mirror of https://github.com/hpcaitech/ColossalAI
332 lines
11 KiB
Python
332 lines
11 KiB
Python
import os
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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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from packaging import version
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from torch import nn
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from torch.nn.parallel import DistributedDataParallel as DDP
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from transformers import AlbertConfig, AlbertForSequenceClassification, BertConfig, BertForSequenceClassification
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import colossalai
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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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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ReplicaSpec, ShardSpec
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from colossalai.utils import get_current_device
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from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper
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CAI_VERSION = colossalai.__version__
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def get_tflops(model_numel, batch_size, seq_len, step_time):
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return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
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def get_profile_context(enable_flag, warmup_steps, active_steps, save_dir):
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from contextlib import nullcontext
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from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler
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if enable_flag:
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return profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
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schedule=schedule(wait=0, warmup=warmup_steps, active=active_steps),
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on_trace_ready=tensorboard_trace_handler(save_dir),
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record_shapes=True,
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profile_memory=True)
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else:
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class DummyProfiler:
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def __init__(self):
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self.step_number = 0
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def step(self):
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self.step_number += 1
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return nullcontext(DummyProfiler())
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def get_time_stamp():
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import time
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cur_time = time.strftime("%d-%H:%M", time.localtime())
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return cur_time
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def get_bert_data(batch_size: int, sequence_length: int, vacob_size: int, n_class: int, device: torch.device):
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input = torch.randint(
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low=0,
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high=vacob_size,
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size=(batch_size, sequence_length),
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device=device,
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dtype=torch.long,
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)
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label = torch.randint(low=0, high=n_class, size=(batch_size,), device=device, dtype=torch.long)
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return input, label
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def parse_args():
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parser = colossalai.get_default_parser()
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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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"--placement",
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type=str,
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default='cpu',
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help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
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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="bert",
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help="bert or albert",
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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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SEQ_LEN = 512
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VOCAB_SIZE = 1000
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NUM_LABELS = 10
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# Parameter Sharding Strategies for Tensor Parallelism
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def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup):
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spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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param.set_tensor_spec(*spec)
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def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup):
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split_param_single_dim_tp1d(0, param, pg)
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def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup):
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split_param_single_dim_tp1d(-1, param, pg)
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def get_cpu_mem():
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return psutil.Process().memory_info().rss / 1024**2
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def get_gpu_mem():
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return torch.cuda.memory_allocated() / 1024**2
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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):
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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_builder(args):
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if args.model_type == "bert":
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cfg = BertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS)
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return BertForSequenceClassification(cfg)
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elif args.model_type == "albert":
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cfg = AlbertConfig(vocab_size=VOCAB_SIZE, num_labels=NUM_LABELS)
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return AlbertForSequenceClassification(cfg)
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else:
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raise RuntimeError
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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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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 = False # The flag of profiling, False by default
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disable_existing_loggers()
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colossalai.launch_from_torch(config={})
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logger = get_dist_logger()
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logger.info(f" {args.distplan}, batch size {BATCH_SIZE}", ranks=[0])
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torch.manual_seed(123)
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if args.distplan.startswith("CAI"):
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# all param must use the same process group.
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world_size = torch.distributed.get_world_size()
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# build a base-bert model
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with ColoInitContext(device=get_current_device(), dtype=torch.half):
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model = model_builder(args)
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# model = BertForSequenceClassification(BertConfig(vocal_size = VOCAB_SIZE))
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# asign running configurations
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gemini_config = None
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if args.distplan.startswith("CAI_ZeRO"):
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optim_config = dict(reduce_bucket_size=12 * 1024 * 1024, overlap_communication=True, verbose=True)
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elif args.distplan == "CAI_Gemini":
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gemini_config = dict(strict_ddp_mode=True,
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device=get_current_device(),
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placement_policy=args.placement,
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pin_memory=True,
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hidden_dim=model.config.hidden_size,
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search_range_mb=128)
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optim_config = dict(gpu_margin_mem_ratio=0.)
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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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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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# wrap your model and optimizer
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model = zero_model_wrapper(model, zero_stage, gemini_config)
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optimizer = zero_optim_wrapper(model, optimizer, optim_config=optim_config)
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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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model = model_builder(args).cuda()
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model = DDP(model)
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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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# 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, labels = get_bert_data(BATCH_SIZE,
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SEQ_LEN,
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VOCAB_SIZE,
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NUM_LABELS,
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device=torch.cuda.current_device())
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optimizer.zero_grad()
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start = time()
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outputs = model(input_ids, labels=labels)
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loss, logits = outputs[:2]
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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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if args.distplan.startswith("CAI"):
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optimizer.backward(loss)
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elif args.distplan.startswith("Pytorch"):
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loss.backward()
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else:
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raise RuntimeError
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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(PROF_FLAG,
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WARMUP_STEPS,
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NUM_STEPS - WARMUP_STEPS,
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save_dir=f"profile/{get_time_stamp()}-demo")
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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 = ((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS
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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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