mirror of https://github.com/hpcaitech/ColossalAI
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
import colossalai
|
|
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, tensor_equal, tensor_shard_equal
|
|
|
|
|
|
class Conv1D(nn.Module):
|
|
"""
|
|
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
|
|
Basically works like a linear layer but the weights are transposed.
|
|
Args:
|
|
nf (`int`): The number of output features.
|
|
nx (`int`): The number of input features.
|
|
"""
|
|
|
|
def __init__(self, nf, nx):
|
|
super().__init__()
|
|
self.nf = nf
|
|
w = torch.empty(nx, nf)
|
|
nn.init.normal_(w, std=0.02)
|
|
self.weight = nn.Parameter(w)
|
|
self.bias = nn.Parameter(torch.ones(nf))
|
|
|
|
def forward(self, x):
|
|
size_out = x.size()[:-1] + (self.nf,)
|
|
x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
|
|
x = x.view(size_out)
|
|
return x
|
|
|
|
|
|
def run_with_spec(spec_init_func, split_bias):
|
|
model = Conv1D(4, 16).cuda()
|
|
world_size = torch.distributed.get_world_size()
|
|
pg = ProcessGroup(tp_degree=world_size)
|
|
|
|
weight = ColoTensor(torch.nn.Parameter(model.weight.detach()), ColoTensorSpec(pg))
|
|
bias = ColoTensor(torch.nn.Parameter(model.bias.detach()), ColoTensorSpec(pg))
|
|
|
|
spec_init_func(weight, pg)
|
|
if split_bias:
|
|
spec_init_func(bias, pg)
|
|
|
|
x = torch.rand(2, 16).cuda()
|
|
out = model(x)
|
|
colo_out = torch.addmm(bias, x, weight)
|
|
colo_out = colo_out.to_replicate()
|
|
assert tensor_equal(out, colo_out)
|
|
grad = torch.rand_like(out)
|
|
out.backward(grad)
|
|
colo_out.backward(grad)
|
|
tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
|
|
tensor_shard_equal(model.bias.grad, bias.grad, pg.tp_local_rank(), pg.tp_world_size())
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_with_spec(spec_init_func=split_param_row_tp1d, split_bias=False)
|
|
run_with_spec(spec_init_func=split_param_col_tp1d, split_bias=True)
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_addmm_1d(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_addmm_1d(4)
|