV->>TM: model.beam_search(src, max_len, start_symbol)TM-->>EV: return translationsEV->>EV: compute_bleu(references, hypotheses)endEV-->>Main: return evaluation metricsMain->>Main: print(f'BLEU Score: {results["bleu"]}') + +---------------------------------------- +[Anything UNCLEAR] +1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available. + diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.3_logic_design.txt b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.3_logic_design.txt new file mode 100644 index 00000000..ef98785d --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.3_logic_design.txt @@ -0,0 +1,86 @@ +---------------------------------------- +[Required packages] +- torch>=1.7.0 +- torchtext>=0.8.0 +- numpy>=1.19.0 +- tqdm>=4.50.0 +- sacrebleu>=1.5.0 +- sentencepiece>=0.1.94 +- matplotlib>=3.3.0 +- tensorboard>=2.4.0 + +---------------------------------------- +[Required Other language third-party packages] +- No third-party dependencies required + +---------------------------------------- +[Logic Analysis] +- ['config.py', "Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\n\nClass:\n- Config: Stores configuration for model architecture, training, and evaluation\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\n - get_model_params(): Returns dictionary of model architecture parameters\n - get_training_params(): Returns dictionary of training parameters\n\nThe config will define:\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\n- Training parameters (learning rate, warmup steps, etc.)\n- Evaluation parameters (beam size, length penalty, etc.)\n- Data processing parameters (max sequence length, batch size, etc.)\n\nNo external dependencies besides standard Python libraries."] +- ['utils.py', 'Utility functions used across the project.\n\nFunctions:\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\n\nImports:\n- torch\n- numpy\n- os\n- math'] +- ['data_processing.py', 'Handles data loading, preprocessing, tokenization, and batching.\n\nClass:\n- DataProcessor:\n - __init__(config: Config): Initialize with configuration\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\n - tokenize(text: str) -> List[str]: Tokenize text\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\n\nThis module will handle:\n- Downloading and extracting WMT14 datasets if not available\n- Tokenization using byte-pair encoding or word-piece encoding\n- Building vocabularies\n- Creating batches of similar sequence lengths\n- Creating attention masks for the transformer model\n\nImports:\n- torch\n- torchtext\n- sentencepiece\n- utils (create_padding_mask, create_subsequent_mask)\n- config (Config)'] +- ['model.py', 'Implements the Transformer architecture as described in the paper.\n\nClasses:\n- PositionalEncoding: Adds positional information to embeddings\n - __init__(d_model: int, dropout: float, max_len: int)\n - forward(x: torch.Tensor) -> torch.Tensor\n\n- MultiHeadAttention: Implements multi-head attention mechanism\n - __init__(d_model: int, n_heads: int, dropout: float)\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\n\n- PositionwiseFeedforward: Implements position-wise feed-forward network\n - __init__(d_model: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor) -> torch.Tensor\n\n- EncoderLayer: Single layer of the encoder\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\n\n- DecoderLayer: Single layer of the decoder\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n\n- TransformerModel: Full transformer model\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\n\nImports:\n- torch\n- math\n- copy\n- numpy\n- config (Config)\n- utils (for mask creation)'] +- ['train.py', 'Handles the training and validation loops for the Transformer model.\n\nClass:\n- Trainer:\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\n - validate(val_data: DataLoader) -> float: Validate model\n - save_checkpoint(path: str) -> None: Save model checkpoint\n - load_checkpoint(path: str) -> None: Load model checkpoint\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\n\nImplements:\n- Adam optimizer with custom learning rate schedule\n- Label smoothing for training\n- Gradient clipping\n- Logging with tensorboard\n- Checkpoint saving and loading\n\nImports:\n- torch\n- tqdm\n- time\n- os\n- tensorboard\n- model (TransformerModel)\n- config (Config)\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\n- data_processing (DataProcessor)'] +- ['evaluate.py', 'Handles evaluation and inference for the trained model.\n\nClass:\n- Evaluator:\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\n - translate_sentence(sentence: str) -> str: Translate a single sentence\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\n\nImplements:\n- Beam search for decoding\n- BLEU score calculation using sacrebleu\n- Model averaging for evaluation\n- Length penalty during beam search\n\nImports:\n- torch\n- tqdm\n- sacrebleu\n- model (TransformerModel)\n- config (Config)\n- data_processing (DataProcessor)\n- utils (average_checkpoints)'] +- ['main.py', 'Entry point for running training and evaluation.\n\nFunctions:\n- train_model(config_path: str = None) -> None: Train the transformer model\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\n- main(): Parse command line arguments and run appropriate function\n\nThis script will:\n- Parse command-line arguments\n- Initialize configuration\n- Set up data processing\n- Create and train model or load pre-trained model\n- Evaluate model performance\n- Allow for single sentence translation\n\nImports:\n- torch\n- argparse\n- os\n- config (Config)\n- model (TransformerModel)\n- data_processing (DataProcessor)\n- train (Trainer)\n- evaluate (Evaluator)'] + +---------------------------------------- +[Task list] +- config.py +- utils.py +- data_processing.py +- model.py +- train.py +- evaluate.py +- main.py + +---------------------------------------- +[Full API spec] + + +---------------------------------------- +[Shared Knowledge] +Several components and concepts are shared across multiple files in this implementation: + +1. Configuration Parameters: + - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py + - Training parameters (learning rate, warmup steps) used in both config.py and train.py + - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py + +2. Masking Functions: + - Padding mask creation logic shared between data_processing.py and model.py + - Sequence masking for autoregressive decoding used in model.py and data_processing.py + +3. Vocabulary and Tokenization: + - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers + - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py + +4. Checkpoint Management: + - Model saving/loading functions in utils.py used by train.py and evaluate.py + - Checkpoint averaging logic shared between utils.py and evaluate.py + +5. Learning Rate Schedule: + - Custom learning rate adjustment function defined in utils.py and used in train.py + - Warmup steps and scaling factors shared between these files + +6. Label Smoothing: + - Implementation in utils.py used during training in train.py + - Epsilon parameter defined in config.py + +7. Model Architecture: + - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder + - Weight sharing between embedding layer and final projection layer as mentioned in the paper + +---------------------------------------- +[Anything UNCLEAR] +1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented. + +2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ. + +3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results. + +4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals. + +5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available. + +6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper. + diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.4_config.yaml b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.4_config.yaml new file mode 100644 index 00000000..ebf83a0b --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_artifacts/1.4_config.yaml @@ -0,0 +1,67 @@ +## config.yaml + +# Transformer Model Configuration + +# Model architecture parameters +model: + base: + d_model: 512 # Dimension of model embeddings + n_heads: 8 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 2048 # Dimension of feed-forward layer + dropout: 0.1 # Dropout rate + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + + big: + d_model: 1024 # Dimension of model embeddings + n_heads: 16 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 4096 # Dimension of feed-forward layer + dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR) + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + +# Training parameters +training: + optimizer: "adam" + beta1: 0.9 + beta2: 0.98 + epsilon: 1.0e-9 + warmup_steps: 4000 + label_smoothing: 0.1 + batch_tokens: 25000 # Target batch size in tokens + base_steps: 100000 # Training steps for base model + big_steps: 300000 # Training steps for big model + checkpoint_interval: 10 # Save checkpoint every 10 minutes + average_checkpoints: + base: 5 # Number of checkpoints to average for base model + big: 20 # Number of checkpoints to average for big model + +# Inference parameters +inference: + beam_size: 4 + length_penalty: 0.6 + max_length_factor: 50 # Maximum output length = input_length + max_length_factor + +# Data parameters +data: + en_de: + source_lang: "en" + target_lang: "de" + train: "wmt14_en_de_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 37000 # Shared BPE vocabulary + tokenization: "bpe" # Byte-pair encoding + + en_fr: + source_lang: "en" + target_lang: "fr" + train: "wmt14_en_fr_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 32000 # Word-piece vocabulary + tokenization: "wordpiece" # Word-piece encoding \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_config.yaml b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_config.yaml new file mode 100644 index 00000000..ebf83a0b --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_config.yaml @@ -0,0 +1,67 @@ +## config.yaml + +# Transformer Model Configuration + +# Model architecture parameters +model: + base: + d_model: 512 # Dimension of model embeddings + n_heads: 8 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 2048 # Dimension of feed-forward layer + dropout: 0.1 # Dropout rate + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + + big: + d_model: 1024 # Dimension of model embeddings + n_heads: 16 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 4096 # Dimension of feed-forward layer + dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR) + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + +# Training parameters +training: + optimizer: "adam" + beta1: 0.9 + beta2: 0.98 + epsilon: 1.0e-9 + warmup_steps: 4000 + label_smoothing: 0.1 + batch_tokens: 25000 # Target batch size in tokens + base_steps: 100000 # Training steps for base model + big_steps: 300000 # Training steps for big model + checkpoint_interval: 10 # Save checkpoint every 10 minutes + average_checkpoints: + base: 5 # Number of checkpoints to average for base model + big: 20 # Number of checkpoints to average for big model + +# Inference parameters +inference: + beam_size: 4 + length_penalty: 0.6 + max_length_factor: 50 # Maximum output length = input_length + max_length_factor + +# Data parameters +data: + en_de: + source_lang: "en" + target_lang: "de" + train: "wmt14_en_de_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 37000 # Shared BPE vocabulary + tokenization: "bpe" # Byte-pair encoding + + en_fr: + source_lang: "en" + target_lang: "fr" + train: "wmt14_en_fr_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 32000 # Word-piece vocabulary + tokenization: "wordpiece" # Word-piece encoding \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_response.json new file mode 100644 index 00000000..e0773d9d --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_response.json @@ -0,0 +1 @@ +[{"text": "# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results."}, {"text": "[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]"}, {"text": "[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]"}, {"text": "## Code: config.yaml\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```"}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_trajectories.json new file mode 100644 index 00000000..b9e88c61 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/planning_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher and strategic planner with a deep understanding of experimental design and reproducibility in scientific research. \nYou will receive a research paper in JSON format. \nYour task is to create a detailed and efficient plan to reproduce the experiments and methodologies described in the paper.\nThis plan should align precisely with the paper's methodology, experimental setup, and evaluation metrics. \n\nInstructions:\n\n1. Align with the Paper: Your plan must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present the plan in a well-organized and easy-to-follow format, breaking it down into actionable steps.\n3. Prioritize Efficiency: Optimize the plan for clarity and practical implementation while ensuring fidelity to the original experiments."}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n## Task\n1. We want to reproduce the method described in the attached paper. \n2. The authors did not release any official code, so we have to plan our own implementation.\n3. Before writing any Python code, please outline a comprehensive plan that covers:\n - Key details from the paper's **Methodology**.\n - Important aspects of **Experiments**, including dataset requirements, experimental settings, hyperparameters, or evaluation metrics.\n4. The plan should be as **detailed and informative** as possible to help us write the final code later.\n\n## Requirements\n- You don't need to provide the actual code yet; focus on a **thorough, clear strategy**.\n- If something is unclear from the paper, mention it explicitly.\n\n## Instruction\nThe response should give us a strong roadmap, making it easier to write the code later."}, {"role": "assistant", "content": "# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results."}, {"role": "user", "content": "Your goal is to create a concise, usable, and complete software system design for reproducing the paper's method. Use appropriate open-source libraries and keep the overall architecture simple.\n \nBased on the plan for reproducing the paper\u2019s main method, please design a concise, usable, and complete software system. \nKeep the architecture simple and make effective use of open-source libraries.\n\n-----\n\n## Format Example\n[CONTENT]\n{\n \"Implementation approach\": \"We will ... ,\n \"File list\": [\n \"main.py\", \n \"dataset_loader.py\", \n \"model.py\", \n \"trainer.py\",\n \"evaluation.py\" \n ],\n \"Data structures and interfaces\": \"\nclassDiagram\n class Main {\n +__init__()\n +run_experiment()\n }\n class DatasetLoader {\n +__init__(config: dict)\n +load_data() -> Any\n }\n class Model {\n +__init__(params: dict)\n +forward(x: Tensor) -> Tensor\n }\n class Trainer {\n +__init__(model: Model, data: Any)\n +train() -> None\n }\n class Evaluation {\n +__init__(model: Model, data: Any)\n +evaluate() -> dict\n }\n Main --> DatasetLoader\n Main --> Trainer\n Main --> Evaluation\n Trainer --> Model\n\",\n \"Program call flow\": \"\nsequenceDiagram\n participant M as Main\n participant DL as DatasetLoader\n participant MD as Model\n participant TR as Trainer\n participant EV as Evaluation\n M->>DL: load_data()\n DL-->>M: return dataset\n M->>MD: initialize model()\n M->>TR: train(model, dataset)\n TR->>MD: forward(x)\n MD-->>TR: predictions\n TR-->>M: training complete\n M->>EV: evaluate(model, dataset)\n EV->>MD: forward(x)\n MD-->>EV: predictions\n EV-->>M: metrics\n\",\n \"Anything UNCLEAR\": \"Need clarification on the exact dataset format and any specialized hyperparameters.\"\n}\n[/CONTENT]\n\n## Nodes: \": # \"\n- Implementation approach: # Summarize the chosen solution strategy.\n- File list: typing.List[str] # Only need relative paths. ALWAYS write a main.py or app.py here.\n- Data structures and interfaces: typing.Optional[str] # Use mermaid classDiagram code syntax, including classes, method(__init__ etc.) and functions with type annotations, CLEARLY MARK the RELATIONSHIPS between classes, and comply with PEP8 standards. The data structures SHOULD BE VERY DETAILED and the API should be comprehensive with a complete design.\n- Program call flow: typing.Optional[str] # Use sequenceDiagram code syntax, COMPLETE and VERY DETAILED, using CLASSES AND API DEFINED ABOVE accurately, covering the CRUD AND INIT of each object, SYNTAX MUST BE CORRECT.\n- Anything UNCLEAR: # Mention ambiguities and ask for clarifications.\n\n## Constraint\nFormat: output wrapped inside [CONTENT][/CONTENT] like the format example, nothing else.\n\n## Action\nFollow the instructions for the nodes, generate the output, and ensure it follows the format example."}, {"role": "assistant", "content": "[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]"}, {"role": "user", "content": "Your goal is break down tasks according to PRD/technical design, generate a task list, and analyze task dependencies. \nYou will break down tasks, analyze dependencies.\n \nYou outline a clear PRD/technical design for reproducing the paper\u2019s method and experiments. \n\nNow, let's break down tasks according to PRD/technical design, generate a task list, and analyze task dependencies.\nThe Logic Analysis should not only consider the dependencies between files but also provide detailed descriptions to assist in writing the code needed to reproduce the paper.\n\n-----\n\n## Format Example\n[CONTENT]\n{\n \"Required packages\": [\n \"numpy==1.21.0\",\n \"torch==1.9.0\" \n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"data_preprocessing.py\",\n \"DataPreprocessing class ........\"\n ],\n [\n \"trainer.py\",\n \"Trainer ....... \"\n ],\n [\n \"dataset_loader.py\",\n \"Handles loading and ........\"\n ],\n [\n \"model.py\",\n \"Defines the model .......\"\n ],\n [\n \"evaluation.py\",\n \"Evaluation class ........ \"\n ],\n [\n \"main.py\",\n \"Entry point .......\"\n ]\n ],\n \"Task list\": [\n \"dataset_loader.py\", \n \"model.py\", \n \"trainer.py\", \n \"evaluation.py\",\n \"main.py\" \n ],\n \"Full API spec\": \"openapi: 3.0.0 ...\",\n \"Shared Knowledge\": \"Both data_preprocessing.py and trainer.py share ........\",\n \"Anything UNCLEAR\": \"Clarification needed on recommended hardware configuration for large-scale experiments.\"\n}\n\n[/CONTENT]\n\n## Nodes: \": # \"\n- Required packages: typing.Optional[typing.List[str]] # Provide required third-party packages in requirements.txt format.(e.g., 'numpy==1.21.0').\n- Required Other language third-party packages: typing.List[str] # List down packages required for non-Python languages. If none, specify \"No third-party dependencies required\".\n- Logic Analysis: typing.List[typing.List[str]] # Provide a list of files with the classes/methods/functions to be implemented, including dependency analysis and imports. Include as much detailed description as possible.\n- Task list: typing.List[str] # Break down the tasks into a list of filenames, prioritized based on dependency order. The task list must include the previously generated file list.\n- Full API spec: # Describe all APIs using OpenAPI 3.0 spec that may be used by both frontend and backend. If front-end and back-end communication is not required, leave it blank.\n- Shared Knowledge: # Detail any shared knowledge, like common utility functions or configuration variables.\n- Anything UNCLEAR: # Mention any unresolved questions or clarifications needed from the paper or project scope.\n\n## Constraint\nFormat: output wrapped inside [CONTENT][/CONTENT] like the format example, nothing else.\n\n## Action\nFollow the node instructions above, generate your output accordingly, and ensure it follows the given format example."}, {"role": "assistant", "content": "[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]"}, {"role": "user", "content": "You write elegant, modular, and maintainable code. Adhere to Google-style guidelines.\n\nBased on the paper, plan, design specified previously, follow the \"Format Example\" and generate the code. \nExtract the training details from the above paper (e.g., learning rate, batch size, epochs, etc.), follow the \"Format example\" and generate the code. \nDO NOT FABRICATE DETAILS \u2014 only use what the paper provides.\n\nYou must write `config.yaml`.\n\nATTENTION: Use '##' to SPLIT SECTIONS, not '#'. Your output format must follow the example below exactly.\n\n-----\n\n# Format Example\n## Code: config.yaml\n```yaml\n## config.yaml\ntraining:\n learning_rate: ...\n batch_size: ...\n epochs: ...\n...\n```\n\n-----\n\n## Code: config.yaml\n"}, {"role": "assistant", "content": "## Code: config.yaml\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```"}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_response.json new file mode 100644 index 00000000..0da1c69a --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis: train.py\n\n## Overview\nThe `train.py` file will handle the training and validation of the Transformer model according to the specifications in the paper \"Attention Is All You Need\". This module will implement the Trainer class that encapsulates all functionalities needed for model training, including learning rate scheduling, optimization, evaluation, and checkpoint management.\n\n## Core Functionality Requirements\n\n### 1. Initialization and Setup\nThe `Trainer` class needs to:\n- Initialize with a model, config object, and data processor\n- Set up the Adam optimizer with parameters specified in the config (\u03b2\u2081 = 0.9, \u03b2\u2082 = 0.98, \u03b5 = 10\u207b\u2079)\n- Configure learning rate scheduling according to the formula in Section 5.3 of the paper\n- Set up loss function with label smoothing (\u03b5_ls = 0.1)\n- Initialize TensorBoard for logging training metrics\n\n### 2. Learning Rate Schedule\nThe paper describes a specific learning rate schedule in Section 5.3:\n```\nlrate = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n```\nWhere:\n- `d_model` is the model dimension (512 for base, 1024 for big model)\n- `step_num` is the current training step\n- `warmup_steps` is 4000 as specified in the paper and config\n\nThis schedule increases the learning rate linearly for the first `warmup_steps` training steps, and decreases it thereafter proportionally to the inverse square root of the step number.\n\n### 3. Training Loop\nThe main training loop should:\n- Iterate through epochs\n- For each epoch, iterate through batches of the training data\n- Forward pass: Run model on input data with appropriate masks\n- Apply label smoothing to loss calculation\n- Backward pass: Compute gradients\n- Apply gradient clipping if necessary (not explicitly mentioned in paper but common practice)\n- Update model parameters with optimizer\n- Adjust learning rate according to schedule\n- Periodically validate on validation set\n- Save checkpoints at regular intervals (every 10 minutes as per config)\n- Log metrics to TensorBoard\n\n### 4. Validation\nThe validation loop should:\n- Set model to evaluation mode\n- Iterate through validation data without computing gradients\n- Calculate validation loss and metrics\n- Return validation metrics for monitoring\n\n### 5. Checkpointing\nCheckpoint management should:\n- Save model state, optimizer state, current epoch, and other training metadata\n- Load checkpoints to resume training\n- Support checkpoint averaging for evaluation (used in the paper)\n\n## Implementation Details\n\n### Class: Trainer\n\n#### Method: __init__\n```python\ndef __init__(self, config, model, data_processor):\n \"\"\"\n Initialize the trainer with model, config, and data processor.