/
hf_gpt2_export.py
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/
hf_gpt2_export.py
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"""
Export Hugging Face GPT2 models to hdf5 format.
"""
import os
import h5py
import numpy as np
from collections import OrderedDict
from transformers import GPT2LMHeadModel
from lightseq.training.ops.pytorch.export import fill_hdf5_layer
from export.util import parse_args
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
"""
For the mapping dictionary: key is the value of the proto parameter,
value is a powerful expression, each && split tensor name of the matching path or expression.
The sub-pattern of the path is separated by spaces, and the expression starts with a expression_.
You can operate separately on each tensor and support multiple expressions. Multiple matching paths
and the expression will finally be concatenated on axis = -1.
"""
enc_layer_mapping_dict = OrderedDict(
{
"multihead_norm_scale": "ln_1 weight",
"multihead_norm_bias": "ln_1 bias",
# GPT2's Conv1D don't need transpose
# https://github.com/huggingface/transformers/blob/9ec0f01b6c3aff4636869aee735859fb6f89aa98/src/transformers/modeling_utils.py#L1400
"multihead_project_kernel_qkv": "attn c_attn weight",
"multihead_project_bias_qkv": "attn c_attn bias",
"multihead_project_kernel_output": "attn c_proj weight",
"multihead_project_bias_output": "attn c_proj bias",
"ffn_norm_scale": "ln_2 weight",
"ffn_norm_bias": "ln_2 bias",
"ffn_first_kernel": "mlp c_fc weight",
"ffn_first_bias": "mlp c_fc bias",
"ffn_second_kernel": "mlp c_proj weight",
"ffn_second_bias": "mlp c_proj bias",
}
)
src_emb_mapping_dict = OrderedDict(
{
"norm_scale": "ln_f weight",
"norm_bias": "ln_f bias",
"token_embedding": "wte",
# manually process position_embedding to customize for max_step
# "position_embedding": "wpe",
}
)
def extract_gpt_weights(
output_file,
model_dir,
generation_method,
beam_size=1,
topk=1,
topp=0.75,
# default eos_id from https://huggingface.co/transformers/model_doc/gpt2.html#gpt2lmheadmodel
eos_id=50256,
pad_id=50257,
max_step=50,
extra_decode_length=0,
):
# load var names
model = GPT2LMHeadModel.from_pretrained(model_dir)
head_num = model.config.n_head
encoder_state_dict = model.state_dict()
enc_var_name_list = list(encoder_state_dict.keys())
# initialize output file
output_file += ".hdf5"
print("Saving model to hdf5...")
print("Writing to {0}".format(output_file))
hdf5_file = h5py.File(output_file, "w")
# fill each encoder layer's params
enc_tensor_names = {}
for name in enc_var_name_list:
name_split = name.split(".")
if len(name_split) <= 2 or not name_split[2].isdigit():
continue
layer_id = int(name_split[2])
enc_tensor_names.setdefault(layer_id, []).append(name)
# fill encoder_stack
for layer_id in sorted(enc_tensor_names.keys()):
fill_hdf5_layer(
enc_tensor_names[layer_id],
encoder_state_dict,
hdf5_file,
f"encoder_stack/{layer_id}/",
enc_layer_mapping_dict,
)
# fill src_embedding - except for position embedding
fill_hdf5_layer(
enc_var_name_list,
encoder_state_dict,
hdf5_file,
"src_embedding/",
src_emb_mapping_dict,
)
# special handling for position embedding
position_emb = encoder_state_dict["transformer.wpe.weight"]
_max_allowed_step, _hidden_size = position_emb.shape
if max_step > _max_allowed_step:
print(f"max_step {max_step} exceed max allowed step, abort.")
return
# truncate position embedding for max_step
position_emb = position_emb[:max_step, :]
print(
f"processed position_embedding with max_step constriant, shape: {position_emb.shape}"
)
position_emb = position_emb.flatten().tolist()
hdf5_file.create_dataset(
"src_embedding/position_embedding", data=position_emb, dtype="f4"
)
# save number of layers metadata
hdf5_file.create_dataset(
"model_conf/n_encoder_stack", data=len(enc_tensor_names), dtype="i4"
)
# fill in model_conf
hdf5_file.create_dataset("model_conf/head_num", data=head_num, dtype="i4")
hdf5_file.create_dataset("model_conf/src_padding_id", data=pad_id, dtype="i4")
hdf5_file.create_dataset(
"model_conf/sampling_method",
data=np.array([ord(c) for c in generation_method]).astype(np.int8),
dtype="i1",
)
hdf5_file.create_dataset("model_conf/beam_size", data=beam_size, dtype="i4")
hdf5_file.create_dataset("model_conf/topp", data=topp, dtype="f4")
hdf5_file.create_dataset("model_conf/topk", data=topk, dtype="i4")
hdf5_file.create_dataset("model_conf/eos_id", data=eos_id, dtype="i4")
hdf5_file.create_dataset(
"model_conf/extra_decode_length", data=extra_decode_length, dtype="i4"
)
hdf5_file.close()
# read-in again to double check
hdf5_file = h5py.File(output_file, "r")
def _print_pair(key, value):
if key == "sampling_method":
value = "".join(map(chr, value[()]))
else:
value = value[()]
print(f"{key}: {value}")
list(map(lambda x: _print_pair(*x), hdf5_file["model_conf"].items()))
if __name__ == "__main__":
args = parse_args()
if args.generation_method not in ["topk", "topp", "ppl", "beam_search"]:
args.generation_method = "topk"
output_lightseq_model_name = "lightseq_gpt2_base" # or "lightseq_gpt2_large"
input_huggingface_gpt_model = "gpt2" # or "gpt2-large"
topk = 1
topp = 0.75
beam_size = 4
# default eos_id from https://huggingface.co/transformers/model_doc/gpt2.html#gpt2lmheadmodel
eos_id = 50256
pad_id = 50257
max_step = 50
extra_decode_length = 0 # use positive length to avtivate it
extract_gpt_weights(
output_lightseq_model_name,
input_huggingface_gpt_model,
generation_method=args.generation_method,
beam_size=beam_size,
topk=topk,
topp=topp,
eos_id=eos_id,
pad_id=pad_id,
max_step=max_step,
extra_decode_length=extra_decode_length,
)