mirror of https://github.com/hpcaitech/ColossalAI
352 lines
12 KiB
Python
352 lines
12 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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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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from torch.nn.parallel import DistributedDataParallel as DDP
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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.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
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CAI_VERSION = colossalai.__version__
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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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"--tp_degree",
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type=int,
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default=1,
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help="Tensor Parallelism Degree. Valid when using colossalai as dist plan.",
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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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"--shardinit",
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action='store_true',
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help=
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"Shard the tensors when init the model to shrink peak memory size on the assigned device. 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="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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# 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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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
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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_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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# Tensor Parallel
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def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
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"""tensor_parallelize
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Sharding the Model Parameters.
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Args:
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model (torch.nn.Module): a torch module to be sharded
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"""
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for mn, module in model.named_modules():
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for pn, param in module.named_parameters(recurse=False):
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# NOTE() a param maybe shared by two modules
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if hasattr(param, 'visited'):
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continue
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# if shard init, then convert param to replica and use the dp-only ProcessGroup
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param: ColoParameter = param
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param.set_dist_spec(ReplicaSpec())
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param.set_process_group(pg)
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# shard it w.r.t tp pattern
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if 'mlp.c_fc' in mn:
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if 'weight' in pn or 'bias' in pn:
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split_param_col_tp1d(param, pg) # colmn slice
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# keep the shape of the output from c_fc
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param.compute_spec.set_output_replicate(False)
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else:
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param.set_dist_spec(ReplicaSpec())
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elif 'mlp.c_proj' in mn:
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if 'weight' in pn:
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split_param_row_tp1d(param, pg) # row slice
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else:
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param.set_dist_spec(ReplicaSpec())
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elif 'wte' in mn or 'wpe' in mn:
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split_param_col_tp1d(param, pg) # colmn slice
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elif 'c_attn' in mn or 'c_proj' in mn:
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split_param_col_tp1d(param, pg) # colmn slice
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else:
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param.set_dist_spec(ReplicaSpec())
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param.visited = True
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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 = 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.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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# all param must use the same process group.
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world_size = torch.distributed.get_world_size()
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shard_pg = ProcessGroup(tp_degree=world_size) if args.shardinit else None
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default_dist_spec = ShardSpec([-1], [world_size]) if args.shardinit else None
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if args.shardinit and args.distplan != "CAI_Gemini":
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raise RuntimeError("You can only use shardinit with CAI_Gemini")
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# build GPT model
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with ColoInitContext(device=get_current_device(),
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dtype=torch.half,
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default_dist_spec=default_dist_spec,
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default_pg=shard_pg):
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model = model_builder(args.model_type)(checkpoint=True)
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tp_pg = ProcessGroup(tp_degree=args.tp_degree)
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# Tensor Parallelism (TP)
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# You should notice that v0.1.10 is not compatible with TP degree > 1
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if args.tp_degree > 1:
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tensor_parallelize(model, tp_pg)
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# asign 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(stage=zero_stage,
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reduce_bucket_size_in_m=12 * 1024 * 1024,
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overlap_communication=True,
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verbose=True)
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elif args.distplan == "CAI_Gemini":
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plugin = GeminiPlugin(device=get_current_device(),
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placement_policy=args.placement,
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pin_memory=True,
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strict_ddp_mode=args.tp_degree == 1,
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search_range_mb=128,
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hidden_dim=model.config.n_embd,
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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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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(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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