\n \n Args:\n config: Configuration object with model and training parameters\n model: The Transformer model instance\n data_processor: Data processor for creating masks and handling data\n \"\"\"\n```\n- Store config, model, and data processor\n- Set device (GPU/CPU)\n- Initialize Adam optimizer with parameters from config\n- Set up learning rate scheduler (from utils)\n- Initialize criterion with label smoothing support\n- Set up TensorBoard writer\n- Initialize training step counter and best validation metrics\n\n#### Method: train\n```python\ndef train(self, train_data, val_data, epochs):\n \"\"\"\n Main training loop\n \n Args:\n train_data: Training data loader\n val_data: Validation data loader\n epochs: Number of training epochs\n \"\"\"\n```\n- Loop through specified number of epochs\n- For each epoch, call train_epoch\n- After each epoch, validate on validation data\n- Track best validation performance\n- Save checkpoints\n- Log metrics to TensorBoard\n\n#### Method: train_epoch\n```python\ndef train_epoch(self, train_data):\n \"\"\"\n Train for one epoch\n \n Args:\n train_data: Training data loader\n \n Returns:\n float: Average training loss for the epoch\n \"\"\"\n```\n- Set model to training mode\n- Initialize metrics tracking\n- Loop through batches with a progress bar\n- For each batch:\n - Move data to device\n - Create source and target masks\n - Forward pass through model\n - Calculate loss with label smoothing\n - Backward pass\n - Apply gradient clipping\n - Step optimizer\n - Adjust learning rate\n - Update metrics\n- Return average metrics for the epoch\n\n#### Method: validate\n```python\ndef validate(self, val_data):\n \"\"\"\n Validate the model\n \n Args:\n val_data: Validation data loader\n \n Returns:\n float: Validation loss\n \"\"\"\n```\n- Set model to evaluation mode\n- Initialize metrics tracking\n- Loop through validation data with progress bar\n- No gradient calculation\n- Forward pass and loss calculation\n- Return validation metrics\n\n#### Method: save_checkpoint\n```python\ndef save_checkpoint(self, path):\n \"\"\"\n Save model checkpoint\n \n Args:\n path: Path to save the checkpoint\n \"\"\"\n```\n- Save model state dict\n- Save optimizer state dict\n- Save current epoch and step\n- Save best validation metrics\n- Use utility function from utils.py\n\n#### Method: load_checkpoint\n```python\ndef load_checkpoint(self, path):\n \"\"\"\n Load model checkpoint\n \n Args:\n path: Path to the checkpoint\n \"\"\"\n```\n- Load checkpoint using utility function\n- Set model state dict\n- Set optimizer state dict\n- Set current epoch and step\n- Set best validation metrics\n\n#### Method: adjust_learning_rate\n```python\ndef adjust_learning_rate(self, step):\n \"\"\"\n Adjust learning rate according to the schedule in the paper\n \n Args:\n step: Current training step\n \"\"\"\n```\n- Calculate new learning rate using the formula from the paper\n- Set learning rate in optimizer\n\n## Integration Points\n\n1. **With model.py**:\n - Uses the TransformerModel for forward passes\n - Creates masks for the model using data_processor\n\n2. **With config.py**:\n - Uses training parameters (learning rate, warmup steps, etc.)\n - Uses model parameters (d_model for learning rate scaling)\n\n3. **With utils.py**:\n - Uses label_smoothed_nll_loss for loss calculation\n - Uses get_lr_scheduler for learning rate scheduling\n - Uses save_checkpoint and load_checkpoint for checkpoint management\n\n4. **With data_processing.py**:\n - Uses data_processor to create masks for the model\n - Processes batched data from dataloaders\n\n## Critical Considerations\n\n1. **Batch Processing**: The paper mentions batching by approximate sequence length with ~25000 tokens per batch. The training loop needs to handle variable batch sizes.\n\n2. **Learning Rate Schedule**: Correctly implementing the learning rate schedule is critical for training stability and convergence.\n\n3. **Regularization**:\n - Apply dropout as specified in the model\n - Use label smoothing with \u03b5_ls = 0.1\n\n4. **Training Time**:\n - Base model: ~12 hours on 8 P100 GPUs (100,000 steps)\n - Big model: ~3.5 days on 8 P100 GPUs (300,000 steps)\n - Implementation needs to adapt to available resources\n\n5. **Checkpointing**:\n - Save checkpoints every 10 minutes as per config\n - For evaluation, base models use the average of the last 5 checkpoints\n - For big models, use the average of the last 20 checkpoints\n\n6. **Performance Monitoring**:\n - Track training and validation loss\n - Monitor learning rate changes\n - Log to TensorBoard for visualization\n\nBy implementing these components according to the paper specifications and configuration settings, the `train.py` module will provide a complete training pipeline for the Transformer model."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..e7f33b3b --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/train.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'train.py', which is intended for 'Handles the training and validation loops for the Transformer model.\n\nClass:\n- Trainer:\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\n - validate(val_data: DataLoader) -> float: Validate model\n - save_checkpoint(path: str) -> None: Save model checkpoint\n - load_checkpoint(path: str) -> None: Load model checkpoint\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\n\nImplements:\n- Adam optimizer with custom learning rate schedule\n- Label smoothing for training\n- Gradient clipping\n- Logging with tensorboard\n- Checkpoint saving and loading\n\nImports:\n- torch\n- tqdm\n- time\n- os\n- tensorboard\n- model (TransformerModel)\n- config (Config)\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\n- data_processing (DataProcessor)'.\n\n-----\n\n## Logic Analysis: train.py"}, {"role": "assistant", "content": "# Logic Analysis: train.py\n\n## Overview\nThe `train.py` file will handle the training and validation of the Transformer model according to the specifications in the paper \"Attention Is All You Need\". This module will implement the Trainer class that encapsulates all functionalities needed for model training, including learning rate scheduling, optimization, evaluation, and checkpoint management.\n\n## Core Functionality Requirements\n\n### 1. Initialization and Setup\nThe `Trainer` class needs to:\n- Initialize with a model, config object, and data processor\n- Set up the Adam optimizer with parameters specified in the config (\u03b2\u2081 = 0.9, \u03b2\u2082 = 0.98, \u03b5 = 10\u207b\u2079)\n- Configure learning rate scheduling according to the formula in Section 5.3 of the paper\n- Set up loss function with label smoothing (\u03b5_ls = 0.1)\n- Initialize TensorBoard for logging training metrics\n\n### 2. Learning Rate Schedule\nThe paper describes a specific learning rate schedule in Section 5.3:\n```\nlrate = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n```\nWhere:\n- `d_model` is the model dimension (512 for base, 1024 for big model)\n- `step_num` is the current training step\n- `warmup_steps` is 4000 as specified in the paper and config\n\nThis schedule increases the learning rate linearly for the first `warmup_steps` training steps, and decreases it thereafter proportionally to the inverse square root of the step number.\n\n### 3. Training Loop\nThe main training loop should:\n- Iterate through epochs\n- For each epoch, iterate through batches of the training data\n- Forward pass: Run model on input data with appropriate masks\n- Apply label smoothing to loss calculation\n- Backward pass: Compute gradients\n- Apply gradient clipping if necessary (not explicitly mentioned in paper but common practice)\n- Update model parameters with optimizer\n- Adjust learning rate according to schedule\n- Periodically validate on validation set\n- Save checkpoints at regular intervals (every 10 minutes as per config)\n- Log metrics to TensorBoard\n\n### 4. Validation\nThe validation loop should:\n- Set model to evaluation mode\n- Iterate through validation data without computing gradients\n- Calculate validation loss and metrics\n- Return validation metrics for monitoring\n\n### 5. Checkpointing\nCheckpoint management should:\n- Save model state, optimizer state, current epoch, and other training metadata\n- Load checkpoints to resume training\n- Support checkpoint averaging for evaluation (used in the paper)\n\n## Implementation Details\n\n### Class: Trainer\n\n#### Method: __init__\n```python\ndef __init__(self, config, model, data_processor):\n \"\"\"\n Initialize the trainer with model, config, and data processor.\n \n Args:\n config: Configuration object with model and training parameters\n model: The Transformer model instance\n data_processor: Data processor for creating masks and handling data\n \"\"\"\n```\n- Store config, model, and data processor\n- Set device (GPU/CPU)\n- Initialize Adam optimizer with parameters from config\n- Set up learning rate scheduler (from utils)\n- Initialize criterion with label smoothing support\n- Set up TensorBoard writer\n- Initialize training step counter and best validation metrics\n\n#### Method: train\n```python\ndef train(self, train_data, val_data, epochs):\n \"\"\"\n Main training loop\n \n Args:\n train_data: Training data loader\n val_data: Validation data loader\n epochs: Number of training epochs\n \"\"\"\n```\n- Loop through specified number of epochs\n- For each epoch, call train_epoch\n- After each epoch, validate on validation data\n- Track best validation performance\n- Save checkpoints\n- Log metrics to TensorBoard\n\n#### Method: train_epoch\n```python\ndef train_epoch(self, train_data):\n \"\"\"\n Train for one epoch\n \n Args:\n train_data: Training data loader\n \n Returns:\n float: Average training loss for the epoch\n \"\"\"\n```\n- Set model to training mode\n- Initialize metrics tracking\n- Loop through batches with a progress bar\n- For each batch:\n - Move data to device\n - Create source and target masks\n - Forward pass through model\n - Calculate loss with label smoothing\n - Backward pass\n - Apply gradient clipping\n - Step optimizer\n - Adjust learning rate\n - Update metrics\n- Return average metrics for the epoch\n\n#### Method: validate\n```python\ndef validate(self, val_data):\n \"\"\"\n Validate the model\n \n Args:\n val_data: Validation data loader\n \n Returns:\n float: Validation loss\n \"\"\"\n```\n- Set model to evaluation mode\n- Initialize metrics tracking\n- Loop through validation data with progress bar\n- No gradient calculation\n- Forward pass and loss calculation\n- Return validation metrics\n\n#### Method: save_checkpoint\n```python\ndef save_checkpoint(self, path):\n \"\"\"\n Save model checkpoint\n \n Args:\n path: Path to save the checkpoint\n \"\"\"\n```\n- Save model state dict\n- Save optimizer state dict\n- Save current epoch and step\n- Save best validation metrics\n- Use utility function from utils.py\n\n#### Method: load_checkpoint\n```python\ndef load_checkpoint(self, path):\n \"\"\"\n Load model checkpoint\n \n Args:\n path: Path to the checkpoint\n \"\"\"\n```\n- Load checkpoint using utility function\n- Set model state dict\n- Set optimizer state dict\n- Set current epoch and step\n- Set best validation metrics\n\n#### Method: adjust_learning_rate\n```python\ndef adjust_learning_rate(self, step):\n \"\"\"\n Adjust learning rate according to the schedule in the paper\n \n Args:\n step: Current training step\n \"\"\"\n```\n- Calculate new learning rate using the formula from the paper\n- Set learning rate in optimizer\n\n## Integration Points\n\n1. **With model.py**:\n - Uses the TransformerModel for forward passes\n - Creates masks for the model using data_processor\n\n2. **With config.py**:\n - Uses training parameters (learning rate, warmup steps, etc.)\n - Uses model parameters (d_model for learning rate scaling)\n\n3. **With utils.py**:\n - Uses label_smoothed_nll_loss for loss calculation\n - Uses get_lr_scheduler for learning rate scheduling\n - Uses save_checkpoint and load_checkpoint for checkpoint management\n\n4. **With data_processing.py**:\n - Uses data_processor to create masks for the model\n - Processes batched data from dataloaders\n\n## Critical Considerations\n\n1. **Batch Processing**: The paper mentions batching by approximate sequence length with ~25000 tokens per batch. The training loop needs to handle variable batch sizes.\n\n2. **Learning Rate Schedule**: Correctly implementing the learning rate schedule is critical for training stability and convergence.\n\n3. **Regularization**:\n - Apply dropout as specified in the model\n - Use label smoothing with \u03b5_ls = 0.1\n\n4. **Training Time**:\n - Base model: ~12 hours on 8 P100 GPUs (100,000 steps)\n - Big model: ~3.5 days on 8 P100 GPUs (300,000 steps)\n - Implementation needs to adapt to available resources\n\n5. **Checkpointing**:\n - Save checkpoints every 10 minutes as per config\n - For evaluation, base models use the average of the last 5 checkpoints\n - For big models, use the average of the last 20 checkpoints\n\n6. **Performance Monitoring**:\n - Track training and validation loss\n - Monitor learning rate changes\n - Log to TensorBoard for visualization\n\nBy implementing these components according to the paper specifications and configuration settings, the `train.py` module will provide a complete training pipeline for the Transformer model."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_response.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_response.json new file mode 100644 index 00000000..fe792fd1 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_response.json @@ -0,0 +1 @@ +[{"text": "# Logic Analysis for utils.py\n\n## Function: create_subsequent_mask(size)\n\n**Purpose**: \nCreates a triangular (look-ahead) mask for the decoder's self-attention mechanism to prevent positions from attending to subsequent positions, ensuring causality in the decoding process.\n\n**Implementation Logic**:\n1. Create a square matrix of size `size` \u00d7 `size` \n2. Fill the upper triangle with `float('-inf')` or a large negative number to effectively zero out those positions after softmax\n3. Fill the lower triangle (including diagonal) with zeros, allowing attention to current and previous positions\n4. This creates a binary mask where positions can only attend to previous positions and themselves\n\n**Mathematical Foundation**:\n- The mask must conform to the attention formula in the paper: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- When we add the mask to QK^T before the softmax operation, values of `-inf` will become zero after softmax, effectively blocking attention to future positions\n\n**Usage Context**:\n- Used during training and inference in the decoder's self-attention mechanism\n- Critical for maintaining the autoregressive property of the model\n- Will be combined with padding masks when necessary\n\n## Function: create_padding_mask(seq, pad_idx)\n\n**Purpose**: \nCreates a mask to hide padding tokens in the input sequences, ensuring the model doesn't attend to padding positions.\n\n**Implementation Logic**:\n1. Create a binary mask where positions equal to `pad_idx` are marked as 1 (to be masked)\n2. All other positions are marked as 0 (to be attended to)\n3. Reshape the mask to be compatible with the attention mechanism's expected dimensions [batch_size, 1, 1, seq_len]\n4. The mask will be added to the scaled dot product (before softmax) as large negative values\n\n**Mathematical Foundation**:\n- In the attention mechanism, this mask effectively zeroes out the contribution of padding tokens\n- Must be compatible with the attention shapes for broadcasting\n\n**Usage Context**:\n- Applied in both encoder self-attention and decoder cross-attention\n- Ensures that the model's predictions are not influenced by padding tokens\n- Will be combined with sequence masks in the decoder\n\n## Function: label_smoothed_nll_loss(pred, target, epsilon)\n\n**Purpose**: \nImplements label smoothing regularization technique mentioned in the paper (section 5.4) to prevent the model from becoming overconfident and improve generalization.\n\n**Implementation Logic**:\n1. Create a distribution with `epsilon/K` probability mass for all K vocabulary items\n2. Assign a probability of `1-epsilon` to the correct label\n3. Calculate the KL-divergence between the predicted distribution and this smoothed distribution\n4. Ignore loss calculations on padding tokens\n\n**Mathematical Foundation**:\n- Standard cross-entropy loss: H(q,p) = -\u2211q(k)log(p(k))\n- Label smoothing modifies the target distribution q from a one-hot distribution to: \n q'(k) = (1-\u03b5)\u03b4(k,y) + \u03b5/K\n where \u03b4(k,y) is 1 when k=y (the correct class) and 0 otherwise\n- As mentioned in the paper, this \"hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score\"\n\n**Usage Context**:\n- Used during training only\n- The paper uses \u03b5_ls = 0.1\n\n## Function: get_lr_scheduler(optimizer, d_model, warmup_steps)\n\n**Purpose**: \nCreates a learning rate scheduler that implements the formula from the paper (section 5.3): lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n\n**Implementation Logic**:\n1. Define a function that calculates learning rate based on step count\n2. Initial phase: Learning rate increases linearly for first `warmup_steps` steps\n3. Later phase: Learning rate decreases proportionally to the inverse square root of step number\n4. Scale by factor of d_model^(-0.5)\n5. Apply this calculated learning rate to the optimizer\n\n**Mathematical Foundation**:\n- Formula from paper: lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n- The initial linear warmup prevents early training instability\n- The square root decay helps maintain stable training for extended periods\n\n**Usage Context**:\n- Applied during training after each step\n- As per paper, warmup_steps = 4000\n- Critical for the model's convergence\n\n## Function: save_checkpoint(model, optimizer, epoch, path)\n\n**Purpose**: \nSaves the model's parameters, optimizer state, and training progress to a file for later resumption of training or evaluation.\n\n**Implementation Logic**:\n1. Create a dictionary containing:\n - Model state dictionary (parameters)\n - Optimizer state dictionary\n - Current epoch\n - Any other training metadata needed\n2. Use torch.save to serialize this dictionary to disk\n3. Handle potential IO errors\n\n**Technical Considerations**:\n- Need to save both model and optimizer states to properly resume training\n- Should be compatible with model averaging functionality\n- Should be efficient with large models\n\n**Usage Context**:\n- Called periodically during training (every 10 minutes as per paper)\n- Used for creating checkpoint ensembles and resuming training\n\n## Function: load_checkpoint(path, model, optimizer=None)\n\n**Purpose**: \nLoads a previously saved model checkpoint for continued training or evaluation.\n\n**Implementation Logic**:\n1. Load the checkpoint dictionary from disk using torch.load\n2. Apply the saved parameters to the model\n3. If optimizer is provided and we're resuming training, restore optimizer state\n4. Return any additional metadata from the checkpoint (epoch, etc.)\n5. Handle potential IO errors and file not found scenarios\n\n**Technical Considerations**:\n- Must handle cases where optimizer is not provided (evaluation mode)\n- Should verify compatibility of loaded weights with current model architecture\n- Handle device mapping (CPU/GPU) gracefully\n\n**Usage Context**:\n- Used when resuming training\n- Used when loading models for evaluation\n- Used when performing checkpoint averaging\n\n## Function: average_checkpoints(paths, model)\n\n**Purpose**: \nImplements checkpoint averaging as mentioned in the paper (section 6.1), which averages parameters from the last N checkpoints for improved performance.\n\n**Implementation Logic**:\n1. Load all specified checkpoint files\n2. Extract model parameters from each checkpoint\n3. Calculate the average value for each parameter across all checkpoints\n4. Update the provided model with these averaged parameters\n5. Leave optimizer state untouched (averaging is only for evaluation)\n\n**Mathematical Foundation**:\n- Simple arithmetic mean of each parameter across checkpoints\n- Averaging parameters often improves generalization compared to using a single checkpoint\n\n**Usage Context**:\n- Used for evaluation only, not during training\n- Paper specifies averaging:\n - Last 5 checkpoints for base models\n - Last 20 checkpoints for big models\n- Checkpoints written at 10-minute intervals\n\n**Implementation Note**:\n- Need to handle potentially large memory usage when loading multiple checkpoints\n- Consider implementing a streaming average to reduce peak memory requirements\n\n## Summary\n\nThe `utils.py` file provides essential utility functions that support the core Transformer implementation, closely following the specifications from the \"Attention Is All You Need\" paper. The masking functions are critical for the attention mechanism's proper operation, ensuring autoregressive behavior in the decoder and proper handling of padded sequences. \n\nThe label smoothing implementation helps improve generalization as specified in section 5.4 of the paper. The learning rate scheduler precisely implements the formula described in section 5.3, which is vital for stable training. \n\nThe checkpoint management functions implement the model averaging technique described in section 6.1, which substantially improves final model performance. According to the paper, the base models use an average of the last 5 checkpoints, while big models average the last 20 checkpoints."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_trajectories.json b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_trajectories.json new file mode 100644 index 00000000..251648e0 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder/utils.py_simple_analysis_trajectories.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You are an expert researcher, strategic analyzer and software engineer with a deep understanding of experimental design and reproducibility in scientific research.\nYou will receive a research paper in JSON format, an overview of the plan, a design in JSON format consisting of \"Implementation approach\", \"File list\", \"Data structures and interfaces\", and \"Program call flow\", followed by a task in JSON format that includes \"Required packages\", \"Required other language third-party packages\", \"Logic Analysis\", and \"Task list\", along with a configuration file named \"config.yaml\". \n\nYour task is to conduct a comprehensive logic analysis to accurately reproduce the experiments and methodologies described in the research paper. \nThis analysis must align precisely with the paper\u2019s methodology, experimental setup, and evaluation criteria.