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104 lines
3.4 KiB
104 lines
3.4 KiB
import argparse
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import time
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import pytest
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import torch
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from model_zoo import GPTLMLoss, get_gpt2_components
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from torch.utils._pytree import tree_map
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import colossalai
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from colossalai.accelerator import get_accelerator
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from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer
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from colossalai.auto_parallel.offload.mem_optimize import memory_optimize
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from colossalai.auto_parallel.offload.solver import NOT_NVML
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from colossalai.fx.profiler import parameter_size
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import spawn
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_type", type=str, default="gpt2_medium")
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parser.add_argument("--batch_size", type=int, default=64)
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parser.add_argument("--solver_type", type=str, default="asyn")
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parser.add_argument("--memory_budget", type=float, default=16)
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return parser.parse_args()
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@pytest.mark.skipif(NOT_NVML, reason="pynvml is not installed")
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def train_gpt(args):
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memory_budget = args.memory_budget * 1024 * 1024 * 1024
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solver_type = args.solver_type
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model_type = args.model_type
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batch_size = args.batch_size
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# build model
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model_builder, data_gen = get_gpt2_components(model_type=model_type, batch_size=batch_size)
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label = torch.randint(
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low=0,
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high=128,
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size=(
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64,
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8,
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),
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device=get_accelerator().get_current_device(),
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)
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criterion = GPTLMLoss()
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start_time = time.time()
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model = model_builder()
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model.train()
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param_size = parameter_size(model) / 1024**2 / 2
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init_time = time.time() - start_time
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print(f"init_param_size={param_size:.3f} MB | init_model_time={init_time:.3f} s")
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data_args = data_gen(device="cpu")
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wrap_fn = lambda x: x.to(dtype=torch.half) if isinstance(x, torch.Tensor) and torch.is_floating_point(x) else x
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data_args = tree_map(wrap_fn, data_args)
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start_time = time.time()
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model = memory_optimize(model, data_args, memory_budget, solver_type)
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solver_time = time.time() - start_time
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print(f"solver_time={solver_time:.3f} s")
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hybrid_optimizer = HybridAdam(model.model.parameters(), lr=1e-3)
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optim = AMPOptimizer(hybrid_optimizer, model)
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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torch.cuda.reset_peak_memory_stats()
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time_list = []
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data_args = data_gen(device="cuda")
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data_args = tree_map(wrap_fn, data_args)
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for step in range(10):
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optim.zero_grad()
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torch.cuda.synchronize()
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start_time = time.time()
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loss = criterion(model(**data_args), label)
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optim.backward(loss)
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torch.cuda.synchronize()
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time_list.append(time.time() - start_time)
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optim.step()
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torch.cuda.synchronize()
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exec_time = sum(sorted(time_list)[:5]) / 5
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runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2
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runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2
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print(f"solver_type: {solver_type} | model_type: {model_type}")
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print(
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f"| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB "
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f"| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|"
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)
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print(time_list)
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def run(rank, world_size, port, args):
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colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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train_gpt(args)
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if __name__ == "__main__":
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args = parse_args()
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spawn(run, 1, args=args)
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