wehub-resource-sync
580 lines
23 KiB
Python
580 lines
23 KiB
Python
# Copyright (c) 2023 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 json
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import os
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import sys
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from functools import partial
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import paddle
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from utils.argument import GenerateArgument
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from utils.data import get_convert_example
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from paddlenlp.data import DataCollatorForSeq2Seq
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from paddlenlp.datasets import (
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ZeroPaddingIterableDataset,
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ZeroPaddingMapDataset,
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load_dataset,
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)
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from paddlenlp.metrics import BLEU, Rouge1, Rouge2, RougeL
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from paddlenlp.peft import LoRAModel
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from paddlenlp.trainer import PdArgumentParser
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from paddlenlp.trainer.trainer_callback import TrainerState
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from paddlenlp.transformers import (
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AutoConfig,
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AutoModelForCausalLM,
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AutoModelForCausalLMPipe,
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AutoTokenizer,
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Llama3Tokenizer,
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LlamaForCausalLM,
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LlamaForCausalLMPipe,
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LlamaTokenizer,
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Qwen2ForCausalLM,
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Qwen2ForCausalLMPipe,
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)
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from paddlenlp.transformers.configuration_utils import LlmMetaConfig
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from paddlenlp.trl import DataConfig, ModelConfig, QuantConfig, SFTConfig, SFTTrainer
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from paddlenlp.trl.llm_utils import (
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ZeroPaddingIterDatasetCallback,
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compute_metrics,
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init_chat_template,
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)
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from paddlenlp.utils.log import logger
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from paddlenlp.utils.tools import get_env_device
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# Fine-tune Environment Variables to support sharding stage1 overlap optimization.
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os.environ["USE_CASUAL_MASK"] = "False"
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flash_mask_support_list = [LlamaForCausalLM, LlamaForCausalLMPipe, Qwen2ForCausalLM, Qwen2ForCausalLMPipe]
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def main():
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parser = PdArgumentParser((GenerateArgument, QuantConfig, ModelConfig, DataConfig, SFTConfig))
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if len(sys.argv) >= 2 and sys.argv[1].endswith(".json"):
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gen_args, quant_args, model_args, data_args, training_args = parser.parse_json_file_and_cmd_lines()
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elif len(sys.argv) >= 2 and sys.argv[1].endswith(".yaml"):
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gen_args, quant_args, model_args, data_args, training_args = parser.parse_yaml_file_and_cmd_lines()
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else:
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gen_args, quant_args, model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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training_args.print_config(model_args, "Model")
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training_args.print_config(data_args, "Data")
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training_args.print_config(quant_args, "Quant")
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training_args.print_config(gen_args, "Generation")
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if sum([quant_args.do_ptq, quant_args.do_qat, quant_args.do_gptq]) > 1:
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raise ValueError(
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"--do_ptq, --do_gptq and --do_qat cannot work at the same time. Please choose only one at a time"
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)
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# Setup GPU & distributed training
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paddle.set_device(training_args.device)
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16 or training_args.bf16}"
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)
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if get_env_device() == "xpu" and training_args.gradient_accumulation_steps > 1:
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try:
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from paddle_xpu.layers.nn.linear import LinearConfig # noqa: F401
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LinearConfig.enable_accumulate_steps_opt()
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LinearConfig.set_accumulate_steps(training_args.gradient_accumulation_steps)
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except ImportError:
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# It's OK, not use accumulate_steps optimization
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pass
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# Load model
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if training_args.fp16_opt_level == "O2":
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if training_args.fp16:
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dtype = "float16"
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elif training_args.bf16:
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dtype = "bfloat16"
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else:
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raise ValueError("Please specific dtype: --fp16 or --bf16")
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else:
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dtype = "float32"
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if hasattr(model_args, "qlora_weight_blocksize"):
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quantization_config = dict(
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weight_quantize_algo=model_args.weight_quantize_algo,
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qlora_weight_blocksize=model_args.qlora_weight_blocksize,
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qlora_weight_double_quant=model_args.qlora_weight_double_quant,
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qlora_weight_double_quant_block_size=model_args.qlora_weight_double_quant_block_size,
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)
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else:
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quantization_config = dict(
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weight_quantize_algo=model_args.weight_quantize_algo,
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weight_blocksize=model_args.weight_blocksize,
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weight_double_quant=model_args.weight_double_quant,
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weight_double_quant_block_size=model_args.weight_double_quant_block_size,
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)
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model_config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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dtype=dtype,
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from_aistudio=model_args.from_aistudio,
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quantization_config=quantization_config,
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)
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LlmMetaConfig.set_llm_config(model_config, training_args)
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model_config.use_fast_layer_norm = model_args.use_fast_layer_norm
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# Config for model using dropout, such as GPT.
