#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Continual Pre-training/Supervised fine-tuning of Colossal-LLaMA-2 developed by Colossal-AI Team
"""
import argparse
import json
import os
import resource
from contextlib import nullcontext
import torch
from colossal_llama.dataset.dummy_dataset import RandomDataset
from colossal_llama.dataset.loader import (
DataCollatorForSupervisedDataset,
StatefulDistributedSampler,
load_tokenized_dataset,
)
from colossal_llama.utils.ckpt_io import load_checkpoint, save_checkpoint
from colossal_llama.utils.froze import freeze_non_embeds_parameters
from colossal_llama.utils.neftune_patch import activate_neftune, deactivate_neftune
from colossal_llama.utils.utils import all_reduce_mean, format_numel_str, get_model_numel
from peft import LoraConfig
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
from colossalai.cluster import DistCoordinator
from colossalai.lazy import LazyInitContext
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
def train(args) -> None:
# ==============================
# Initialize Distributed Training
# ==============================
colossalai.launch_from_torch()
accelerator = get_accelerator()
coordinator = DistCoordinator()
# ==============================
# Initialize Tensorboard and Save Config
# ==============================
if coordinator.is_master():
os.makedirs(args.tensorboard_dir, exist_ok=True)
writer = SummaryWriter(args.tensorboard_dir)
with open(args.config_file, "w") as f:
json.dump(args.__dict__, f, indent=4)
# ==============================
# Initialize Booster
# ==============================
if args.plugin == "ddp":
plugin = TorchDDPPlugin(find_unused_parameters=True if args.use_grad_checkpoint is False else False)
elif args.plugin == "gemini":
plugin = GeminiPlugin(
precision=args.mixed_precision,
initial_scale=2**16,
max_norm=args.grad_clip,
enable_gradient_accumulation=(args.accumulation_steps > 1),
enable_fused_normalization=get_accelerator().is_available(),
enable_flash_attention=args.use_flash_attn,
)
elif args.plugin == "gemini_auto":
plugin = GeminiPlugin(
precision=args.mixed_precision,
placement_policy="auto",
initial_scale=2**16,
max_norm=args.grad_clip,
enable_gradient_accumulation=(args.accumulation_steps > 1),
enable_fused_normalization=get_accelerator().is_available(),
enable_flash_attention=args.use_flash_attn,
)
elif args.plugin == "zero2":
plugin = LowLevelZeroPlugin(
stage=2,
precision=args.mixed_precision,
initial_scale=2**16,
max_norm=args.grad_clip,
)
elif args.plugin == "zero2_cpu":
plugin = LowLevelZeroPlugin(
stage=2,
precision=args.mixed_precision,
initial_scale=2**16,
cpu_offload=True,
max_norm=args.grad_clip,
)
elif args.plugin == "3d":
plugin = HybridParallelPlugin(
tp_size=args.tp,
pp_size=args.pp,
sp_size=args.sp,
sequence_parallelism_mode=args.sp_mode,
zero_stage=args.zero_stage,
enable_flash_attention=args.use_flash_attn,
enable_fused_normalization=get_accelerator().is_available(),
enable_sequence_parallelism=args.enable_sequence_parallelism,
cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
max_norm=args.grad_clip,
precision=args.mixed_precision,
microbatch_size=args.microbatch_size,
)
else:
raise ValueError(f"Unknown plugin {args.plugin}")
booster = Booster(plugin=plugin)
# ======================================================
# Initialize Tokenizer, Dataset, Collator and Dataloader
# ======================================================
tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
if args.pad_token == "eos":
try:
tokenizer.pad_token = tokenizer.eos_token
except AttributeError:
coordinator.print_on_master(f"pad_token can't be set")
elif args.pad_token == "unk":
try:
tokenizer.pad_token = tokenizer.unk_token
except AttributeError:
coordinator.print_on_master(f"pad_token can't be set")
tokenizer.add_bos_token = False
tokenizer.add_eos_token = False
coordinator.print_on_master(
f"Training Info:\nConfig file: {args.config_file} \nTensorboard logs: {args.tensorboard_dir} \nModel checkpoint: {args.save_dir}"
)
if args.benchmark:
coordinator.print_on_master(f"Run benchmark with {args.num_samples} random samples.")
