mirror of https://github.com/InternLM/InternLM
Test(pp): test pipeline parallel (#413)
* test: pp * feat: add pp test * test pp * pp test * pp test * test pppull/424/head
jiaxingli
GitHub
parent
aa5e34d815
commit
30f610b1fa
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import multiprocessing as mp
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import random
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import numpy as np
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import pytest
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import torch
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from torch import nn
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from torch.testing import assert_close
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import internlm
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from internlm.core.context import ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.core.context.parallel_context import Config
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from internlm.core.engine import Engine
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from internlm.core.gradient_handler import PipelineSharedModuleGradientHandler
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from internlm.core.scheduler import (
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InterleavedPipelineScheduler,
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PipelineScheduler,
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SchedulerMetricHook,
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)
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from internlm.solver.pipeline_utils import partition_uniform
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from internlm.train import initialize_optimizer
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class MlpModel(nn.Module):
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"""
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Custom model
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"""
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def __init__(self, start, end, model_type=None):
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super().__init__()
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self.part = [start, end]
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self.blocks = nn.ModuleList([nn.Linear(8, 8, bias=False) for lid in range(end - start)])
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self.model_type = model_type
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def forward(self, hidden_states=None, input_ids=None):
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if self.model_type != "torch" and self.part[0] != 0:
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input_ids = hidden_states
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for i in range(self.part[1] - self.part[0]):
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input_ids = self.blocks[i](input_ids)
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return input_ids
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class MyLoss(nn.Module):
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"""
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Custom loss
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"""
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def __init__(self):
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super().__init__()
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def forward(self, logits, labels):
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loss = torch.nn.MSELoss(reduction="sum")
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return loss(logits, labels)
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config = Config(
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dict(
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gradient_handler=[dict(type="PipelineSharedModuleGradientHandler")],
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parallel=dict(zero1=1, pipeline=dict(size=8, interleaved_overlap=False), sequence_parallel=False, tensor=1),
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model_type="INTERNLM",
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data=dict(seq_len=8, micro_num=16, micro_bsz=1, pack_sample_into_one=False, min_length=0, total_steps=9999),
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model=dict(
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dtype=torch.bfloat16,
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num_chunks=2,
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use_flash_attn=True,
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),
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resume_tb_folder="",
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tensorboard_folder="",
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alert_address=None,
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monitor=dict(alert=dict(enable_feishu_alert=False, feishu_alert_address=None, light_monitor_address=None)),
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grad_scaler=dict(
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fp16=dict(
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initial_scale=1,
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min_scale=1,
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growth_interval=1,
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),
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growth_factor=1.1,
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backoff_factor=0.9,
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max_scale=1,
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hysteresis=1,
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),
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adam=dict(
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lr=1e-4,
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adam_beta1=0.9,
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adam_beta2=0.95,
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adam_beta2_c=0,
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adam_eps=1e-8,
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weight_decay=0.01,
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),
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hybrid_zero_optimizer=dict(
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overlap_sync_grad=False,
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overlap_sync_param=False,
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reduce_bucket_size=512 * 1024 * 1024,
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clip_grad_norm=1.0,
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),
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beta2_scheduler=dict(
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init_beta2=0.95,
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c=0,
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cur_iter=-1,
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),
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lr_scheduler=dict(
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total_steps=100,
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init_steps=0,
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warmup_ratio=0.01,
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eta_min=1e-5,
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last_epoch=-1,
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),
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)
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)
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def build_environment(rank, world_size):
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import os
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os.environ["RANK"] = str(rank)
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os.environ["LOCAL_RANK"] = str(rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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os.environ["MASTER_ADDR"] = "127.0.0.1"
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os.environ["MASTER_PORT"] = "33333"
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torch.cuda.empty_cache()
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# launcher="torch"
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internlm.launch_from_torch(config=config, seed=1024)
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def loose_close(a, b, dtype: torch.dtype = torch.float32):
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if dtype is torch.float32:
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rtol = 1.3e-6
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atol = 1e-5
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elif dtype is torch.bfloat16:
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rtol = 2e-2
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atol = 2e-2
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if isinstance(a, torch.Tensor):
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a = a.detach().to(dtype)
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b = b.detach().to(dtype)
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assert_close(a, b, rtol=rtol, atol=atol)
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def seed_all(seed, cuda_deterministic=False):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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if cuda_deterministic: # slower, more reproducible
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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else:
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = True
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def _build_generic_model_1d(num_layers, num_chunks):
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pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE)
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pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
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all_parts = partition_uniform(num_layers, pipeline_size, num_chunks)
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parts = all_parts[pipeline_rank]
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if gpc.is_rank_for_log():
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print(f"The layer sharding is {all_parts}.", flush=True)
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models = []
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for start, end in parts:
