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[NFC] polish colossalai/nn/layer/parallel_2p5d/layers.py code style (#972)

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Zirui Zhu 2022-05-16 11:27:33 +08:00 committed by binmakeswell
parent 632e94abde
commit 598cde4a0f
1 changed files with 8 additions and 8 deletions
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16
colossalai/nn/layer/parallel_2p5d/layers.py
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@ -189,7 +189,7 @@ class Linear2p5D(ParallelLayer):
def forward(self, x: Tensor) -> Tensor: def forward(self, x: Tensor) -> Tensor:
# input: [m/dq, n/q, k/q] # input: [m/dq, n/q, k/q]
# output: [m/dq, n/q, h/q] # output: [m/dq, 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_2p5D.apply( output = Matmul_AB_2p5D.apply(
x, x,
@ -254,7 +254,7 @@ class LayerNorm2p5D(ParallelLayer):
self.tesseract_dim, _ = get_tesseract_dim_dep_from_env() self.tesseract_dim, _ = get_tesseract_dim_dep_from_env()
# partitioning dimension # partitioning dimension
self.partitioned_partition = divide(normalized_shape, self.tesseract_dim) # * self.partitioned_partition = divide(normalized_shape, self.tesseract_dim) # *
# create parameters # create parameters
factory_kwargs = {'device': get_current_device(), 'dtype': dtype} factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
@ -357,16 +357,16 @@ class LayerNorm2p5D(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_2P5D_ROW)) torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_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_2P5D_ROW)) torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_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)
@ -589,7 +589,7 @@ class PatchEmbedding2p5D(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_2p5d(self.cls_token, -1, ParallelMode.PARALLEL_2P5D_COL) cls_token = all_gather_tensor_2p5d(self.cls_token, -1, ParallelMode.PARALLEL_2P5D_COL)
pos_embed = all_gather_tensor_2p5d(self.pos_embed, -1, ParallelMode.PARALLEL_2P5D_COL) pos_embed = all_gather_tensor_2p5d(self.pos_embed, -1, ParallelMode.PARALLEL_2P5D_COL)
@ -1038,7 +1038,7 @@ class Classifier2p5D(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_2p5d(input_, self.weight, self.bias, self.tesseract_dim, out_shape, self.row_rank, return classifier_2p5d(input_, self.weight, self.bias, self.tesseract_dim, out_shape, self.row_rank,
self.col_rank, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL, self.col_rank, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL,
@ -1172,7 +1172,7 @@ class VocabParallelClassifier2p5D(ParallelLayer):
def forward(self, x: Tensor) -> Tensor: def forward(self, x: Tensor) -> Tensor:
# input: [m/dq, n/q, k/q] # input: [m/dq, n/q, k/q]
# output: [m/dq, n/q, h/q] # output: [m/dq, 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_ABT_2p5D.apply( output = Matmul_ABT_2p5D.apply(
x, x,