mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
252 lines
7.9 KiB
Python
252 lines
7.9 KiB
Python
"""
|
|
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
|
|
import paddle
|
|
import os
|
|
from paddlenlp.trainer import strtobool
|
|
from efficientllm.models.utils import load_checkpoint
|
|
from efficientllm.inference_args import InferenceArgs
|
|
from paddlenlp.utils.log import logger
|
|
from efficientllm.models.configuration import ErnieBotConfig
|
|
from efficientllm.models.tokenizer import ErnieBotTokenizer
|
|
from safetensors.numpy import save_file as safe_save_file
|
|
from paddlenlp.utils.env import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME
|
|
import shutil
|
|
import argparse
|
|
import importlib
|
|
import json
|
|
from paddlenlp.transformers.model_utils import shard_checkpoint
|
|
|
|
MODEL_LIB_NAMES = [
|
|
"efficientllm.models.modeling_ernie_bot",
|
|
]
|
|
|
|
|
|
def parse_arguments():
|
|
"""
|
|
parse_arguments
|
|
"""
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--model_name_or_path",
|
|
default=None,
|
|
required=True,
|
|
help="The directory of model.",
|
|
)
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
default="merged_output",
|
|
required=True,
|
|
help="The directory of merged model output.",
|
|
)
|
|
parser.add_argument(
|
|
"--safe_serialization",
|
|
type=strtobool,
|
|
default="True",
|
|
help="Whether merge the model into safetensors format.",
|
|
)
|
|
parser.add_argument(
|
|
"--predict_model_type",
|
|
type=str,
|
|
default="",
|
|
help="Quantization type for the model.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--draft_type",
|
|
type=str,
|
|
default=None,
|
|
choices=["autoregressive", "inference_with_reference", "hydra", "mtp"],
|
|
help="Quantization type for the model.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--moe_quant_type",
|
|
default="default",
|
|
type=str,
|
|
choices=["weight_only_int4", "weight_only_int8", "w4a8", "fp8", "default"],
|
|
help="quant type for moe part",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--use_ep",
|
|
type=strtobool,
|
|
default="True",
|
|
help="Whether merge the model into safetensors format.",
|
|
)
|
|
parser.add_argument("--dtype", type=str, default="bfloat16")
|
|
return parser.parse_args()
|
|
|
|
|
|
def get_model_cls(config):
|
|
"""
|
|
Get model class from model configuration.
|
|
"""
|
|
init_class = "ErnieBotFusedModel"
|
|
for lib_name in MODEL_LIB_NAMES:
|
|
eb_lib = importlib.import_module(lib_name)
|
|
if hasattr(eb_lib, init_class):
|
|
cls = getattr(eb_lib, init_class)
|
|
return cls
|
|
|
|
raise RuntimeError(f"Cannot find model architecture({init_class}) from eb_lib")
|
|
|
|
|
|
def save_safetensors(state_dict, args):
|
|
"""
|
|
save_safetensors
|
|
"""
|
|
logger.info("Move to numpy.")
|
|
for k in list(state_dict.keys()):
|
|
if isinstance(state_dict[k], paddle.Tensor):
|
|
state_dict[k] = state_dict.pop(k).cpu().numpy()
|
|
|
|
logger.info("Save safetensors files.")
