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116 lines
3.6 KiB
116 lines
3.6 KiB
import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.testing import assert_close
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import colossalai
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from colossalai.shardformer.layer import LinearConv1D_Col, LinearConv1D_Row
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from colossalai.shardformer.layer.linear_conv import split_fused_qkv
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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# This code is copied from https://github.com/huggingface/transformers
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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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self.weight = nn.Parameter(torch.empty(nx, nf))
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self.bias = nn.Parameter(torch.zeros(nf))
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nn.init.normal_(self.weight, std=0.02)
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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 rearrange(tensor: torch.Tensor, dim: int):
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tensor = tensor.clone()
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world_size = 2
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order = torch.arange(world_size * 3)
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new_order = []
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for i in range(world_size):
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new_order.append(order[i::world_size])
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new_order = torch.cat(new_order)
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tensor_chunks = torch.chunk(tensor, world_size * 3, dim=dim)
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rearanged_tensor_chunks = [tensor_chunks[i] for i in new_order]
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rearanged_tensor = torch.cat(rearanged_tensor_chunks, dim=dim)
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return rearanged_tensor
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def check_linear_conv_1d_col():
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linear = Conv1D(192, 48).cuda()
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linear_conv_col = LinearConv1D_Col.from_native_module(linear, process_group=None, gather_output=True, n_fused=3)
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assert linear.weight.shape == torch.Size([48, 192])
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assert linear.bias.shape == torch.Size([192])
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assert linear_conv_col.weight.shape == torch.Size([48, 96])
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assert linear_conv_col.bias.shape == torch.Size([96])
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# ensure weights are reversibly loadable
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linear_conv_col.load_state_dict(linear.state_dict())
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linear.load_state_dict(linear_conv_col.state_dict())
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# check computation correctness
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x = torch.rand(4, 48).cuda()
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out = linear(x)
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gather_out = linear_conv_col(x)
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assert_close(rearrange(out, 1), gather_out)
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# check backward correctness
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out.sum().backward()
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gather_out.sum().backward()
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target_grad = split_fused_qkv(linear.weight.grad, 3, None)
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assert_close(target_grad, linear_conv_col.weight.grad)
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def check_linear_conv_1d_row():
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linear = Conv1D(192, 48).cuda()
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linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False)
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assert linear.weight.shape == torch.Size([48, 192])
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assert linear_row.weight.shape == torch.Size([24, 192])
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assert linear_row.bias.shape == torch.Size([192])
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# check computation correctness
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x = torch.rand(4, 48).cuda()
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out = linear(x)
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gather_out = linear_row(x)
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assert_close(out, gather_out)
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# check backward correctness
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out.sum().backward()
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gather_out.sum().backward()
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rank = dist.get_rank()
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target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank]
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assert_close(target_grad, linear_row.weight.grad)
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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check_linear_conv_1d_col()
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check_linear_conv_1d_row()
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@rerun_if_address_is_in_use()
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def test_linearconv():
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spawn(run_dist, nprocs=2)
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if __name__ == '__main__':
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test_linearconv()
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