mirror of https://github.com/hpcaitech/ColossalAI
137 lines
4.9 KiB
Python
137 lines
4.9 KiB
Python
from copy import copy
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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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import torch
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.tensor import ColoTensor, distspec
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from functools import partial
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import colossalai
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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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import torch.nn.functional as F
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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.core import global_context as gpc
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from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager, register_colo_module, init_colo_module, ColoLinear
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from _utils import tensor_equal, tensor_shard_equal, set_seed
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from tests.components_to_test.registry import non_distributed_component_funcs
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def run_simplenet_with_spec(label):
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get_components_func = non_distributed_component_funcs.get_callable('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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set_seed(1)
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with ColoInitContext(device=get_current_device()):
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model = model_builder(checkpoint=True)
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if rank == 0:
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model_seq = model_builder(checkpoint=True)
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model_seq = model_seq.cuda()
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# Make two models have the same init params
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for p1, p2 in zip(model.parameters(), model_seq.parameters()):
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p2.data.copy_(p1.data)
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parallel_action = ParallelAction(ComputePattern.TP1D)
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init_colo_module(model, parallel_action, recursive=True, label=label)
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model = model.cuda()
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for i, (data, label) in enumerate(train_dataloader):
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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torch.distributed.broadcast(data, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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# For reference
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if rank == 0:
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if criterion:
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output_seq = model_seq(data)
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loss_seq = criterion(output_seq, label)
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else:
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output_seq = model_seq(data, label)
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loss_seq = output_seq
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if rank == 0:
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with torch.no_grad():
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assert torch.allclose(loss, loss_seq, rtol=1e-2)
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loss.backward()
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if rank == 0:
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loss_seq.backward()
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with torch.no_grad():
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# check param
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for p1, p2 in zip(model.parameters(), model_seq.parameters()):
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if p1.size() == p2.size():
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assert torch.allclose(p1, p2)
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else:
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if p1.size(-1) < p2.size(-1): # col
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world_size = p2.size(-1) // p1.size(-1)
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split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
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elif p1.size(0) < p2.size(0): # row
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world_size = p2.size(0) // p1.size(0)
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split_p2 = torch.chunk(p2, world_size, dim=0)[0]
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assert torch.allclose(p1, split_p2)
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if i > 3:
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break
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def run_linear_with_spec(label):
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with ColoInitContext(device=get_current_device()):
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model = torch.nn.Linear(4, 8)
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model_handy = copy(model)
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parallel_action = ParallelAction(ComputePattern.TP1D)
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init_colo_module(model, parallel_action, recursive=True, label=label)
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x = torch.rand(2, 4).cuda()
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out = model(x)
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colo_out = model_handy(x)
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assert tensor_equal(out, colo_out)
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grad = torch.rand_like(out)
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out.backward(grad)
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colo_out.backward(grad)
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assert tensor_shard_equal(model.weight.grad, model_handy.weight.grad)
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assert tensor_shard_equal(model.bias.grad, model_handy.bias.grad)
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def run_dist(rank, world_size, port, func):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=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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func('col')
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func('row')
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func('default')
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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_module_linear_1d(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port(), func=run_linear_with_spec)
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mp.spawn(run_func, nprocs=world_size)
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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_module_simplenet(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port(), func=run_simplenet_with_spec)
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_module_simplenet(4) |