From 18542b47fc7be2f90b1dd6b2d24b206194ec618f Mon Sep 17 00:00:00 2001 From: shenggan Date: Mon, 16 May 2022 12:33:22 +0800 Subject: [PATCH] [NFC] polish colossalai/nn/layer/parallel_2d/layers.py code style (#976) --- colossalai/nn/layer/parallel_2d/layers.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/colossalai/nn/layer/parallel_2d/layers.py b/colossalai/nn/layer/parallel_2d/layers.py index cec7cb8f7..5fc5c63e5 100644 --- a/colossalai/nn/layer/parallel_2d/layers.py +++ b/colossalai/nn/layer/parallel_2d/layers.py @@ -182,7 +182,7 @@ class Linear2D(ParallelLayer): 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, @@ -337,16 +337,16 @@ class LayerNorm2D(ParallelLayer): 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) @@ -569,7 +569,7 @@ class PatchEmbedding2D(ParallelLayer): 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) @@ -1012,7 +1012,7 @@ class Classifier2D(ParallelLayer): 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, @@ -1186,7 +1186,7 @@ class VocabParallelClassifier2D(ParallelLayer): 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,