mirror of https://github.com/hpcaitech/ColossalAI
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
import time
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import torch
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import tqdm
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import transformers
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from args import parse_benchmark_args
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from transformers import ViTConfig, ViTForImageClassification
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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, LowLevelZeroPlugin, TorchDDPPlugin
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from colossalai.cluster import DistCoordinator
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from colossalai.logging import disable_existing_loggers, get_dist_logger
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from colossalai.nn.optimizer import HybridAdam
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def format_num(num: int, bytes=False):
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"""Scale bytes to its proper format, e.g. 1253656 => '1.20MB'"""
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factor = 1024 if bytes else 1000
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suffix = "B" if bytes else ""
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for unit in ["", " K", " M", " G", " T", " P"]:
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if num < factor:
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return f"{num:.2f}{unit}{suffix}"
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num /= factor
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def get_data(batch_size, num_labels, num_channels=3, height=224, width=224):
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pixel_values = torch.randn(batch_size,
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num_channels,
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height,
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width,
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device=torch.cuda.current_device(),
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dtype=torch.float)
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labels = torch.randint(0, num_labels, (batch_size,), device=torch.cuda.current_device(), dtype=torch.int64)
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return pixel_values, labels
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def colo_memory_cap(size_in_GB):
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from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device
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cuda_capacity = colo_device_memory_capacity(get_current_device())
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if size_in_GB * (1024**3) < cuda_capacity:
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colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity)
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print(f"Limiting GPU memory usage to {size_in_GB} GB")
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def main():
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args = parse_benchmark_args()
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# Launch ColossalAI
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colossalai.launch_from_torch(config={}, seed=args.seed)
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coordinator = DistCoordinator()
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world_size = coordinator.world_size
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# Manage loggers
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disable_existing_loggers()
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logger = get_dist_logger()
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if coordinator.is_master():
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transformers.utils.logging.set_verbosity_info()
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else:
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transformers.utils.logging.set_verbosity_error()
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# Whether to set limit on memory capacity
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if args.mem_cap > 0:
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colo_memory_cap(args.mem_cap)
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# Build ViT model
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config = ViTConfig.from_pretrained(args.model_name_or_path)
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model = ViTForImageClassification(config)
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logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0])
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# Enable gradient checkpointing
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model.gradient_checkpointing_enable()
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# Set plugin
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booster_kwargs = {}
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if args.plugin == 'torch_ddp_fp16':
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booster_kwargs['mixed_precision'] = 'fp16'
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if args.plugin.startswith('torch_ddp'):
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plugin = TorchDDPPlugin()
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elif args.plugin == 'gemini':
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plugin = GeminiPlugin(offload_optim_frac=1.0, pin_memory=True, initial_scale=2**5)
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elif args.plugin == 'low_level_zero':
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plugin = LowLevelZeroPlugin(initial_scale=2**5)
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logger.info(f"Set plugin as {args.plugin}", ranks=[0])
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# Set optimizer
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optimizer = HybridAdam(model.parameters(), lr=(args.learning_rate * world_size))
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# Set booster
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booster = Booster(plugin=plugin, **booster_kwargs)
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model, optimizer, _, _, _ = booster.boost(model, optimizer)
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# Start training.
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logger.info(f"Start testing", ranks=[0])
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progress_bar = tqdm.tqdm(total=args.max_train_steps, desc="Training Step", disable=not coordinator.is_master())
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torch.cuda.synchronize()
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model.train()
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start_time = time.time()
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for _ in range(args.max_train_steps):
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pixel_values, labels = get_data(args.batch_size, args.num_labels, 3, 224, 224)
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optimizer.zero_grad()
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outputs = model(pixel_values=pixel_values, labels=labels)
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loss = outputs['loss']
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booster.backward(loss, optimizer)
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optimizer.step()
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torch.cuda.synchronize()
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progress_bar.update(1)
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# Compute Statistics
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end_time = time.time()
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throughput = "{:.4f}".format((world_size * args.max_train_steps * args.batch_size) / (end_time - start_time))
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max_mem = format_num(torch.cuda.max_memory_allocated(device=torch.cuda.current_device()), bytes=True)
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logger.info(
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f"Testing finished, "
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f"batch size per gpu: {args.batch_size}, "
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f"plugin: {args.plugin}, "
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f"throughput: {throughput}, "
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f"maximum memory usage per gpu: {max_mem}.",
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ranks=[0])
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if __name__ == "__main__":
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main()
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