\n\n1. Align with the Paper: Your analysis must strictly follow the methods, datasets, model configurations, hyperparameters, and experimental setups described in the paper.\n2. Be Clear and Structured: Present your analysis in a logical, well-organized, and actionable format that is easy to follow and implement.\n3. Prioritize Efficiency: Optimize the analysis for clarity and practical implementation while ensuring fidelity to the original experiments.\n4. Follow design: YOU MUST FOLLOW \"Data structures and interfaces\". DONT CHANGE ANY DESIGN. Do not use public member functions that do not exist in your design.\n5. REFER TO CONFIGURATION: Always reference settings from the config.yaml file. Do not invent or assume any values\u2014only use configurations explicitly provided.\n \n"}, {"role": "user", "content": "## Paper\n{'paper_id': 'Transformer', 'title': 'Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works. Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\\n\u2020 Work performed while at Google Brain.\\n\u2021 Work performed while at Google Research.', 'pdf_parse': {'paper_id': 'Transformer', 'abstract': [{'text': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2020 Work performed while at Google Brain.', 'section': 'Abstract', 'sec_num': None}, {'text': '\u2021 Work performed while at Google Research.', 'section': 'Abstract', 'sec_num': None}], 'body_text': [{'text': 'Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5] . Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15] .', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t-1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32] , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] . In all but a few cases [27] , however, such attention mechanisms are used in conjunction with a recurrent network.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.', 'section': 'Introduction', 'sec_num': '1'}, {'text': 'The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16] , ByteNet [18] and ConvS2S [9] , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12] . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.', 'section': 'Background', 'sec_num': '2'}, {'text': 'Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9] .', 'section': 'Background', 'sec_num': '2'}, {'text': 'Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35] . Here, the encoder maps an input sequence of symbol representations (x 1 , ..., x n ) to a sequence of continuous representations z = (z 1 , ..., z n ). Given z, the decoder then generates an output sequence (y 1 , ..., y m ) of symbols one element at a time. At each step the model is auto-regressive [10] , consuming the previously generated symbols as additional input when generating the next. The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1 , respectively.', 'section': 'Model Architecture', 'sec_num': '3'}, {'text': 'Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection [11] around each of the two sub-layers, followed by layer normalization [1] . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.', 'section': 'Encoder and Decoder Stacks', 'sec_num': '3.1'}, {'text': 'An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum Scaled Dot-Product Attention Multi-Head Attention of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.', 'section': 'Attention', 'sec_num': '3.2'}, {'text': 'We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2 ). The input consists of queries and keys of dimension d k , and values of dimension d v . We compute the dot products of the query with all keys, divide each by \u221a d k , and apply a softmax function to obtain the weights on the values.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'EQUATION', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'The two most commonly used attention functions are additive attention [2] , and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3] . We suspect that for large values of d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients4 . To counteract this effect, we scale the dot products by 1', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': '\u221a d k .', 'section': 'Scaled Dot-Product Attention', 'sec_num': '3.2.1'}, {'text': 'Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2 .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'MultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'where', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'head i = Attention(QW Q i , KW K i , V W V i )', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Where the projections are parameter matrices', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'W Q i \u2208 R dmodel\u00d7d k , W K i \u2208 R dmodel\u00d7d k , W V i \u2208 R dmodel\u00d7dv and W O \u2208 R hdv\u00d7dmodel .', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'In this work we employ h = 8 parallel attention layers, or heads. For each of these we use', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'd k = d v = d model /h = 64.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.', 'section': 'Multi-Head Attention', 'sec_num': '3.2.2'}, {'text': 'The Transformer uses multi-head attention in three different ways:', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9] .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': '\u2022 Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to -\u221e) all values in the input of the softmax which correspond to illegal connections. See Figure 2 .', 'section': 'Applications of Attention in our Model', 'sec_num': '3.2.3'}, {'text': 'In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'EQUATION', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'The dimensionality of input and output is d model = 512, and the inner-layer has dimensionality d f f = 2048.', 'section': 'Position-wise Feed-Forward Networks', 'sec_num': '3.3'}, {'text': 'Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [30] . In the embedding layers, we multiply those weights by \u221a d model . ', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': '(n 2 \u2022 d) O(1) O(1) Recurrent O(n \u2022 d 2 ) O(n) O(n) Convolutional O(k \u2022 n \u2022 d 2 ) O(1) O(log k (n)) Self-Attention (restricted) O(r \u2022 n \u2022 d) O(1) O(n/r)', 'section': 'Embeddings and Softmax', 'sec_num': '3.4'}, {'text': 'Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension d model as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [9] .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this work, we use sine and cosine functions of different frequencies:', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'P E (pos,2i) = sin(pos/10000 2i/dmodel ) P E (pos,2i+1) = cos(pos/10000 2i/dmodel )', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2\u03c0 to 10000 \u2022 2\u03c0. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P E pos+k can be represented as a linear function of P E pos .', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'We also experimented with using learned positional embeddings [9] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.', 'section': 'Positional Encoding', 'sec_num': '3.5'}, {'text': 'In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x 1 , ..., x n ) to another sequence of equal length (z 1 , ..., z n ), with x i , z i \u2208 R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [12] . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As noted in Table 1 , a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [38] and byte-pair [31] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(log k (n)) in the case of dilated convolutions [18] , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6] , however, decrease the complexity considerably, to O(k', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': '\u2022 n \u2022 d + n \u2022 d 2 ).', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.', 'section': 'Why Self-Attention', 'sec_num': '4'}, {'text': 'This section describes the training regime for our models.', 'section': 'Training', 'sec_num': '5'}, {'text': 'We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3] , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [38] . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.', 'section': 'Training Data and Batching', 'sec_num': '5.1'}, {'text': 'We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3 ), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).', 'section': 'Hardware and Schedule', 'sec_num': '5.2'}, {'text': 'We used the Adam optimizer [20] with \u03b2 1 = 0.9, \u03b2 2 = 0.98 and \u03f5 = 10 -9 . We varied the learning rate over the course of training, according to the formula:', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'EQUATION', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.', 'section': 'Optimizer', 'sec_num': '5.3'}, {'text': 'We employ three types of regularization during training: Residual Dropout We apply dropout [33] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P drop = 0.1.', 'section': 'Regularization', 'sec_num': '5.4'}, {'text': 'During training, we employed label smoothing of value \u03f5 ls = 0.1 [36] . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.', 'section': 'Label Smoothing', 'sec_num': None}, {'text': '6 Results', 'section': 'Label Smoothing', 'sec_num': None}, {'text': 'On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2 ) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3 . Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P drop = 0.1, instead of 0.3.', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty \u03b1 = 0.6 [38] . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [38] .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU5 .', 'section': 'Machine Translation', 'sec_num': '6.1'}, {'text': 'To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the . We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3 .', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [9] , and observe nearly identical results to the base model.', 'section': 'Model Variations', 'sec_num': '6.2'}, {'text': 'To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25] , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [37] . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and \u03b1 = 0.3 for both WSJ only and the semi-supervised setting.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8] .', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In contrast to RNN sequence-to-sequence models [37] , the Transformer outperforms the Berkeley-Parser [29] even when training only on the WSJ training set of 40K sentences.', 'section': 'English Constituency Parsing', 'sec_num': '6.3'}, {'text': 'In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.', 'section': 'Conclusion', 'sec_num': '7'}, {'text': 'The code we used to train and evaluate our models is available at https://github.com/ tensorflow/tensor2tensor. ', 'section': 'Conclusion', 'sec_num': '7'}, {'text': \"The Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'The ', 'section': 'Input-Input Layer5', 'sec_num': None}, {'text': 'To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1. Then their dot product, q \u2022 k = d k i=1 qiki, has mean 0 and variance d k .', 'section': '', 'sec_num': None}, {'text': 'We used values of 2.8, 3.7,', 'section': '', 'sec_num': None}, {'text': '6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.', 'section': '', 'sec_num': None}], 'back_matter': [{'text': 'Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.', 'section': 'acknowledgement', 'sec_num': None}], 'ref_entries': {'FIGREF0': {'text': 'Figure 1: The Transformer -model architecture.', 'num': None, 'type_str': 'figure', 'fig_num': '1', 'uris': None}, 'FIGREF1': {'text': 'Figure 2: (left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.', 'num': None, 'type_str': 'figure', 'fig_num': '2', 'uris': None}, 'FIGREF2': {'text': \"Figure 3: An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color.\", 'num': None, 'type_str': 'figure', 'fig_num': '3', 'uris': None}, 'FIGREF3': {'text': \"Figure4: Two attention heads, also in layer 5 of 6, apparently involved in anaphora resolution. Top: Full attentions for head 5. Bottom: Isolated attentions from just the word 'its' for attention heads 5 and 6. Note that the attentions are very sharp for this word.\", 'num': None, 'type_str': 'figure', 'fig_num': '4', 'uris': None}, 'FIGREF4': {'text': 'Figure5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence. We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6. The heads clearly learned to perform different tasks.', 'num': None, 'type_str': 'figure', 'fig_num': '5', 'uris': None}, 'TABREF0': {'text': 'Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types. n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
Layer TypeComplexity per Layer Sequential Maximum Path Length
Operations
Self-AttentionO
'}, 'TABREF1': {'text': 'The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ModelBLEU EN-DE EN-FRTraining Cost (FLOPs) EN-DE EN-FR
ByteNet [18]23.75
Deep-Att + PosUnk [39]39.21.0 \u2022 10 20
GNMT + RL [38]24.639.922.3 \u2022 10 19 1.4 \u2022 10 20
ConvS2S [9]25.1640.469.6 \u2022 10 18 1.5 \u2022 10 20
MoE [32]26.0340.562.0 \u2022 10 19 1.2 \u2022 10 20
Deep-Att + PosUnk Ensemble [39]40.48.0 \u2022 10 20
GNMT + RL Ensemble [38]26.3041.161.8 \u2022 10 20 1.1 \u2022 10 21
ConvS2S Ensemble [9]26.3641.297.7 \u2022 10 19 1.2 \u2022 10 21
Transformer (base model)27.338.13.3 \u2022 10 18
Transformer (big)28.441.82.3 \u2022 10 19
'}, 'TABREF2': {'text': 'Variations on the Transformer architecture. Unlisted values are identical to those of the base model. All metrics are on the English-to-German translation development set, newstest2013. Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.', 'num': None, 'type_str': 'table', 'html': None, 'content': '
N d modeld ffhd kd vP drop \u03f5 lstrain steps (dev) (dev) PPL BLEU params \u00d710 6
base 65122048 864640.10.1 100K 4.9225.865
1 512 5125.2924.9
(A)4 128 128 16 32 325.00 4.9125.5 25.8
32 16165.0125.4
(B)16 325.16 5.0125.1 25.458 60
26.1123.736
45.1925.350
84.8825.580
(C)25632325.7524.528
1024128 1284.6626.0168
10245.1225.453
40964.7526.290
0.05.7724.6
(D)0.20.04.95 4.6725.5 25.3
0.25.4725.7
(E)positional embedding instead of sinusoids4.9225.7
big61024 4096 160.3300K 4.3326.4213
development set, newstest2013
'}, 'TABREF3': {'text': 'The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)', 'num': None, 'type_str': 'table', 'html': None, 'content': '
ParserTrainingWSJ 23 F1
Vinyals & Kaiser el al. (2014) [37] WSJ only, discriminative88.3
Petrov et al. (2006) [29]WSJ only, discriminative90.4
Zhu et al. (2013) [40]WSJ only, discriminative90.4
Dyer et al. (2016) [8]WSJ only, discriminative91.7
Transformer (4 layers)WSJ only, discriminative91.3
Zhu et al. (2013) [40]semi-supervised91.3
Huang & Harper (2009) [14]semi-supervised91.3
McClosky et al. (2006) [26]semi-supervised92.1
Vinyals & Kaiser el al. (2014) [37]semi-supervised92.1
Transformer (4 layers)semi-supervised92.7
Luong et al. (2015) [23]multi-task93.0
Dyer et al. (2016) [8]generative93.3
'}}}}\n\n-----\n\n## Overview of the plan\n# Reproduction Plan for \"Attention Is All You Need\" (Transformer Paper)\n\n## Overview\n\nThis plan outlines how to reproduce the Transformer architecture and experiments described in the paper. The Transformer is a novel sequence transduction model that relies entirely on self-attention mechanisms, dispensing with recurrence and convolutions.\n\n## Part 1: Transformer Architecture Implementation\n\n### 1. Model Architecture\n\nThe Transformer follows an encoder-decoder architecture with the following specifications:\n\n#### 1.1 Overall Architecture\n- **Encoder**: Stack of N=6 identical layers\n- **Decoder**: Stack of N=6 identical layers\n- **Output**: Linear layer + softmax for next-token prediction\n- **Dimension**: All sub-layers output dimension d_model = 512\n\n#### 1.2 Encoder Components\nEach encoder layer has:\n- Multi-head self-attention sub-layer\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n\n#### 1.3 Decoder Components\nEach decoder layer has:\n- Masked multi-head self-attention sub-layer\n- Multi-head attention over encoder output\n- Position-wise fully connected feed-forward network\n- Residual connections around each sub-layer + layer normalization\n- Masking to prevent positions from attending to subsequent positions\n\n### 2. Key Components\n\n#### 2.1 Attention Mechanism\n\n**Scaled Dot-Product Attention**:\n- Formula: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- Inputs: Queries and keys of dimension d_k, values of dimension d_v\n- Scaling factor: 1/\u221ad_k to prevent small gradients in softmax\n\n**Multi-Head Attention**:\n- Perform attention function h times in parallel\n- Parameters:\n - Number of heads h = 8\n - Dimension per head d_k = d_v = d_model/h = 64\n- Projection matrices:\n - W_Q^i \u2208 R^(d_model\u00d7d_k)\n - W_K^i \u2208 R^(d_model\u00d7d_k)\n - W_V^i \u2208 R^(d_model\u00d7d_v)\n - W_O \u2208 R^(hd_v\u00d7d_model)\n\n#### 2.2 Position-wise Feed-Forward Networks\n- Two linear transformations with ReLU activation:\n - FFN(x) = max(0, xW_1 + b_1)W_2 + b_2\n- Parameters:\n - Input and output dimensionality: d_model = 512\n - Inner layer dimensionality: d_ff = 2048\n\n#### 2.3 Embeddings and Positional Encoding\n- Token embeddings: Learned embeddings of dimension d_model = 512\n- Positional encodings: Using sine and cosine functions\n - PE(pos,2i) = sin(pos/10000^(2i/d_model))\n - PE(pos,2i+1) = cos(pos/10000^(2i/d_model))\n- Position embeddings added to token embeddings\n- Embedding weights multiplied by \u221ad_model\n- Weight sharing between embedding layers and pre-softmax linear transformation\n\n#### 2.4 Regularization Techniques\n- Residual dropout: Applied to each sub-layer output before addition and normalization, P_drop = 0.1\n- Embedding dropout: Applied to sums of embeddings and positional encodings\n- Label smoothing: \u03f5_ls = 0.1\n\n## Part 2: Training and Evaluation\n\n### 1. Datasets\n\n#### 1.1 Machine Translation\n- **English-to-German**: WMT 2014 EN-DE dataset (~4.5M sentence pairs)\n - Evaluation on newstest2014\n - Development set: newstest2013\n- **English-to-French**: WMT 2014 EN-FR dataset (~36M sentence pairs)\n - Evaluation on newstest2014\n\n#### 1.2 Preprocessing\n- Tokenization using byte-pair encoding (BPE)\n - EN-DE: Shared vocabulary of ~37,000 tokens\n - EN-FR: Word-piece vocabulary of 32,000 tokens\n\n### 2. Training Configuration\n\n#### 2.1 Base Model Hyperparameters\n- N = 6 layers for both encoder and decoder\n- d_model = 512\n- d_ff = 2048\n- h = 8 attention heads\n- d_k = d_v = 64\n- P_drop = 0.1\n- \u03f5_ls = 0.1 (label smoothing)\n\n#### 2.2 Big Model Hyperparameters\n- N = 6 layers\n- d_model = 1024\n- d_ff = 4096\n- h = 16 attention heads\n- P_drop = 0.3 (EN-DE) or P_drop = 0.1 (EN-FR)\n\n#### 2.3 Training Settings\n- Batch size: ~25,000 source tokens and ~25,000 target tokens per batch\n- Adam optimizer: \u03b21 = 0.9, \u03b22 = 0.98, \u03b5 = 10^-9\n- Learning rate schedule:\n - lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n - warmup_steps = 4000\n- Training steps:\n - Base model: 100,000 steps (~12 hours on 8 P100 GPUs)\n - Big model: 300,000 steps (~3.5 days on 8 P100 GPUs)\n\n### 3. Inference and Evaluation\n\n#### 3.1 Decoding Strategy\n- Beam search with beam size = 4\n- Length penalty \u03b1 = 0.6\n- Maximum output length = input length + 50, with early termination\n\n#### 3.2 Model Averaging\n- Base models: Average last 5 checkpoints (saved at 10-minute intervals)\n- Big models: Average last 20 checkpoints\n\n#### 3.3 Evaluation Metrics\n- BLEU score for machine translation\n- Expected scores:\n - EN-DE: 27.3 BLEU (base), 28.4 BLEU (big)\n - EN-FR: 38.1 BLEU (base), 41.8 BLEU (big)\n\n## Part 3: Implementation Plan Breakdown\n\n### Phase 1: Basic Implementation\n\n1. **Set up project structure and dependencies**\n - PyTorch/TensorFlow\n - Data processing libraries\n - Evaluation tools (SACREBLEU for BLEU scoring)\n\n2. **Implement core Transformer components**\n - Scaled dot-product attention\n - Multi-head attention\n - Position-wise feed-forward networks\n - Positional encoding\n - Embedding layers with weight sharing\n\n3. **Assemble encoder and decoder**\n - Encoder layer with self-attention and feed-forward\n - Decoder layer with masked self-attention, cross-attention, and feed-forward\n - Full encoder and decoder with N=6 layers\n - Attention masking for decoder\n\n4. **Implement full Transformer model**\n - Connect encoder and decoder\n - Add final linear layer and softmax\n - Implement label smoothing\n\n### Phase 2: Training Infrastructure\n\n1. **Data preprocessing pipeline**\n - Download WMT 2014 datasets (EN-DE, EN-FR)\n - Implement BPE/word-piece tokenization\n - Create batching logic (grouping by similar sequence length)\n\n2. **Training loop**\n - Implement Adam optimizer with custom learning rate schedule\n - Set up gradient clipping and optimization\n - Implement dropout regularization\n - Configure logging and checkpointing\n\n3. **Model initialization**\n - Initialize model parameters according to paper specifications\n - Weight sharing between embedding layers and pre-softmax projection\n\n### Phase 3: Inference and Evaluation\n\n1. **Beam search implementation**\n - Implement beam search with configurable beam size\n - Add length penalty\n - Handle early termination\n\n2. **Checkpoint averaging**\n - Implement logic to average model weights from checkpoints\n\n3. **Evaluation pipeline**\n - Set up BLEU score computation\n - Compare results with paper benchmarks\n\n### Phase 4: Experimentation and Validation\n\n1. **Base model training**\n - Train on EN-DE dataset with base configuration\n - Validate on newstest2013\n - Test on newstest2014\n\n2. **Big model training** (if resources permit)\n - Train with larger configuration\n - Validate and test as above\n\n3. **Ablation studies** (optional but recommended)\n - Vary number of attention heads\n - Test different key/value dimensions\n - Experiment with model size variations\n\n## Additional Implementation Details\n\n### Key Nuances to Consider\n\n1. **Attention Implementation Details**:\n - Ensure proper masking in decoder self-attention\n - Implement attention scaling correctly (1/\u221ad_k)\n - Handle padding tokens in attention calculations\n\n2. **Positional Encoding**:\n - Implement sine/cosine functions properly\n - Add to token embeddings before each encoder/decoder stack\n\n3. **Training Stability**:\n - Monitor loss for instability\n - Implement learning rate warmup carefully\n - Consider gradient clipping if not mentioned in paper\n\n4. **Efficiency Considerations**:\n - Implement attention batching efficiently\n - Memory management for large model variants\n - Parallelize training if possible\n\n### Potential Challenges\n\n1. **Computational Resources**:\n - The paper used 8 P100 GPUs; adjust batch size/accumulation if fewer resources available\n - Consider using mixed precision training if memory is limited\n\n2. **Hyperparameter Sensitivity**:\n - Pay special attention to learning rate schedule\n - Label smoothing implementation details\n\n3. **Dataset Preparation**:\n - Ensure correct BPE implementation\n - Handle large dataset sizes efficiently\n\n## Final Verification\n\nTo validate successful reproduction:\n\n1. Train base model on EN-DE WMT 2014\n2. Evaluate on newstest2014 using beam search\n3. Compare BLEU scores (target: ~27.3 for base model)\n4. If resources permit, train and evaluate big model (target: ~28.4 BLEU)\n\nThis plan should provide all the necessary details to implement the Transformer architecture as described in \"Attention Is All You Need\" and reproduce the paper's main results.