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models.append(MlpModel(start, end).cuda())
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torch.distributed.barrier()
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if len(models) == 1:
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model = models[0]
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else:
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model = nn.ModuleList(models)
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return model
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def exam_pipeline_parallel(args):
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# init
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rank, world_size, micro_num, num_chunks, interleaved_overlap = args
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config.data.micro_num = micro_num
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config.model.num_chunks = num_chunks
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config.parallel.pipeline.interleaved_overlap = interleaved_overlap
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build_environment(rank, world_size)
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device = torch.device(f"cuda:{rank}")
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dtype = config.model["dtype"]
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# set seed
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seed_all(1024)
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# pp model
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pp_model = _build_generic_model_1d(num_layers=32, num_chunks=num_chunks)
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pp_model = pp_model.to(dtype)
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# pp scheduler
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scheduler_hooks = [
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SchedulerMetricHook(skip=True),
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]
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seq_len = gpc.config.data.seq_len
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gpc.config.NUM_MICRO_BATCHES = micro_num
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communication_overlap = interleaved_overlap
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if num_chunks == 1:
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# noninterleaved pp
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scheduler = PipelineScheduler(
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data_process_func=None,
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num_microbatches=micro_num,
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dtype=dtype,
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tensor_shape=[1, 8],
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scatter_gather_tensors=False,
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scheduler_hooks=scheduler_hooks,
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)
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else:
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# interleaved pp
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if micro_num < gpc.get_world_size(ParallelMode.PIPELINE):
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try:
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scheduler = InterleavedPipelineScheduler(
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num_microbatches=micro_num,
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num_chunks=gpc.config.model.num_chunks,
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dtype=dtype,
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tensor_shape=[1, 8],
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scatter_gather_tensors=False,
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scheduler_hooks=scheduler_hooks,
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communication_overlap=communication_overlap,
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)
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except AssertionError:
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return
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else:
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raise RuntimeError("Error: AssertionError should occur when micro_num < Pipeline parrallel world size")
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else:
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scheduler = InterleavedPipelineScheduler(
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num_microbatches=micro_num,
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num_chunks=gpc.config.model.num_chunks,
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dtype=dtype,
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tensor_shape=[1, 8],
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scatter_gather_tensors=False,
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scheduler_hooks=scheduler_hooks,
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communication_overlap=communication_overlap,
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)
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# pp optimizer and engine
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optimizer, beta2_scheduler, lr_scheduler = initialize_optimizer(model=pp_model)
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engine = Engine(
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model=pp_model,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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beta2_scheduler=beta2_scheduler,
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criterion=MyLoss().to(dtype),
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gradient_handlers=[PipelineSharedModuleGradientHandler(model=pp_model, optimizer=optimizer)],
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clip_grad_norm=gpc.config.hybrid_zero_optimizer.get("clip_grad_norm", 0.0),
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)
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scheduler.pre_processing(engine)
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engine.train()
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# create input
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x_list = []
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y_list = []
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for _ in range(micro_num):
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x_list.append(list(range(seq_len)))
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y_list.append(list(range(seq_len)))
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xs = torch.tensor(x_list).to(device).to(dtype)
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yx = torch.tensor(y_list).to(device).to(dtype)
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input_list = [{"input_ids": xs}, yx]
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# pp forward and backward
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output, _, loss = scheduler.forward_backward_step(
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engine, input_list, forward_only=False, return_loss=True, return_output_label=True
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)
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engine.step()
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# torch related
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if gpc.is_last_rank(ParallelMode.PIPELINE):
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torch_xs = torch.tensor(x_list).to(device).to(torch.float32)
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torch_ys = torch.tensor(y_list).to(device).to(torch.float32)
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torch_model = MlpModel(0, 32, "torch").to(device)
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torch_optimizer = torch.optim.AdamW(
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params=[{"params": torch_model.parameters(), "weight_decay": config.adam.weight_decay}],
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lr=config.adam.lr,
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betas=(config.adam.adam_beta1, config.adam.adam_beta2),
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eps=config.adam.adam_eps,
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)
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# check output
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torch_output = torch_model(input_ids=torch_xs) # pylint: disable=E1102
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loose_close(torch_output, output, dtype=dtype)
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torch_criterion = MyLoss().to(torch.float32)
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torch_loss = torch_criterion(torch_output, torch_ys) / micro_num # pylint: disable=E1102
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torch_loss.backward()
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torch_optimizer.step()
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# check loss
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loose_close(torch_loss, loss[0], dtype=dtype)
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@pytest.mark.parametrize("micro_num", [4, 8, 16])
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@pytest.mark.parametrize("num_chunks", [1, 2, 4])
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@pytest.mark.parametrize("interleaved_overlap", [True, False])
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def test_pipeline_parallel(micro_num, num_chunks, interleaved_overlap):
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ctx = mp.get_context("spawn")
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with ctx.Pool(processes=8) as pool:
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pool.map(
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exam_pipeline_parallel,
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[[rank, 8, micro_num, num_chunks, interleaved_overlap] for rank in range(8)],
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)
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pool.close()
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pool.join()
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if __name__ == "__main__":
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pytest.main(["-s", "-q", "test_pipeline.py"])
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