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397 lines
16 KiB
397 lines
16 KiB
import argparse
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import resource
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import time
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import warnings
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from contextlib import nullcontext
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import torch
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import torch.distributed as dist
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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, get_profile_context
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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 import AutoConfig, AutoModelForCausalLM
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from transformers.models.llama.configuration_llama import LlamaConfig
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import colossalai
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from colossalai.accelerator import get_accelerator
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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.pipeline.schedule.v_schedule import PipelineGraph
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from colossalai.shardformer import PipelineGradientCheckpointConfig
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warnings.filterwarnings("ignore")
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# ==============================
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# Constants
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# ==============================
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# We have lots of llamas for your choice!
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MODEL_CONFIGS = {
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"100m": LlamaConfig(
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max_position_embeddings=4096,
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num_hidden_layers=4,
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num_attention_heads=32,
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intermediate_size=2048,
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hidden_size=1024,
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),
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"5b": LlamaConfig(max_position_embeddings=4096, num_key_value_heads=8),
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"7b": LlamaConfig(max_position_embeddings=4096),
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# "7b": LlamaConfig(num_hidden_layers=4, 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("--sp", type=int, default=1, help="Sequence parallel size")
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parser.add_argument("--extra_dp", type=int, default=1, help="Extra data parallel size, used for Gemini")
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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, help="Micro batch size of pipeline parallel")
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parser.add_argument("--zero", type=int, default=0, help="Zero Stage when hybrid plugin is enabled")
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parser.add_argument("--custom-ckpt", action="store_true", help="Customize checkpoint", default=False)
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parser.add_argument("--pp_style", default="1f1b", choices=["1f1b", "interleaved", "zbv"])
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parser.add_argument("--n_chunks", default=1, help="number of model chunks", type=eval)
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parser.add_argument("--profile", action="store_true", help="Profile the code")
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parser.add_argument(
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"--nsys",
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action="store_true",
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help="Use nsys for profiling. \
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You should put something like this before colossalai launch: \
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nsys profile -w true -t cuda,cudnn,cublas -s cpu --capture-range=cudaProfilerApi --capture-range-end=stop --cudabacktrace=true -x true --python-backtrace=cuda -o prof_out",
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)
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parser.add_argument("--disable-async-reduce", action="store_true", help="Disable the asynchronous reduce operation")
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parser.add_argument("--prefetch_num", type=int, default=0, help="chunk prefetch max number")
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parser.add_argument("--no_cache", action="store_true")
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parser.add_argument("--use_fp8_comm", action="store_true", default=False, help="for using fp8 during communication")
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parser.add_argument("--use_fp8", action="store_true", default=False, help="for using fp8 linear")
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parser.add_argument("--overlap_p2p", action="store_true", default=True, help="for using overlap p2p")
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parser.add_argument("--overlap_allgather", action="store_true")
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parser.add_argument(
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"--sp_mode",
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default="all_to_all",
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choices=["all_to_all", "ring_attn", "ring", "split_gather"],
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help="Sequence parallelism mode",
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)
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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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# ckpt config for LLaMA3-70B on 64 H100 GPUs
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hybrid_kwargs = (
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{
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"gradient_checkpoint_config": PipelineGradientCheckpointConfig(
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num_ckpt_layers_per_stage=[19, 19, 19, 13],
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),
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"num_layers_per_stage": [19, 20, 20, 21],
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"pp_style": "interleaved",
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}
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if args.custom_ckpt
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else {}
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)
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# ==============================
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# Initialize Booster
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# ==============================
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if args.config in MODEL_CONFIGS:
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config = MODEL_CONFIGS[args.config]
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else:
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config = AutoConfig.from_pretrained(args.config, trust_remote_code=True)
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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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tp_size=args.tp,
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extra_dp_size=args.extra_dp,
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enable_fused_normalization=torch.cuda.is_available(),
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enable_flash_attention=args.xformers,
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max_prefetch=args.prefetch_num,
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enable_async_reduce=not args.disable_async_reduce,
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use_fp8=args.use_fp8,
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fp8_communication=args.use_fp8_comm,
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)
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elif args.plugin == "gemini_auto":
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plugin = GeminiPlugin(
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placement_policy="auto",
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precision="bf16",
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warmup_non_model_data_ratio=args.warmup_ratio,
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tp_size=args.tp,
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extra_dp_size=args.extra_dp,
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enable_fused_normalization=torch.cuda.is_available(),
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max_prefetch=args.prefetch_num,
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enable_async_reduce=not args.disable_async_reduce,
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enable_flash_attention=args.xformers,
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use_fp8=args.use_fp8,
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fp8_communication=args.use_fp8_comm,
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)
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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,
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reduce_dtype=torch.float16,
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buffer_dtype=torch.float16,
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),
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param_init_fn=empty_init(),
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fp8_communication=args.use_fp8_comm,
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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,
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reduce_dtype=torch.float16,
