mirror of https://github.com/hpcaitech/ColossalAI
133 lines
5.5 KiB
Python
133 lines
5.5 KiB
Python
from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from utils.util import set_seed, tensor_equal, tensor_shard_equal
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from vit import get_training_components
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import colossalai
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.nn.parallel.data_parallel import ColoDDP
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from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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# Only for all Linear, it's 1d_row split because Linear will be transposed when calculating.
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# But for other layers, it's 1d_col split.
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# Layernorm is not supported for now.
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# patch_embeddings.projection has nn.Conv2d
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# https://github.com/huggingface/transformers/blob/dcb08b99f44919425f8ba9be9ddcc041af8ec25e/src/transformers/models/vit/modeling_vit.py#L182
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def init_1d_row_for_linear_weight_spec(model, world_size: int):
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pg = ProcessGroup(tp_degree=world_size)
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spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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with DistSpecManager.no_grad():
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for n, p in model.named_parameters():
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if 'weight' in n and 'layernorm' not in n and 'embeddings.patch_embeddings.projection.weight' not in n:
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p.set_process_group(pg)
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p.set_tensor_spec(*spec)
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# Similarly, it's col split for Linear but row split for others.
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def init_1d_col_for_linear_weight_bias_spec(model, world_size: int):
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pg = ProcessGroup(tp_degree=world_size)
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spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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with DistSpecManager.no_grad():
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for n, p in model.named_parameters():
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if ('weight' in n
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or 'bias' in n) and 'layernorm' not in n and 'embeddings.patch_embeddings.projection' not in n:
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p.set_process_group(pg)
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p.set_tensor_spec(*spec)
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def check_param_equal(model, torch_model):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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assert tensor_shard_equal(torch_p, p)
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def check_grad_equal(model, torch_model):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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if (torch_p.grad.shape == p.grad.shape):
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assert torch.allclose(torch_p.grad, p.grad, rtol=1e-3, atol=2.0) == True
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else:
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dims_not_eq = torch.nonzero(torch.tensor(torch_p.grad.shape) != torch.tensor(p.grad.shape))
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dim = dims_not_eq.item()
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world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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assert torch.allclose(torch_p.grad.chunk(world_size, dim)[rank], p.grad, rtol=1e-3, atol=2.0) == True
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def run_vit(init_spec_func, use_ddp):
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_training_components()
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = model.cuda()
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torch_model = model_builder().cuda()
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if use_ddp:
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model = ColoDDP(model)
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torch_model = DDP(torch_model,
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device_ids=[gpc.get_global_rank()],
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process_group=gpc.get_group(ParallelMode.DATA))
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p)
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world_size = torch.distributed.get_world_size()
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init_spec_func(model, world_size)
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check_param_equal(model, torch_model)
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model.train()
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torch_model.train()
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set_seed(gpc.get_local_rank(ParallelMode.DATA))
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optimizer = optimizer_class(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
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torch_optimizer = optimizer_class(torch_model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
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for i, image_dict in enumerate(train_dataloader):
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if use_ddp:
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model.zero_grad()
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else:
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optimizer.zero_grad()
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logits = model(image_dict['pixel_values'])
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torch_logits = torch_model(image_dict['pixel_values'])
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assert tensor_equal(torch_logits.logits, logits.logits)
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loss = criterion(logits.logits, image_dict['label'])
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torch_loss = criterion(torch_logits.logits, image_dict['label'])
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if use_ddp:
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model.backward(loss)
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else:
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loss.backward()
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torch_loss.backward()
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check_grad_equal(model, torch_model)
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optimizer.step()
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torch_optimizer.step()
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check_param_equal(model, torch_model)
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break
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def run_dist(rank, world_size, port, use_ddp):
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if use_ddp and world_size == 1:
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return
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tp_world_size = world_size // 2 if use_ddp else world_size
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config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_vit(init_1d_row_for_linear_weight_spec, use_ddp)
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run_vit(init_1d_col_for_linear_weight_bias_spec, use_ddp)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.parametrize('use_ddp', [False, True])
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@rerun_if_address_is_in_use()
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def test_vit(world_size, use_ddp):
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run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_vit(1, False)
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