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# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
"""Basic HuggingFace -> ONNX export script.
This scripts show a basic HuggingFace -> ONNX export workflow. This works for a MPT model
that has been saved using `MPT.save_pretrained`. For more details and examples
of exporting and working with HuggingFace models with ONNX, see https://huggingface.co/docs/transformers/serialization#export-to-onnx.
Example usage:
1) Local export
python convert_hf_to_onnx.py --pretrained_model_name_or_path local/path/to/huggingface/folder --output_folder local/folder
2) Remote export
python convert_hf_to_onnx.py --pretrained_model_name_or_path local/path/to/huggingface/folder --output_folder s3://bucket/remote/folder
3) Verify the exported model
python convert_hf_to_onnx.py --pretrained_model_name_or_path local/path/to/huggingface/folder --output_folder local/folder --verify_export
4) Change the batch size or max sequence length
python convert_hf_to_onnx.py --pretrained_model_name_or_path local/path/to/huggingface/folder --output_folder local/folder --export_batch_size 1 --max_seq_len 32000
"""
import argparse
import os
from argparse import ArgumentTypeError
from pathlib import Path
from typing import Any, Dict, Optional, Union
import torch
from composer.utils import (maybe_create_object_store_from_uri, parse_uri,
reproducibility)
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
def str2bool(v: Union[str, bool]):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise ArgumentTypeError('Boolean value expected.')
def str_or_bool(v: Union[str, bool]):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
return v
def gen_random_batch(batch_size: int, vocab_size: int, max_seq_len: int):
# generate input batch of random data
batch = {
'input_ids':
torch.randint(
low=0,
high=vocab_size,
size=(batch_size, max_seq_len),
dtype=torch.int64,
),
'attention_mask':
torch.ones(size=(batch_size, max_seq_len), dtype=torch.bool)
}
return batch
def export_to_onnx(
pretrained_model_name_or_path: str,
output_folder: str,
export_batch_size: int,
max_seq_len: Optional[int],
verify_export: bool,
from_pretrained_kwargs: Dict[str, Any],
):
reproducibility.seed_all(42)
save_object_store = maybe_create_object_store_from_uri(output_folder)
_, _, parsed_save_path = parse_uri(output_folder)
print('Loading HF config/model/tokenizer...')
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path,
**from_pretrained_kwargs)
config = AutoConfig.from_pretrained(pretrained_model_name_or_path,
**from_pretrained_kwargs)
# specifically for MPT, switch to the torch version of attention for ONNX export
if hasattr(config, 'attn_config'):
config.attn_config['attn_impl'] = 'torch'
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path,
config=config,
**from_pretrained_kwargs)
model.eval()
if max_seq_len is None and not hasattr(model.config, 'max_seq_len'):
raise ValueError(
'max_seq_len must be specified in either the model config or as an argument to this function.'
)
elif max_seq_len is None:
max_seq_len = model.config.max_seq_len
assert isinstance(max_seq_len, int) # pyright
print('Creating random batch...')
sample_input = gen_random_batch(
export_batch_size,
len(tokenizer),
max_seq_len,
)
with torch.no_grad():
model(**sample_input)
output_file = Path(parsed_save_path) / 'model.onnx'
os.makedirs(parsed_save_path, exist_ok=True)
print('Exporting the model with ONNX...')
torch.onnx.export(
model,
(sample_input,),
str(output_file),
input_names=['input_ids', 'attention_mask'],
output_names=['output'],
opset_version=16,
)
if verify_export:
with torch.no_grad():
orig_out = model(**sample_input)
import onnx
import onnx.checker
import onnxruntime as ort
_ = onnx.load(str(output_file))
onnx.checker.check_model(str(output_file))
ort_session = ort.InferenceSession(str(output_file))
for key, value in sample_input.items():
sample_input[key] = value.cpu().numpy()
loaded_model_out = ort_session.run(None, sample_input)
torch.testing.assert_close(
orig_out.logits.detach().numpy(),
loaded_model_out[0],
rtol=1e-2,
atol=1e-2,
msg=f'output mismatch between the orig and onnx exported model',
)
print('exported model ouptut matches with unexported model!!')
if save_object_store is not None:
print('Uploading files to object storage...')
for filename in os.listdir(parsed_save_path):
full_path = str(Path(parsed_save_path) / filename)
save_object_store.upload_object(full_path, full_path)
def parse_args():
parser = argparse.ArgumentParser(description='Convert HF model to ONNX',)
parser.add_argument(
'--pretrained_model_name_or_path',
type=str,
required=True,
)
parser.add_argument(
'--output_folder',
type=str,
required=True,
)
parser.add_argument(
'--export_batch_size',
type=int,
default=8,
)
parser.add_argument(
'--max_seq_len',
type=int,
default=None,
)
parser.add_argument(
'--verify_export',
action='store_true',
)
parser.add_argument('--trust_remote_code',
type=str2bool,
nargs='?',
const=True,
default=True)
parser.add_argument('--use_auth_token',
type=str_or_bool,
nargs='?',
const=True,
default=None)
parser.add_argument('--revision', type=str, default=None)
return parser.parse_args()
def main(args: argparse.Namespace):
from_pretrained_kwargs = {
'use_auth_token': args.use_auth_token,
'trust_remote_code': args.trust_remote_code,
'revision': args.revision,
}
export_to_onnx(
pretrained_model_name_or_path=args.pretrained_model_name_or_path,
output_folder=args.output_folder,
export_batch_size=args.export_batch_size,
max_seq_len=args.max_seq_len,
verify_export=args.verify_export,
from_pretrained_kwargs=from_pretrained_kwargs)
if __name__ == '__main__':
main(parse_args())
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