mirror of https://github.com/hpcaitech/ColossalAI
[misc] Accelerate CI for zero and dist optim (#5758)
* remove fp16 from lamb * remove d2h copy in checking states --------- Co-authored-by: Edenzzzz <wtan45@wisc.edu>pull/5782/head
Edenzzzz
6 months ago
committed by
GitHub
parent
50b4c8e8cf
commit
79f7a7b211
@ -1,126 +0,0 @@
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import pytest
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.testing import assert_close
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import colossalai
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from colossalai.accelerator import get_accelerator
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from colossalai.legacy.amp import convert_to_apex_amp
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils import set_seed
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from colossalai.zero import GeminiDDP, GeminiOptimizer
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from colossalai.zero.gemini.chunk import search_chunk_configuration
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from tests.kit.model_zoo import model_zoo, run_fwd_bwd
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PLACEMENT_CONFIGS = [
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{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
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{"placement_policy": "static", "shard_param_frac": 1.0}, # zero3
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{"placement_policy": "static", "shard_param_frac": 0.5}, # zero3-half
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{"placement_policy": "auto"},
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]
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def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
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chunk_manager = model.chunk_manager
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param_list = [p for p in model.parameters()]
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chunk_list = chunk_manager.get_chunks(param_list)
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if not model.chunk_manager.reuse_fp16_chunk:
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chunk_list = [chunk.grad_chunk for chunk in chunk_list]
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for chunk in chunk_list:
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chunk_manager.access_chunk(chunk)
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for p0, p1 in zip(model.parameters(), torch_model.parameters()):
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assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5)
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@parameterize("placement_config", PLACEMENT_CONFIGS)
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@parameterize("keep_gather", [False, True])
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@parameterize("model_name", ["transformers_gpt_lm"])
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@parameterize("use_grad_checkpoint", [False, True])
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@parameterize("master_weights", [False, True])
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@parameterize("max_prefetch", [0, 4])
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@parameterize("enable_async_reduce", [False, True])
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def exam_gpt_fwd_bwd(
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placement_config,
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keep_gather,
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model_name: str,
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use_grad_checkpoint: bool = False,
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master_weights: bool = True,
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max_prefetch: int = 0,
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enable_async_reduce=True,
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):
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init_device = get_accelerator().get_current_device()
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model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
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iter(model_zoo.get_sub_registry(model_name).values())
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)
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set_seed(42)
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model = model_builder()
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set_seed(42)
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torch_model = model_builder().cuda()
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p.data)
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if use_grad_checkpoint:
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model.gradient_checkpointing_enable()
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torch_model.gradient_checkpointing_enable()
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world_size = torch.distributed.get_world_size()
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config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
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config_dict[world_size]["chunk_size"] = 5000
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config_dict[world_size]["keep_gathered"] = keep_gather
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model = GeminiDDP(
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model,
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config_dict,
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init_device,
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pin_memory=True,
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**placement_config,
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master_weights=master_weights,
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max_prefetch=max_prefetch,
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enable_async_reduce=enable_async_reduce,
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)
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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zero_optim = GeminiOptimizer(optimizer, model, initial_scale=1)
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rank = dist.get_rank()
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amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=1, master_weights=master_weights)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
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torch_model = DDP(torch_model, device_ids=[rank])
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set_seed(rank)
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data = data_gen_fn()
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data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
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torch_optim.zero_grad()
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zero_optim.zero_grad()
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# set random seed is same as torch_model.eval()
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set_seed(42)
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torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
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set_seed(42)
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loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
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assert_close(torch_loss.float(), loss.float())
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check_grad(model, torch_model)
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def run_dist(rank, world_size, port):
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colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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exam_gpt_fwd_bwd()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 4])
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@rerun_if_address_is_in_use()
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def test_gpt(world_size):
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spawn(run_dist, world_size)
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if __name__ == "__main__":
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test_gpt(1)
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