mirror of https://github.com/hpcaitech/ColossalAI
258 lines
8.5 KiB
Python
258 lines
8.5 KiB
Python
import argparse
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import os
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from contextlib import nullcontext
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from functools import partial
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from time import time
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import psutil
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import torch
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import torch.nn as nn
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from commons.model_zoo import model_builder
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from commons.utils import get_data, get_profile_context, get_tflops, get_time_stamp
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from packaging import version
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import colossalai
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from colossalai.accelerator import get_accelerator
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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.lazy import LazyInitContext
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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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CAI_VERSION = colossalai.__version__
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--distplan",
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type=str,
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default="CAI_Gemini",
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help="The distributed plan [colossalai, zero1, zero2, torch_ddp, torch_zero].",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=8,
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help="batch size per DP group of training.",
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)
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parser.add_argument(
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"--model_type",
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type=str,
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default="gpt2_medium",
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help="model model scale",
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)
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parser.add_argument(
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"--train_step",
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type=int,
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default=10,
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help="training iterations for test",
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)
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args = parser.parse_args()
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return args
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class GPTLMLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss_fn = nn.CrossEntropyLoss()
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def forward(self, logits, labels):
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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def get_cpu_mem():
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return psutil.Process().memory_info().rss / 1024**2 # 返回值是B，转换成MB
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def get_gpu_mem():
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return torch.cuda.memory_allocated() / 1024**2 # 转换成MB
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def get_mem_info(prefix=""):
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return f"{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB"
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def get_model_size(model: nn.Module): # 得到模型参数量
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total_numel = 0
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for module in model.modules():
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for p in module.parameters(recurse=False):
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total_numel += p.numel()
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return total_numel
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def model_size_formatter(numel: int) -> str:
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GB_SIZE = 10**9
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MB_SIZE = 10**6
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KB_SIZE = 10**3
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if numel >= GB_SIZE:
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return f"{numel / GB_SIZE:.1f}B"
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elif numel >= MB_SIZE:
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return f"{numel / MB_SIZE:.1f}M"
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elif numel >= KB_SIZE:
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return f"{numel / KB_SIZE:.1f}K"
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else:
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return str(numel)
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def set_cpu_maximum_parallelism():
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conf_str = torch.__config__.parallel_info()
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inter_str = conf_str.split("hardware_concurrency() : ")[1]
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max_concurrency = inter_str.split("\n")[0]
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os.environ["OMP_NUM_THREADS"] = max_concurrency
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print(f"environmental variable OMP_NUM_THREADS is set to {max_concurrency}.")
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def main():
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# version check
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# this example is supposed to work for versions greater than 0.2.0
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assert version.parse(CAI_VERSION) >= version.parse("0.2.0")
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set_cpu_maximum_parallelism()
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args = parse_args()
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# if args.distplan not in ["colossalai", "torch_ddp", "torch_zero", "zero1", "zero2"]:
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if args.distplan not in ["CAI_ZeRO1", "CAI_ZeRO2", "CAI_Gemini", "Pytorch_DDP", "Pytorch_ZeRO"]:
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raise TypeError(f"{args.distplan} is error")
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# batch size per DP degree
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BATCH_SIZE = args.batch_size
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SEQ_LEN = 1024
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VOCAB_SIZE = 50257
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NUM_STEPS = args.train_step
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WARMUP_STEPS = 1
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assert WARMUP_STEPS < NUM_STEPS, "warmup steps should smaller than the total steps"
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assert (NUM_STEPS - WARMUP_STEPS) % 2 == 1, "the number of valid steps should be odd to take the median"
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PROF_FLAG = True # The flag of profiling, False by default
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disable_existing_loggers()
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colossalai.launch_from_torch()
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logger = get_dist_logger()
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logger.info(f"{args.model_type}, {args.distplan}, batch size {BATCH_SIZE}", ranks=[0])
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# build criterion
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criterion = GPTLMLoss()
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torch.manual_seed(123)
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if args.distplan.startswith("CAI"):
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ctx = (
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LazyInitContext(default_device=get_accelerator().get_current_device())
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if args.distplan == "CAI_Gemini"
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else nullcontext()
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)
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# build GPT model
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with ctx:
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model = model_builder(args.model_type)(checkpoint=True)
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# assign running configurations
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if args.distplan == "CAI_ZeRO1":
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zero_stage = 1
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elif args.distplan == "CAI_ZeRO2":
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zero_stage = 2
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elif args.distplan == "CAI_Gemini":
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zero_stage = 3
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else:
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raise RuntimeError
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plugin = None
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if args.distplan.startswith("CAI_ZeRO"):
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plugin = LowLevelZeroPlugin(
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stage=zero_stage, reduce_bucket_size_in_m=12, overlap_communication=True, verbose=True
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)
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elif args.distplan == "CAI_Gemini":
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plugin = GeminiPlugin(search_range_m=128, hidden_dim=model.config.n_embd, max_prefetch=1)
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else:
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raise RuntimeError
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# build a highly optimized gpu/cpu optimizer
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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logger.info(get_mem_info(prefix="After init optim, "), ranks=[0])
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elif args.distplan.startswith("Pytorch"):
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assert args.tp_degree == 1, "The degree of TP should be 1 for DDP examples."
