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minor : improve C++ and Python style (#768)

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* use some STL functions

* use self.field than setattr, use pathlib.Path

* recover some format

* const some iter

* Keep the original

* 2 space
This commit is contained in:
AsukaMinato
2023-04-29 16:06:25 +09:00
committed by GitHub
parent 4d89ee2e59
commit 94aa56f19e
4 changed files with 100 additions and 105 deletions
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38
models/convert-h5-to-ggml.py
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@ -23,6 +23,7 @@ import json
import code
import torch
import numpy as np
from pathlib import Path
from transformers import WhisperForConditionalGeneration
@ -75,16 +76,13 @@ 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]
dir_model = Path(sys.argv[1])
dir_whisper = Path(sys.argv[2])
dir_out = Path(sys.argv[3])
with open(dir_model + "/vocab.json", "r", encoding="utf8") as f:
encoder = json.load(f)
with open(dir_model + "/added_tokens.json", "r", encoding="utf8") as f:
encoder_added = json.load(f)
with open(dir_model + "/config.json", "r", encoding="utf8") as f:
hparams = json.load(f)
encoder = json.load((dir_model / "vocab.json").open("r", encoding="utf8"))
encoder_added = json.load((dir_model / "added_tokens.json").open( "r", encoding="utf8"))
hparams = json.load((dir_model / "config.json").open("r", encoding="utf8") )
model = WhisperForConditionalGeneration.from_pretrained(dir_model)
@ -96,16 +94,15 @@ with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as
dir_tokenizer = dir_model
fname_out = dir_out + "/ggml-model.bin"
fname_out = dir_out / "ggml-model.bin"
with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
tokens = json.load(f)
tokens = json.load(open(dir_tokenizer / "vocab.json", "r", encoding="utf8"))
# 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"
fname_out = dir_out / "ggml-model-f32.bin"
fout = open(fname_out, "wb")
@ -171,10 +168,9 @@ for name in list_vars.keys():
data = data.astype(np.float16)
# reshape conv bias from [n] to [n, 1]
if name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias":
if name in ["encoder.conv1.bias", "encoder.conv2.bias"]:
data = data.reshape(data.shape[0], 1)
print(" Reshaped variable: " + name + " to shape: ", data.shape)
print(" Reshaped variable: " , name , " to shape: ", data.shape)
n_dims = len(data.shape)
print(name, n_dims, data.shape)
@ -182,7 +178,7 @@ for name in list_vars.keys():
# 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;
ftype = 1
if use_f16:
if n_dims < 2 or \
name == "encoder.conv1.bias" or \
@ -197,16 +193,16 @@ for name in list_vars.keys():
ftype = 0
# header
str = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str), ftype))
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);
fout.write(str_)
# data
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("Done. Output file: " , fname_out)
print("")