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if hasattr(model_config, "hidden_dropout_prob"):
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model_config.hidden_dropout_prob = model_args.hidden_dropout_prob
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if hasattr(model_config, "attention_probs_dropout_prob"):
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model_config.attention_probs_dropout_prob = model_args.attention_probs_dropout_prob
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if hasattr(model_config, "ignore_index"):
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model_config.ignore_index = -100
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if model_args.fuse_attention_qkv is not None:
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model_config.fuse_attention_qkv = model_args.fuse_attention_qkv
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if model_args.fuse_attention_ffn is not None:
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model_config.fuse_attention_ffn = model_args.fuse_attention_ffn
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model_config.seq_length = data_args.max_length
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logger.info(f"Final model config: {model_config}")
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model_class = AutoModelForCausalLM
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if training_args.pipeline_parallel_degree > 1:
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if data_args.eval_with_do_generation and training_args.do_eval:
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raise ValueError("Please set eval_with_do_generation to false in pipeline parallel mode.")
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model_class = AutoModelForCausalLMPipe
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if model_args.continue_training and not training_args.autotuner_benchmark:
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model = model_class.from_pretrained(
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model_args.model_name_or_path,
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config=model_config,
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from_aistudio=model_args.from_aistudio,
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)
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else:
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# NOTE(gongenlei): new add autotuner_benchmark
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model = model_class.from_config(model_config, dtype=dtype)
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if model_args.flash_mask and (not data_args.zero_padding or not model.config.use_flash_attention):
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logger.warning("`flash_mask` must use with zero padding and flash attention.")
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data_args.zero_padding = True
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model.config.use_flash_attention = True
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if model_args.flash_mask and not any(isinstance(model, cls) for cls in flash_mask_support_list):
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raise NotImplementedError(f"{model.__class__} not support flash mask.")
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# Load tokenizer & dataset
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tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, from_aistudio=model_args.from_aistudio)
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# init chat_template for tokenizer
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init_chat_template(tokenizer, model_args.model_name_or_path, data_args.chat_template)
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# if using chat_template, data_args.eval_with_do_generation must be false
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if tokenizer.chat_template is not None:
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data_args.eval_with_do_generation = False
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if isinstance(tokenizer, LlamaTokenizer) or isinstance(tokenizer, Llama3Tokenizer):
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tokenizer.pad_token_id = tokenizer.eos_token_id
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if data_args.dataset_name_or_path is None:
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raise ValueError(f"Please specific dataset name or path (got {data_args.dataset_name_or_path})")
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elif (
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os.path.exists(os.path.join(data_args.dataset_name_or_path, "train.json"))
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or os.path.exists(os.path.join(data_args.dataset_name_or_path, "dev.json"))
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or os.path.exists(os.path.join(data_args.dataset_name_or_path, "quant.json"))
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):
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if quant_args.do_qat:
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train_ds = load_dataset(
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"json",
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data_files=os.path.join(data_args.dataset_name_or_path, "train.json"),
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lazy=data_args.lazy,
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)[0]
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else:
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train_ds = None
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if training_args.do_eval:
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dev_ds = load_dataset(
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"json",
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data_files=os.path.join(data_args.dataset_name_or_path, "dev.json"),
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lazy=data_args.lazy,
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)[0]
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else:
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dev_ds = None
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if os.path.exists(os.path.join(data_args.dataset_name_or_path, "quant.json")):
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ptq_ds = load_dataset(
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"json",
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data_files=os.path.join(data_args.dataset_name_or_path, "quant.json"),
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lazy=data_args.lazy,
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)[0]
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elif os.path.exists(os.path.join(data_args.dataset_name_or_path, "train.json")):
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ptq_ds = load_dataset(
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"json",
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data_files=os.path.join(data_args.dataset_name_or_path, "train.json"),
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lazy=data_args.lazy,
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)[0]
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logger.info(
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f"Not found quant.json in {data_args.dataset_name_or_path}. Set train dataset as PTQ calibration dataset."