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buffer_dtype=torch.float16,
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),
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fp8_communication=args.use_fp8_comm,
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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,
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reduce_dtype=torch.float16,
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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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fp8_communication=args.use_fp8_comm,
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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,
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reduce_dtype=torch.float16,
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buffer_dtype=torch.float16,
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),
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cpu_offload=CPUOffload(offload_params=True),
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fp8_communication=args.use_fp8_comm,
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)
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elif args.plugin == "3d":
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if args.pp_style == "zbv":
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mem_f = 34 * config.hidden_size + 5 * config.num_attention_heads * args.max_length
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mem_w = -32 * config.hidden_size
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mem_b = -mem_w - mem_f
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scheduler_nodes = PipelineGraph(
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n_stage=args.pp,
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n_micro=args.batch_size // args.mbs,
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f_cost=1000,
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b_cost=1000,
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w_cost=1000,
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c_cost=1,
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f_mem=mem_f * 1.5,
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b_mem=mem_b * 1.5,
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w_mem=mem_w * 1.5,
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).get_v_schedule()
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else:
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scheduler_nodes = None
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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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pp_style=args.pp_style,
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num_model_chunks=args.n_chunks,
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zero_stage=args.zero,
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sp_size=args.sp,
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sequence_parallelism_mode=args.sp_mode,
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enable_sequence_parallelism=args.sp > 1,
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enable_fused_normalization=torch.cuda.is_available(),
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enable_flash_attention=args.xformers,
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microbatch_size=args.mbs,
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precision="bf16",
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enable_metadata_cache=not args.no_cache,
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overlap_allgather=args.overlap_allgather,
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use_fp8=args.use_fp8,
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fp8_communication=args.use_fp8_comm,
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scheduler_nodes=scheduler_nodes,
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**hybrid_kwargs,
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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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pp_style=args.pp_style,
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num_model_chunks=args.n_chunks,
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zero_stage=args.zero,
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cpu_offload=True,
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enable_fused_normalization=torch.cuda.is_available(),
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enable_flash_attention=args.xformers,
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microbatch_size=args.mbs,
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initial_scale=2**8,
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precision="bf16",
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overlap_p2p=args.overlap_p2p,
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use_fp8=args.use_fp8,
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fp8_communication=args.use_fp8_comm,
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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 = getattr(plugin, "dp_size", coordinator.world_size)
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if args.config in MODEL_CONFIGS:
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config = MODEL_CONFIGS[args.config]
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else:
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config = AutoConfig.from_pretrained(args.config, trust_remote_code=True)
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torch.cuda.manual_seed(42)
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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, seed=42)
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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_accelerator().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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init_kwargs = {}
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if config.model_type == "chatglm":
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init_kwargs["empty_init"] = False
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with init_ctx:
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model = AutoModelForCausalLM.from_config(
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config,
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trust_remote_code=True,
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**init_kwargs,
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16,
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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 config.model_type == "chatglm":
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model.transformer.encoder.gradient_checkpointing = True
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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,
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args.ignore_steps,
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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(
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f"Booster init max CUDA memory: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB"
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)
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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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with get_profile_context(
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args.profile,
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args.ignore_steps,
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1, # avoid creating massive log files
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save_dir=f"./profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-llama-{args.config}",
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nsys=args.nsys,
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) as prof:
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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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outputs = booster.execute_pipeline(
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data_iter,
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model,
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criterion=lambda outputs, inputs: outputs[0],
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optimizer=optimizer,
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return_loss=True,
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)
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loss = outputs["loss"]
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if args.pp_style == "zbv":
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if coordinator.is_master():
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print(f"Step {step} loss: {loss}")
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else:
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if coordinator.is_last_process():
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print(f"Step {step} loss: {loss}")
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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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prof.step()
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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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del outputs # free memory
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if dist.get_rank() == dist.get_world_size() - 1:
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print(f"Step {step} loss: {loss}")
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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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prof.step()
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performance_evaluator.on_fit_end()
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coordinator.print_on_master(f"Max CUDA memory usage: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB")
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if __name__ == "__main__":
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main()
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