mirror of https://github.com/hpcaitech/ColossalAI
90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
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import time
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from argparse import ArgumentParser
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from functools import partial
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import matplotlib.pyplot as plt
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import torch
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import torch.multiprocessing as mp
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import torchvision.models as tm
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from bench_utils import GPTLMLoss, bench_rotor, data_gen_gpt2, data_gen_resnet, gpt2_medium
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import colossalai
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from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
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from colossalai.fx import metainfo_trace, symbolic_trace
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from colossalai.utils import free_port
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def _benchmark(rank, world_size, port, args):
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"""
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Auto activation checkpoint solver benchmark, we provide benchmark on two models: gpt2_medium and resnet50.
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The benchmark will sample in a range of memory budget for each model and output the benchmark summary and
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data visualization of peak memory vs. budget memory and relative step time vs. peak memory.
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"""
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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if args.model == 'resnet50':
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model = tm.resnet50()
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data_gen = partial(data_gen_resnet, batch_size=128, shape=(3, 224, 224))
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gm = symbolic_trace(model)
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gm = metainfo_trace(gm, torch.empty(128, 3, 224, 224, device='meta'))
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loss = torch.nn.CrossEntropyLoss()
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else:
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model = gpt2_medium()
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data_gen = partial(data_gen_gpt2, batch_size=8, seq_len=1024, vocab_size=50257)
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data, mask = data_gen(device='meta')[0]
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gm = symbolic_trace(model, meta_args={'input_ids': data, 'attention_mask': mask})
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gm = metainfo_trace(gm, data, mask)
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loss = GPTLMLoss()
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free_memory = 11000 * 1024**2 if args.model == 'resnet50' else 56000 * 1024**2
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start_factor = 4 if args.model == 'resnet50' else 10
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# trace and benchmark
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budgets, peak_hist, step_hist = bench_rotor(gm,
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loss,
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data_gen,
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num_steps=5,
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sample_points=15,
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free_memory=free_memory,
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start_factor=start_factor)
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# print summary
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print("==============benchmark summary==============")
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for budget, peak, step in zip(budgets, peak_hist, step_hist):
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print(f'memory budget: {budget:.3f} MB, peak memory: {peak:.3f} MB, step time: {step:.3f} MS')
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# plot valid results
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fig, axs = plt.subplots(1, 2, figsize=(16, 8))
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valid_idx = step_hist.index(next(step for step in step_hist if step != float("inf")))
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# plot peak memory vs. budget memory
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axs[0].plot(budgets[valid_idx:], peak_hist[valid_idx:])
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axs[0].plot([budgets[valid_idx], budgets[-1]], [budgets[valid_idx], budgets[-1]], linestyle='--')
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axs[0].set_xlabel("Budget Memory (MB)")
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axs[0].set_ylabel("Peak Memory (MB)")
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axs[0].set_title("Peak Memory vs. Budget Memory")
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# plot relative step time vs. budget memory
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axs[1].plot(peak_hist[valid_idx:], [step_time / step_hist[-1] for step_time in step_hist[valid_idx:]])
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axs[1].plot([peak_hist[valid_idx], peak_hist[-1]], [1.0, 1.0], linestyle='--')
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axs[1].set_xlabel("Peak Memory (MB)")
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axs[1].set_ylabel("Relative Step Time")
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axs[1].set_title("Step Time vs. Peak Memory")
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axs[1].set_ylim(0.8, 1.5)
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# save plot
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fig.savefig(f"{args.model}_benchmark.png")
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def auto_activation_checkpoint_benchmark(args):
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world_size = 1
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run_func_module = partial(_benchmark, world_size=world_size, port=free_port(), args=args)
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mp.spawn(run_func_module, nprocs=world_size)
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if __name__ == "__main__":
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parser = ArgumentParser("Auto Activation Checkpoint Solver Benchmark")
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parser.add_argument("--model", type=str, default='gpt2', choices=['gpt2', 'resnet50'])
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args = parser.parse_args()
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auto_activation_checkpoint_benchmark(args)
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