mirror of
https://github.com/hpcaitech/ColossalAI.git
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4c8e85ee0d
* update dpo * remove unsupport plugin * update msg * update dpo * remove unsupport plugin * update msg * update template * update dataset * add pp for dpo * update dpo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add dpo fn * update dpo * update dpo * update dpo * update dpo * minor update * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update loss * update help * polish code --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
384 lines
16 KiB
Python
Executable File
384 lines
16 KiB
Python
Executable File
import argparse
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import json
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import os
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import resource
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from contextlib import nullcontext
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import torch
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from coati.dataset import DataCollatorForPreferenceDataset, StatefulDistributedSampler, load_tokenized_dataset
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from coati.models import LoraConfig, convert_to_lora_module, disable_dropout
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from coati.trainer import DPOTrainer
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from coati.utils import load_checkpoint
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
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from colossalai.cluster import DistCoordinator
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from colossalai.logging import get_dist_logger
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from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
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from colossalai.nn.optimizer import HybridAdam
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logger = get_dist_logger()
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def train(args):
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lora_config = None
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if args.lora_config is not None:
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lora_config = LoraConfig.from_file(args.lora_config)
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# check lora compatibility
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if "gemini" in args.plugin and lora_config is not None and lora_config.r > 0:
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raise ValueError("LoRA is not supported in GeminiPlugin. Please use other plugin")
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# ==============================
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# Initialize Distributed Training
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# ==============================
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colossalai.launch_from_torch()
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coordinator = DistCoordinator()
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# ==============================
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# Initialize Booster
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# ==============================
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if args.plugin == "ddp":
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"""
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Default torch ddp plugin without any acceleration, for
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debugging purpose acceleration, for debugging purpose
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"""
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plugin = TorchDDPPlugin(find_unused_parameters=not args.grad_checkpoint)
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elif args.plugin == "gemini":
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plugin = GeminiPlugin(
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precision=args.mixed_precision,
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placement_policy="static",
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initial_scale=2**16,
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max_norm=args.grad_clip,
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enable_gradient_accumulation=True,
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enable_flash_attention=args.use_flash_attn,
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)
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elif args.plugin == "zero2":
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plugin = LowLevelZeroPlugin(
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stage=2,
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precision=args.mixed_precision,
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initial_scale=2**16,
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max_norm=args.grad_clip,
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)
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elif args.plugin == "zero2_cpu":
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plugin = LowLevelZeroPlugin(
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stage=2,
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precision=args.mixed_precision,
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initial_scale=2**16,
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cpu_offload=True,
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max_norm=args.grad_clip,
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)
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elif args.plugin == "3d":
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plugin = HybridParallelPlugin(
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tp_size=args.tp,
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pp_size=args.pp,
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sp_size=args.sp,
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sequence_parallelism_mode=args.sp_mode,
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zero_stage=args.zero_stage,
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enable_flash_attention=args.use_flash_attn,
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enable_sequence_parallelism=args.enable_sequence_parallelism,
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cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
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parallel_output=False,
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max_norm=args.grad_clip,
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precision=args.mixed_precision,
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microbatch_size=args.microbatch_size,
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)
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else:
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raise ValueError(f"Unknown plugin {args.plugin}")
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booster = Booster(plugin=plugin)
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ref_plugin = HybridParallelPlugin(
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tp_size=args.ref_tp,
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pp_size=1,
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zero_stage=args.zero_stage,
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enable_flash_attention=args.use_flash_attn,
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cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
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parallel_output=False,
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max_norm=args.grad_clip,
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precision=args.mixed_precision,
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)
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ref_booster = Booster(plugin=ref_plugin)
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init_ctx = nullcontext()
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with init_ctx:
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if args.use_flash_attn:
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model = AutoModelForCausalLM.from_pretrained(
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args.pretrain,
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torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
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use_flash_attention_2=True,
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)
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coordinator.print_on_master(msg="Flash-attention enabled successfully")
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else:
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model = AutoModelForCausalLM.from_pretrained(args.pretrain)
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if not args.disable_reference_model:
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if args.use_flash_attn:
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ref_model = AutoModelForCausalLM.from_pretrained(
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args.pretrain,
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torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
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use_flash_attention_2=True,
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)
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else:
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ref_model = AutoModelForCausalLM.from_pretrained(args.pretrain)
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else:
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ref_model = None
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if args.lora_config is not None:
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model = convert_to_lora_module(model, lora_config=lora_config)
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for name, module in model.named_modules():
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if "norm" in name or "gate" in name:
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module = module.to(torch.float32)
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disable_dropout(model)
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disable_dropout(ref_model)
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if args.grad_checkpoint:
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# Make sure gradient checkpointing can be activated.
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model.train()
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# Note, for some models, lora may not be compatible with gradient checkpointing.
