wehub-resource-sync
267 lines
10 KiB
Python
267 lines
10 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import copy
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import os
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import numpy as np
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import paddle
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from paddle.nn.quant import weight_dequantize
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from paddlenlp.peft import LoRAConfig, LoRAModel
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try:
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from paddlenlp.quantization.qlora import qlora_weight_quantize_dequantize
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from paddlenlp.quantization.quantization_config import QuantizationConfig
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from paddlenlp.quantization.quantization_linear import QuantizationLinear
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except:
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pass
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from paddlenlp.trainer.argparser import strtobool
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from paddlenlp.transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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from paddlenlp.transformers.utils import device_guard
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from paddlenlp.utils.env import CONFIG_NAME
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from paddlenlp.utils.log import logger
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name_or_path", default=None, required=True, type=str, help="The directory of model.")
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parser.add_argument(
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"--lora_path", default=None, type=str, required=True, help="The directory of LoRA parameters. Default to None"
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)
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parser.add_argument("--output_path", default=None, type=str, required=True, help="The directory of saved model ")
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parser.add_argument("--safe_serialization", default="False", type=strtobool, help="Whether save as safetensor.")
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parser.add_argument(
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"--device",
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type=str,
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default="gpu",
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choices=["gpu", "npu", "cpu"],
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help="Device for selecting for merging lora weights, currently only supports gpu/npu/cpu.",
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)
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return parser.parse_args()
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def weight_process(name, quant_config, lora_config, state_dict, device):
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target_device = device if device == "cpu" else device + ":0"
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if (name + ".weight") not in state_dict.keys():
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return
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if quant_config.weight_quantize_algo is None:
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return
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elif quant_config.weight_quantize_algo in ["nf4", "fp4"]:
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weight = state_dict.pop(name + ".weight").to(target_device)
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state_dict[name + ".weight"] = qlora_weight_quantize_dequantize(
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weight,
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quant_algo=quant_config.weight_quantize_algo,
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double_quant=quant_config.qlora_weight_double_quant,
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block_size=quant_config.qlora_weight_blocksize,
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double_quant_block_size=quant_config.qlora_weight_double_quant_block_size,
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).cpu()
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elif quant_config.weight_quantize_algo in ["weight_only_int8"]:
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quant_weight = state_dict.pop(name + ".quant_weight").to(target_device)
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quant_scale = state_dict.pop(name + ".quant_scale").to(target_device)
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state_dict[name + ".weight"] = weight_dequantize(quant_weight, quant_scale, out_dtype=lora_config.dtype).cpu()
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else:
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raise ValueError(f"quant_config.weight_quantize_algo {quant_config.weight_quantize_algo} is not supported.")
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def get_mixer(mixer, mixer_num, index=0):
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if index == mixer_num - 1:
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return mixer[index]
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else:
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return mixer[index] @ get_mixer(mixer, mixer_num, index + 1)
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def lora_process(name, layer, lora_config, state_dict, device, lora_state_dict=None):
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target_device = device if device == "cpu" else device + ":0"
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if (name + ".weight") not in state_dict.keys():
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return
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weight = state_dict.pop(name + ".weight")
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lora_use_mixer = lora_config.lora_use_mixer
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mixer_num = lora_config.mixer_num
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mixer = {}
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use_mora = lora_config.use_mora
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if lora_state_dict is None:
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lora_A = state_dict.pop(name + ".lora_A")
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if not use_mora:
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lora_B = state_dict.pop(name + ".lora_B")
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if lora_use_mixer:
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for i in range(mixer_num):
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mixer[i] = state_dict.pop(name + ".lora_mixer_" + str(i))
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else:
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lora_A = lora_state_dict.pop(name + ".lora_A")
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if not use_mora:
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lora_B = lora_state_dict.pop(name + ".lora_B")
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if lora_use_mixer:
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for i in range(mixer_num):
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mixer[i] = state_dict.pop(name + ".lora_mixer_" + str(i))
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if device != "cpu":
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weight = weight.to(target_device)
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lora_A = lora_A.to(target_device)
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if not use_mora:
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lora_B = lora_B.to(target_device)
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if lora_use_mixer:
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for key in mixer.keys():
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mixer[key] = mixer[key].to(target_device)
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if device == "cpu" and weight.dtype.name == "BF16":
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weight = weight.astype("float32")
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lora_A = lora_A.astype("float32")
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if not use_mora:
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lora_B = lora_B.astype("float32")
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if lora_use_mixer:
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for key in mixer.keys():
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mixer[key] = mixer[key].astype(lora_config.dtype)
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delta_weight = layer.get_delta_weight(lora_A, lora_B, get_mixer(mixer, mixer_num))
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elif use_mora:
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delta_weight = layer.get_delta_weight(lora_A)
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else:
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delta_weight = layer.get_delta_weight(lora_A, lora_B)
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out = (weight + delta_weight).astype(lora_config.dtype)
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else:
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if lora_use_mixer:
