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samsja
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| def get_model(config: Config) -> GPT: | ||
| # Load model | ||
| config_model = LlamaConfig.from_pretrained(config.path_model, attn_implementation=config.attn_implementation) | ||
| return LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path=config.path_model, config=config_model) | ||
| if isinstance(config.llama_config, ModelConfig): | ||
| llama_config = config.llama_config | ||
| else: | ||
| with open(config.llama_config) as f: | ||
| llama_config = ModelConfig(**json.load(f)) | ||
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| llama_config.attention_impl = config.attention_impl | ||
| return GPT(llama_config) |
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Hrmm would the initialisation be the same across diloco workers?
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yes indeed, is there a way to use hf hub to push the init ckpt like we did before ?
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seems like GPT inherits from nn.Module instead of transformers PreTrainedModel so it doesnt have the from_pretrained function. Maybe we can change it to allow loading from hub
| input_ids = inputs_ids[:, :-1] | ||
| target = inputs_ids[:, 1:] | ||
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| output = model(input_ids) |
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fyi @Jackmin801 this pr was not meant to be merged for now, it was more to compare. Tho we have a 20% better mfu with it (even with torch compile) so we might want it. I will address your comments |
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where are the MFU speedups coming from? if its from better layer implementations I think it might be better to just modify (copy over to our repo and modify) the HF Its also easier to have FP8 support this way (I have an implementation using transformer engine that uses this implementation method; https://github.com/PrimeIntellect-ai/OpenDiLoCo_internal/pull/71) |
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add llama