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)
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else:
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raise ValueError(f"Quant strategy requires quant.json or train.json in {data_args.dataset_name_or_path}")
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elif (
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os.path.exists(os.path.join(data_args.dataset_name_or_path, "train"))
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or os.path.exists(os.path.join(data_args.dataset_name_or_path, "dev"))
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or os.path.exists(os.path.join(data_args.dataset_name_or_path, "quant"))
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):
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import glob
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if quant_args.do_qat:
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train_ds = load_dataset(
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"json",
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data_files=glob.glob(os.path.join(data_args.dataset_name_or_path, "train", "*.json")),
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lazy=data_args.lazy,
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)[0]
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else:
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train_ds = None
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if training_args.do_eval:
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dev_ds = load_dataset(
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"json",
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data_files=glob.glob(os.path.join(data_args.dataset_name_or_path, "dev", "*.json")),
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lazy=data_args.lazy,
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)[0]
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else:
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dev_ds = None
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if os.path.exists(os.path.join(data_args.dataset_name_or_path, "quant")):
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ptq_ds = load_dataset(
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"json",
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data_files=glob.glob(os.path.join(data_args.dataset_name_or_path, "quant", "*.json")),
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lazy=data_args.lazy,
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)[0]
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elif os.path.exists(os.path.join(data_args.dataset_name_or_path, "train")):
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ptq_ds = load_dataset(
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"json",
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data_files=glob.glob(os.path.join(data_args.dataset_name_or_path, "train", "*.json")),
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lazy=data_args.lazy,
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)[0]
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logger.info(
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f"Not found quant.json in {data_args.dataset_name_or_path}. Set train dataset as PTQ calibration dataset."
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)
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else:
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raise ValueError(f"Quant strategy requires quant or train folder in {data_args.dataset_name_or_path}")
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else:
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if quant_args.do_qat:
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train_ds = load_dataset(data_args.dataset_name_or_path, splits=["train"])[0]
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else:
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train_ds = None
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if training_args.do_eval:
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dev_ds = load_dataset(data_args.dataset_name_or_path, splits=["dev"])[0]
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else:
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dev_ds = None
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if quant_args.do_ptq or quant_args.do_gptq or quant_args.load_quant_model:
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ptq_ds = load_dataset(data_args.dataset_name_or_path, splits=["train"])[0]
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logger.info("Set train dataset as PTQ calibration dataset.")
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else:
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ptq_ds = None
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# TODO(ZHUI & sijunhe): Temporary implementation. Generalize this logic and move to Trainer later.
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if training_args.resume_from_checkpoint is not None and data_args.lazy:
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logger.info(
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f"Loading from '{training_args.resume_from_checkpoint}' with `lazy=True`, manually skipping dataset and setting `ignore_data_skip` to True."
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)
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training_args.ignore_data_skip = True
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state = TrainerState.load_from_json(os.path.join(training_args.resume_from_checkpoint, "trainer_state.json"))
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if state.trial_params is not None and "zero_padding_global_step" in state.trial_params:
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consumed_samples = state.trial_params["zero_padding_global_step"]
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else:
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consumed_samples = (
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state.global_step
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* training_args.per_device_train_batch_size
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* training_args.gradient_accumulation_steps
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* training_args.dataset_world_size
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)
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logger.info(
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f"Skipping the first {consumed_samples} samples to warmup the dataset from checkpoint '{training_args.resume_from_checkpoint}'."
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)
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train_ds = train_ds.skip(consumed_samples)
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if training_args.pipeline_parallel_degree > 1:
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from utils.data import convert_example_common
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trans_func = partial(convert_example_common, tokenizer=tokenizer, data_args=data_args)
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else:
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trans_func = partial(get_convert_example(model), tokenizer=tokenizer, data_args=data_args)
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train_ds = (
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train_ds.map(
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partial(trans_func, is_test=False, zero_padding=data_args.zero_padding, flash_mask=model_args.flash_mask)
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)
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if train_ds is not None
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else None
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)
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ptq_ds = (
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ptq_ds.map(
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partial(trans_func, is_test=False, zero_padding=data_args.zero_padding, flash_mask=model_args.flash_mask)
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)
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if ptq_ds is not None
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else None
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)
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eval_zero_padding = data_args.zero_padding
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if data_args.zero_padding and data_args.eval_with_do_generation:
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logger.warning(
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"`zero_padding` conflicts with `eval_with_do_generation`. Setting zero_padding to False for the eval_dataset."
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)
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eval_zero_padding = False
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dev_ds = (
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dev_ds.map(
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partial(
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trans_func,
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is_test=data_args.eval_with_do_generation,
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zero_padding=eval_zero_padding,
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flash_mask=model_args.flash_mask,
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)
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)
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if dev_ds is not None
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else None
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)
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if data_args.zero_padding:
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if data_args.lazy:
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intoken_dataset = ZeroPaddingIterableDataset
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else:
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intoken_dataset = ZeroPaddingMapDataset
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logger.info("Creating Zero Padding Data Stream. This may take a few minutes.")