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delta_weight = layer.get_delta_weight(lora_A, lora_B, get_mixer(mixer, mixer_num))
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elif use_mora:
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delta_weight = layer.get_delta_weight(lora_A)
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else:
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delta_weight = layer.get_delta_weight(lora_A, lora_B)
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out = (weight + delta_weight).cpu()
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state_dict[name + ".weight"] = out
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def merge_old_lora(lora_config, args):
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lora_config.merge_weights = True
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model = AutoModelForCausalLM.from_pretrained(
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args.model_name_or_path,
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dtype=lora_config.dtype,
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)
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model = LoRAModel.from_pretrained(model, args.lora_path)
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try:
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model.merge()
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model.eval()
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except:
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model.eval()
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model_state_dict = model.model.state_dict()
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for key in list(model_state_dict):
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if "lora" in key:
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del model_state_dict[key]
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return model, model_state_dict
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def read_file(file_name):
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if file_name.endswith("safetensors"):
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try:
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from paddlenlp.utils.safetensors import fast_load_file as load_file
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except:
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from safetensors.numpy import load_file
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read_tensors = load_file(file_name)
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for key in list(read_tensors.keys()):
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if isinstance(read_tensors[key], np.ndarray):
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with device_guard("cpu"):
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read_tensors[key] = paddle.Tensor(read_tensors.pop(key), zero_copy=True)
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else:
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with device_guard("cpu"):
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read_tensors = paddle.load(file_name)
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return read_tensors
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def save_file(output_path, file_name, tensors, safe_serialization=True):
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if safe_serialization:
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from safetensors.numpy import save_file as _save_file
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if file_name == "model_state.pdparams":
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file_name = "model.safetensors"
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for key in list(tensors.keys()):
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if isinstance(tensors[key], paddle.Tensor):
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tensors[key] = tensors.pop(key).cpu().numpy()
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_save_file(tensors, os.path.join(output_path, file_name), metadata={"format": "np"})
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else:
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paddle.save(tensors, os.path.join(output_path, file_name))
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def merge():
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args = parse_arguments()
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paddle.set_device(args.device)
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lora_config = LoRAConfig.from_pretrained(args.lora_path)
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if os.path.isfile(os.path.join(args.lora_path, CONFIG_NAME)):
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config = AutoConfig.from_pretrained(args.lora_path)
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elif args.model_name_or_path is not None:
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config = AutoConfig.from_pretrained(args.model_name_or_path)
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else:
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raise ValueError(
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f"We can not find config.json in lora_path: {args.lora_path} or find a valid model_name_or_path."
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)
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config.dtype = lora_config.dtype
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quant_config = copy.deepcopy(config.quantization_config)
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lora_config.merge_weights = False
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
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tokenizer.save_pretrained(args.output_path)
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if lora_config.enable_lora_list is not None:
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model, model_state_dict = merge_old_lora(lora_config, args)
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else:
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if quant_config.weight_quantize_algo in ["nf4", "fp4"]:
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config.quantization_config = QuantizationConfig()
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with device_guard(args.device):
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model = AutoModelForCausalLM.from_pretrained(
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args.model_name_or_path,
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config=config,
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low_cpu_mem_usage=True,
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)
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logger.info("load model done")
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model = LoRAModel.from_pretrained(model=model, lora_path=args.lora_path, lora_config=lora_config)
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logger.info("load lora model done")
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if quant_config.weight_quantize_algo in ["weight_only_int8"]:
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model.config.quantization_config = QuantizationConfig()
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model.eval()
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model_state_dict = model.model.state_dict()
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if quant_config.weight_quantize_algo in ["nf4", "fp4", "weight_only_int8"]:
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for name, layer in model.model.named_sublayers():
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if isinstance(layer, paddle.nn.Linear) or isinstance(layer, QuantizationLinear):
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weight_process(name, quant_config, lora_config, model_state_dict, args.device)
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lora_info = {}
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for sublayer_name, sublayer in model.named_sublayers():
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if isinstance(sublayer, paddle.nn.Linear):
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for param_name, param in sublayer.named_parameters():
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if "lora_A" in param_name:
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lora_info[sublayer_name[6:]] = sublayer
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for name, layer in lora_info.items():
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lora_process(name, layer, lora_config, model_state_dict, args.device)
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logger.info("Begin to save merged model")
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if args.safe_serialization:
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model.model.save_pretrained(
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args.output_path, state_dict=model_state_dict, safe_serialization=args.safe_serialization
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)
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else:
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model.model.save_pretrained(args.output_path, state_dict=model_state_dict, max_shard_size="100GB")
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if __name__ == "__main__":
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merge()
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