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model = model_builder(args.model_type)(checkpoint=True).cuda()
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plugin = TorchDDPPlugin()
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if args.distplan.endswith("DDP"):
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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elif args.distplan.endswith("ZeRO"):
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from torch.distributed.optim import ZeroRedundancyOptimizer
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optimizer = ZeroRedundancyOptimizer(model.parameters(), optimizer_class=torch.optim.Adam, lr=1e-3)
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else:
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raise RuntimeError
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# wrap your model and optimizer
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booster = Booster(plugin=plugin)
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model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
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# model is shared after TP
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numel = get_model_size(model)
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logger.info(f"the size of testing model size is {model_size_formatter(numel)}.")
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logger.info(get_mem_info(prefix="After init model, "), ranks=[0])
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# Tflops_per_GPU = global_batch * global_numel * seq_len * 8 / #gpu
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# = (batch_per_DP_group * dp_degree) * (numel * tp_degree) * seq_len * 8 / (tp_degree * dp_degree)
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# = batch_per_DP_group * numel * seq_len * 8
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get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN)
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torch.cuda.synchronize()
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model.train()
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tflops_list = []
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def train_step():
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# we just use randomly generated data here
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input_ids, attn_mask = get_data(BATCH_SIZE, SEQ_LEN, VOCAB_SIZE)
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optimizer.zero_grad()
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start = time()
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outputs = model(input_ids, attn_mask)
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loss = criterion(outputs, input_ids)
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torch.cuda.synchronize()
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fwd_end = time()
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fwd_time = fwd_end - start
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logger.info(get_mem_info(prefix=f"[{n + 1}/{NUM_STEPS}] Forward "), ranks=[0])
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booster.backward(loss, optimizer)
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torch.cuda.synchronize()
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bwd_end = time()
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bwd_time = bwd_end - fwd_end
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logger.info(get_mem_info(prefix=f"[{n + 1}/{NUM_STEPS}] Backward "), ranks=[0])
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optimizer.step()
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torch.cuda.synchronize()
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optim_time = time() - bwd_end
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step_time = time() - start
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logger.info(get_mem_info(prefix=f"[{n + 1}/{NUM_STEPS}] Optimizer step "), ranks=[0])
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step_tflops = get_tflops_func(step_time)
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logger.info(
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f"[{n + 1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}, FWD time: {fwd_time:.3f}s, BWD time: {bwd_time:.3f}s, OPTIM time: {optim_time:.3f}s",
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ranks=[0],
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)
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if n >= WARMUP_STEPS:
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tflops_list.append(step_tflops)
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demo_profiler = get_profile_context(
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PROF_FLAG, WARMUP_STEPS, NUM_STEPS - WARMUP_STEPS, save_dir=f"profile/{get_time_stamp()}-demo"
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)
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with demo_profiler as prof:
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for n in range(NUM_STEPS):
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train_step()
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prof.step()
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tflops_list.sort()
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median_index = min(((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS, len(tflops_list) - 1)
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logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}")
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torch.cuda.synchronize()
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if __name__ == "__main__":
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main()
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