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models : fix HF fine-tuned model conversion script (#157)

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It works now
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Georgi Gerganov 2022-11-23 23:14:11 +02:00
parent 388e9f79ad
commit 5698bddbc9
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GPG Key ID: 449E073F9DC10735
1 changed files with 27 additions and 26 deletions
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21
models/convert-h5-to-ggml.py
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@ -9,14 +9,15 @@ import numpy as np
from transformers import WhisperForConditionalGeneration
conv_map = {'self_attn_layer_norm': 'attn_ln',
'encoder_attn.k_proj': 'attn.key',
'self_attn.out_proj': 'attn.out',
'encoder_attn.out_proj': 'cross_attn.out',
conv_map = {
'self_attn.k_proj' : 'attn.key',
'self_attn.q_proj' : 'attn.query',
'encoder_attn.q_proj': 'cross_attn.query',
'self_attn.v_proj' : 'attn.value',
'self_attn.out_proj' : 'attn.out',
'self_attn_layer_norm' : 'attn_ln',
'encoder_attn.q_proj' : 'cross_attn.query',
'encoder_attn.v_proj' : 'cross_attn.value',
'encoder_attn.out_proj' : 'cross_attn.out',
'encoder_attn_layer_norm' : 'cross_attn_ln',
'fc1' : 'mlp.0',
'fc2' : 'mlp.2',
@ -95,12 +96,12 @@ fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["max_source_positions"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
fout.write(struct.pack("i", hparams["decoder_layers"]))
fout.write(struct.pack("i", hparams["max_length"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
fout.write(struct.pack("i", hparams["encoder_layers"]))
fout.write(struct.pack("i", hparams["max_length"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
fout.write(struct.pack("i", hparams["decoder_layers"]))
fout.write(struct.pack("i", hparams["num_mel_bins"]))
fout.write(struct.pack("i", use_f16))
@ -139,7 +140,7 @@ for name in list_vars.keys():
if nn[1] == "layers":
nn[1] = "blocks"
if ".".join(nn[3:-1]) == "self_attn.k_proj":
if ".".join(nn[3:-1]) == "encoder_attn.k_proj":
mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
else:
mapped = conv_map[".".join(nn[3:-1])]