|
|
shards, index = shard_checkpoint(
|
|
state_dict,
|
|
max_shard_size="5GB",
|
|
weights_name=SAFE_WEIGHTS_NAME,
|
|
shard_format="naive",
|
|
)
|
|
for shard_file, shard in shards.items():
|
|
save_file = os.path.join(args.output_dir, shard_file)
|
|
logger.info(f"Saving {save_file}")
|
|
safe_save_file(shard, save_file, metadata={"format": "np"})
|
|
|
|
save_index_file = os.path.join(args.output_dir, SAFE_WEIGHTS_INDEX_NAME)
|
|
with open(save_index_file, "w", encoding="utf-8") as f:
|
|
content = json.dumps(index, indent=2) + "\n"
|
|
f.write(content)
|
|
|
|
|
|
def quanted_tensor(cls, state_dict, config):
|
|
"""
|
|
quanted_tensor
|
|
"""
|
|
name_action_mappings = cls._get_tensor_quantization_mappings(config)
|
|
state_keys_map = cls._resolve_prefix_keys(
|
|
name_action_mappings.keys(), state_dict.keys()
|
|
)
|
|
for k, v in state_keys_map.items():
|
|
name_action_mappings[v] = name_action_mappings.pop(k)
|
|
state_dict_to_save = {}
|
|
from efficientllm.layers.utils import get_tensor
|
|
from tqdm import tqdm
|
|
for key in tqdm(state_dict.keys(), desc="process quantized weights "):
|
|
tensor_path = state_dict[key]
|
|
if key in name_action_mappings:
|
|
ret = state_dict[key]
|
|
action = name_action_mappings.pop(key)
|
|
quanted_weight_tensor, weight_scale_tensor = action(get_tensor(ret))
|
|
if quanted_weight_tensor._is_initialized():
|
|
state_dict_to_save[key + ".quant_weight"] = quanted_weight_tensor.cpu()
|
|
if weight_scale_tensor._is_initialized():
|
|
state_dict_to_save[key + ".quant_scale"] = weight_scale_tensor.cpu()
|
|
else:
|
|
state_dict_to_save[key] = quanted_weight_tensor.cpu()
|
|
else:
|
|
state_dict_to_save[key] = get_tensor(tensor_path).cpu()
|
|
|
|
if len(name_action_mappings) > 0:
|
|
for x in name_action_mappings.keys():
|
|
logger.debug(
|
|
f"key <{x}> need to merge tensor parallel but we can't find in model state."
|
|
)
|
|
return state_dict_to_save
|
|
|
|
|
|
def get_quant_type(args):
|
|
"""
|
|
get_quant_type
|
|
"""
|
|
quant_type = args.predict_model_type.lower()
|
|
if quant_type == "default":
|
|
quant_type = ""
|
|
moe_quant_type = args.moe_quant_type.lower()
|
|
if moe_quant_type == "default":
|
|
moe_quant_type = ""
|
|
paddle.set_default_dtype(args.dtype)
|
|
offline_args = InferenceArgs(
|
|
quant_type=quant_type,
|
|
num_layers=1,
|
|
num_attention_heads=1,
|
|
num_key_value_heads=1,
|
|
hidden_size=1,
|
|
ffn_hidden_size=1,
|
|
mp_rank=1,
|
|
mp_size=1,
|
|
)
|
|
weight_dtype, act_dtype, cachekv_dtype = (
|
|
offline_args.weight_dtype,
|
|
offline_args.act_dtype,
|
|
offline_args.cachekv_dtype,
|
|
)
|
|
return weight_dtype, act_dtype, cachekv_dtype, quant_type, moe_quant_type
|
|
|
|
|
|
def main():
|
|
"""
|
|
main
|
|
"""
|
|
args = parse_arguments()
|
|
tokenizer = ErnieBotTokenizer.from_pretrained(args.model_name_or_path)
|
|
config = ErnieBotConfig.from_pretrained(args.model_name_or_path)
|
|
(
|
|
config.weight_dtype,
|
|
config.act_dtype,
|
|
config.cachekv_dtype,
|
|
config.quant_type,
|
|
config.moe_quant_type,
|
|
) = get_quant_type(args)
|
|
config.is_mtp = args.draft_type in ["eagle", "mtp"]
|
|
config.use_ep = args.use_ep
|
|
cls = get_model_cls(config)
|
|
# load
|
|
state_dict = load_checkpoint(
|
|
args.model_name_or_path, cls, config, return_numpy=True
|
|
)
|
|
import time
|
|
|
|
start = time.perf_counter()
|
|
state_dict_to_save = quanted_tensor(cls=cls, state_dict=state_dict, config=config)
|
|
end = time.perf_counter()
|
|
logger.info("Finish Quantize.")
|
|
logger.info(f"load和量化耗时: {end - start:.6f} 秒")
|
|
|
|
logger.info("Begin to save model")
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
start = time.perf_counter()
|
|
if not args.safe_serialization:
|
|
paddle.save(
|
|
state_dict_to_save,
|
|
os.path.join(args.output_dir, "model_state.pdparams"),
|
|
)
|
|
else:
|
|
save_safetensors(state_dict_to_save, args)
|
|
|
|
config.save_pretrained(args.output_dir)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
if config.moe_quant_type == "w4a8":
|
|
# cp act_scales.json
|
|
shutil.copy(args.model_name_or_path + '/act_scales.json', args.output_dir)
|
|
shutil.copy(args.model_name_or_path + '/weight_scales.json', args.output_dir)
|
|
end = time.perf_counter()
|
|
logger.info(f"save耗时: {end - start:.6f} 秒")
|
|
logger.info("Finish.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |