mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2026-07-15 15:29:48 +00:00
b2ad0d9e8f
* Use p2p * Cannot bidirectonal send p2p * Refactor tensor creation and serialization in P2P communication * Fix llama forward args in flash attention * Add flop estimate from megatron * Support loading weight not in weight_map when strict=False in hybrid_parallel * Use send_forward_recv_backward, etc in 1f1b * Use dataclass for metdata Remove torch.cuda.synchronize() as suggested * Add comment about the torch.cuda.synchronize for potential error * Typo * Update hybrid_parallel_checkpoint_io.py * Update p2p.py * Update one_f_one_b.py * Update p2p.py --------- Co-authored-by: flybird11111 <1829166702@qq.com>
228 lines
8.9 KiB
Python
228 lines
8.9 KiB
Python
import argparse
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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 attn import SUPPORT_FLASH, replace_xformers
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from data_utils import RandomDataset
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from model_utils import format_numel_str, get_model_numel
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from performance_evaluator import PerformanceEvaluator
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from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision
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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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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, TorchFSDPPlugin
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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.optimizer import HybridAdam
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from colossalai.utils import get_current_device
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# ==============================
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# Constants
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# ==============================
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MODEL_CONFIGS = {
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"7b": LlamaConfig(max_position_embeddings=4096),
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"13b": LlamaConfig(
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hidden_size=5120,
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intermediate_size=13824,
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num_hidden_layers=40,
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num_attention_heads=40,
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max_position_embeddings=4096,
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),
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"70b": LlamaConfig(
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hidden_size=8192,
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intermediate_size=28672,
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num_hidden_layers=80,
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num_attention_heads=64,
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max_position_embeddings=4096,
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num_key_value_heads=8,
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),
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}
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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("-c", "--config", type=str, default="7b", help="Model configuration")
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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", "fsdp", "fsdp_cpu", "3d", "3d_cpu"],
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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("-b", "--batch_size", type=int, default=2, help="Batch size")
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parser.add_argument("-s", "--num_steps", type=int, default=5, help="Number of steps to run")
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parser.add_argument("-i", "--ignore_steps", type=int, default=2, help="Number of steps to ignore")
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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(
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"-w", "--warmup_ratio", type=float, default=0.8, help="warm up ratio of non-model data. Only for gemini-auto"
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)
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parser.add_argument("-m", "--memory_limit", type=int, help="Gemini memory limit in mb")
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parser.add_argument("-x", "--xformers", action="store_true", help="Use xformers")
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parser.add_argument("--shard_param_frac", type=float, default=1.0, help="Shard param fraction. Only for gemini")
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parser.add_argument("--offload_optim_frac", type=float, default=0.0, help="Offload optim fraction. Only for gemini")
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parser.add_argument("--offload_param_frac", type=float, default=0.0, help="Offload param fraction. Only for gemini")
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parser.add_argument("--tp", type=int, default=1, help="Tensor parallel size")
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parser.add_argument("--pp", type=int, default=1, help="Pipeline parallel size")
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parser.add_argument("--mbs", type=int, default=1)
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parser.add_argument("--zero", type=int, default=0)
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args = parser.parse_args()
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colossalai.launch_from_torch({})
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coordinator = DistCoordinator()
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def empty_init():
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pass
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# ==============================
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# Initialize Booster
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# ==============================
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use_empty_init = True
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if args.plugin == "gemini":
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plugin = GeminiPlugin(
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precision="bf16",
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shard_param_frac=args.shard_param_frac,
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offload_optim_frac=args.offload_optim_frac,
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offload_param_frac=args.offload_param_frac,
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)
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elif args.plugin == "gemini_auto":
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plugin = GeminiPlugin(placement_policy="auto", precision="bf16", warmup_non_model_data_ratio=args.warmup_ratio)
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elif args.plugin == "fsdp":
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if use_empty_init:
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plugin = TorchFSDPPlugin(
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mixed_precision=MixedPrecision(
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param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16
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),
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param_init_fn=empty_init(),
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)
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else:
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plugin = TorchFSDPPlugin(
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mixed_precision=MixedPrecision(
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param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16
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)
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)
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elif args.plugin == "fsdp_cpu":
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if use_empty_init:
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plugin = TorchFSDPPlugin(
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mixed_precision=MixedPrecision(
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param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16
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),
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cpu_offload=CPUOffload(offload_params=True),
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param_init_fn=empty_init(),
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)
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else:
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plugin = TorchFSDPPlugin(
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mixed_precision=MixedPrecision(
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param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16
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),
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cpu_offload=CPUOffload(offload_params=True),
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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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zero_stage=args.zero,
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enable_fused_normalization=True,
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num_microbatches=args.mbs,
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precision="bf16",
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)
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elif args.plugin == "3d_cpu":
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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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zero_stage=args.zero,
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cpu_offload=True,
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enable_fused_normalization=True,
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num_microbatches=args.mbs,
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initial_scale=2**8,
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precision="bf16",
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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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# ==============================
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# Initialize Dataset and Dataloader
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# ==============================
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dp_size = plugin.dp_size if isinstance(plugin, HybridParallelPlugin) else coordinator.world_size
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config = MODEL_CONFIGS[args.config]
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dataset = RandomDataset(
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num_samples=args.batch_size * args.num_steps * dp_size, max_length=args.max_length, vocab_size=config.vocab_size
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)
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dataloader = plugin.prepare_dataloader(dataset, batch_size=args.batch_size, shuffle=True, drop_last=True)
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# ==============================
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# Initialize Model and Optimizer
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# ==============================
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init_ctx = (
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LazyInitContext(default_device=get_current_device())
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if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin))
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else nullcontext()
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)
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with init_ctx:
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model = LlamaForCausalLM(config)
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if args.grad_checkpoint:
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model.gradient_checkpointing_enable()
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if args.xformers:
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assert SUPPORT_FLASH, "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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performance_evaluator = PerformanceEvaluator(
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model_numel,
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model.config.num_hidden_layers,
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model.config.hidden_size,
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model.config.vocab_size,
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args.grad_checkpoint, args.ignore_steps, dp_world_size=dp_size
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)
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optimizer = HybridAdam(model.parameters())
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torch.set_default_dtype(torch.bfloat16)
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model, optimizer, _, dataloader, _ = booster.boost(model, optimizer, dataloader=dataloader)
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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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if isinstance(plugin, HybridParallelPlugin) and args.pp > 1:
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data_iter = iter(dataloader)
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for step in tqdm(range(len(dataloader)), desc="Step", disable=not coordinator.is_master()):
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performance_evaluator.on_step_start(step)
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booster.execute_pipeline(
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data_iter, model, criterion=lambda outputs, inputs: outputs[0], optimizer=optimizer, return_loss=False
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)
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optimizer.step()
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optimizer.zero_grad()
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performance_evaluator.on_step_end(input_ids=torch.empty(args.batch_size, args.max_length))
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else:
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for step, batch in enumerate(tqdm(dataloader, desc="Step", disable=not coordinator.is_master())):
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performance_evaluator.on_step_start(step)
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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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optimizer.zero_grad()
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performance_evaluator.on_step_end(**batch)
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performance_evaluator.on_fit_end()
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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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