mirror of https://github.com/hpcaitech/ColossalAI
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
import colossalai
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import torch
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import pytest
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import torch.nn as nn
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import torch.multiprocessing as mp
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from colossalai.tensor import ColoTensor
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from colossalai.tensor import dist_spec
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from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
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from colossalai.context import ParallelMode
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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 functools import partial
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from colossalai.core import global_context as gpc
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class Conv1D(nn.Module):
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"""
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1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
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Basically works like a linear layer but the weights are transposed.
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Args:
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nf (`int`): The number of output features.
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nx (`int`): The number of input features.
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"""
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def __init__(self, nf, nx):
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super().__init__()
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self.nf = nf
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w = torch.empty(nx, nf)
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nn.init.normal_(w, std=0.02)
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self.weight = nn.Parameter(w)
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self.bias = nn.Parameter(torch.ones(nf))
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def forward(self, x):
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size_out = x.size()[:-1] + (self.nf,)
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x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
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x = x.view(size_out)
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return x
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def init_1d_row(weight, bias):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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def check_grad_1d_row(model: torch.nn.Module, weight, bias):
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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assert torch.allclose(model.weight.grad.chunk(size, 0)[rank], weight.grad)
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assert torch.allclose(model.bias.grad, bias.grad)
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def init_1d_col(weight, bias):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1D, parallel_mode=ParallelMode.PARALLEL_1D)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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bias.set_spec(spec)
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def check_grad_1d_col(model: torch.nn.Module, weight, bias):
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rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
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assert torch.allclose(model.weight.grad.chunk(size, -1)[rank], weight.grad)
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assert torch.allclose(model.bias.grad.chunk(size, -1)[rank], bias.grad)
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def run_with_spec(spec_init_func, check_grad_func):
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model = Conv1D(4, 16).cuda()
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weight = ColoTensor.init_from_torch_tensor(torch.nn.Parameter(model.weight.detach()))
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bias = ColoTensor.init_from_torch_tensor(torch.nn.Parameter(model.bias.detach()))
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spec_init_func(weight, bias)
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x = torch.rand(2, 16).cuda()
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out = model(x)
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colo_out = torch.addmm(bias, x, weight)
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assert torch.allclose(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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check_grad_func(model, weight, bias)
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def run_dist(rank, world_size, port):
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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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run_with_spec(init_1d_row, check_grad_1d_row)
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run_with_spec(init_1d_col, check_grad_1d_col)
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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_addmm_1d(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_addmm_1d(4)
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