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model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
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coordinator.print_on_master(msg="Gradient checkpointing enabled successfully")
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# configure tokenizer
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tokenizer_dir = args.tokenizer_dir if args.tokenizer_dir is not None else args.pretrain
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=False, trust_remote_code=True)
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if hasattr(tokenizer, "pad_token") and hasattr(tokenizer, "eos_token") and tokenizer.eos_token is not None:
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try:
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# Some tokenizers doesn't allow to set pad_token mannually e.g., Qwen
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tokenizer.pad_token = tokenizer.eos_token
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except AttributeError as e:
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logger.warning(f"Unable to set pad token to eos token, {str(e)}")
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if not hasattr(tokenizer, "pad_token") or tokenizer.pad_token is None:
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logger.warning(
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"The tokenizer does not have a pad token which is required. May lead to unintended behavior in training, Please consider manually set them."
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)
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tokenizer.add_bos_token = False
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tokenizer.add_eos_token = False
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# configure optimizer
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optim = HybridAdam(
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model_params=model.parameters(),
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lr=args.lr,
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betas=(0.9, 0.95),
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weight_decay=args.weight_decay,
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adamw_mode=True,
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)
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# Configure dataset
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coordinator.print_on_master(f"Load dataset: {args.dataset}")
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mode_map = {"train": "train", "valid": "validation", "test": "test"}
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train_dataset = load_tokenized_dataset(dataset_paths=args.dataset, mode="train", mode_map=mode_map)
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data_collator = DataCollatorForPreferenceDataset(tokenizer=tokenizer, max_length=args.max_length)
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train_dataloader = plugin.prepare_dataloader(
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dataset=train_dataset,
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batch_size=args.batch_size,
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shuffle=True,
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drop_last=True,
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collate_fn=data_collator,
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distributed_sampler_cls=StatefulDistributedSampler,
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)
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eval_dataloader = None
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if args.eval_dataset:
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eval_dataset = load_tokenized_dataset(dataset_paths=args.eval_dataset, mode="dev")
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eval_data_collator = DataCollatorForPreferenceDataset(tokenizer=tokenizer, max_length=args.max_length)
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eval_dataloader = plugin.prepare_dataloader(
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dataset=eval_dataset,
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batch_size=args.batch_size,
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shuffle=True,
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drop_last=True,
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collate_fn=eval_data_collator,
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distributed_sampler_cls=StatefulDistributedSampler,
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)
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else:
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logger.warning("No evaluation dataset is provided, skip evaluation")
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num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps
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if args.warmup_steps is None:
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args.warmup_steps = int(args.max_epochs * 0.025 * (len(train_dataloader) // args.accumulation_steps))
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coordinator.print_on_master(f"Warmup steps is set to {args.warmup_steps}")
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lr_scheduler = CosineAnnealingWarmupLR(
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optimizer=optim,
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total_steps=args.max_epochs * num_update_steps_per_epoch,
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warmup_steps=args.warmup_steps,
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eta_min=0.1 * args.lr,
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)
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default_dtype = torch.float16 if args.mixed_precision == "fp16" else torch.bfloat16
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torch.set_default_dtype(default_dtype)
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model, optim, _, train_dataloader, lr_scheduler = booster.boost(
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model=model,
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optimizer=optim,
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lr_scheduler=lr_scheduler,
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dataloader=train_dataloader,
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)
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ref_model, _, _, _, _ = ref_booster.boost(model=ref_model)
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torch.set_default_dtype(torch.float)
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coordinator.print_on_master(f"Booster init max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
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coordinator.print_on_master(
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f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
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)
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start_epoch = 0
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sampler_start_idx = 0
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start_step = 0
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if args.checkpoint_path is not None:
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if "modeling" in args.checkpoint_path:
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coordinator.print_on_master(f"Continued pretrain from checkpoint {args.checkpoint_path}")
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booster.load_model(model, args.checkpoint_path)
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else:
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coordinator.print_on_master(f"Load model checkpoint from {args.checkpoint_path}")
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start_epoch, start_step, sampler_start_idx = load_checkpoint(
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load_dir=args.checkpoint_path,
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booster=booster,
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model=model,
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optimizer=optim,
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lr_scheduler=lr_scheduler,
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)
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assert isinstance(train_dataloader.sampler, StatefulDistributedSampler)
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train_dataloader.sampler.set_start_index(start_index=sampler_start_idx)
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coordinator.print_on_master(
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f"Loaded checkpoint {args.checkpoint_path} at epoch {start_epoch} step {start_step}"
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)
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coordinator.print_on_master(f"Loaded sample at index {sampler_start_idx}")
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coordinator.print_on_master(
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f"Checkpoint loaded max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB"
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)
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coordinator.print_on_master(
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f"Checkpoint loaded CUDA memory: {torch.cuda.memory_allocated() / 1024 ** 2:.2f} MB"
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)
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coordinator.print_on_master(
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f"Checkpoint loaded max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
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)
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trainer = DPOTrainer(
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actor=model,
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ref_model=ref_model,
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booster=booster,
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actor_optim=optim,
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plugin=plugin,
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actor_lr_scheduler=lr_scheduler,