\n\n-----\n\n## Design\n[CONTENT]\n{\n \"Implementation approach\": \"We will implement the Transformer architecture as described in 'Attention Is All You Need' using PyTorch as the main deep learning framework. The implementation will focus on reproducibility and readability while making use of efficient libraries like torchtext for data processing and sacrebleu for evaluation. The system will be organized into modules for model architecture, data processing, training, and evaluation. We'll implement both base and big Transformer variants with appropriate hyperparameter configurations.\",\n \n \"File list\": [\n \"main.py\",\n \"config.py\",\n \"model.py\",\n \"data_processing.py\",\n \"train.py\",\n \"evaluate.py\",\n \"utils.py\"\n ],\n \n \"Data structures and interfaces\": \"\nclassDiagram\n class Config {\n +int d_model\n +int n_heads\n +int num_encoder_layers\n +int num_decoder_layers\n +int dim_feedforward\n +float dropout\n +int max_seq_length\n +str device\n +float learning_rate\n +int warmup_steps\n +float label_smoothing\n +int beam_size\n +float length_penalty\n +__init__(model_size: str)\n +get_model_params() -> dict\n +get_training_params() -> dict\n }\n \n class TransformerModel {\n +torch.nn.Module encoder\n +torch.nn.Module decoder\n +torch.nn.Module src_embed\n +torch.nn.Module tgt_embed\n +torch.nn.Module generator\n +__init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n +forward(src: Tensor, tgt: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n +encode(src: Tensor, src_mask: Tensor) -> Tensor\n +decode(memory: Tensor, src_mask: Tensor, tgt: Tensor, tgt_mask: Tensor) -> Tensor\n +beam_search(src: Tensor, max_len: int, start_symbol: int) -> Tensor\n }\n \n class PositionalEncoding {\n +torch.nn.Module dropout\n +Tensor pe\n +__init__(d_model: int, dropout: float, max_len: int)\n +forward(x: Tensor) -> Tensor\n }\n \n class MultiHeadAttention {\n +int d_model\n +int n_heads\n +int d_k\n +int d_v\n +torch.nn.Module W_q\n +torch.nn.Module W_k\n +torch.nn.Module W_v\n +torch.nn.Module W_o\n +__init__(d_model: int, n_heads: int, dropout: float)\n +forward(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tensor\n -attention(query: Tensor, key: Tensor, value: Tensor, mask: Tensor) -> Tuple[Tensor]\n }\n \n class PositionwiseFeedforward {\n +torch.nn.Module linear1\n +torch.nn.Module linear2\n +torch.nn.Module dropout\n +__init__(d_model: int, d_ff: int, dropout: float)\n +forward(x: Tensor) -> Tensor\n }\n \n class EncoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, mask: Tensor) -> Tensor\n }\n \n class DecoderLayer {\n +torch.nn.Module self_attn\n +torch.nn.Module cross_attn\n +torch.nn.Module feed_forward\n +torch.nn.Module norm1\n +torch.nn.Module norm2\n +torch.nn.Module norm3\n +float dropout\n +__init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\n +forward(x: Tensor, memory: Tensor, src_mask: Tensor, tgt_mask: Tensor) -> Tensor\n }\n \n class DataProcessor {\n +torchtext.vocab.Vocab src_vocab\n +torchtext.vocab.Vocab tgt_vocab\n +int max_seq_length\n +str device\n +__init__(config: Config)\n +load_data(dataset_path: str) -> Tuple[DataLoader]\n +build_vocab(train_data: List) -> Tuple[Vocab]\n +tokenize(text: str) -> List[str]\n +apply_bpe(tokens: List[str]) -> List[str]\n +batch_data(data: List) -> Iterator\n +create_masks(src: Tensor, tgt: Tensor) -> Tuple[Tensor]\n }\n \n class Trainer {\n +TransformerModel model\n +torch.optim.Optimizer optimizer\n +torch.nn.Module criterion\n +DataProcessor data_processor\n +str device\n +int warmup_steps\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None\n +train_epoch(train_data: DataLoader) -> float\n +validate(val_data: DataLoader) -> float\n +save_checkpoint(path: str) -> None\n +load_checkpoint(path: str) -> None\n -adjust_learning_rate(step: int) -> None\n }\n \n class Evaluator {\n +TransformerModel model\n +DataProcessor data_processor\n +int beam_size\n +float length_penalty\n +__init__(config: Config, model: TransformerModel, data_processor: DataProcessor)\n +evaluate(test_data: DataLoader) -> dict\n +translate_sentence(sentence: str) -> str\n +compute_bleu(references: List[str], hypotheses: List[str]) -> float\n +average_checkpoints(paths: List[str]) -> None\n }\n \n TransformerModel --> MultiHeadAttention\n TransformerModel --> PositionalEncoding\n TransformerModel --> EncoderLayer\n TransformerModel --> DecoderLayer\n EncoderLayer --> MultiHeadAttention\n EncoderLayer --> PositionwiseFeedforward\n DecoderLayer --> MultiHeadAttention\n DecoderLayer --> PositionwiseFeedforward\n Trainer --> TransformerModel\n Trainer --> DataProcessor\n Evaluator --> TransformerModel\n Evaluator --> DataProcessor\n\",\n \n \"Program call flow\": \"\nsequenceDiagram\n participant Main as main.py\n participant Cfg as Config\n participant DP as DataProcessor\n participant TM as TransformerModel\n participant TR as Trainer\n participant EV as Evaluator\n \n Main->>Cfg: config = Config('base')\n Main->>DP: data_processor = DataProcessor(config)\n Main->>DP: train_data, val_data, test_data = data_processor.load_data('wmt14_en_de')\n DP-->>Main: return dataloaders\n \n Main->>TM: model = TransformerModel(config, len(data_processor.src_vocab), len(data_processor.tgt_vocab))\n TM->>TM: init encoder/decoder layers\n TM->>PositionalEncoding: create positional encoding\n PositionalEncoding-->>TM: return module\n TM->>MultiHeadAttention: create attention modules\n MultiHeadAttention-->>TM: return modules\n TM-->>Main: return initialized model\n \n Main->>TR: trainer = Trainer(config, model, data_processor)\n \n Main->>TR: trainer.train(train_data, val_data, epochs=100)\n loop For each epoch\n TR->>TR: train_epoch(train_data)\n loop For each batch\n TR->>DP: src_mask, tgt_mask = data_processor.create_masks(src, tgt)\n DP-->>TR: return masks\n TR->>TM: output = model(src, tgt, src_mask, tgt_mask)\n TM->>TM: encode(src, src_mask)\n TM->>TM: decode(memory, src_mask, tgt, tgt_mask)\n TM-->>TR: return output\n TR->>TR: calculate loss with label smoothing\n TR->>TR: backward() and optimizer.step()\n TR->>TR: adjust_learning_rate(step)\n end\n TR->>TR: validate(val_data)\n TR->>TR: save_checkpoint('checkpoint.pt')\n end\n TR-->>Main: training complete\n \n Main->>TR: trainer.save_checkpoint('final_model.pt')\n \n Main->>EV: evaluator = Evaluator(config, model, data_processor)\n Main->>EV: evaluator.average_checkpoints(['checkpoint_1.pt', 'checkpoint_2.pt', ...])\n \n Main->>EV: results = evaluator.evaluate(test_data)\n loop For each test batch\n EV->>TM: model.beam_search(src, max_len, start_symbol)\n TM-->>EV: return translations\n EV->>EV: compute_bleu(references, hypotheses)\n end\n EV-->>Main: return evaluation metrics\n \n Main->>Main: print(f'BLEU Score: {results[\\\"bleu\\\"]}')\n\",\n \n \"Anything UNCLEAR\": \"1. The exact format of WMT14 dataset and preprocessing details are not fully specified in the paper. We may need to refer to additional resources for detailed preprocessing steps. 2. The paper doesn't explicitly mention if they used any specific weight initialization scheme beyond the standard practices. 3. The exact implementation of label smoothing isn't detailed in the paper. We're assuming standard implementation. 4. The paper mentions using 8 GPUs, but our implementation will need to adapt to available resources, potentially using distributed training techniques if multiple GPUs are available.\"\n}\n[/CONTENT]\n\n-----\n\n## Task\n[CONTENT]\n{\n \"Required packages\": [\n \"torch>=1.7.0\",\n \"torchtext>=0.8.0\",\n \"numpy>=1.19.0\",\n \"tqdm>=4.50.0\",\n \"sacrebleu>=1.5.0\",\n \"sentencepiece>=0.1.94\",\n \"matplotlib>=3.3.0\",\n \"tensorboard>=2.4.0\"\n ],\n \"Required Other language third-party packages\": [\n \"No third-party dependencies required\"\n ],\n \"Logic Analysis\": [\n [\n \"config.py\",\n \"Contains the Config class to store all hyperparameters and configuration settings for the Transformer model.\\n\\nClass:\\n- Config: Stores configuration for model architecture, training, and evaluation\\n - __init__(model_size: str): Initialize configuration based on model size ('base' or 'big')\\n - get_model_params(): Returns dictionary of model architecture parameters\\n - get_training_params(): Returns dictionary of training parameters\\n\\nThe config will define:\\n- Model architecture parameters (d_model, n_heads, num_layers, etc.)\\n- Training parameters (learning rate, warmup steps, etc.)\\n- Evaluation parameters (beam size, length penalty, etc.)\\n- Data processing parameters (max sequence length, batch size, etc.)\\n\\nNo external dependencies besides standard Python libraries.\"\n ],\n [\n \"utils.py\",\n \"Utility functions used across the project.\\n\\nFunctions:\\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\\n\\nImports:\\n- torch\\n- numpy\\n- os\\n- math\"\n ],\n [\n \"data_processing.py\",\n \"Handles data loading, preprocessing, tokenization, and batching.\\n\\nClass:\\n- DataProcessor:\\n - __init__(config: Config): Initialize with configuration\\n - load_data(dataset_path: str) -> Tuple[DataLoader]: Load and prepare train/val/test data\\n - build_vocab(train_data: List) -> Tuple[Vocab]: Build source and target vocabularies\\n - tokenize(text: str) -> List[str]: Tokenize text\\n - apply_bpe(tokens: List[str]) -> List[str]: Apply byte-pair encoding\\n - batch_data(data: List) -> Iterator: Create batches of similar lengths\\n - create_masks(src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor]: Create attention masks\\n\\nThis module will handle:\\n- Downloading and extracting WMT14 datasets if not available\\n- Tokenization using byte-pair encoding or word-piece encoding\\n- Building vocabularies\\n- Creating batches of similar sequence lengths\\n- Creating attention masks for the transformer model\\n\\nImports:\\n- torch\\n- torchtext\\n- sentencepiece\\n- utils (create_padding_mask, create_subsequent_mask)\\n- config (Config)\"\n ],\n [\n \"model.py\",\n \"Implements the Transformer architecture as described in the paper.\\n\\nClasses:\\n- PositionalEncoding: Adds positional information to embeddings\\n - __init__(d_model: int, dropout: float, max_len: int)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- MultiHeadAttention: Implements multi-head attention mechanism\\n - __init__(d_model: int, n_heads: int, dropout: float)\\n - forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n - attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: torch.Tensor) -> Tuple[torch.Tensor]\\n\\n- PositionwiseFeedforward: Implements position-wise feed-forward network\\n - __init__(d_model: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor) -> torch.Tensor\\n\\n- EncoderLayer: Single layer of the encoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor\\n\\n- DecoderLayer: Single layer of the decoder\\n - __init__(d_model: int, n_heads: int, d_ff: int, dropout: float)\\n - forward(x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n\\n- TransformerModel: Full transformer model\\n - __init__(config: Config, src_vocab_size: int, tgt_vocab_size: int)\\n - forward(src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - encode(src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor\\n - decode(memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor) -> torch.Tensor\\n - beam_search(src: torch.Tensor, max_len: int, start_symbol: int) -> torch.Tensor\\n\\nImports:\\n- torch\\n- math\\n- copy\\n- numpy\\n- config (Config)\\n- utils (for mask creation)\"\n ],\n [\n \"train.py\",\n \"Handles the training and validation loops for the Transformer model.\\n\\nClass:\\n- Trainer:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize trainer\\n - train(train_data: DataLoader, val_data: DataLoader, epochs: int) -> None: Main training loop\\n - train_epoch(train_data: DataLoader) -> float: Train for one epoch\\n - validate(val_data: DataLoader) -> float: Validate model\\n - save_checkpoint(path: str) -> None: Save model checkpoint\\n - load_checkpoint(path: str) -> None: Load model checkpoint\\n - adjust_learning_rate(step: int) -> None: Adjust learning rate according to schedule\\n\\nImplements:\\n- Adam optimizer with custom learning rate schedule\\n- Label smoothing for training\\n- Gradient clipping\\n- Logging with tensorboard\\n- Checkpoint saving and loading\\n\\nImports:\\n- torch\\n- tqdm\\n- time\\n- os\\n- tensorboard\\n- model (TransformerModel)\\n- config (Config)\\n- utils (label_smoothed_nll_loss, get_lr_scheduler, save_checkpoint, load_checkpoint)\\n- data_processing (DataProcessor)\"\n ],\n [\n \"evaluate.py\",\n \"Handles evaluation and inference for the trained model.\\n\\nClass:\\n- Evaluator:\\n - __init__(config: Config, model: TransformerModel, data_processor: DataProcessor): Initialize evaluator\\n - evaluate(test_data: DataLoader) -> dict: Evaluate model on test data\\n - translate_sentence(sentence: str) -> str: Translate a single sentence\\n - compute_bleu(references: List[str], hypotheses: List[str]) -> float: Compute BLEU score\\n - average_checkpoints(paths: List[str]) -> None: Average model weights from checkpoints\\n\\nImplements:\\n- Beam search for decoding\\n- BLEU score calculation using sacrebleu\\n- Model averaging for evaluation\\n- Length penalty during beam search\\n\\nImports:\\n- torch\\n- tqdm\\n- sacrebleu\\n- model (TransformerModel)\\n- config (Config)\\n- data_processing (DataProcessor)\\n- utils (average_checkpoints)\"\n ],\n [\n \"main.py\",\n \"Entry point for running training and evaluation.\\n\\nFunctions:\\n- train_model(config_path: str = None) -> None: Train the transformer model\\n- evaluate_model(model_path: str, config_path: str = None) -> None: Evaluate the trained model\\n- translate(model_path: str, sentence: str, config_path: str = None) -> str: Translate a single sentence\\n- main(): Parse command line arguments and run appropriate function\\n\\nThis script will:\\n- Parse command-line arguments\\n- Initialize configuration\\n- Set up data processing\\n- Create and train model or load pre-trained model\\n- Evaluate model performance\\n- Allow for single sentence translation\\n\\nImports:\\n- torch\\n- argparse\\n- os\\n- config (Config)\\n- model (TransformerModel)\\n- data_processing (DataProcessor)\\n- train (Trainer)\\n- evaluate (Evaluator)\"\n ]\n ],\n \"Task list\": [\n \"config.py\",\n \"utils.py\",\n \"data_processing.py\",\n \"model.py\",\n \"train.py\",\n \"evaluate.py\",\n \"main.py\"\n ],\n \"Full API spec\": \"\",\n \"Shared Knowledge\": \"Several components and concepts are shared across multiple files in this implementation:\\n\\n1. Configuration Parameters:\\n - Model architecture parameters (d_model, n_heads, etc.) defined in config.py but used across model.py, train.py, and evaluate.py\\n - Training parameters (learning rate, warmup steps) used in both config.py and train.py\\n - Evaluation parameters (beam size, length penalty) shared between config.py and evaluate.py\\n\\n2. Masking Functions:\\n - Padding mask creation logic shared between data_processing.py and model.py\\n - Sequence masking for autoregressive decoding used in model.py and data_processing.py\\n\\n3. Vocabulary and Tokenization:\\n - Source and target vocabularies created in data_processing.py but used in model.py for embedding layers\\n - Special tokens (PAD, BOS, EOS) shared across data_processing.py, model.py, and evaluate.py\\n\\n4. Checkpoint Management:\\n - Model saving/loading functions in utils.py used by train.py and evaluate.py\\n - Checkpoint averaging logic shared between utils.py and evaluate.py\\n\\n5. Learning Rate Schedule:\\n - Custom learning rate adjustment function defined in utils.py and used in train.py\\n - Warmup steps and scaling factors shared between these files\\n\\n6. Label Smoothing:\\n - Implementation in utils.py used during training in train.py\\n - Epsilon parameter defined in config.py\\n\\n7. Model Architecture:\\n - The transformer architecture components (attention, feed-forward, etc.) are shared across encoder and decoder\\n - Weight sharing between embedding layer and final projection layer as mentioned in the paper\",\n \"Anything UNCLEAR\": \"1. The paper doesn't provide specific details about the tokenization process beyond mentioning byte-pair encoding for English-German and word-piece for English-French. We might need to refer to additional sources to ensure the tokenization is correctly implemented.\\n\\n2. The exact implementation of label smoothing isn't detailed in the paper. We've assumed a standard implementation but might need to adjust if results differ.\\n\\n3. The warm-up learning rate schedule is described at a high level, but implementation details might require some experimentation for optimal results.\\n\\n4. The paper mentions averaging checkpoints for evaluation, but doesn't specify exactly how many checkpoints to save during training or at what intervals.\\n\\n5. The paper was trained on 8 P100 GPUs. Our implementation will need to be adapted to available resources, which might affect batch sizes and training times. Distributed training might be necessary for full reproduction if 8 GPUs aren't available.\\n\\n6. The exact pre-processing of the WMT14 dataset (filtering criteria, handling of special characters, etc.) is not fully specified in the paper.\"\n}\n[/CONTENT]\n\n-----\n\n## Configuration file\n```yaml\n## config.yaml\n\n# Transformer Model Configuration\n\n# Model architecture parameters\nmodel:\n base:\n d_model: 512 # Dimension of model embeddings\n n_heads: 8 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 2048 # Dimension of feed-forward layer\n dropout: 0.1 # Dropout rate\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n \n big:\n d_model: 1024 # Dimension of model embeddings\n n_heads: 16 # Number of attention heads\n num_encoder_layers: 6 # Number of encoder layers\n num_decoder_layers: 6 # Number of decoder layers\n dim_feedforward: 4096 # Dimension of feed-forward layer\n dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR)\n max_seq_length: 5000 # Maximum sequence length\n share_weights: true # Share embedding weights with output projection\n\n# Training parameters\ntraining:\n optimizer: \"adam\"\n beta1: 0.9\n beta2: 0.98\n epsilon: 1.0e-9\n warmup_steps: 4000\n label_smoothing: 0.1\n batch_tokens: 25000 # Target batch size in tokens\n base_steps: 100000 # Training steps for base model\n big_steps: 300000 # Training steps for big model\n checkpoint_interval: 10 # Save checkpoint every 10 minutes\n average_checkpoints:\n base: 5 # Number of checkpoints to average for base model\n big: 20 # Number of checkpoints to average for big model\n\n# Inference parameters\ninference:\n beam_size: 4\n length_penalty: 0.6\n max_length_factor: 50 # Maximum output length = input_length + max_length_factor\n\n# Data parameters\ndata:\n en_de:\n source_lang: \"en\"\n target_lang: \"de\"\n train: \"wmt14_en_de_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 37000 # Shared BPE vocabulary\n tokenization: \"bpe\" # Byte-pair encoding\n \n en_fr:\n source_lang: \"en\"\n target_lang: \"fr\"\n train: \"wmt14_en_fr_train\"\n valid: \"newstest2013\"\n test: \"newstest2014\"\n vocab_size: 32000 # Word-piece vocabulary\n tokenization: \"wordpiece\" # Word-piece encoding\n```\n-----\n\n## Instruction\nConduct a Logic Analysis to assist in writing the code, based on the paper, the plan, the design, the task and the previously specified configuration file (config.yaml). \nYou DON'T need to provide the actual code yet; focus on a thorough, clear analysis.\n\nWrite the logic analysis in 'utils.py', which is intended for 'Utility functions used across the project.\n\nFunctions:\n- create_subsequent_mask(size: int) -> torch.Tensor: Creates mask for decoder self-attention\n- create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: Creates mask for padding tokens\n- label_smoothed_nll_loss(pred: torch.Tensor, target: torch.Tensor, epsilon: float) -> torch.Tensor: Implements label smoothing\n- get_lr_scheduler(optimizer: torch.optim.Optimizer, d_model: int, warmup_steps: int) -> function: Creates learning rate scheduler\n- save_checkpoint(model: torch.nn.Module, optimizer: torch.optim.Optimizer, epoch: int, path: str) -> None: Saves model checkpoint\n- load_checkpoint(path: str, model: torch.nn.Module, optimizer: torch.optim.Optimizer = None) -> dict: Loads model checkpoint\n- average_checkpoints(paths: List[str], model: torch.nn.Module) -> None: Averages model weights from multiple checkpoints\n\nImports:\n- torch\n- numpy\n- os\n- math'.\n\n-----\n\n## Logic Analysis: utils.py"}, {"role": "assistant", "content": "# Logic Analysis for utils.py\n\n## Function: create_subsequent_mask(size)\n\n**Purpose**: \nCreates a triangular (look-ahead) mask for the decoder's self-attention mechanism to prevent positions from attending to subsequent positions, ensuring causality in the decoding process.\n\n**Implementation Logic**:\n1. Create a square matrix of size `size` \u00d7 `size` \n2. Fill the upper triangle with `float('-inf')` or a large negative number to effectively zero out those positions after softmax\n3. Fill the lower triangle (including diagonal) with zeros, allowing attention to current and previous positions\n4. This creates a binary mask where positions can only attend to previous positions and themselves\n\n**Mathematical Foundation**:\n- The mask must conform to the attention formula in the paper: Attention(Q,K,V) = softmax(QK^T/\u221ad_k)V\n- When we add the mask to QK^T before the softmax operation, values of `-inf` will become zero after softmax, effectively blocking attention to future positions\n\n**Usage Context**:\n- Used during training and inference in the decoder's self-attention mechanism\n- Critical for maintaining the autoregressive property of the model\n- Will be combined with padding masks when necessary\n\n## Function: create_padding_mask(seq, pad_idx)\n\n**Purpose**: \nCreates a mask to hide padding tokens in the input sequences, ensuring the model doesn't attend to padding positions.\n\n**Implementation Logic**:\n1. Create a binary mask where positions equal to `pad_idx` are marked as 1 (to be masked)\n2. All other positions are marked as 0 (to be attended to)\n3. Reshape the mask to be compatible with the attention mechanism's expected dimensions [batch_size, 1, 1, seq_len]\n4. The mask will be added to the scaled dot product (before softmax) as large negative values\n\n**Mathematical Foundation**:\n- In the attention mechanism, this mask effectively zeroes out the contribution of padding tokens\n- Must be compatible with the attention shapes for broadcasting\n\n**Usage Context**:\n- Applied in both encoder self-attention and decoder cross-attention\n- Ensures that the model's predictions are not influenced by padding tokens\n- Will be combined with sequence masks in the decoder\n\n## Function: label_smoothed_nll_loss(pred, target, epsilon)\n\n**Purpose**: \nImplements label smoothing regularization technique mentioned in the paper (section 5.4) to prevent the model from becoming overconfident and improve generalization.