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whisper.cpp/models/convert-h5-to-ggml.py

213 lines
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# Convert Hugging Face fine-tuned models to ggml format
#
# Usage:
#
# git clone https://github.com/openai/whisper
# git clone https://github.com/ggerganov/whisper.cpp
# git clone https://huggingface.co/openai/whisper-medium
#
# python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .
#
# This script is similar to "convert-pt-to-ggml.py"
#
# For more info:
#
# https://github.com/ggerganov/whisper.cpp/issues/157
#
import io
import os
import sys
import struct
import json
import code
import torch
import numpy as np
from transformers import WhisperForConditionalGeneration
conv_map = {
'self_attn.k_proj' : 'attn.key',
'self_attn.q_proj' : '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',
'final_layer_norm' : 'mlp_ln',
'encoder.layer_norm.bias' : 'encoder.ln_post.bias',
'encoder.layer_norm.weight' : 'encoder.ln_post.weight',
'encoder.embed_positions.weight': 'encoder.positional_embedding',
'decoder.layer_norm.bias' : 'decoder.ln.bias',
'decoder.layer_norm.weight' : 'decoder.ln.weight',
'decoder.embed_positions.weight': 'decoder.positional_embedding',
'decoder.embed_tokens.weight' : 'decoder.token_embedding.weight',
'proj_out.weight' : 'decoder.proj.weight',
}
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if len(sys.argv) < 4:
print("Usage: convert-h5-to-ggml.py dir_model path-to-whisper-repo dir-output [use-f32]\n")
sys.exit(1)
dir_model = sys.argv[1]
dir_whisper = sys.argv[2]
dir_out = sys.argv[3]
with open(dir_model + "/vocab.json", "r") as f:
encoder = json.load(f)
with open(dir_model + "/added_tokens.json", "r") as f:
encoder_added = json.load(f)
with open(dir_model + "/config.json", "r") as f:
hparams = json.load(f)
model = WhisperForConditionalGeneration.from_pretrained(dir_model)
#code.interact(local=locals())
n_mels = hparams["num_mel_bins"]
with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
filters = torch.from_numpy(f[f"mel_{n_mels}"])
dir_tokenizer = dir_model
fname_out = dir_out + "/ggml-model.bin"
with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
tokens = json.load(f)
# use 16-bit or 32-bit floats
use_f16 = True
if len(sys.argv) > 4:
use_f16 = False
fname_out = dir_out + "/ggml-model-f32.bin"
fout = open(fname_out, "wb")
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["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))
fout.write(struct.pack("i", filters.shape[0]))
fout.write(struct.pack("i", filters.shape[1]))
for i in range(filters.shape[0]):
for j in range(filters.shape[1]):
fout.write(struct.pack("f", filters[i][j]))
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
fout.write(struct.pack("i", len(tokens)))
tokens = sorted(tokens.items(), key=lambda x: x[1])
for key in tokens:
text = bytearray([byte_decoder[c] for c in key[0]])
fout.write(struct.pack("i", len(text)))
fout.write(text)
list_vars = model.state_dict()
for name in list_vars.keys():
# this seems to not be used
# ref: https://github.com/huggingface/transformers/blob/9a5b84a0076a04fe9596da72e8668069d4f09ea0/src/transformers/models/whisper/modeling_whisper.py#L1099-L1106
if name == "proj_out.weight":
print('Skipping', name)
continue
src = name
nn = name
if name != "proj_out.weight":
nn = nn.split(".")[1:]
else:
nn = nn.split(".")
if nn[1] == "layers":
nn[1] = "blocks"
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])]
name = ".".join(nn[:3] + [mapped] + nn[-1:])
else:
name = ".".join(nn)
name = conv_map[name] if name in conv_map else name
print(src, ' -> ', name)
data = list_vars[src].squeeze().numpy()
data = data.astype(np.float16)
# reshape conv bias from [n] to [n, 1]
if name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias":
data = data.reshape(data.shape[0], 1)
print(" Reshaped variable: " + name + " to shape: ", data.shape)
n_dims = len(data.shape)
print(name, n_dims, data.shape)
# looks like the whisper models are in f16 by default
# so we need to convert the small tensors to f32 until we fully support f16 in ggml
# ftype == 0 -> float32, ftype == 1 -> float16
ftype = 1;
if use_f16:
if n_dims < 2 or \
name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias" or \
name == "encoder.positional_embedding" or \
name == "decoder.positional_embedding":
print(" Converting to float32")
data = data.astype(np.float32)
ftype = 0
else:
data = data.astype(np.float32)
ftype = 0
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype))
for i in range(n_dims):
fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
fout.write(str);
# data
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("")