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tokenizer=tokenizer,
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max_epochs=args.max_epochs,
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accumulation_steps=args.accumulation_steps,
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start_epoch=start_epoch,
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save_interval=args.save_interval,
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save_dir=args.save_dir,
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coordinator=coordinator,
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beta=args.beta,
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gamma=args.gamma,
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length_normalization=args.length_normalization,
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apply_loss_mask=not args.disable_loss_mask,
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)
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trainer.fit(
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train_preference_dataloader=train_dataloader,
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eval_preference_dataloader=eval_dataloader,
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log_dir=args.log_dir,
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use_wandb=args.use_wandb,
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)
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if lora_config is not None and lora_config.r > 0:
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# NOTE: set model to eval to merge LoRA weights
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model.eval()
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# save model checkpoint after fitting on only rank0
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if args.save_dir is not None:
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coordinator.print_on_master("Start saving final model checkpoint")
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booster.save_model(model, os.path.join(args.save_dir, "modeling"), shard=True)
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coordinator.print_on_master(
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f"Saved final model checkpoint at epoch {args.max_epochs} at folder {args.save_dir}"
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)
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coordinator.print_on_master(f"Max CUDA memory usage: {torch.cuda.max_memory_allocated()/1024**2:.2f} MB")
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if __name__ == "__main__":
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# ==============================
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# Parse Arguments
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# ==============================
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--plugin",
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type=str,
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default="gemini",
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choices=["gemini", "zero2", "zero2_cpu", "3d", "ddp"],
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help="Choose which plugin to use",
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)
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parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value")
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parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay")
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parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
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parser.add_argument("--tp", type=int, default=1)
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parser.add_argument("--pp", type=int, default=1)
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parser.add_argument("--sp", type=int, default=1)
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parser.add_argument("--loss_type", type=str, default="dpo_loss", help="dpo_loss or simpo_loss")
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parser.add_argument("--beta", type=float, default=0.1, help="beta in DPO loss")
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parser.add_argument("--gamma", type=float, default=0.0, help="gamma in SimPO loss")
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parser.add_argument("--length_normalization", default=False, action="store_true")
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parser.add_argument("--enable_sequence_parallelism", default=False, action="store_true")
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parser.add_argument("--zero_stage", type=int, default=0, help="Zero stage", choices=[0, 1, 2])
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parser.add_argument("--zero_cpu_offload", default=False, action="store_true")
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parser.add_argument("--sp_mode", type=str, default="split_gather", choices=["split_gather", "ring", "all_to_all"])
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parser.add_argument("--pretrain", type=str, default=None)
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parser.add_argument("--model_type", type=str, default=None)
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parser.add_argument("--tokenizer_dir", type=str, default=None)
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parser.add_argument("--dataset", nargs="+", default=[])
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parser.add_argument("--eval_dataset", nargs="+", default=[])
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parser.add_argument(
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"--checkpoint_path", type=str, default=None, help="Checkpoint path if need to resume training form a checkpoint"
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)
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parser.add_argument("--config_file", type=str, default=None, help="Config file")
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parser.add_argument("--save_dir", type=str, default=None)
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parser.add_argument("--max_length", type=int, default=2048, help="Model max length")
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parser.add_argument("--max_epochs", type=int, default=3)
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parser.add_argument("--batch_size", type=int, default=4)
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parser.add_argument("--disable_loss_mask", default=False, action="store_true")
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parser.add_argument("--mixed_precision", type=str, default="fp16", choices=["fp16", "bf16"], help="Mixed precision")
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parser.add_argument("--lora_config", type=str, default=None, help="low-rank adaptation config file path")
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parser.add_argument("--save_interval", type=int, default=1000, help="number of step between two checkpoints")
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parser.add_argument("--lr", type=float, default=5e-6)
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parser.add_argument("--accumulation_steps", type=int, default=1)
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parser.add_argument("--log_dir", default=None, type=str)
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parser.add_argument("--use_wandb", default=False, action="store_true")
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parser.add_argument("--grad_checkpoint", default=False, action="store_true")
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parser.add_argument("--use_flash_attn", default=False, action="store_true")
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parser.add_argument(
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"--microbatch_size",
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type=int,
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default=2,
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help="Micro batch size for PP training. To activate PP training for DPO-like algorithm, you must keep size even and the size should be equal or greater than 2.",
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)
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# Parameter for reference model
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parser.add_argument(
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"--disable_reference_model",
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action="store_true",
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default=False,
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help="Disable the reference model (enabled by default)",
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)
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parser.add_argument(
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"--ref_tp",
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type=int,
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default=1,
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help="TP size for reference model; used only when reference model is too large.",
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)
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args = parser.parse_args()
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# fool proof hyperparameter setup
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if args.loss_type == "simpo_loss":
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args.length_normalization = True
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args.gamma = args.gamma if args.gamma > 0 else 1.4
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if args.config_file is not None:
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os.makedirs(os.path.dirname(args.config_file), exist_ok=True)
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with open(args.config_file, "w") as f:
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json.dump(args.__dict__, f, indent=4)
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train(args)
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