mirror of https://github.com/hpcaitech/ColossalAI
[example]add gpt2 benchmark example script. (#5295)
* benchmark gpt2 * fix fix fix fix * [doc] fix typo in Colossal-LLaMA-2/README.md (#5247) * [workflow] fixed build CI (#5240) * [workflow] fixed build CI * polish * polish * polish * polish * polish * [ci] fixed booster test (#5251) * [ci] fixed booster test * [ci] fixed booster test * [ci] fixed booster test * [ci] fixed ddp test (#5254) * [ci] fixed ddp test * polish * fix typo in applications/ColossalEval/README.md (#5250) * [ci] fix shardformer tests. (#5255) * fix ci fix * revert: revert p2p * feat: add enable_metadata_cache option * revert: enable t5 tests --------- Co-authored-by: Wenhao Chen <cwher@outlook.com> * [doc] fix doc typo (#5256) * [doc] fix annotation display * [doc] fix llama2 doc * [hotfix]: add pp sanity check and fix mbs arg (#5268) * fix: fix misleading mbs arg * feat: add pp sanity check * fix: fix 1f1b sanity check * [workflow] fixed incomplete bash command (#5272) * [workflow] fixed oom tests (#5275) * [workflow] fixed oom tests * polish * polish * polish * [ci] fix test_hybrid_parallel_plugin_checkpoint_io.py (#5276) * fix ci fix * fix test * revert: revert p2p * feat: add enable_metadata_cache option * revert: enable t5 tests * fix --------- Co-authored-by: Wenhao Chen <cwher@outlook.com> * [shardformer] hybridparallelplugin support gradients accumulation. (#5246) * support gradients acc fix fix fix fix fix fix fix fix fix fix fix fix fix * fix fix * fix fix fix * [hotfix] Fix ShardFormer test execution path when using sequence parallelism (#5230) * fix auto loading gpt2 tokenizer (#5279) * [doc] add llama2-13B disyplay (#5285) * Update README.md * fix 13b typo --------- Co-authored-by: binmakeswell <binmakeswell@gmail.com> * fix llama pretrain (#5287) * fix * fix * fix fix * fix fix fix * fix fix * benchmark gpt2 * fix fix fix fix * [workflow] fixed build CI (#5240) * [workflow] fixed build CI * polish * polish * polish * polish * polish * [ci] fixed booster test (#5251) * [ci] fixed booster test * [ci] fixed booster test * [ci] fixed booster test * fix fix * fix fix fix * fix * fix fix fix fix fix * fix * Update shardformer.py --------- Co-authored-by: digger yu <digger-yu@outlook.com> Co-authored-by: Frank Lee <somerlee.9@gmail.com> Co-authored-by: Wenhao Chen <cwher@outlook.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com> Co-authored-by: Michelle <97082656+MichelleMa8@users.noreply.github.com> Co-authored-by: Desperado-Jia <502205863@qq.com>pull/5427/head
flybird11111
9 months ago
committed by
GitHub
parent
4b8312c08e
commit
29695cf70c
@ -0,0 +1,228 @@
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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 torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision
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from torch.optim import Adam
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from tqdm import tqdm
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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
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import colossalai
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# import colossalai.utils.device as device_utils
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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.utils import get_current_device
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from examples.language.data_utils import RandomDataset
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from examples.language.model_utils import format_numel_str, get_model_numel
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from examples.language.performance_evaluator import PerformanceEvaluator
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# ==============================
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# Constants
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# ==============================
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MODEL_CONFIGS = {
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"118M": GPT2Config(activation_function="gelu"),
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"338M": GPT2Config(n_embd=1024, n_head=16, n_layer=24, activation_function="gelu"),
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"738M": GPT2Config(n_embd=1280, n_head=20, n_layer=36, activation_function="gelu"),
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"6.21B": GPT2Config(n_embd=4096, n_head=32, n_layer=32, n_positions=4096, activation_function="gelu"),
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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="6.21B", 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=200, help="Number of steps to run")
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parser.add_argument("-i", "--ignore_steps", type=int, default=3, 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("--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("--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)
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parser.add_argument("--zero", type=int, default=0)
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parser.add_argument("--pp_style", type=str, default="1f1b")
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parser.add_argument("--num_model_chunks", type=int, default=2)
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parser.add_argument("--cpu_offload", action="store_true", help="Use gradient checkpointing")
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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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tp_size=args.tp,
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extra_dp_size=args.extra_dp,
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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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)
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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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pp_style=args.pp_style,
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zero_stage=args.zero,
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num_model_chunks=args.num_model_chunks,
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enable_all_optimization=True,
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num_microbatches=args.mbs,
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cpu_offload=args.cpu_offload,
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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=torch.cuda.is_available(),
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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 = GPT2LMHeadModel(config)
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if args.grad_checkpoint:
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model.gradient_checkpointing_enable()
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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.n_layer,
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model.config.n_embd,
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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 = Adam(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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coordinator.print_on_master(f"Max CUDA memory usage: {torch.cuda.max_memory_allocated()/1024**2:.2f} MB")
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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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