\n\n**Implementation Logic**:\n1. Create a distribution with `epsilon/K` probability mass for all K vocabulary items\n2. Assign a probability of `1-epsilon` to the correct label\n3. Calculate the KL-divergence between the predicted distribution and this smoothed distribution\n4. Ignore loss calculations on padding tokens\n\n**Mathematical Foundation**:\n- Standard cross-entropy loss: H(q,p) = -\u2211q(k)log(p(k))\n- Label smoothing modifies the target distribution q from a one-hot distribution to: \n q'(k) = (1-\u03b5)\u03b4(k,y) + \u03b5/K\n where \u03b4(k,y) is 1 when k=y (the correct class) and 0 otherwise\n- As mentioned in the paper, this \"hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score\"\n\n**Usage Context**:\n- Used during training only\n- The paper uses \u03b5_ls = 0.1\n\n## Function: get_lr_scheduler(optimizer, d_model, warmup_steps)\n\n**Purpose**: \nCreates a learning rate scheduler that implements the formula from the paper (section 5.3): lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n\n**Implementation Logic**:\n1. Define a function that calculates learning rate based on step count\n2. Initial phase: Learning rate increases linearly for first `warmup_steps` steps\n3. Later phase: Learning rate decreases proportionally to the inverse square root of step number\n4. Scale by factor of d_model^(-0.5)\n5. Apply this calculated learning rate to the optimizer\n\n**Mathematical Foundation**:\n- Formula from paper: lr = d_model^(-0.5) * min(step_num^(-0.5), step_num * warmup_steps^(-1.5))\n- The initial linear warmup prevents early training instability\n- The square root decay helps maintain stable training for extended periods\n\n**Usage Context**:\n- Applied during training after each step\n- As per paper, warmup_steps = 4000\n- Critical for the model's convergence\n\n## Function: save_checkpoint(model, optimizer, epoch, path)\n\n**Purpose**: \nSaves the model's parameters, optimizer state, and training progress to a file for later resumption of training or evaluation.\n\n**Implementation Logic**:\n1. Create a dictionary containing:\n - Model state dictionary (parameters)\n - Optimizer state dictionary\n - Current epoch\n - Any other training metadata needed\n2. Use torch.save to serialize this dictionary to disk\n3. Handle potential IO errors\n\n**Technical Considerations**:\n- Need to save both model and optimizer states to properly resume training\n- Should be compatible with model averaging functionality\n- Should be efficient with large models\n\n**Usage Context**:\n- Called periodically during training (every 10 minutes as per paper)\n- Used for creating checkpoint ensembles and resuming training\n\n## Function: load_checkpoint(path, model, optimizer=None)\n\n**Purpose**: \nLoads a previously saved model checkpoint for continued training or evaluation.\n\n**Implementation Logic**:\n1. Load the checkpoint dictionary from disk using torch.load\n2. Apply the saved parameters to the model\n3. If optimizer is provided and we're resuming training, restore optimizer state\n4. Return any additional metadata from the checkpoint (epoch, etc.)\n5. Handle potential IO errors and file not found scenarios\n\n**Technical Considerations**:\n- Must handle cases where optimizer is not provided (evaluation mode)\n- Should verify compatibility of loaded weights with current model architecture\n- Handle device mapping (CPU/GPU) gracefully\n\n**Usage Context**:\n- Used when resuming training\n- Used when loading models for evaluation\n- Used when performing checkpoint averaging\n\n## Function: average_checkpoints(paths, model)\n\n**Purpose**: \nImplements checkpoint averaging as mentioned in the paper (section 6.1), which averages parameters from the last N checkpoints for improved performance.\n\n**Implementation Logic**:\n1. Load all specified checkpoint files\n2. Extract model parameters from each checkpoint\n3. Calculate the average value for each parameter across all checkpoints\n4. Update the provided model with these averaged parameters\n5. Leave optimizer state untouched (averaging is only for evaluation)\n\n**Mathematical Foundation**:\n- Simple arithmetic mean of each parameter across checkpoints\n- Averaging parameters often improves generalization compared to using a single checkpoint\n\n**Usage Context**:\n- Used for evaluation only, not during training\n- Paper specifies averaging:\n - Last 5 checkpoints for base models\n - Last 20 checkpoints for big models\n- Checkpoints written at 10-minute intervals\n\n**Implementation Note**:\n- Need to handle potentially large memory usage when loading multiple checkpoints\n- Consider implementing a streaming average to reduce peak memory requirements\n\n## Summary\n\nThe `utils.py` file provides essential utility functions that support the core Transformer implementation, closely following the specifications from the \"Attention Is All You Need\" paper. The masking functions are critical for the attention mechanism's proper operation, ensuring autoregressive behavior in the decoder and proper handling of padded sequences. \n\nThe label smoothing implementation helps improve generalization as specified in section 5.4 of the paper. The learning rate scheduler precisely implements the formula described in section 5.3, which is vital for stable training. \n\nThe checkpoint management functions implement the model averaging technique described in section 6.1, which substantially improves final model performance. According to the paper, the base models use an average of the last 5 checkpoints, while big models average the last 20 checkpoints."}] \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.py new file mode 100644 index 00000000..81c266f5 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.py @@ -0,0 +1,199 @@ +""" +Configuration module for the Transformer model implementation. +Contains the Config class to store all hyperparameters and configuration settings. +""" +import os +import yaml +from typing import Dict, Any, Optional + + +class Config: + """ + Configuration class for the Transformer model. + Stores parameters for model architecture, training, and evaluation. + """ + def __init__(self, model_size: str = 'base', config_path: Optional[str] = None): + """ + Initialize configuration based on model size ('base' or 'big') + + Args: + model_size: Model size, either 'base' or 'big' + config_path: Path to config.yaml file (optional) + """ + self.model_size = model_size + if model_size not in ['base', 'big']: + raise ValueError(f"Model size must be 'base' or 'big', got {model_size}") + + # Load configuration from YAML file + if config_path is None: + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml') + + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + # Load model architecture parameters + model_config = config['model'][model_size] + self.d_model = model_config['d_model'] + self.n_heads = model_config['n_heads'] + self.num_encoder_layers = model_config['num_encoder_layers'] + self.num_decoder_layers = model_config['num_decoder_layers'] + self.dim_feedforward = model_config['dim_feedforward'] + self.dropout = model_config['dropout'] + self.max_seq_length = model_config['max_seq_length'] + self.share_weights = model_config['share_weights'] + + # Derived parameters + self.d_k = self.d_model // self.n_heads # Dimension of key per head + self.d_v = self.d_model // self.n_heads # Dimension of value per head + + # Load training parameters + self.optimizer = config['training']['optimizer'] + self.beta1 = config['training']['beta1'] + self.beta2 = config['training']['beta2'] + self.epsilon = config['training']['epsilon'] + self.warmup_steps = config['training']['warmup_steps'] + self.label_smoothing = config['training']['label_smoothing'] + self.batch_tokens = config['training']['batch_tokens'] + + if self.model_size == 'base': + self.total_steps = config['training']['base_steps'] + self.average_checkpoints = config['training']['average_checkpoints']['base'] + else: + self.total_steps = config['training']['big_steps'] + self.average_checkpoints = config['training']['average_checkpoints']['big'] + + self.checkpoint_interval = config['training']['checkpoint_interval'] + + # Load inference parameters + self.beam_size = config['inference']['beam_size'] + self.length_penalty = config['inference']['length_penalty'] + self.max_length_factor = config['inference']['max_length_factor'] + + # Load data parameters (default to English-German) + self.data_config = config['data']['en_de'] + + # Set device (will be determined at runtime) + self.device = 'cuda' if self._is_cuda_available() else 'cpu' + + # Learning rate setup - depends on d_model + self.learning_rate = None # Will be calculated dynamically using scheduler + + def get_model_params(self) -> Dict[str, Any]: + """ + Return a dictionary of model architecture parameters + + Returns: + Dictionary containing model architecture parameters + """ + return { + 'd_model': self.d_model, + 'n_heads': self.n_heads, + 'num_encoder_layers': self.num_encoder_layers, + 'num_decoder_layers': self.num_decoder_layers, + 'dim_feedforward': self.dim_feedforward, + 'dropout': self.dropout, + 'd_k': self.d_k, + 'd_v': self.d_v, + 'max_seq_length': self.max_seq_length, + 'share_weights': self.share_weights + } + + def get_training_params(self) -> Dict[str, Any]: + """ + Return a dictionary of training parameters + + Returns: + Dictionary containing training parameters + """ + return { + 'optimizer': self.optimizer, + 'beta1': self.beta1, + 'beta2': self.beta2, + 'epsilon': self.epsilon, + 'warmup_steps': self.warmup_steps, + 'label_smoothing': self.label_smoothing, + 'batch_tokens': self.batch_tokens, + 'total_steps': self.total_steps, + 'checkpoint_interval': self.checkpoint_interval, + 'average_checkpoints': self.average_checkpoints, + 'device': self.device + } + + def get_inference_params(self) -> Dict[str, Any]: + """ + Return a dictionary of inference parameters + + Returns: + Dictionary containing inference parameters + """ + return { + 'beam_size': self.beam_size, + 'length_penalty': self.length_penalty, + 'max_length_factor': self.max_length_factor, + 'device': self.device + } + + def get_data_params(self) -> Dict[str, Any]: + """ + Return a dictionary of data parameters + + Returns: + Dictionary containing data parameters + """ + return self.data_config + + def set_language_pair(self, language_pair: str) -> None: + """ + Set the language pair for data configuration + + Args: + language_pair: Language pair code ('en_de' or 'en_fr') + """ + if language_pair not in ['en_de', 'en_fr']: + raise ValueError(f"Language pair must be 'en_de' or 'en_fr', got {language_pair}") + + # Load configuration from YAML file + config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml') + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + self.data_config = config['data'][language_pair] + + # Update dropout for EN-FR if using big model + if language_pair == 'en_fr' and self.model_size == 'big': + self.dropout = 0.1 # As mentioned in the paper for EN-FR big model + + def _is_cuda_available(self) -> bool: + """ + Check if CUDA is available for GPU acceleration + + Returns: + Boolean indicating CUDA availability + """ + try: + import torch + return torch.cuda.is_available() + except ImportError: + return False + + +# For testing +if __name__ == "__main__": + # Create configurations for base and big models + base_config = Config(model_size='base') + big_config = Config(model_size='big') + + # Print some configuration values + print("Base model parameters:") + print(f"d_model: {base_config.d_model}") + print(f"n_heads: {base_config.n_heads}") + print(f"dropout: {base_config.dropout}") + + print("\nBig model parameters:") + print(f"d_model: {big_config.d_model}") + print(f"n_heads: {big_config.n_heads}") + print(f"dropout: {big_config.dropout}") + + # Change language pair and check dropout for big model + big_config.set_language_pair('en_fr') + print(f"\nBig model dropout after setting language to EN-FR: {big_config.dropout}") diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.yaml b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.yaml new file mode 100644 index 00000000..ebf83a0b --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/config.yaml @@ -0,0 +1,67 @@ +## config.yaml + +# Transformer Model Configuration + +# Model architecture parameters +model: + base: + d_model: 512 # Dimension of model embeddings + n_heads: 8 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 2048 # Dimension of feed-forward layer + dropout: 0.1 # Dropout rate + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + + big: + d_model: 1024 # Dimension of model embeddings + n_heads: 16 # Number of attention heads + num_encoder_layers: 6 # Number of encoder layers + num_decoder_layers: 6 # Number of decoder layers + dim_feedforward: 4096 # Dimension of feed-forward layer + dropout: 0.3 # Dropout rate for EN-DE (0.1 for EN-FR) + max_seq_length: 5000 # Maximum sequence length + share_weights: true # Share embedding weights with output projection + +# Training parameters +training: + optimizer: "adam" + beta1: 0.9 + beta2: 0.98 + epsilon: 1.0e-9 + warmup_steps: 4000 + label_smoothing: 0.1 + batch_tokens: 25000 # Target batch size in tokens + base_steps: 100000 # Training steps for base model + big_steps: 300000 # Training steps for big model + checkpoint_interval: 10 # Save checkpoint every 10 minutes + average_checkpoints: + base: 5 # Number of checkpoints to average for base model + big: 20 # Number of checkpoints to average for big model + +# Inference parameters +inference: + beam_size: 4 + length_penalty: 0.6 + max_length_factor: 50 # Maximum output length = input_length + max_length_factor + +# Data parameters +data: + en_de: + source_lang: "en" + target_lang: "de" + train: "wmt14_en_de_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 37000 # Shared BPE vocabulary + tokenization: "bpe" # Byte-pair encoding + + en_fr: + source_lang: "en" + target_lang: "fr" + train: "wmt14_en_fr_train" + valid: "newstest2013" + test: "newstest2014" + vocab_size: 32000 # Word-piece vocabulary + tokenization: "wordpiece" # Word-piece encoding \ No newline at end of file diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/data_processing.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/data_processing.py new file mode 100644 index 00000000..d6e067d6 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/data_processing.py @@ -0,0 +1,530 @@ +""" +Data processing module for the Transformer model implementation. +Handles data loading, preprocessing, tokenization, and batching. +""" + +import os +import math +import numpy as np +import torch +import torch.utils.data as data +from torch.nn.utils.rnn import pad_sequence +from torchtext.data import Dataset, Example, Field, Iterator +from torchtext.vocab import Vocab, build_vocab_from_iterator +from torchtext.datasets import WMT14 +from torchtext.data.utils import get_tokenizer +import sentencepiece as spm +from typing import List, Tuple, Dict, Iterator as IterType, Union, Optional, Callable + +# Import from project modules +from config import Config +from utils import create_masks, create_padding_mask, create_subsequent_mask + + +class DataProcessor: + """ + Handles data loading, preprocessing, tokenization, and batching. + """ + def __init__(self, config: Config): + """ + Initialize with configuration. + + Args: + config: Configuration object + """ + self.config = config + self.data_config = config.get_data_params() + self.model_config = config.get_model_params() + self.device = config.device + self.max_seq_length = self.model_config['max_seq_length'] + + # Get language pair info + self.source_lang = self.data_config['source_lang'] + self.target_lang = self.data_config['target_lang'] + + # Set vocabulary size and tokenization method + self.vocab_size = self.data_config['vocab_size'] + self.tokenization = self.data_config['tokenization'] + + # Special tokens + self.PAD_IDX = 0 + self.BOS_IDX = 1 + self.EOS_IDX = 2 + self.UNK_IDX = 3 + + # Initialize vocabularies as None (to be built) + self.src_vocab = None + self.tgt_vocab = None + + # Initialize tokenizers + self._init_tokenizers() + + def _init_tokenizers(self) -> None: + """ + Initialize tokenizers based on configuration. + """ + # Base tokenizer function (word-level tokenization) + self.base_tokenizer = get_tokenizer('spacy', language=f'{self.source_lang}_core_web_sm') + + # Initialize BPE or WordPiece tokenizers if needed + if self.tokenization == 'bpe': + self._init_bpe_tokenizer() + elif self.tokenization == 'wordpiece': + self._init_wordpiece_tokenizer() + + def _init_bpe_tokenizer(self) -> None: + """ + Initialize byte-pair encoding tokenizer. + Will train the model if it doesn't exist or load a pre-trained model. + """ + # Create directory for tokenizer models if it doesn't exist + os.makedirs('tokenizers', exist_ok=True) + + # Define model path + model_prefix = f'tokenizers/bpe_{self.source_lang}_{self.target_lang}' + model_path = f'{model_prefix}.model' + + # Check if model exists, otherwise train it (when datasets are loaded) + self.bpe_model_path = model_path + self.bpe_model_prefix = model_prefix + + # We'll train or load the model later when data is available + + def _init_wordpiece_tokenizer(self) -> None: + """ + Initialize WordPiece tokenizer. + Will train the model if it doesn't exist or load a pre-trained model. + """ + # Create directory for tokenizer models if it doesn't exist + os.makedirs('tokenizers', exist_ok=True) + + # Define model path + model_prefix = f'tokenizers/wp_{self.source_lang}_{self.target_lang}' + model_path = f'{model_prefix}.model' + + # Check if model exists, otherwise train it (when datasets are loaded) + self.wp_model_path = model_path + self.wp_model_prefix = model_prefix + + # We'll train or load the model later when data is available + + def load_data(self, dataset_path: Optional[str] = None) -> Tuple[data.DataLoader, data.DataLoader, data.DataLoader]: + """ + Load and prepare train/val/test data. + + Args: + dataset_path: Path to dataset (optional, will use default if not provided) + + Returns: + Tuple of (train_dataloader, val_dataloader, test_dataloader) + """ + print(f"Loading {self.source_lang}-{self.target_lang} dataset...") + + # Create dataset paths + if dataset_path is None: + dataset_path = '.data' + + # Define dataset splits based on config + train_split = self.data_config['train'] + valid_split = self.data_config['valid'] + test_split = self.data_config['test'] + + # Create Fields for source and target + src_field = Field( + tokenize=self.tokenize, + init_token='', + eos_token='', + pad_token='', + unk_token='', + lower=True, + batch_first=True + ) + + tgt_field = Field( + tokenize=self.tokenize, + init_token='', + eos_token='', + pad_token='', + unk_token='', + lower=True, + batch_first=True + ) + + # Specify fields for torchtext dataset + fields = [(self.source_lang, src_field), (self.target_lang, tgt_field)] + + # Load datasets + train_data, valid_data, test_data = WMT14.splits( + exts=(f'.{self.source_lang}', f'.{self.target_lang}'), + fields=fields, + root=dataset_path, + filter_pred=lambda x: len(vars(x)[self.source_lang]) <= self.max_seq_length and + len(vars(x)[self.target_lang]) <= self.max_seq_length + ) + + print(f"Number of training examples: {len(train_data)}") + print(f"Number of validation examples: {len(valid_data)}") + print(f"Number of testing examples: {len(test_data)}") + + # Build vocabularies from training data + if self.tokenization in ['bpe', 'wordpiece']: + # For subword tokenization, we need to train the tokenizer first + if self.tokenization == 'bpe': + self._train_bpe_tokenizer(train_data) + else: + self._train_wordpiece_tokenizer(train_data) + + # Apply subword tokenization to the datasets + train_data = self._apply_subword_tokenization(train_data) + valid_data = self._apply_subword_tokenization(valid_data) + test_data = self._apply_subword_tokenization(test_data) + + # Build vocabularies + self.build_vocab(train_data, src_field, tgt_field) + + # Create bucketed iterators to efficiently batch sequences of similar lengths + train_iterator, valid_iterator, test_iterator = self.batch_data(train_data, valid_data, test_data) + + # Convert iterators to PyTorch DataLoader format + train_dataloader = self._convert_to_dataloader(train_iterator) + valid_dataloader = self._convert_to_dataloader(valid_iterator) + test_dataloader = self._convert_to_dataloader(test_iterator) + + return train_dataloader, valid_dataloader, test_dataloader + + def _train_bpe_tokenizer(self, train_data: Dataset) -> None: + """ + Train byte-pair encoding tokenizer on training data. + + Args: + train_data: Training dataset + """ + # Check if model already exists + if os.path.exists(self.bpe_model_path): + print(f"Loading existing BPE model from {self.bpe_model_path}") + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpe_model_path) + return + + # Create corpus file for training + corpus_file = f'{self.bpe_model_prefix}.corpus' + with open(corpus_file, 'w', encoding='utf-8') as f: + # Write source sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.source_lang]) + '\n') + + # Write target sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.target_lang]) + '\n') + + # Train SentencePiece model + print(f"Training BPE tokenizer with vocabulary size {self.vocab_size}") + spm.SentencePieceTrainer.train( + f'--input={corpus_file} ' + f'--model_prefix={self.bpe_model_prefix} ' + f'--vocab_size={self.vocab_size} ' + f'--character_coverage=0.9995 ' + f'--model_type=bpe ' + f'--pad_id=0 --bos_id=1 --eos_id=2 --unk_id=3 ' + f'--user_defined_symbols=,,,' + ) + + # Load trained model + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.bpe_model_path) + + # Remove corpus file + os.remove(corpus_file) + + def _train_wordpiece_tokenizer(self, train_data: Dataset) -> None: + """ + Train WordPiece tokenizer on training data. + + Args: + train_data: Training dataset + """ + # Check if model already exists + if os.path.exists(self.wp_model_path): + print(f"Loading existing WordPiece model from {self.wp_model_path}") + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.wp_model_path) + return + + # Create corpus file for training + corpus_file = f'{self.wp_model_prefix}.corpus' + with open(corpus_file, 'w', encoding='utf-8') as f: + # Write source sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.source_lang]) + '\n') + + # Write target sentences + for example in train_data.examples: + f.write(' '.join(vars(example)[self.target_lang]) + '\n') + + # Train SentencePiece model with WordPiece + print(f"Training WordPiece tokenizer with vocabulary size {self.vocab_size}") + spm.SentencePieceTrainer.train( + f'--input={corpus_file} ' + f'--model_prefix={self.wp_model_prefix} ' + f'--vocab_size={self.vocab_size} ' + f'--character_coverage=0.9995 ' + f'--model_type=word ' + f'--pad_id=0 --bos_id=1 --eos_id=2 --unk_id=3 ' + f'--user_defined_symbols=,,,' + ) + + # Load trained model + self.sp = spm.SentencePieceProcessor() + self.sp.load(self.wp_model_path) + + # Remove corpus file + os.remove(corpus_file) + + def _apply_subword_tokenization(self, dataset: Dataset) -> Dataset: + """ + Apply subword tokenization to a dataset. + + Args: + dataset: Dataset to tokenize + + Returns: + Tokenized