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

pull/997/head
shenggan 2022-05-16 12:33:22 +08:00 committed by binmakeswell
parent b67eebd20f
commit 18542b47fc
1 changed files with 7 additions and 7 deletions
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14
colossalai/nn/layer/parallel_2d/layers.py
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@ -182,7 +182,7 @@ class Linear2D(ParallelLayer):
def forward(self, x: Tensor) -> Tensor: def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q] # input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/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, 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, 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: def forward(self, x: Tensor) -> Tensor:
with torch.no_grad(): 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)) torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
E_x /= self.normalized_shape E_x /= self.normalized_shape
# Var_x in the block below is the sum of input^2 # 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)) torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
Var_x /= self.normalized_shape 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) # this time 1/sqrt(Var_x + epsilon)
Var_x = 1.0 / torch.sqrt(Var_x + self.variance_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) output = F.conv2d(input_, weight, bias, stride=self.patch_size)
if self.flatten: 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) 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) 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) destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor: 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, 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, 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: def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q] # input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/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, 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, ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,