mirror of https://github.com/hpcaitech/ColossalAI
314 lines
12 KiB
Python
314 lines
12 KiB
Python
import argparse
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import math
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import os
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import resource
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from contextlib import nullcontext
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from functools import partial
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from typing import Optional, Tuple
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from attn import SUPPORT_XFORMERS, replace_xformers
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from data_utils import load_json, prepare_dataloader, save_json
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from datasets import load_dataset
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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from transformers.models.llama.configuration_llama import LlamaConfig
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from transformers.models.llama.modeling_llama import LlamaForCausalLM
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from transformers.models.llama.tokenization_llama import LlamaTokenizer
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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
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from colossalai.cluster import DistCoordinator
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from colossalai.lazy import LazyInitContext
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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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from colossalai.utils import get_current_device
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def get_model_numel(model: nn.Module) -> int:
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return sum(p.numel() for p in model.parameters())
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def format_numel_str(numel: int) -> str:
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B = 1024**3
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M = 1024**2
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K = 1024
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if numel >= B:
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return f"{numel / B:.2f} B"
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elif numel >= M:
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return f"{numel / M:.2f} M"
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elif numel >= K:
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return f"{numel / K:.2f} K"
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else:
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return f"{numel}"
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def tokenize_batch_for_finetune(batch, tokenizer: Optional[LlamaTokenizer] = None, max_length: int = 2048):
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texts = [sample["prompt"] + sample["completion"] for sample in batch]
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data = tokenizer(texts, return_tensors="pt", padding="max_length", truncation=True, max_length=max_length)
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data = {k: v.cuda() for k, v in data.items()}
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data["labels"] = data["input_ids"].clone()
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return data
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def all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
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dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
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tensor.div_(dist.get_world_size())
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return tensor
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def save(
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booster: Booster,
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model: nn.Module,
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optimizer: Optimizer,
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lr_scheduler: _LRScheduler,
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epoch: int,
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step: int,
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batch_size: int,
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coordinator: DistCoordinator,
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save_dir: str,
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):
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save_dir = os.path.join(save_dir, f"epoch{epoch}-step{step}")
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os.makedirs(os.path.join(save_dir, "model"), exist_ok=True)
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booster.save_model(model, os.path.join(save_dir, "model"), shard=True)
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booster.save_optimizer(optimizer, os.path.join(save_dir, "optimizer"), shard=True)
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booster.save_lr_scheduler(lr_scheduler, os.path.join(save_dir, "lr_scheduler"))
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running_states = {
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"epoch": epoch,
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"step": step,
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"sample_start_index": step * batch_size,
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}
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if coordinator.is_master():
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save_json(running_states, os.path.join(save_dir, "running_states.json"))
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def load(
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booster: Booster, model: nn.Module, optimizer: Optimizer, lr_scheduler: _LRScheduler, load_dir: str
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) -> Tuple[int, int, int]:
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booster.load_model(model, os.path.join(load_dir, "model"))
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booster.load_optimizer(optimizer, os.path.join(load_dir, "optimizer"))
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booster.load_lr_scheduler(lr_scheduler, os.path.join(load_dir, "lr_scheduler"))
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running_states = load_json(os.path.join(load_dir, "running_states.json"))
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return running_states["epoch"], running_states["step"], running_states["sample_start_index"]
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def _criterion(outputs, inputs):
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return outputs.loss
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def 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("--model_path", type=str, help="pretrained checkpoint path, used with mode==finetune")
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parser.add_argument(
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"-p",
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"--plugin",
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choices=["gemini", "gemini_auto", "zero2", "zero2_cpu", "hybrid_parallel"],
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default="gemini",
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help="Choose which plugin to use",
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)
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parser.add_argument("-d", "--dataset", type=str, default="yizhongw/self_instruct", help="Data set path")
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parser.add_argument("--task_name", type=str, default="super_natural_instructions", help="task to run")
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parser.add_argument("-e", "--num_epochs", type=int, default=1, help="Number of epochs")
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parser.add_argument("-b", "--batch_size", type=int, default=2, help="Local batch size")
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parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
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parser.add_argument("-w", "--weigth_decay", type=float, default=0.1, help="Weight decay")
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parser.add_argument("-g", "--grad_checkpoint", action="store_true", help="Use gradient checkpointing")
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parser.add_argument("-l", "--max_length", type=int, default=4096, help="Max sequence length")
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parser.add_argument("-x", "--mixed_precision", default="fp16", choices=["fp16", "bf16"], help="Mixed precision")
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parser.add_argument("-i", "--save_interval", type=int, default=1000, help="Save interval")
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parser.add_argument("-o", "--save_dir", type=str, default="checkpoint", help="Checkpoint directory")
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parser.add_argument("-f", "--load", type=str, default=None, help="Load checkpoint")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping")
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parser.add_argument("-t", "--tensorboard_dir", type=str, default="tb_logs", help="Tensorboard directory")
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parser.add_argument("-a", "--flash_attention", action="store_true", help="Use Flash Attention")
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args = parser.parse_args()
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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 == "gemini":
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plugin = GeminiPlugin(precision=args.mixed_precision, initial_scale=2**16, max_norm=args.grad_clip)
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elif args.plugin == "gemini_auto":
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plugin = GeminiPlugin(
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precision=args.mixed_precision, placement_policy="auto", initial_scale=2**16, max_norm=args.grad_clip
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)
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elif args.plugin == "zero2":
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plugin = LowLevelZeroPlugin(