dataset + """ + # Create a new dataset with subword-tokenized examples + examples = [] + for example in dataset.examples: + src_text = ' '.join(vars(example)[self.source_lang]) + tgt_text = ' '.join(vars(example)[self.target_lang]) + + # Apply subword tokenization + src_tokens = self.sp.encode(src_text, out_type=str) + tgt_tokens = self.sp.encode(tgt_text, out_type=str) + + # Create a new example with tokenized text + new_example = Example() + setattr(new_example, self.source_lang, src_tokens) + setattr(new_example, self.target_lang, tgt_tokens) + examples.append(new_example) + + # Create new dataset with tokenized examples + return Dataset(examples, dataset.fields) + + def build_vocab(self, train_data: Dataset, src_field: Field, tgt_field: Field) -> Tuple[Vocab, Vocab]: + """ + Build source and target vocabularies. + + Args: + train_data: Training dataset + src_field: Source field + tgt_field: Target field + + Returns: + Tuple of (source vocabulary, target vocabulary) + """ + if self.tokenization in ['bpe', 'wordpiece']: + # For subword tokenization, use the vocabulary from SentencePiece + sp_vocab = {self.sp.id_to_piece(i): i for i in range(self.sp.get_piece_size())} + src_field.vocab = Vocab(sp_vocab, specials=[]) + tgt_field.vocab = Vocab(sp_vocab, specials=[]) + else: + # For word-level tokenization, build vocabulary from training data + src_field.build_vocab(train_data, max_size=self.vocab_size) + tgt_field.build_vocab(train_data, max_size=self.vocab_size) + + # Store vocabularies + self.src_vocab = src_field.vocab + self.tgt_vocab = tgt_field.vocab + + print(f"Source vocabulary size: {len(self.src_vocab)}") + print(f"Target vocabulary size: {len(self.tgt_vocab)}") + + return self.src_vocab, self.tgt_vocab + + def tokenize(self, text: str) -> List[str]: + """ + Tokenize text. + + Args: + text: Text to tokenize + + Returns: + List of tokens + """ + # Use base tokenizer for initial tokenization + tokens = self.base_tokenizer(text) + return tokens + + def apply_bpe(self, tokens: List[str]) -> List[str]: + """ + Apply byte-pair encoding to tokens. + + Args: + tokens: List of tokens + + Returns: + List of BPE tokens + """ + # Join tokens and apply BPE + text = ' '.join(tokens) + bpe_tokens = self.sp.encode(text, out_type=str) + return bpe_tokens + + def batch_data(self, train_data: Dataset, valid_data: Dataset, test_data: Dataset) -> Tuple[Iterator, Iterator, Iterator]: + """ + Create batches of similar lengths. + + Args: + train_data: Training dataset + valid_data: Validation dataset + test_data: Test dataset + + Returns: + Tuple of (train iterator, validation iterator, test iterator) + """ + # Calculate batch size based on target tokens per batch + # We'll do dynamic batching in the bucket iterator + batch_size = self.config.batch_tokens // self.max_seq_length + batch_size = max(1, batch_size) # Ensure at least 1 + + # Create BucketIterator for batching similar-length sequences + train_iterator, valid_iterator, test_iterator = Iterator.splits( + (train_data, valid_data, test_data), + batch_size=batch_size, + sort_key=lambda x: len(getattr(x, self.source_lang)), + sort_within_batch=True, + device=self.device + ) + + return train_iterator, valid_iterator, test_iterator + + def _convert_to_dataloader(self, iterator: Iterator) -> data.DataLoader: + """ + Convert torchtext iterator to PyTorch DataLoader. + + Args: + iterator: torchtext iterator + + Returns: + PyTorch DataLoader + """ + # Create a dataset that yields batches from the iterator + dataset = _IteratorDataset(iterator, self.source_lang, self.target_lang) + + # Create a DataLoader with the dataset + return data.DataLoader( + dataset, + batch_size=None, # Batching is already done by the iterator + collate_fn=None # No need for collation + ) + + def create_masks(self, src: torch.Tensor, tgt: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Create attention masks for the transformer model. + + Args: + src: Source tensor of shape (batch_size, src_len) + tgt: Target tensor of shape (batch_size, tgt_len) (optional, for training) + + Returns: + Tuple of (source mask, target mask) where target mask is None if tgt is None + """ + return create_masks(src, tgt, self.PAD_IDX) + + def encode_sentence(self, sentence: str, is_source: bool = True) -> torch.Tensor: + """ + Encode a sentence to tensor with vocabulary indices. + + Args: + sentence: Sentence to encode + is_source: Whether this is a source sentence (or target) + + Returns: + Tensor with vocabulary indices + """ + # Tokenize the sentence + tokens = self.tokenize(sentence) + + # Apply subword tokenization if needed + if self.tokenization in ['bpe', 'wordpiece']: + tokens = self.sp.encode(' '.join(tokens), out_type=str) + + # Get vocabulary (source or target) + vocab = self.src_vocab if is_source else self.tgt_vocab + + # Convert tokens to indices + indices = [vocab.stoi.get(token, self.UNK_IDX) for token in tokens] + + # Add BOS and EOS tokens + indices = [self.BOS_IDX] + indices + [self.EOS_IDX] + + # Convert to tensor + return torch.tensor(indices, dtype=torch.long).unsqueeze(0) + + def decode_sentence(self, indices: torch.Tensor, is_source: bool = False) -> str: + """ + Decode indices to sentence. + + Args: + indices: Tensor with vocabulary indices + is_source: Whether this is a source sentence (or target) + + Returns: + Decoded sentence + """ + # Get vocabulary (source or target) + vocab = self.src_vocab if is_source else self.tgt_vocab + + # Convert indices to tokens, skipping special tokens + tokens = [] + for idx in indices: + if idx == self.EOS_IDX: + break + if idx != self.BOS_IDX and idx != self.PAD_IDX: + tokens.append(vocab.itos[idx]) + + # For subword tokenization, join with spaces and then remove separators + if self.tokenization in ['bpe', 'wordpiece']: + return self.sp.decode(tokens) + else: + # For word-level tokenization, join with spaces + return ' '.join(tokens) + + +class _IteratorDataset(data.Dataset): + """ + Dataset adapter for torchtext iterator to PyTorch DataLoader. + """ + def __init__(self, iterator: Iterator, src_field: str, tgt_field: str): + """ + Initialize with iterator and field names. + + Args: + iterator: torchtext iterator + src_field: Source field name + tgt_field: Target field name + """ + self.iterator = iterator + self.src_field = src_field + self.tgt_field = tgt_field + self._iterator = iter(iterator) + self.length = len(iterator) + + def __len__(self) -> int: + """Get dataset length.""" + return self.length + + def __iter__(self) -> IterType: + """Reset and return iterator.""" + self._iterator = iter(self.iterator) + return self + + def __next__(self) -> Tuple[torch.Tensor, torch.Tensor]: + """Get next batch.""" + batch = next(self._iterator) + src = getattr(batch, self.src_field) + tgt = getattr(batch, self.tgt_field) + return src, tgt + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: + """Get item is not meaningful for iterator, but needed for DataLoader.""" + raise NotImplementedError("This dataset does not support random access") diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/evaluate.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/evaluate.py new file mode 100644 index 00000000..ca7d3200 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/evaluate.py @@ -0,0 +1,391 @@ +""" +Evaluation module for the Transformer model implementation. +Handles evaluation and inference for the trained model. +""" + +import os +import time +import math +from typing import Optional, Tuple, Dict, List, Any, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +import sacrebleu +from tqdm import tqdm + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from utils import average_checkpoints, create_masks + + +class Evaluator: + """ + Handles evaluation and inference for the trained Transformer model. + """ + def __init__( + self, + config: Config, + model: TransformerModel, + data_processor: DataProcessor + ): + """ + Initialize evaluator. + + Args: + config: Configuration object + model: TransformerModel instance + data_processor: DataProcessor instance + """ + self.config = config + self.model = model + self.data_processor = data_processor + + # Get inference parameters + inference_params = config.get_inference_params() + self.device = inference_params['device'] + self.beam_size = inference_params['beam_size'] + self.length_penalty = inference_params['length_penalty'] + self.max_length_factor = inference_params['max_length_factor'] + + # Move model to device + self.model.to(self.device) + + # Set model to evaluation mode + self.model.eval() + + def evaluate(self, test_data: DataLoader) -> Dict[str, float]: + """ + Evaluate model on test data. + + Args: + test_data: Test data loader + + Returns: + Dictionary containing evaluation metrics (e.g., BLEU score) + """ + self.model.eval() + + # Lists to store references and hypotheses + references = [] + hypotheses = [] + + print("Evaluating model...") + + # Create progress bar + pbar = tqdm(test_data, desc="Evaluating") + + with torch.no_grad(): + for src, tgt in pbar: + # Move data to device + src = src.to(self.device) + + # Get batch size and max possible length + batch_size = src.size(0) + max_len = min(src.size(1) + self.max_length_factor, + self.config.model_config['max_seq_length']) + + # Generate translations using beam search + generated = self.model.beam_search( + src, + max_len, + start_symbol=self.data_processor.BOS_IDX, + end_symbol=self.data_processor.EOS_IDX + ) + + # Convert tensors to sentences + for i in range(batch_size): + # Get reference sentence (target) + ref_sentence = self.data_processor.decode_sentence(tgt[i], is_source=False) + references.append(ref_sentence) + + # Get hypothesis sentence (generated) + hyp_sentence = self.data_processor.decode_sentence(generated[i], is_source=False) + hypotheses.append(hyp_sentence) + + # Calculate BLEU score + bleu_score = self.compute_bleu(references, hypotheses) + + print(f"BLEU score: {bleu_score}") + + # Return metrics + return { + 'bleu': bleu_score, + 'num_samples': len(references) + } + + def translate_sentence(self, sentence: str) -> str: + """ + Translate a single sentence. + + Args: + sentence: Input sentence in source language + + Returns: + Translated sentence in target language + """ + self.model.eval() + + # Encode the sentence + src_tensor = self.data_processor.encode_sentence(sentence, is_source=True).to(self.device) + + # Calculate maximum output length + max_len = min(src_tensor.size(1) + self.max_length_factor, + self.config.model_config['max_seq_length']) + + # Generate translation + with torch.no_grad(): + generated = self.model.beam_search( + src_tensor, + max_len, + start_symbol=self.data_processor.BOS_IDX, + end_symbol=self.data_processor.EOS_IDX + ) + + # Decode the generated tensor to text + translation = self.data_processor.decode_sentence(generated[0], is_source=False) + + return translation + + def compute_bleu(self, references: List[str], hypotheses: List[str]) -> float: + """ + Compute BLEU score using sacrebleu. + + Args: + references: List of reference sentences + hypotheses: List of hypothesis sentences + + Returns: + BLEU score + """ + # Convert single references to list of lists format required by sacrebleu + references_list = [[ref] for ref in references] + + # Calculate corpus BLEU score + bleu = sacrebleu.corpus_bleu(hypotheses, references_list) + + # Return the score as a float + return bleu.score + + def average_checkpoints(self, paths: List[str]) -> None: + """ + Average model weights from multiple checkpoints as described in the paper. + + Args: + paths: List of paths to checkpoints + """ + if not paths: + print("No checkpoint paths provided for averaging.") + return + + print(f"Averaging {len(paths)} checkpoints...") + average_checkpoints(paths, self.model) + print("Checkpoint averaging complete.") + + def find_latest_checkpoints( + self, + checkpoint_dir: str, + num_checkpoints: int + ) -> List[str]: + """ + Find the latest checkpoints in a directory. + + Args: + checkpoint_dir: Directory containing checkpoints + num_checkpoints: Number of latest checkpoints to find + + Returns: + List of paths to latest checkpoints + """ + # List all checkpoint files + checkpoint_files = [ + os.path.join(checkpoint_dir, f) for f in os.listdir(checkpoint_dir) + if f.startswith('checkpoint_') and f.endswith('.pt') + ] + + # Sort by modification time (newest first) + checkpoint_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + + # Return the specified number of checkpoints + return checkpoint_files[:num_checkpoints] + + def generate_translations( + self, + src_sentences: List[str], + output_file: Optional[str] = None + ) -> List[str]: + """ + Generate translations for a list of source sentences. + + Args: + src_sentences: List of source language sentences + output_file: Path to write translations to (optional) + + Returns: + List of translated sentences + """ + self.model.eval() + translations = [] + + # Create progress bar + pbar = tqdm(src_sentences, desc="Generating translations") + + for sentence in pbar: + translation = self.translate_sentence(sentence) + translations.append(translation) + + # Write translations to file if specified + if output_file: + with open(output_file, 'w', encoding='utf-8') as f: + for trans in translations: + f.write(trans + '\n') + print(f"Translations written to {output_file}") + + return translations + + def evaluate_from_checkpoint(self, checkpoint_path: str, test_data: DataLoader) -> Dict[str, float]: + """ + Load a checkpoint and evaluate the model. + + Args: + checkpoint_path: Path to checkpoint + test_data: Test data loader + + Returns: + Dictionary containing evaluation metrics + """ + # Load checkpoint + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + + # Load model state + self.model.load_state_dict(checkpoint['model']) + + # Move model to device + self.model.to(self.device) + + # Set model to evaluation mode + self.model.eval() + + print(f"Loaded checkpoint from {checkpoint_path}") + + # Evaluate model + return self.evaluate(test_data) + + def evaluate_averaged_model( + self, + checkpoint_dir: str, + test_data: DataLoader, + num_checkpoints: Optional[int] = None + ) -> Dict[str, float]: + """ + Average checkpoints and evaluate the resulting model. + + Args: + checkpoint_dir: Directory containing checkpoints + test_data: Test data loader + num_checkpoints: Number of checkpoints to average (if None, use config value) + + Returns: + Dictionary containing evaluation metrics + """ + # Get number of checkpoints to average from config if not specified + if num_checkpoints is None: + num_checkpoints = self.config.average_checkpoints + + # Find the latest checkpoints + checkpoint_paths = self.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + + if not checkpoint_paths: + raise ValueError(f"No checkpoints found in {checkpoint_dir}") + + # Average the checkpoints + self.average_checkpoints(checkpoint_paths) + + # Evaluate the averaged model + return self.evaluate(test_data) + + def compute_perplexity(self, data_loader: DataLoader) -> float: + """ + Compute perplexity on a dataset. + + Args: + data_loader: Data loader for evaluation + + Returns: + Perplexity score + """ + self.model.eval() + total_loss = 0 + total_tokens = 0 + + with torch.no_grad(): + for src, tgt in tqdm(data_loader, desc="Computing perplexity"): + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss + loss = F.cross_entropy( + logits, + tgt_output, + ignore_index=self.data_processor.PAD_IDX, + reduction='sum' + ) + + # Update statistics + total_loss += loss.item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Calculate perplexity + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + perplexity = math.exp(avg_loss) + + print(f"Perplexity: {perplexity:.2f}") + + return perplexity + + +if __name__ == "__main__": + # Simple test for the evaluator + print("Testing Evaluator class...") + + # Load configuration + config = Config(model_size='base') + + # Create dummy data processor + data_processor = DataProcessor(config) + + # Create dummy model + model = TransformerModel( + config, + src_vocab_size=1000, # Dummy value + tgt_vocab_size=1000 # Dummy value + ) + + # Create evaluator + evaluator = Evaluator(config, model, data_processor) + + print("Evaluator initialized successfully!") + print(f"Beam size: {evaluator.beam_size}") + print(f"Length penalty: {evaluator.length_penalty}") + print(f"Device: {evaluator.device}") diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/main.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/main.py new file mode 100644 index 00000000..518d6d77 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/main.py @@ -0,0 +1,439 @@ +""" +Main module for the Transformer model implementation. +Entry point for running training and evaluation of the model. +""" + +import os +import argparse +import torch +import time +from typing import Optional, List, Tuple + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from train import Trainer +from evaluate import Evaluator +from utils import average_checkpoints + + +def train_model( + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + checkpoint_dir: str = "checkpoints", + resume_checkpoint: Optional[str] = None, + epochs: Optional[int] = None +) -> None: + """ + Train the transformer model. + + Args: + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to train on ('en_de' or 'en_fr') + config_path: Path to configuration file + checkpoint_dir: Directory to save checkpoints + resume_checkpoint: Path to checkpoint to resume training from + epochs: Number of epochs to train (if None, will use steps from config) + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Create directory for checkpoints + os.makedirs(checkpoint_dir, exist_ok=True) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load data + print(f"Loading {language_pair} dataset...") + train_data, val_data, test_data = data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Count model parameters + param_count = sum(p.numel() for p in model.parameters()) + print(f"Model has {param_count:,} parameters") + + # Initialize trainer + trainer = Trainer(config, model, data_processor) + + # Resume from checkpoint if specified + if resume_checkpoint: + print(f"Resuming from checkpoint: {resume_checkpoint}") + trainer.load_checkpoint(resume_checkpoint) + + # Train model + print("Starting training...") + trainer.train(train_data, val_data, epochs=epochs, checkpoint_dir=checkpoint_dir) + + print("Training complete!") + + +def evaluate_model( + model_path: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + is_averaged: bool = False, + checkpoint_dir: Optional[str] = None, + num_checkpoints: Optional[int] = None, + output_file: Optional[str] = None +) -> float: + """ + Evaluate the trained model. + + Args: + model_path: Path to trained model + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to evaluate on ('en_de' or 'en_fr') + config_path: Path to configuration file + is_averaged: Whether to average checkpoints + checkpoint_dir: Directory containing checkpoints (for averaging) + num_checkpoints: Number of checkpoints to average + output_file: Path to write translations to + + Returns: + BLEU score + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load data + print(f"Loading {language_pair} test dataset...") + _, _, test_data = data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + if is_averaged and checkpoint_dir: + # Average checkpoints + if num_checkpoints is None: + # Use default from config + num_checkpoints = config.average_checkpoints + + print(f"Averaging {num_checkpoints} checkpoints from {checkpoint_dir}...") + checkpoint_paths = evaluator.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + evaluator.average_checkpoints(checkpoint_paths) + else: + # Load single model + print(f"Loading model from {model_path}...") + evaluator.evaluate_from_checkpoint(model_path, test_data) + + # Evaluate model + print("Evaluating model...") + eval_results = evaluator.evaluate(test_data) + + # Print results + bleu_score = eval_results['bleu'] + print(f"BLEU score: {bleu_score:.2f}") + + # Generate translations for test set and save to file if specified + if output_file: + print(f"Generating translations and saving to {output_file}...") + # Extract source sentences from test data + src_sentences = [] + for batch in test_data: + src, _ = batch + for i in range(src.size(0)): + src_sentence = data_processor.decode_sentence(src[i], is_source=True) + src_sentences.append(src_sentence) + + # Generate translations + evaluator.generate_translations(src_sentences, output_file) + + return bleu_score + + +def translate( + model_path: str, + sentence: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None +) -> str: + """ + Translate a single sentence. + + Args: + model_path: Path to trained model + sentence: Sentence to translate + model_size: Size of the model ('base' or 'big') + language_pair: Language pair to translate ('en_de' or 'en_fr') + config_path: Path to configuration file + + Returns: + Translated sentence + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load vocabularies + print("Building vocabularies...") + # Need to load some data to build vocabularies + data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Load model + print(f"Loading model from {model_path}...") + checkpoint = torch.load(model_path, map_location=torch.device('cpu')) + model.load_state_dict(checkpoint['model']) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + # Translate sentence + print("Translating sentence...") + translation = evaluator.translate_sentence(sentence) + + return translation + + +def average_model_checkpoints( + checkpoint_dir: str, + output_path: str, + model_size: str = 'base', + language_pair: str = 'en_de', + config_path: Optional[str] = None, + num_checkpoints: Optional[int] = None +) -> None: + """ + Average multiple model checkpoints and save the result. + + Args: + checkpoint_dir: Directory containing checkpoints + output_path: Path to save the averaged model + model_size: Size of the model ('base' or 'big') + language_pair: Language pair ('en_de' or 'en_fr') + config_path: Path to configuration file + num_checkpoints: Number of checkpoints to average + """ + # Load configuration + config = Config(model_size=model_size, config_path=config_path) + + # Set language pair + config.set_language_pair(language_pair) + + # Initialize data processor (needed for vocabulary sizes) + print("Initializing data processor...") + data_processor = DataProcessor(config) + + # Load vocabularies + print("Building vocabularies...") + data_processor.load_data() + + # Get vocabulary sizes + src_vocab_size = len(data_processor.src_vocab) + tgt_vocab_size = len(data_processor.tgt_vocab) + + # Initialize model + print(f"Initializing {model_size} transformer model...") + model = TransformerModel(config, src_vocab_size, tgt_vocab_size) + + # Initialize evaluator + evaluator = Evaluator(config, model, data_processor) + + # If num_checkpoints is not specified, use from config + if num_checkpoints is None: + num_checkpoints = config.average_checkpoints + + # Find the latest checkpoints + checkpoint_paths = evaluator.find_latest_checkpoints(checkpoint_dir, num_checkpoints) + + if not checkpoint_paths: + print(f"No checkpoints found in {checkpoint_dir}") + return + + # Average checkpoints + print(f"Averaging {len(checkpoint_paths)} checkpoints...") + evaluator.average_checkpoints(checkpoint_paths) + + # Save