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[NFC] polish colossalai/nn/layer/parallel_2d/layers.py code style #976

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Merged
binmakeswell merged 1 commit into from
May 16, 2022
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[NFC] polish colossalai/nn/layer/parallel_2d/layers.py code style #976

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14 changes: 7 additions & 7 deletions colossalai/nn/layer/parallel_2d/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -182,7 +182,7 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):
def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/q]
out_shape = x.shape[:-1] + (self.hidden_size_per_partition, )
out_shape = x.shape[:-1] + (self.hidden_size_per_partition,)

output = Matmul_AB_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
Expand Down Expand Up @@ -337,16 +337,16 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):

def forward(self, x: Tensor) -> Tensor:
with torch.no_grad():
E_x = torch.sum(x, dim=-1, keepdim=True) # [b/q, s, 1]
E_x = torch.sum(x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
E_x /= self.normalized_shape

# Var_x in the block below is the sum of input^2
Var_x = torch.sum(x * x, dim=-1, keepdim=True) # [b/q, s, 1]
Var_x = torch.sum(x * x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
Var_x /= self.normalized_shape

Var_x = Var_x - E_x * E_x # variance of x [b/q, s, 1]
Var_x = Var_x - E_x * E_x # variance of x [b/q, s, 1]
# this time 1/sqrt(Var_x + epsilon)
Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)

Expand Down Expand Up @@ -569,7 +569,7 @@ def forward(self, input_: Tensor) -> Tensor:

output = F.conv2d(input_, weight, bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC

cls_token = all_gather_tensor_2d(self.cls_token, -1, ParallelMode.PARALLEL_2D_COL)
pos_embed = all_gather_tensor_2d(self.pos_embed, -1, ParallelMode.PARALLEL_2D_COL)
Expand Down Expand Up @@ -1012,7 +1012,7 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):
destination.update(local_state)

def forward(self, input_: Tensor) -> Tensor:
out_shape = input_.shape[:-1] + (self.num_classes, )
out_shape = input_.shape[:-1] + (self.num_classes,)

return classifier_2d(input_, self.weight, self.bias, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
Expand Down Expand Up @@ -1186,7 +1186,7 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):
def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/q]
out_shape = x.shape[:-1] + (self.output_size_per_partition, )
out_shape = x.shape[:-1] + (self.output_size_per_partition,)

output = Matmul_ABT_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
Expand Down