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stage=2, precision=args.mixed_precision, initial_scale=2**16, 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, precision=args.mixed_precision, initial_scale=2**16, cpu_offload=True, max_norm=args.grad_clip
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)
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elif args.plugin == "hybrid_parallel":
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# modify the param accordingly, default configuration is for llama2-7b
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plugin = HybridParallelPlugin(
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tp_size=4,
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pp_size=2,
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num_microbatches=None,
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microbatch_size=1,
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enable_jit_fused=False,
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zero_stage=0,
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precision="fp32",
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initial_scale=1,
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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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use_pipeline = isinstance(booster.plugin, HybridParallelPlugin) and booster.plugin.pp_size > 1
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is_pp_last_stage = use_pipeline and booster.plugin.stage_manager.is_last_stage()
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print_flag = (not use_pipeline and coordinator.is_master()) or (use_pipeline and is_pp_last_stage)
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# ==============================
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# Initialize Tensorboard
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# ==============================
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if print_flag:
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os.makedirs(args.tensorboard_dir, exist_ok=True)
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writer = SummaryWriter(args.tensorboard_dir)
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# ==============================
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# Initialize Model, Optimizer and LR Scheduler
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# ==============================
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config = LlamaConfig.from_pretrained(args.model_path)
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# use lazy init when using GeminiPlugin
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init_ctx = (
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LazyInitContext(default_device=get_current_device()) if isinstance(plugin, GeminiPlugin) else nullcontext()
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)
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with init_ctx:
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model = LlamaForCausalLM(config)
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# ==============================
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# Initialize Tokenizer, Dataset and Dataloader
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# ==============================
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tokenizer = LlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
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# follows fast chat: https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py#L257
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tokenizer.pad_token = tokenizer.unk_token
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dataset = load_dataset(args.dataset, args.task_name)
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train_ds = dataset["train"]
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dataloader = prepare_dataloader(
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train_ds,
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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=partial(tokenize_batch_for_finetune, tokenizer=tokenizer, max_length=args.max_length),
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)
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if args.grad_checkpoint:
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model.gradient_checkpointing_enable()
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if args.flash_attention:
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assert SUPPORT_XFORMERS, "Use flash attention while xfomers is not installed"
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replace_xformers(model)
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model_numel = get_model_numel(model)
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coordinator.print_on_master(f"Model params: {format_numel_str(model_numel)}")
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optimizer = HybridAdam(model.parameters(), lr=args.lr, betas=(0.9, 0.95), weight_decay=args.weigth_decay)
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total_step = args.num_epochs * len(dataloader)
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lr_scheduler = CosineAnnealingWarmupLR(
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optimizer, total_steps=total_step, warmup_steps=math.ceil(total_step * 0.03), 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, optimizer, _, dataloader, lr_scheduler = booster.boost(
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model, optimizer, dataloader=dataloader, lr_scheduler=lr_scheduler
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)
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torch.set_default_dtype(torch.float)
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booster.load_model(model, args.model_path)
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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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# load checkpoint if specified
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start_epoch = 0
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start_step = 0
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sampler_start_idx = 0
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if args.load is not None:
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coordinator.print_on_master("Loading checkpoint")
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start_epoch, start_step, sampler_start_idx = load(booster, model, optimizer, lr_scheduler, args.load)
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coordinator.print_on_master(f"Loaded checkpoint {args.load} at epoch {start_epoch} step {start_step}")
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num_steps_per_epoch = len(dataloader)
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# if resume training, set the sampler start index to the correct value
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dataloader.sampler.set_start_index(sampler_start_idx)
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for epoch in range(start_epoch, args.num_epochs):
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dataloader.sampler.set_epoch(epoch)
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step_nums = num_steps_per_epoch - start_step
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dataloader_iter = iter(dataloader)
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with tqdm(
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range(step_nums),
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desc=f"Epoch {epoch}",
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disable=not print_flag,
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total=num_steps_per_epoch,
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initial=start_step,
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) as pbar:
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for step in pbar:
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if use_pipeline:
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outputs = booster.execute_pipeline(
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dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
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)
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loss = outputs["loss"]
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else:
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batch = next(dataloader_iter)
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outputs = model(**batch)
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loss = outputs[0]
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booster.backward(loss, optimizer)
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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if not use_pipeline:
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all_reduce_mean(loss)
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if print_flag:
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pbar.set_postfix({"loss": loss.item()})
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writer.add_scalar("loss", loss.item(), epoch * num_steps_per_epoch + step)
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if args.save_interval > 0 and (step + 1) % args.save_interval == 0:
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coordinator.print_on_master(f"Saving checkpoint")
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save(
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booster,
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model,
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optimizer,
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lr_scheduler,
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epoch,
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step + 1,
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args.batch_size,
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coordinator,
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args.save_dir,
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)
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coordinator.print_on_master(f"Saved checkpoint at epoch {epoch} step {step + 1}")
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# the continue epochs are not resumed, so we need to reset the sampler start index and start step
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dataloader.sampler.set_start_index(0)
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start_step = 0
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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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main()
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