averaged model + print(f"Saving averaged model to {output_path}...") + # Create a simple checkpoint structure + checkpoint = { + 'model': model.state_dict(), + 'epoch': 0, # Not relevant for averaged model + 'step': 0, # Not relevant for averaged model + 'loss': 0.0 # Not relevant for averaged model + } + + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + # Save the model + torch.save(checkpoint, output_path) + + print(f"Averaged model saved to {output_path}") + + +def main() -> None: + """ + Parse command line arguments and run the appropriate function. + """ + parser = argparse.ArgumentParser(description='Train and evaluate Transformer models') + + # Create subparsers for different commands + subparsers = parser.add_subparsers(dest='command', help='Command to run') + + # Train command + train_parser = subparsers.add_parser('train', help='Train a model') + train_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + train_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to train on') + train_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + train_parser.add_argument('--checkpoint-dir', type=str, default='checkpoints', + help='Directory to save checkpoints') + train_parser.add_argument('--resume', type=str, default=None, + help='Path to checkpoint to resume training from') + train_parser.add_argument('--epochs', type=int, default=None, + help='Number of epochs to train (if None, will use steps from config)') + + # Evaluate command + eval_parser = subparsers.add_parser('evaluate', help='Evaluate a model') + eval_parser.add_argument('model_path', type=str, + help='Path to trained model') + eval_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + eval_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to evaluate on') + eval_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + eval_parser.add_argument('--average', action='store_true', + help='Average checkpoints before evaluation') + eval_parser.add_argument('--checkpoint-dir', type=str, default=None, + help='Directory containing checkpoints (for averaging)') + eval_parser.add_argument('--num-checkpoints', type=int, default=None, + help='Number of checkpoints to average') + eval_parser.add_argument('--output-file', type=str, default=None, + help='Path to write translations to') + + # Translate command + translate_parser = subparsers.add_parser('translate', help='Translate a sentence') + translate_parser.add_argument('model_path', type=str, + help='Path to trained model') + translate_parser.add_argument('sentence', type=str, + help='Sentence to translate') + translate_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + translate_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair to translate') + translate_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + + # Average command + average_parser = subparsers.add_parser('average', help='Average model checkpoints') + average_parser.add_argument('checkpoint_dir', type=str, + help='Directory containing checkpoints') + average_parser.add_argument('output_path', type=str, + help='Path to save the averaged model') + average_parser.add_argument('--model-size', choices=['base', 'big'], default='base', + help='Model size (base or big)') + average_parser.add_argument('--language-pair', choices=['en_de', 'en_fr'], default='en_de', + help='Language pair') + average_parser.add_argument('--config-path', type=str, default=None, + help='Path to configuration file') + average_parser.add_argument('--num-checkpoints', type=int, default=None, + help='Number of checkpoints to average') + + # Parse arguments + args = parser.parse_args() + + # Check if a command was specified + if args.command is None: + parser.print_help() + return + + # Run the appropriate function + if args.command == 'train': + train_model( + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + checkpoint_dir=args.checkpoint_dir, + resume_checkpoint=args.resume, + epochs=args.epochs + ) + elif args.command == 'evaluate': + evaluate_model( + model_path=args.model_path, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + is_averaged=args.average, + checkpoint_dir=args.checkpoint_dir, + num_checkpoints=args.num_checkpoints, + output_file=args.output_file + ) + elif args.command == 'translate': + translation = translate( + model_path=args.model_path, + sentence=args.sentence, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path + ) + print(f"Translation: {translation}") + elif args.command == 'average': + average_model_checkpoints( + checkpoint_dir=args.checkpoint_dir, + output_path=args.output_path, + model_size=args.model_size, + language_pair=args.language_pair, + config_path=args.config_path, + num_checkpoints=args.num_checkpoints + ) + + +if __name__ == "__main__": + # Record start time + start_time = time.time() + + # Run main function + main() + + # Print elapsed time + elapsed_time = time.time() - start_time + print(f"Elapsed time: {elapsed_time:.2f} seconds") diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/model.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/model.py new file mode 100644 index 00000000..66cfaf03 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/model.py @@ -0,0 +1,777 @@ +""" +Model module for the Transformer architecture implementation. +Implements the Transformer model as described in 'Attention Is All You Need' paper. + +This module contains the full Transformer architecture with encoder and decoder stacks, +multi-head attention mechanisms, position-wise feed-forward networks, and positional encoding. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +import copy +import numpy as np +from typing import Optional, Tuple, List + +# Import from project modules +from config import Config +from utils import clones, create_padding_mask, create_subsequent_mask + + +class PositionalEncoding(nn.Module): + """ + Implement the positional encoding as described in section 3.5 of the paper. + Adds positional information to the token embeddings. + """ + def __init__(self, d_model: int, dropout: float, max_len: int = 5000): + """ + Initialize positional encoding. + + Args: + d_model: Dimension of the model + dropout: Dropout rate + max_len: Maximum sequence length + """ + super(PositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + + # Create a matrix of shape (max_len, d_model) + pe = torch.zeros(max_len, d_model) + + # Create a vector of shape (max_len) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) + + # Create a vector of shape (d_model) + div_term = torch.exp( + torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model) + ) + + # Apply sine to even indices and cosine to odd indices + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + # Add batch dimension and transpose to shape (1, max_len, d_model) + pe = pe.unsqueeze(0) + + # Register buffer (not a parameter, but part of the module) + self.register_buffer('pe', pe) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Add positional encoding to the input embeddings. + + Args: + x: Input embeddings of shape (batch_size, seq_len, d_model) + + Returns: + Output with positional encoding added + """ + # Add positional encoding to the input + x = x + self.pe[:, :x.size(1)] + + # Apply dropout + return self.dropout(x) + + +class MultiHeadAttention(nn.Module): + """ + Implement the multi-head attention mechanism as described in section 3.2.2 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, dropout: float): + """ + Initialize multi-head attention. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + dropout: Dropout rate + """ + super(MultiHeadAttention, self).__init__() + + # Ensure d_model is divisible by n_heads + assert d_model % n_heads == 0, "d_model must be divisible by n_heads" + + # Store parameters + self.d_model = d_model + self.n_heads = n_heads + self.d_k = d_model // n_heads # Dimension of key per head + self.d_v = d_model // n_heads # Dimension of value per head + + # Linear projections + self.W_q = nn.Linear(d_model, d_model) # Query projection + self.W_k = nn.Linear(d_model, d_model) # Key projection + self.W_v = nn.Linear(d_model, d_model) # Value projection + self.W_o = nn.Linear(d_model, d_model) # Output projection + + # Dropout + self.dropout = nn.Dropout(p=dropout) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Apply multi-head attention. + + Args: + query: Query tensor of shape (batch_size, query_len, d_model) + key: Key tensor of shape (batch_size, key_len, d_model) + value: Value tensor of shape (batch_size, value_len, d_model) + mask: Optional mask tensor of shape (batch_size, 1, 1, key_len) or (batch_size, 1, query_len, key_len) + + Returns: + Output tensor of shape (batch_size, query_len, d_model) + """ + batch_size = query.size(0) + + # Linear projections and split into h heads + # Shape: (batch_size, seq_len, n_heads, d_k) -> (batch_size, n_heads, seq_len, d_k) + q = self.W_q(query).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2) + k = self.W_k(key).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2) + v = self.W_v(value).view(batch_size, -1, self.n_heads, self.d_v).transpose(1, 2) + + # Apply attention + scores, attn = self.attention(q, k, v, mask) + + # Concatenate heads and apply final linear projection + # Shape: (batch_size, n_heads, seq_len, d_v) -> (batch_size, seq_len, d_model) + concat = scores.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model) + output = self.W_o(concat) + + return output + + def attention( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: Optional[torch.Tensor] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Compute scaled dot-product attention as described in section 3.2.1 of the paper. + + Args: + query: Query tensor of shape (batch_size, n_heads, query_len, d_k) + key: Key tensor of shape (batch_size, n_heads, key_len, d_k) + value: Value tensor of shape (batch_size, n_heads, value_len, d_v) + mask: Optional mask tensor of shape (batch_size, 1, 1, key_len) or (batch_size, 1, query_len, key_len) + + Returns: + Tuple of (output tensor of shape (batch_size, n_heads, query_len, d_v), + attention weights of shape (batch_size, n_heads, query_len, key_len)) + """ + # Get dimensions + d_k = query.size(-1) + + # Compute scaled dot product + # Shape: (batch_size, n_heads, query_len, key_len) + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) + + # Apply mask if provided + if mask is not None: + scores = scores.masked_fill(mask == 1, -1e9) + + # Apply softmax to get attention weights + attn = F.softmax(scores, dim=-1) + + # Apply dropout to attention weights + attn = self.dropout(attn) + + # Apply attention weights to values + # Shape: (batch_size, n_heads, query_len, d_v) + output = torch.matmul(attn, value) + + return output, attn + + +class PositionwiseFeedforward(nn.Module): + """ + Implement the position-wise feed-forward network as described in section 3.3 of the paper. + """ + def __init__(self, d_model: int, d_ff: int, dropout: float): + """ + Initialize position-wise feed-forward network. + + Args: + d_model: Dimension of the model + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(PositionwiseFeedforward, self).__init__() + + # Two linear transformations with a ReLU activation in between + self.linear1 = nn.Linear(d_model, d_ff) + self.linear2 = nn.Linear(d_ff, d_model) + + # Dropout + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Apply position-wise feed-forward network. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # First linear transformation with ReLU activation + x = F.relu(self.linear1(x)) + + # Apply dropout + x = self.dropout(x) + + # Second linear transformation + x = self.linear2(x) + + return x + + +class LayerNorm(nn.Module): + """ + Layer normalization module as described in the paper. + """ + def __init__(self, features: int, eps: float = 1e-6): + """ + Initialize layer normalization. + + Args: + features: Number of features + eps: Small constant for numerical stability + """ + super(LayerNorm, self).__init__() + + # Learnable parameters + self.a_2 = nn.Parameter(torch.ones(features)) + self.b_2 = nn.Parameter(torch.zeros(features)) + + # Epsilon for numerical stability + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Apply layer normalization. + + Args: + x: Input tensor of shape (batch_size, seq_len, features) + + Returns: + Normalized tensor of shape (batch_size, seq_len, features) + """ + # Calculate mean and standard deviation + mean = x.mean(-1, keepdim=True) + std = x.std(-1, keepdim=True) + + # Normalize + x = (x - mean) / (std + self.eps) + + # Scale and shift + return self.a_2 * x + self.b_2 + + +class EncoderLayer(nn.Module): + """ + Single layer of the encoder as described in section 3.1 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float): + """ + Initialize encoder layer. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(EncoderLayer, self).__init__() + + # Multi-head self-attention + self.self_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Position-wise feed-forward network + self.feed_forward = PositionwiseFeedforward(d_model, d_ff, dropout) + + # Layer normalization + self.norm1 = LayerNorm(d_model) + self.norm2 = LayerNorm(d_model) + + # Dropout + self.dropout = dropout + self.dropout_layer = nn.Dropout(p=dropout) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + """ + Process input through encoder layer. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + mask: Mask tensor of shape (batch_size, 1, 1, seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Self-attention with residual connection and layer normalization + # (Apply layer norm before sublayer as per paper section 5.4) + attn_output = self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), mask) + x = x + self.dropout_layer(attn_output) + + # Position-wise feed-forward with residual connection and layer normalization + ff_output = self.feed_forward(self.norm2(x)) + x = x + self.dropout_layer(ff_output) + + return x + + +class DecoderLayer(nn.Module): + """ + Single layer of the decoder as described in section 3.1 of the paper. + """ + def __init__(self, d_model: int, n_heads: int, d_ff: int, dropout: float): + """ + Initialize decoder layer. + + Args: + d_model: Dimension of the model + n_heads: Number of attention heads + d_ff: Dimension of the feed-forward layer + dropout: Dropout rate + """ + super(DecoderLayer, self).__init__() + + # Multi-head self-attention + self.self_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Multi-head cross-attention + self.cross_attn = MultiHeadAttention(d_model, n_heads, dropout) + + # Position-wise feed-forward network + self.feed_forward = PositionwiseFeedforward(d_model, d_ff, dropout) + + # Layer normalization + self.norm1 = LayerNorm(d_model) + self.norm2 = LayerNorm(d_model) + self.norm3 = LayerNorm(d_model) + + # Dropout + self.dropout = dropout + self.dropout_layer = nn.Dropout(p=dropout) + + def forward( + self, + x: torch.Tensor, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through decoder layer. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Self-attention with residual connection and layer normalization + # (Apply layer norm before sublayer as per paper section 5.4) + attn_output = self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), tgt_mask) + x = x + self.dropout_layer(attn_output) + + # Cross-attention with residual connection and layer normalization + attn_output = self.cross_attn(self.norm2(x), memory, memory, src_mask) + x = x + self.dropout_layer(attn_output) + + # Position-wise feed-forward with residual connection and layer normalization + ff_output = self.feed_forward(self.norm3(x)) + x = x + self.dropout_layer(ff_output) + + return x + + +class Encoder(nn.Module): + """ + Encoder stack as described in section 3.1 of the paper. + """ + def __init__(self, layer: EncoderLayer, N: int): + """ + Initialize encoder stack. + + Args: + layer: Encoder layer to be stacked + N: Number of layers + """ + super(Encoder, self).__init__() + + # Create a stack of N identical encoder layers + self.layers = clones(layer, N) + + # Layer normalization for the final output + self.norm = LayerNorm(layer.self_attn.d_model) + + def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + """ + Process input through encoder stack. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + mask: Mask tensor of shape (batch_size, 1, 1, seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Pass through each encoder layer + for layer in self.layers: + x = layer(x, mask) + + # Apply final layer normalization + return self.norm(x) + + +class Decoder(nn.Module): + """ + Decoder stack as described in section 3.1 of the paper. + """ + def __init__(self, layer: DecoderLayer, N: int): + """ + Initialize decoder stack. + + Args: + layer: Decoder layer to be stacked + N: Number of layers + """ + super(Decoder, self).__init__() + + # Create a stack of N identical decoder layers + self.layers = clones(layer, N) + + # Layer normalization for the final output + self.norm = LayerNorm(layer.self_attn.d_model) + + def forward( + self, + x: torch.Tensor, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through decoder stack. + + Args: + x: Input tensor of shape (batch_size, seq_len, d_model) + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, seq_len, d_model) + """ + # Pass through each decoder layer + for layer in self.layers: + x = layer(x, memory, src_mask, tgt_mask) + + # Apply final layer normalization + return self.norm(x) + + +class TransformerModel(nn.Module): + """ + Full Transformer model as described in the paper. + """ + def __init__(self, config: Config, src_vocab_size: int, tgt_vocab_size: int): + """ + Initialize Transformer model. + + Args: + config: Configuration object + src_vocab_size: Size of source vocabulary + tgt_vocab_size: Size of target vocabulary + """ + super(TransformerModel, self).__init__() + + # Get model parameters from config + model_params = config.get_model_params() + d_model = model_params['d_model'] + n_heads = model_params['n_heads'] + num_encoder_layers = model_params['num_encoder_layers'] + num_decoder_layers = model_params['num_decoder_layers'] + d_ff = model_params['dim_feedforward'] + dropout = model_params['dropout'] + max_seq_length = model_params['max_seq_length'] + share_weights = model_params['share_weights'] + + # Create embedding layers + self.src_embed = nn.Embedding(src_vocab_size, d_model) + self.tgt_embed = nn.Embedding(tgt_vocab_size, d_model) + + # Scale embeddings by sqrt(d_model) as mentioned in the paper + self.src_scale = math.sqrt(d_model) + self.tgt_scale = math.sqrt(d_model) + + # Positional encoding + self.pos_encoding = PositionalEncoding(d_model, dropout, max_seq_length) + + # Create encoder and decoder components + encoder_layer = EncoderLayer(d_model, n_heads, d_ff, dropout) + decoder_layer = DecoderLayer(d_model, n_heads, d_ff, dropout) + + self.encoder = Encoder(encoder_layer, num_encoder_layers) + self.decoder = Decoder(decoder_layer, num_decoder_layers) + + # Output projection + self.generator = nn.Linear(d_model, tgt_vocab_size) + + # Weight sharing between embedding layers and output projection + if share_weights: + self.generator.weight = self.tgt_embed.weight + + # Initialize parameters + self._init_parameters() + + def _init_parameters(self): + """ + Initialize model parameters. + """ + # Initialize parameters with Xavier uniform distribution + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward( + self, + src: torch.Tensor, + tgt: torch.Tensor, + src_mask: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Process input through the full Transformer model. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Output tensor of shape (batch_size, tgt_seq_len, tgt_vocab_size) + """ + # Encode source sequence + memory = self.encode(src, src_mask) + + # Decode target sequence + output = self.decode(memory, src_mask, tgt, tgt_mask) + + # Generate final output logits + return self.generator(output) + + def encode(self, src: torch.Tensor, src_mask: torch.Tensor) -> torch.Tensor: + """ + Encode source sequence. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + + Returns: + Memory tensor of shape (batch_size, src_seq_len, d_model) + """ + # Apply input embedding, scaling, and positional encoding + src_embedded = self.pos_encoding(self.src_embed(src) * self.src_scale) + + # Encode + return self.encoder(src_embedded, src_mask) + + def decode( + self, + memory: torch.Tensor, + src_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor + ) -> torch.Tensor: + """ + Decode target sequence. + + Args: + memory: Output from encoder of shape (batch_size, src_seq_len, d_model) + src_mask: Source mask tensor of shape (batch_size, 1, 1, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + tgt_mask: Target mask tensor of shape (batch_size, 1, tgt_seq_len, tgt_seq_len) + + Returns: + Decoded output tensor of shape (batch_size, tgt_seq_len, d_model) + """ + # Apply target embedding, scaling, and positional encoding + tgt_embedded = self.pos_encoding(self.tgt_embed(tgt) * self.tgt_scale) + + # Decode + return self.decoder(tgt_embedded, memory, src_mask, tgt_mask) + + def create_masks(self, src: torch.Tensor, tgt: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Create masks for transformer model. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + tgt: Target tensor of shape (batch_size, tgt_seq_len) + + Returns: + Tuple of (source mask, target mask) + """ + # Determine padding index (assuming 0) + pad_idx = 0 + + # Create source padding mask + src_mask = create_padding_mask(src, pad_idx) + + # Create target mask (combining padding and subsequent masks) + tgt_padding_mask = create_padding_mask(tgt, pad_idx) + tgt_subsequent_mask = create_subsequent_mask(tgt.size(1)).to(tgt.device) + + # Combine masks (broadcasting to the right shape) + tgt_mask = tgt_padding_mask | tgt_subsequent_mask + + return src_mask, tgt_mask + + def beam_search(self, src: torch.Tensor, max_len: int, start_symbol: int, end_symbol: int = 2) -> torch.Tensor: + """ + Perform beam search decoding for inference. + + Args: + src: Source tensor of shape (batch_size, src_seq_len) + max_len: Maximum length of output sequence + start_symbol: Start symbol index + end_symbol: End symbol index (default: 2) + + Returns: + Generated output sequence tensor of shape (batch_size, seq_len) + """ + # Get device from source tensor + device = src.device + + # Get beam size from config (default to 4 if not in config) + beam_size = 4 # Will be overridden by actual config value + length_penalty = 0.6 # Will be overridden by actual config value + + # Try to get beam size and length penalty from global config + try: + from config import Config + cfg = Config() + inference_params = cfg.get_inference_params() + beam_size = inference_params.get('beam_size', beam_size) + length_penalty = inference_params.get('length_penalty', length_penalty) + except: + pass + + # Get batch size and encode the source sequence + batch_size = src.size(0) + src_mask = create_padding_mask(src, 0) + memory = self.encode(src, src_mask) + + # We'll process each batch item separately + generated_sequences = [] + + for batch_idx in range(batch_size): + # Take the memory for this batch item and repeat it for beam size + batch_memory = memory[batch_idx:batch_idx+1].repeat(beam_size, 1, 1) + batch_src_mask = src_mask[batch_idx:batch_idx+1].repeat(beam_size, 1, 1, 1) + + # Initialize beams with start symbol + beams = torch.full((beam_size, 1), start_symbol, dtype=torch.long, device=device) + + # Initialize beam scores + beam_scores = torch.zeros(beam_size, device=device) + + # Flag to indicate which beams are completed + completed_beams = torch.zeros(beam_size, dtype=torch.bool, device=device) + + # Generate tokens one by one + for step in range(max_len - 1): + # Don't decode further for completed beams + if completed_beams.all(): + break + + # Create mask for current beams + beam_tgt_mask = create_subsequent_mask(beams.size(1)).to(device) + + # Decode one step for all beams + decoder_output = self.decode(batch_memory, batch_src_mask, beams, beam_tgt_mask) + + # Get next token predictions (last position) + logits = self.generator(decoder_output[:, -1]) + + # Convert to log probabilities + log_probs = F.log_softmax(logits, dim=-1) + + # For completed beams, only the end_symbol should be considered + for b in range(beam_size): + if completed_beams[b]: + log_probs[b, :] = float('-inf') + log_probs[b, end_symbol] = 0.0 + + # Calculate scores for next tokens based on current beam scores + vocab_size = log_probs.size(1) + next_scores = beam_scores.unsqueeze(1) + log_probs + + # For first step, consider only single beam + if step == 0: + # For first step, treat log_probs for first beam only + next_scores = next_scores[0].unsqueeze(0) + + # Flatten scores to select top-k + flat_next_scores = next_scores.view(-1) + + # Select top-k scores and convert to beam indices and token indices + if step == 0: + # For first step, select top-k from the first beam only + top_scores, top_indices = flat_next_scores.topk(beam_size, dim=0) + beam_indices = torch.zeros(beam_size, dtype=torch.long, device=device) + token_indices = top_indices + else: + # For subsequent steps, select top-k from all beams + top_scores, top_indices = flat_next_scores.topk(beam_size, dim=0) + beam_indices = top_indices // vocab_size + token_indices = top_indices % vocab_size + + # Update beam scores + beam_scores = top_scores + + # Create new beams by appending selected tokens to selected beams + new_beams = [] + for i in range(beam_size): + beam_idx = beam_indices[i] + token_idx = token_indices[i] + + # Update completed beams flag + if token_idx == end_symbol: + completed_beams[i] = True + + # Create new beam by appending token to selected beam + new_beam = torch.cat([beams[beam_idx], token_idx.unsqueeze(0)], dim=0).unsqueeze(0) + new_beams.append(new_beam) + + # Concatenate new beams + beams = torch.cat(new_beams, dim=0) + + # Apply length penalty + beam_lengths = torch.sum(beams != 0, dim=1).float() + normalized_scores = beam_scores / ((5 + beam_lengths) ** length_penalty / (5 + 1) ** length_penalty) + + # Select the best beam + best_beam_idx = normalized_scores.argmax() + best_beam = beams[best_beam_idx].unsqueeze(0) + + # Add to generated sequences + generated_sequences.append(best_beam) + + # Concatenate all batch results + return torch.cat(generated_sequences, dim=0) + diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/train.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/train.py new file mode 100644 index 00000000..886f3b75 --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/train.py @@ -0,0 +1,381 @@ +""" +Training module for the Transformer model implementation. +Handles the training and validation loops for the Transformer model. +""" + +import os +import time +import math +from typing import Optional, Tuple, Dict, List, Any + +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +# Import from project modules +from config import Config +from model import TransformerModel +from data_processing import DataProcessor +from utils import ( + label_smoothed_nll_loss, + get_lr_scheduler, + save_checkpoint, + load_checkpoint, + create_masks +) + + +class Trainer: + """ + Handles the training and validation loops for the Transformer model. + """ + def __init__( + self, + config: Config, + model: TransformerModel, + data_processor: DataProcessor + ): + """ + Initialize trainer. + + Args: + config: Configuration object + model: TransformerModel instance + data_processor: DataProcessor instance + """ + self.config = config + self.model = model + self.data_processor = data_processor + + # Get training parameters + training_params = config.get_training_params() + self.device = training_params['device'] + self.warmup_steps = training_params['warmup_steps'] + self.label_smoothing = training_params['label_smoothing'] + self.total_steps = training_params['total_steps'] + self.checkpoint_interval = training_params['checkpoint_interval'] + + # Move model to device + self.model.to(self.device) + + # Initialize optimizer with betas and epsilon as specified in the paper + self.optimizer = optim.Adam( + model.parameters(), + lr=0.0, # Will be set by scheduler + betas=(training_params['beta1'], training_params['beta2']), + eps=training_params['epsilon'] + ) + + # Initialize learning rate scheduler + self.lr_scheduler = get_lr_scheduler( + self.optimizer, + config.get_model_params()['d_model'], + self.warmup_steps + ) + + # Initialize tensorboard writer + self.writer = SummaryWriter(log_dir="logs") + + # Track training statistics + self.step = 0 + self.epoch = 0 + self.best_val_loss = float('inf') + + def train( + self, + train_data: DataLoader, + val_data: DataLoader, + epochs: Optional[int] = None, + checkpoint_dir: str = "checkpoints" + ) -> None: + """ + Main training loop. + + Args: + train_data: Training data loader + val_data: Validation data loader + epochs: Number of epochs to train (if None, will use total_steps) + checkpoint_dir: Directory to save checkpoints + """ + # Create checkpoint directory if it doesn't exist + os.makedirs(checkpoint_dir, exist_ok=True) + + # Start time for checkpoint saving + last_checkpoint_time = time.time() + + print(f"Starting training on device: {self.device}") + print(f"Model size: {self.config.model_size}") + + # Main training loop + while True: + self.epoch += 1 + print(f"\nEpoch {self.epoch}") + + # Train for one epoch + train_loss = self.train_epoch(train_data) + + # Evaluate on validation data + val_loss = self.validate(val_data) + + # Log losses + self.writer.add_scalar('Loss/train', train_loss, self.epoch) + self.writer.add_scalar('Loss/val', val_loss, self.epoch) + + print(f"Epoch {self.epoch}: Train loss = {train_loss:.4f}, Val loss = {val_loss:.4f}") + + # Save best model + if val_loss < self.best_val_loss: + self.best_val_loss = val_loss + self.save_checkpoint(os.path.join(checkpoint_dir, "best_model.pt")) + print(f"New best model saved with validation loss: {val_loss:.4f}") + + # Save checkpoint periodically + current_time = time.time() + if current_time - last_checkpoint_time > self.checkpoint_interval * 60: # Convert to seconds + checkpoint_path = os.path.join(checkpoint_dir, f"checkpoint_epoch{self.epoch}_step{self.step}.pt") + self.save_checkpoint(checkpoint_path) + last_checkpoint_time = current_time + + # Check stopping conditions + if self.step >= self.total_steps: + print(f"Reached {self.total_steps} steps. Training complete.") + # Save final model + self.save_checkpoint(os.path.join(checkpoint_dir, "final_model.pt")) + break + + if epochs is not None and self.epoch >= epochs: + print(f"Reached {epochs} epochs. Training complete.") + # Save final model + self.save_checkpoint(os.path.join(checkpoint_dir, "final_model.pt")) + break + + def train_epoch(self, train_data: DataLoader) -> float: + """ + Train for one epoch. + + Args: + train_data: Training data loader + + Returns: + Average loss for the epoch + """ + self.model.train() + total_loss = 0 + total_tokens = 0 + start_time = time.time() + + # Create progress bar + pbar = tqdm(train_data, desc=f"Training epoch {self.epoch}") + + for i, (src, tgt) in enumerate(pbar): + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + # Input: w1 w2 w3 + # Target: w1 w2 w3 + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss with label smoothing + loss, nll_loss = label_smoothed_nll_loss( + logits, + tgt_output, + self.label_smoothing, + ignore_index=self.data_processor.PAD_IDX + ) + + # Backward pass and optimization + self.optimizer.zero_grad() + loss.backward() + + # Clip gradients as mentioned in paper (not explicitly stated value, using common default) + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + + # Adjust learning rate according to schedule + lr = self.lr_scheduler(self.step) + + # Update parameters + self.optimizer.step() + + # Update statistics + self.step += 1 + total_loss += nll_loss.item() * tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Update progress bar + pbar.set_postfix({ + 'loss': f"{loss.item():.4f}", + 'lr': f"{lr:.7f}", + 'step': self.step + }) + + # Log to tensorboard + self.writer.add_scalar('Loss/train_step', loss.item(), self.step) + self.writer.add_scalar('Learning rate', lr, self.step) + + # Check if total steps reached + if self.step >= self.total_steps: + break + + # Calculate average loss + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + + # Calculate training time + elapsed_time = time.time() - start_time + tokens_per_sec = total_tokens / elapsed_time if elapsed_time > 0 else 0 + + print(f"Epoch {self.epoch} completed in {elapsed_time:.2f} seconds") + print(f"Training throughput: {tokens_per_sec:.2f} tokens/sec") + + return avg_loss + + def validate(self, val_data: DataLoader) -> float: + """ + Validate model. + + Args: + val_data: Validation data loader + + Returns: + Validation loss + """ + self.model.eval() + total_loss = 0 + total_tokens = 0 + + # Create progress bar + pbar = tqdm(val_data, desc=f"Validating epoch {self.epoch}") + + with torch.no_grad(): + for src, tgt in pbar: + # Move data to device + src = src.to(self.device) + tgt = tgt.to(self.device) + + # Create masks + src_mask, tgt_mask = self.data_processor.create_masks(src, tgt) + + # Prepare target for loss calculation (shift by 1) + tgt_input = tgt[:, :-1] + tgt_output = tgt[:, 1:] + + # Create mask for target input + _, tgt_input_mask = self.data_processor.create_masks(src, tgt_input) + + # Forward pass + logits = self.model(src, tgt_input, src_mask, tgt_input_mask) + + # Flatten logits and targets for loss calculation + logits = logits.contiguous().view(-1, logits.size(-1)) + tgt_output = tgt_output.contiguous().view(-1) + + # Calculate loss with label smoothing + _, nll_loss = label_smoothed_nll_loss( + logits, + tgt_output, + self.label_smoothing, + ignore_index=self.data_processor.PAD_IDX + ) + + # Update statistics + total_loss += nll_loss.item() * tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + total_tokens += tgt_output.ne(self.data_processor.PAD_IDX).sum().item() + + # Calculate average loss + avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf') + + return avg_loss + + def save_checkpoint(self, path: str) -> None: + """ + Save model checkpoint. + + Args: + path: Path to save checkpoint + """ + save_checkpoint( + self.model, + self.optimizer, + self.epoch, + self.step, + self.best_val_loss, + path + ) + + def load_checkpoint(self, path: str) -> Dict[str, Any]: + """ + Load model checkpoint. + + Args: + path: Path to load checkpoint from + + Returns: + Dictionary with checkpoint metadata + """ + metadata = load_checkpoint(path, self.model, self.optimizer) + + # Update trainer state + self.epoch = metadata.get('epoch', 0) + self.step = metadata.get('step', 0) + self.best_val_loss = metadata.get('loss', float('inf')) + + print(f"Loaded checkpoint from {path}") + print(f"Epoch: {self.epoch}, Step: {self.step}, Best val loss: {self.best_val_loss:.4f}") + + return metadata + + def adjust_learning_rate(self, step: int) -> float: + """ + Adjust learning rate according to schedule. + + Args: + step: Current step number + + Returns: + New learning rate + """ + return self.lr_scheduler(step) + + +if __name__ == "__main__": + # Simple test for the trainer + print("Testing Trainer class...") + + # Load configuration + config = Config(model_size='base') + + # Create dummy data processor + data_processor = DataProcessor(config) + + # Create dummy model + model = TransformerModel( + config, + src_vocab_size=1000, # Dummy value + tgt_vocab_size=1000 # Dummy value + ) + + # Create trainer + trainer = Trainer(config, model, data_processor) + + print("Trainer initialized successfully!") + print(f"Model will be trained for {trainer.total_steps} steps with {trainer.warmup_steps} warmup steps") + print(f"Label smoothing: {trainer.label_smoothing}") + print(f"Device: {trainer.device}") diff --git a/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/utils.py b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/utils.py new file mode 100644 index 00000000..0a03f33c --- /dev/null +++ b/outputs_Transformer_claude-3-7-sonnet-20250219-v1/Transformer_dscoder_repo/utils.py @@ -0,0 +1,376 @@ +""" +Utility functions for the Transformer model implementation. + +This module contains helper functions used across the project: +- Attention masking functions +- Label smoothing loss +- Learning rate scheduler +- Checkpoint management +- Model weight averaging +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import os +import math +import copy +from typing import Dict, List, Optional, Callable, Tuple + + +def create_subsequent_mask(size: int) -> torch.Tensor: + """ + Create a mask for decoder self-attention to prevent attending to future positions. + + Args: + size: Size of the square mask + + Returns: + Mask tensor of shape (1, size, size) with zeros for allowed positions and -inf for masked positions + """ + # Create upper triangular matrix with ones (including diagonal) + mask = torch.triu(torch.ones(1, size, size), diagonal=1) + # Convert to boolean mask where True means position should be masked + return mask == 1 + + +def create_padding_mask(seq: torch.Tensor, pad_idx: int) -> torch.Tensor: + """ + Create a mask to hide padding tokens in the input sequence. + + Args: + seq: Sequence tensor of shape (batch_size, seq_len) + pad_idx: Index used for padding + + Returns: + Mask tensor of shape (batch_size, 1, 1, seq_len) with zeros for tokens and ones for padding + """ + # Create mask where 1 indicates a padding token + mask = (seq == pad_idx).unsqueeze(1).unsqueeze(2) + return mask + + +def label_smoothed_nll_loss( + pred: torch.Tensor, + target: torch.Tensor, + epsilon: float, + ignore_index: int = -100 +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate cross entropy loss with label smoothing. + + Args: + pred: Predicted logits of shape (batch_size, seq_len, vocab_size) + target: Target indices of shape (batch_size, seq_len) + epsilon: Label smoothing factor + ignore_index: Index to ignore in the loss calculation (e.g., padding) + + Returns: + Tuple of (smoothed loss, regular nll loss for logging purposes) + """ + # Get vocab size from predictions + vocab_size = pred.size(-1) + + # Calculate standard NLL loss for logging + nll_loss = F.nll_loss( + F.log_softmax(pred, dim=-1).view(-1, vocab_size), + target.view(-1), + ignore_index=ignore_index, + reduction='sum' + ) + + # If no label smoothing, return standard loss + if epsilon == 0: + return nll_loss, nll_loss + + # Create smoothed target distribution: + # - (1-epsilon) for correct class + # - epsilon/(vocab_size-1) for all other classes + + # Create a mask for non-ignored positions + non_pad_mask = target != ignore_index + num_non_pad_tokens = non_pad_mask.sum().item() + + # Apply log_softmax to get log probabilities + log_probs = F.log_softmax(pred, dim=-1) + + # Reshape to (N, vocab_size) + log_probs = log_probs.view(-1, vocab_size) + target_flat = target.view(-1) + + # Calculate smoothed loss: + # - Loss with correct targets weighted by (1-epsilon) + # - Loss with uniform distribution weighted by epsilon + smooth_loss = -log_probs.sum(dim=-1) # Sum over vocab (uniform part) + smooth_loss = smooth_loss.masked_select(non_pad_mask.view(-1)).sum() # Sum over non-pad tokens + smooth_loss = smooth_loss * (epsilon / vocab_size) + + # Add the non-smoothed component + correct_log_probs = log_probs.gather(dim=-1, index=target_flat.unsqueeze(1)).squeeze(1) + correct_log_probs = correct_log_probs.masked_select(non_pad_mask.view(-1)) + smooth_loss -= correct_log_probs.sum() * (epsilon / vocab_size) # Subtract from uniform part + smooth_loss += (1 - epsilon) * (-correct_log_probs.sum()) # Add weighted correct part + + # Return both smoothed loss and original NLL loss (for logging) + return smooth_loss / num_non_pad_tokens, nll_loss / num_non_pad_tokens + + +def get_lr_scheduler( + optimizer: torch.optim.Optimizer, + d_model: int, + warmup_steps: int +) -> Callable[[int], float]: + """ + Create a learning rate scheduler as described in the paper: + lr = d_model^(-0.5) * min(step^(-0.5), step * warmup_steps^(-1.5)) + + Args: + optimizer: Optimizer to schedule + d_model: Model dimension + warmup_steps: Number of warmup steps + + Returns: + Function that updates learning rate based on step number + """ + # Define the learning rate schedule function + def lr_lambda(step: int) -> float: + """Calculate learning rate multiplier based on step.""" + # Protect against division by zero + step = max(1, step) + + # Apply formula from paper: lrate = d_model^(-0.5) * min(step^(-0.5), step * warmup_steps^(-1.5)) + factor = d_model ** (-0.5) + arg1 = step ** (-0.5) + arg2 = step * (warmup_steps ** (-1.5)) + + return factor * min(arg1, arg2) + + # Create a function to update optimizer learning rate based on step + def update_lr(step: int) -> None: + """Update optimizer learning rate based on step number.""" + lr = lr_lambda(step) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + return lr + + return update_lr + + +def save_checkpoint( + model: nn.Module, + optimizer: torch.optim.Optimizer, + epoch: int, + step: int, + loss: float, + path: str +) -> None: + """ + Save model checkpoint. + + Args: + model: Model to save + optimizer: Optimizer to save + epoch: Current epoch number + step: Current step number + loss: Current loss value + path: Path to save checkpoint + """ + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(path), exist_ok=True) + + # Save checkpoint with model state, optimizer state, and metadata + checkpoint = { + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + 'step': step, + 'loss': loss + } + + torch.save(checkpoint, path) + print(f"Checkpoint saved to {path}") + + +def load_checkpoint( + path: str, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None +) -> Dict: + """ + Load model checkpoint. + + Args: + path: Path to load checkpoint from + model: Model to load weights into + optimizer: Optimizer to load state into (optional) + + Returns: + Dictionary containing checkpoint metadata (epoch, step, loss) + """ + # Check if checkpoint file exists + if not os.path.isfile(path): + raise FileNotFoundError(f"Checkpoint file not found: {path}") + + # Load checkpoint + checkpoint = torch.load(path, map_location=torch.device('cpu')) + + # Load model state + model.load_state_dict(checkpoint['model']) + + # Load optimizer state if provided + if optimizer is not None and 'optimizer' in checkpoint: + optimizer.load_state_dict(checkpoint['optimizer']) + + # Return metadata + return { + 'epoch': checkpoint.get('epoch', 0), + 'step': checkpoint.get('step', 0), + 'loss': checkpoint.get('loss', float('inf')) + } + + +def average_checkpoints(paths: List[str], model: nn.Module) -> None: + """ + Average weights from multiple model checkpoints as described in the paper. + + Args: + paths: List of paths to checkpoints + model: Model to load averaged weights into + """ + # Ensure there are checkpoints to average + if not paths: + raise ValueError("No checkpoint paths provided for averaging") + + # Initialize dictionary to store sum of parameters + averaged_params = {} + + # Load and sum parameters from each checkpoint + for i, path in enumerate(paths): + # Load checkpoint + checkpoint = torch.load(path, map_location=torch.device('cpu')) + model_params = checkpoint['model'] + + # Initialize parameter sum or add to it + if i == 0: + for name, param in model_params.items(): + averaged_params[name] = param.clone() + else: + for name, param in model_params.items(): + averaged_params[name] += param + + # Average parameters by dividing by number of checkpoints + for name, param in averaged_params.items(): + averaged_params[name] = param / len(paths) + + # Load averaged parameters into model + model.load_state_dict(averaged_params) + print(f"Loaded averaged weights from {len(paths)} checkpoints") + + +def greedy_decode( + model: nn.Module, + src: torch.Tensor, + src_mask: torch.Tensor, + max_len: int, + start_symbol: int, + end_symbol: int +) -> torch.Tensor: + """ + Greedy decoding for inference (used for ablation studies). + + Args: + model: Transformer model + src: Source tensor of shape (batch_size, src_len) + src_mask: Source mask tensor + max_len: Maximum output sequence length + start_symbol: Start symbol index + end_symbol: End symbol index + + Returns: + Generated output sequence tensor of shape (batch_size, seq_len) + """ + # Get device from source tensor + device = src.device + + # Get batch size + batch_size = src.size(0) + + # Encode the source sequence + memory = model.encode(src, src_mask) + + # Initialize decoder input with start symbol for each batch item + ys = torch.ones(batch_size, 1).fill_(start_symbol).long().to(device) + + # Generate output sequence + for i in range(max_len - 1): + # Create target mask for current output + tgt_mask = create_subsequent_mask(ys.size(1)).to(device) + + # Decode one step + out = model.decode(memory, src_mask, ys, tgt_mask) + + # Get next token prediction (last position) + prob = model.generator(out[:, -1]) + + # Select most likely next token + _, next_word = torch.max(prob, dim=1) + + # Add predicted token to output + next_word = next_word.unsqueeze(1) + ys = torch.cat([ys, next_word], dim=1) + + # Stop if all sequences have end symbol + if (next_word == end_symbol).all(): + break + + return ys + + +def create_masks( + src: torch.Tensor, + tgt: Optional[torch.Tensor], + pad_idx: int +) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Create masks for transformer model. + + Args: + src: Source tensor of shape (batch_size, src_len) + tgt: Target tensor of shape (batch_size, tgt_len) or None for inference + pad_idx: Padding token index + + Returns: + Tuple of (src_mask, tgt_mask) where tgt_mask is None if tgt is None + """ + # Create source padding mask + src_mask = create_padding_mask(src, pad_idx) + + # Create target mask if target is provided + tgt_mask = None + if tgt is not None: + # Create padding mask + tgt_padding_mask = create_padding_mask(tgt, pad_idx) + + # Create subsequent mask + tgt_subsequent_mask = create_subsequent_mask(tgt.size(1)).to(tgt.device) + + # Combine padding and subsequent masks + tgt_mask = tgt_padding_mask | tgt_subsequent_mask + + return src_mask, tgt_mask + + +def clones(module: nn.Module, N: int) -> nn.ModuleList: + """ + Produce N identical layers. + Used for creating multiple identical encoder/decoder layers. + + Args: + module: Module to clone + N: Number of clones to make + + Returns: + ModuleList containing N clones of the module + """ + return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) diff --git a/requirements.txt b/requirements.txt index 9e374b21..58a13f4b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,6 @@ openai>=1.65.4 vllm>=0.6.4.post1 transformers>=4.46.3 tiktoken>=0.9.0 +python-dotenv>=1.0.0 +litellm>=1.17.0 +boto3>=1.28.0 \ No newline at end of file