mirror of https://github.com/hpcaitech/ColossalAI
[SC] remove redundant hands on (#1939)
* [sc] SC tutorial for auto checkpoint * [sc] polish examples * [sc] polish readme * [sc] polish readme and help information * [sc] polish readme and help information * [sc] modify auto checkpoint benchmark * [sc] remove imgs * [sc] remove redundant handsonpull/1941/head
Boyuan Yao
GitHub
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9f4fb3f28a
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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 bench_rotor
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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 data_gen(batch_size, shape, device='cuda'):
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"""
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Generate random data for benchmarking
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"""
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data = torch.empty(batch_size, *shape, device=device)
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label = torch.empty(batch_size, dtype=torch.long, device=device).random_(1000)
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return (data,), label
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def _resnet50_benchmark(rank, world_size, port, batch_size, num_steps, sample_points, free_memory, start_factor):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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model = tm.resnet50()
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# trace and benchmark
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gm = symbolic_trace(model)
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gm = metainfo_trace(gm, torch.empty(batch_size, 3, 224, 224, device='meta'))
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budgets, peak_hist, step_hist = bench_rotor(gm,
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torch.nn.CrossEntropyLoss(),
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partial(data_gen, batch_size=batch_size, shape=(3, 224, 224)),
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num_steps=num_steps,
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sample_points=sample_points,
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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("==============test 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("resnet50_benchmark.png")
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def resnet50_benchmark(batch_size, num_steps, sample_points, free_memory, start_factor):
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world_size = 1
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run_func_module = partial(_resnet50_benchmark,
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world_size=world_size,
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port=free_port(),
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batch_size=batch_size,
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num_steps=num_steps,
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sample_points=sample_points,
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free_memory=free_memory,
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start_factor=start_factor)
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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("ResNet50 Auto Activation Benchmark")
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parser.add_argument("--batch_size", type=int, default=128, help="batch size for benchmark, default 128")
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parser.add_argument("--num_steps", type=int, default=5, help="number of test steps for benchmark, default 5")
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parser.add_argument(
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"--sample_points",
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type=int,
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default=15,
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help=
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"number of sample points for benchmark from start memory budget to maximum memory budget (free_memory), default 15"
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)
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parser.add_argument("--free_memory",
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type=int,
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default=11000,
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help="maximum memory budget in MB for benchmark, default 11000 MB")
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parser.add_argument(
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"--start_factor",
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type=int,
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default=4,
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help=
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"start memory budget factor for benchmark, the start memory budget will be free_memory / start_factor, default 4"
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)
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args = parser.parse_args()
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resnet50_benchmark(args.batch_size, args.num_steps, args.sample_points, args.free_memory * 1024**2,
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args.start_factor)
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