dataset = RandomDataset(
num_samples=args.num_samples, max_length=args.max_length, vocab_size=tokenizer.vocab_size
)
dataloader = plugin.prepare_dataloader(
dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
seed=42,
distributed_sampler_cls=StatefulDistributedSampler,
)
else:
coordinator.print_on_master(f"Load dataset: {args.dataset}")
dataset = load_tokenized_dataset(dataset_paths=args.dataset, mode="train")
data_collator = DataCollatorForSupervisedDataset(
tokenizer=tokenizer, max_length=args.max_length, padding=args.padding_mode
)
dataloader = plugin.prepare_dataloader(
dataset=dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
collate_fn=data_collator,
distributed_sampler_cls=StatefulDistributedSampler,
)
coordinator.print_on_master(
f"Max device memory after data loader: {accelerator.max_memory_allocated() / 1024 ** 2:.2f} MB"
)
# ======================================================
# Initialize Model, Objective, Optimizer and LR Scheduler
# ======================================================
# When training the ChatGLM model, LoRA and gradient checkpointing are incompatible.
init_ctx = (
LazyInitContext(default_device=get_current_device())
if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin)) and args.lora_rank == 0
else nullcontext()
)
with init_ctx:
model = AutoModelForCausalLM.from_pretrained(
args.pretrained,
torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
trust_remote_code=True,
)
# Freeze part of parameters.
if args.freeze_non_embeds_params:
freeze_non_embeds_parameters(model=model)
if args.lora_rank > 0:
lora_config = LoraConfig(task_type="CAUSAL_LM", r=args.lora_rank, lora_alpha=32, lora_dropout=0.1)
model = booster.enable_lora(model, lora_config=lora_config)
# this is essential, otherwise the grad checkpoint will not work.
model.train()
if args.use_grad_checkpoint:
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
coordinator.print_on_master(msg="Gradient checkpointing enabled successfully")
model_numel = get_model_numel(model)
coordinator.print_on_master(f"Model params: {format_numel_str(model_numel)}")
optimizer = HybridAdam(
model_params=(
filter(lambda p: p.requires_grad, model.parameters())
if args.freeze_non_embeds_params
else model.parameters()
),
lr=args.lr,
betas=(0.9, 0.95),
weight_decay=args.weight_decay,
adamw_mode=True,
)
if args.warmup_steps is None:
args.warmup_steps = int(args.num_epochs * 0.025 * (len(dataloader) // args.accumulation_steps))
coordinator.print_on_master(f"Warmup steps is set to {args.warmup_steps}")
lr_scheduler = CosineAnnealingWarmupLR(
optimizer=optimizer,
total_steps=args.num_epochs * (len(dataloader) // args.accumulation_steps),
warmup_steps=args.warmup_steps,
eta_min=0.1 * args.lr,
)
# Flash attention will be disabled because it does NOT support fp32.
default_dtype = torch.float16 if args.mixed_precision == "fp16" else torch.bfloat16
torch.set_default_dtype(default_dtype)
model, optimizer, _, dataloader, lr_scheduler = booster.boost(
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
dataloader=dataloader,
)
torch.set_default_dtype(torch.float)
coordinator.print_on_master(
f"Booster init max device memory: {accelerator.max_memory_allocated() / 1024 ** 2:.2f} MB"
)
coordinator.print_on_master(
f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
)
start_epoch = 0
start_step = 0
sampler_start_idx = 0
if args.load_checkpoint is not None:
if "modeling" in args.load_checkpoint:
coordinator.print_on_master(f"Continued pretrain from checkpoint {args.load_checkpoint}")
booster.load_model(model, args.load_checkpoint)
else:
coordinator.print_on_master(f"Load model checkpoint from {args.load_checkpoint}")
start_epoch, start_step, sampler_start_idx = load_checkpoint(
load_dir=args.load_checkpoint,
booster=booster,
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
)
coordinator.print_on_master(
f"Loaded checkpoint {args.load_checkpoint} at epoch {start_epoch} step {start_step}"
)
coordinator.print_on_master(f"Loaded sample at index {sampler_start_idx}")
coordinator.print_on_master(
f"Checkpoint loaded max device memory: {accelerator.max_memory_allocated() / 1024 ** 2:.2f} MB"
)
coordinator.print_on_master(
f"Checkpoint loaded device memory: {accelerator.memory_allocated() / 1024 ** 2:.2f} MB"
)
coordinator.print_on_master(
f"Checkpoint loaded max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
)
if args.use_neft:
coordinator.print_on_master("Activate NEFTune.")
model, handle = activate_neftune(model)
num_steps_per_epoch = len(dataloader) // args.accumulation_steps
# If resume training, set the sampler start index to the correct value
assert isinstance(dataloader.sampler, StatefulDistributedSampler)
dataloader.sampler.set_start_index(start_index=sampler_start_idx)
for epoch in range(start_epoch, args.num_epochs):
dataloader.sampler.set_epoch(epoch=epoch)
if isinstance(plugin, HybridParallelPlugin) and plugin.pp_size > 1:
data_iter = iter(dataloader)
step_bar = tqdm(
range(len(dataloader)),
desc="Step",
disable=not (coordinator._local_rank == coordinator._world_size - 1),
)
for step in step_bar:
outputs = booster.execute_pipeline(
data_iter,
model,
criterion=lambda outputs, inputs: outputs[0],
optimizer=optimizer,
return_loss=True,
)
loss = outputs["loss"]
if booster.plugin.stage_manager.is_last_stage():
global_loss = all_reduce_mean(loss, plugin)
if coordinator._local_rank == coordinator._world_size - 1:
step_bar.set_postfix({"train/loss": global_loss.item()})
optimizer.step()
optimizer.zero_grad()
# Save modeling.
save_model_condition = args.save_interval > 0 and (step + 1) % args.save_interval == 0
if not args.skip_save_each_epoch:
save_model_condition = save_model_condition or (step + 1) == len(dataloader)
if save_model_condition and not args.benchmark:
coordinator.print_on_master("\nStart saving model checkpoint with running states")
if args.use_neft:
coordinator.print_on_master("Deactivate NEFTune before saving model.")
deactivate_neftune(model, handle)
accelerator.empty_cache()
save_checkpoint(
save_dir=args.save_dir,
booster=booster,
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
epoch=epoch,
step=step + 1,
batch_size=args.batch_size,
coordinator=coordinator,
use_lora=(args.lora_rank > 0),
)
coordinator.print_on_master(
f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
)
if args.use_neft:
coordinator.print_on_master("Activate NEFTune.")
model, handle = activate_neftune(model)
else:
pbar = tqdm(
desc=f"Epoch {epoch}",
disable=not coordinator.is_master(),
total=num_steps_per_epoch,
initial=start_step // args.accumulation_steps,
)
total_loss = torch.tensor(0.0, device=get_current_device())
for step, batch in enumerate(dataloader, start=start_step):
batch = {k: v.to(get_current_device()) for k, v in batch.items() if isinstance(v, torch.Tensor)}
batch_output = model(**batch)
loss = batch_output.loss / args.accumulation_steps
total_loss.add_(loss.data)
booster.backward(loss=loss, optimizer=optimizer)
if (step + 1) % args.accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
all_reduce_mean(tensor=total_loss)
pbar.set_postfix({"Loss": f"{total_loss.item():.4f}"})
if coordinator.is_master():
global_step = (epoch * num_steps_per_epoch) + (step + 1) // args.accumulation_steps
writer.add_scalar(tag="Loss", scalar_value=total_loss.item(), global_step=global_step)
writer.add_scalar(
tag="Learning Rate",
scalar_value=lr_scheduler.get_last_lr()[0],
global_step=global_step,
)
total_loss.fill_(0.0)
pbar.update()
# Save modeling.
save_model_condition = (
args.save_interval > 0 and (step + 1) % (args.save_interval * args.accumulation_steps) == 0
)
if not args.skip_save_each_epoch:
save_model_condition = save_model_condition or (step + 1) == len(dataloader)
if save_model_condition and not args.benchmark:
coordinator.print_on_master("\nStart saving model checkpoint with running states")
if args.use_neft:
coordinator.print_on_master("Deactivate NEFTune before saving model.")
deactivate_neftune(model, handle)
accelerator.empty_cache()
save_checkpoint(
save_dir=args.save_dir,
booster=booster,
model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
epoch=epoch,
step=step + 1,
batch_size=args.batch_size,
coordinator=coordinator,
use_lora=(args.lora_rank > 0),
)
coordinator.print_on_master(
f"Saved checkpoint at epoch {epoch} step {step + 1} at folder {args.save_dir}"
)
if args.use_neft:
coordinator.print_on_master("Activate NEFTune.")
