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Bugfix: missing hparam type_vocab_size #32

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FFengIll wants to merge 4 commits into from
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Bugfix: missing hparam type_vocab_size #32

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dd15c0f
bugfix: `type_vocab_size` is also a hparam (can not use const as 2).
FFengIll Sep 14, 2023
a3c8548
bugfix: add `type_vocab_size`.
FFengIll Sep 14, 2023
7ef3126
bugfix: missing read for new n_vocab_size.
FFengIll Sep 19, 2023
79825d8
bugfix: add n_vocab_size into quantize.
FFengIll Sep 21, 2023
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5 changes: 4 additions & 1 deletion bert.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@ struct bert_hparams
int32_t n_intermediate = 1536;
int32_t n_head = 12;
int32_t n_layer = 6;
int32_t n_vocab_size = 2;
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@FFengIll FFengIll Sep 19, 2023

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Here is a break change since new field.
I believe some others will happen in future.
So we may try to shift into GGUF.

https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md

int32_t f16 = 1;
};

Expand Down Expand Up @@ -489,11 +490,13 @@ struct bert_ctx * bert_load_from_file(const char *fname)
const int n_intermediate = hparams.n_intermediate;
const int n_max_tokens = hparams.n_max_tokens;
const int n_vocab = hparams.n_vocab;
const int n_vocab_size = hparams.n_vocab_size;


model.layers.resize(n_layer);

model.word_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.token_type_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, 2);
model.token_type_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab_size);
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@FFengIll FFengIll Sep 19, 2023

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@skeskinen
here is a change since the tensor is related to n_vocab_size.
for many case, it is 2, but not a const.

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@FFengIll FFengIll Sep 19, 2023

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some ref
google-research/bert#16
https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertConfig

model.position_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_max_tokens);

model.ln_e_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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1 change: 1 addition & 0 deletions models/convert-to-ggml.py
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Expand Up @@ -61,6 +61,7 @@
fout.write(struct.pack("i", hparams["intermediate_size"]))
fout.write(struct.pack("i", hparams["num_attention_heads"]))
fout.write(struct.pack("i", hparams["num_hidden_layers"]))
fout.write(struct.pack("i", hparams["type_vocab_size"]))
fout.write(struct.pack("i", ftype))

for i in range(hparams["vocab_size"]):
Expand Down