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train_ds = (
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intoken_dataset(
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train_ds,
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tokenizer=tokenizer,
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max_length=data_args.max_length,
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greedy_zero_padding=data_args.greedy_zero_padding,
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)
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if train_ds is not None
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else None
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)
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ptq_ds = (
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intoken_dataset(
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ptq_ds,
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tokenizer=tokenizer,
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max_length=data_args.max_length,
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greedy_zero_padding=data_args.greedy_zero_padding,
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)
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if ptq_ds is not None
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else None
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)
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if eval_zero_padding:
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dev_ds = (
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intoken_dataset(
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dev_ds,
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tokenizer=tokenizer,
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max_length=data_args.max_length,
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)
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if dev_ds is not None
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else None
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)
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def compute_metrics_do_generation(eval_preds):
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rouge1 = Rouge1()
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rouge2 = Rouge2()
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rougel = RougeL()
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bleu4 = BLEU(n_size=4)
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predictions = [x[x != -100].tolist() for x in eval_preds.predictions]
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references = [x[x != -100].tolist() for x in eval_preds.label_ids]
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predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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references = tokenizer.batch_decode(references, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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if data_args.save_generation_output:
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with open(os.path.join(training_args.output_dir, "generated_output.json"), "w", encoding="utf-8") as f:
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for pred, ref in zip(predictions, references):
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out = {"output": pred, "tgt": ref}
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f.write(json.dumps(out, ensure_ascii=False) + "\n")
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# for pred in predictions:
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rouge1_score = rouge1.score(predictions, references)
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rouge2_score = rouge2.score(predictions, references)
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for pred, ref in zip(predictions, references):
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rougel.add_inst(pred, [ref])
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bleu4.add_inst(pred, [ref])
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return {
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"rouge1": rouge1_score,
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"rouge2": rouge2_score,
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"rougel": rougel.score(),
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"bleu4": bleu4.score(),
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}
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# Create trainer
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if (
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training_args.pipeline_parallel_degree > 1
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or training_args.sequence_parallel
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or training_args.autotuner_benchmark
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or data_args.zero_padding
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or data_args.pad_to_max_length
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):
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# NOTE(gongenlei): new add autotuner_benchmark
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max_length = data_args.max_length
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padding = "max_length"
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else:
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max_length = None
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padding = True
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if training_args.pipeline_parallel_degree > 1:
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metrics = None
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elif data_args.eval_with_do_generation:
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metrics = compute_metrics_do_generation
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else:
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metrics = compute_metrics
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=train_ds,
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eval_dataset=dev_ds,
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tokenizer=tokenizer,
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compute_metrics=metrics,
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data_collator=DataCollatorForSeq2Seq(
|
|
tokenizer=tokenizer,
|
|
max_length=max_length,
|
|
padding=padding,
|
|
max_label_length=max_length,
|
|
return_tensors="np",
|
|
return_attention_mask=not model_args.flash_mask,
|
|
pad_to_multiple_of=data_args.pad_to_multiple_of,
|
|
),
|
|
do_generation=data_args.eval_with_do_generation,
|
|
callbacks=[ZeroPaddingIterDatasetCallback()] if isinstance(train_ds, ZeroPaddingIterableDataset) else None,
|
|
gen_args=gen_args,
|
|
data_args=data_args,
|
|
)
|
|
trainable_parameters = [p for p in model.parameters() if not p.stop_gradient]
|
|
trainer.set_optimizer_grouped_parameters(trainable_parameters)
|
|
|
|
# QAT
|
|
if quant_args.do_qat:
|
|
from utils.quant import create_qat_model
|
|
|
|
trainer.model = create_qat_model(quant_args, trainer.model, dtype)
|
|
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
|
|
trainer.save_model(merge_tensor_parallel=training_args.tensor_parallel_degree > 1)
|
|
trainer.log_metrics("qat", train_result.metrics)
|
|
trainer.save_metrics("qat", train_result.metrics)
|
|
trainer.save_state()
|
|
|
|
# PTQ
|
|
if quant_args.do_ptq:
|
|
if isinstance(model, LoRAModel):
|
|
raise NotImplementedError(
|
|
"PTQ strategy not supported for LoRA model. Please merge lora parameters to pretrain model first."