model, handle = activate_neftune(model)
# Delete cache.
# del batch, batch_labels, batch_output, loss
accelerator.empty_cache()
# the continue epochs are not resumed, so we need to reset the sampler start index and start step
dataloader.sampler.set_start_index(start_index=0)
start_step = 0
if args.use_neft:
coordinator.print_on_master("Deactivate NEFTune.")
deactivate_neftune(model, handle)
# Final save.
if not args.benchmark:
coordinator.print_on_master("Start saving final model checkpoint")
booster.save_model(model, os.path.join(args.save_dir, "modeling"), shard=True)
coordinator.print_on_master(f"Saved final model checkpoint at epoch {epoch} at folder {args.save_dir}")
coordinator.print_on_master(f"Max device memory usage: {accelerator.max_memory_allocated()/1024**2:.2f} MB")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Basic training information.
parser.add_argument(
"--pretrained",
type=str,
default=None,
help="Address of the pre-trained model",
)
parser.add_argument("--load_checkpoint", type=str, default=None, help="Load checkpoint for continuous training.")
parser.add_argument("--dataset", nargs="+", default=[])
parser.add_argument(
"--plugin",
type=str,
default="gemini",
choices=["gemini", "gemini_auto", "zero2", "zero2_cpu", "3d", "ddp"],
help="Choose which plugin to use",
)
parser.add_argument("--save_interval", type=int, default=1000, help="Save interval")
parser.add_argument("--save_dir", type=str, default="checkpoint_dir", help="Checkpoint directory")
parser.add_argument("--tensorboard_dir", type=str, default="logs_dir", help="Tensorboard directory")
parser.add_argument("--config_file", type=str, default="config_file", help="Config file")
# Training parameters
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--accumulation_steps", type=int, default=1, help="Number of accumulation steps")
parser.add_argument("--batch_size", type=int, default=2, help="Global Batch size of each process")
parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--max_length", type=int, default=8192, help="Model max length")
parser.add_argument(
"--mixed_precision",
type=str,
default="fp16",
choices=["fp16", "bf16"],
help="Mixed precision",
)
parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value")
parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay")
parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
parser.add_argument(
"--use_grad_checkpoint",
action="store_true",
default=False,
help="Use gradient checkpointing",
)
parser.add_argument(
"--use_flash_attn",
action="store_true",
default=False,
help="Use flash-attention",
)
parser.add_argument(
"--use_neft",
action="store_true",
default=False,
help="Use NEFTune",
)
parser.add_argument(
"--freeze_non_embeds_params",
action="store_true",
default=False,
help="Freeze non embeddings parameters",
)
parser.add_argument("--pad_token", choices=["eos", "unk"], default="eos")
parser.add_argument("--padding_mode", choices=["max_length", "longest"], default="max_length")
parser.add_argument(
"--skip_save_each_epoch",
action="store_true",
default=False,
help="Skip saving the model checkpoint after each epoch is completed.",
)
# Additional arguments for 3d plugin.
parser.add_argument("--tp", type=int, default=1, help="TP size, used for 3d plugin.")
parser.add_argument("--pp", type=int, default=1, help="PP size, used for 3d plugin.")
parser.add_argument("--sp", type=int, default=1, help="SP size, used for 3d plugin.")
parser.add_argument("--zero_stage", type=int, default=0, help="Zero stage, used for 3d plugin.", choices=[0, 1, 2])
parser.add_argument(
"--sp_mode",
type=str,
default="split_gather",
choices=["split_gather", "ring", "all_to_all"],
help="SP mode, used for 3d plugin.",
)
parser.add_argument(
"--enable_sequence_parallelism",
default=False,
action="store_true",
help="Whether to enable SP, used for 3d plugin.",
)
parser.add_argument(
"--zero_cpu_offload", default=False, action="store_true", help="Whether to use offloading, used for 3d plugin."
)
parser.add_argument(
"--microbatch_size", type=int, default=1, help="Batch size for each process in PP, used for 3d plugin."
)
parser.add_argument("--lora_rank", type=int, default=0, help="lora rank when using lora to train.")
# Additional arguments for benchmark.
parser.add_argument("--num_samples", type=int, default=500, help="Number of samples for benchmarking.")
parser.add_argument(
"--benchmark", action="store_true", default=False, help="Benchmark performance using random dataset."
)
args = parser.parse_args()
train(args)