|
|
)
|
|
from utils.quant import (
|
|
apply_autoclip,
|
|
apply_ptq,
|
|
apply_shift,
|
|
apply_smooth,
|
|
get_ptq_model_config,
|
|
)
|
|
|
|
trainer.model.eval()
|
|
trainer.model.config.quantization_config.quant_type = quant_args.quant_type
|
|
trainer.model.config.quantization_config.smooth = quant_args.smooth
|
|
trainer.model.config.quantization_config.shift = quant_args.shift
|
|
trainer.model.config.quantization_config.shift_smooth_all_linears = (
|
|
quant_args.smooth_all_linears or quant_args.shift_all_linears
|
|
)
|
|
ptq_dataloader = trainer.get_ptq_dataloader(ptq_ds)
|
|
if quant_args.shift or quant_args.smooth:
|
|
ptq_model_config = get_ptq_model_config(trainer.model)
|
|
|
|
if quant_args.shift:
|
|
apply_shift(quant_args, trainer, ptq_dataloader, ptq_model_config)
|
|
|
|
if quant_args.smooth:
|
|
apply_smooth(quant_args, trainer, ptq_dataloader, ptq_model_config)
|
|
|
|
if quant_args.auto_clip:
|
|
apply_autoclip(quant_args, trainer, ptq_dataloader)
|
|
|
|
apply_ptq(quant_args, trainer, ptq_dataloader)
|
|
trainer.save_model(merge_tensor_parallel=training_args.tensor_parallel_degree > 1)
|
|
|
|
if quant_args.do_gptq:
|
|
if isinstance(model, LoRAModel):
|
|
raise NotImplementedError(
|
|
"PTQ strategy not supported for LoRA model. Please merge lora parameters to pretrain model first."
|
|
)
|
|
from utils.quant import apply_gptq
|
|
|
|
ptq_dataloader = trainer.get_ptq_dataloader(ptq_ds)
|
|
apply_gptq(quant_args, trainer, ptq_dataloader)
|
|
trainer.save_model(merge_tensor_parallel=training_args.tensor_parallel_degree > 1)
|
|
|
|
# Evaluation test set
|
|
if training_args.do_predict:
|
|
test_ds = load_dataset(
|
|
"json",
|
|
data_files=os.path.join(data_args.dataset_name_or_path, "test.json"),
|
|
lazy=data_args.lazy,
|
|
)[0]
|
|
|
|
test_ds = test_ds.map(partial(trans_func, is_test=data_args.eval_with_do_generation))
|
|
if eval_zero_padding:
|
|
test_ds = intoken_dataset(
|
|
test_ds,
|
|
tokenizer=tokenizer,
|
|
max_length=data_args.max_length,
|
|
)
|
|
eval_result = trainer.predict(test_ds).metrics
|
|
trainer.log_metrics("test", eval_result)
|
|
|
|
if quant_args.load_quant_model and not quant_args.do_ptq:
|
|
if isinstance(model, LoRAModel):
|
|
raise NotImplementedError(
|
|
"PTQ strategy not supported for LoRA model. Please merge lora parameters to pretrain model first."
|
|
)
|
|
from utils.quant import (
|
|
apply_autoclip,
|
|
apply_ptq,
|
|
apply_shift,
|
|
apply_smooth,
|
|
get_ptq_model_config,
|
|
load_quant_model,
|
|
)
|
|
|
|
trainer.model.eval()
|
|
trainer.model.config.quantization_config.quant_type = quant_args.quant_type
|
|
trainer.model.config.quantization_config.smooth = quant_args.smooth
|
|
trainer.model.config.quantization_config.shift = quant_args.shift
|
|
trainer.model.config.quantization_config.shift_smooth_all_linears = (
|
|
quant_args.smooth_all_linears or quant_args.shift_all_linears
|
|
)
|
|
ptq_dataloader = trainer.get_ptq_dataloader(ptq_ds)
|
|
if quant_args.shift or quant_args.smooth:
|
|
ptq_model_config = get_ptq_model_config(trainer.model)
|
|
|
|
if quant_args.shift:
|
|
apply_shift(quant_args, trainer, ptq_dataloader, ptq_model_config)
|
|
|
|
if quant_args.smooth:
|
|
apply_smooth(quant_args, trainer, ptq_dataloader, ptq_model_config)
|
|
|
|
load_quant_model(trainer.model, quant_args, training_args.output_dir)
|
|
|
|
# Evaluation dev set
|
|
if training_args.do_eval:
|
|
|
|
logger.info("*** Evaluate result after ptq/qat/ etc.***")
|
|
eval_result = trainer.evaluate(dev_ds)
|
|
trainer.log_metrics("eval", eval_result)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|