[rename] convert_to_dist -> redistribute (#1243)

colossalai/nn/_ops/addmm.py

@@ -11,7 +11,7 @@ def colo_addmm_1Drow(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTenso
     # mat1:S[1] x mat2:S[0] = Output:P
     # beta * input + alpha * All-Reduce(Output) = res
 
-    mat1 = mat1.convert_to_dist_spec(distspec.shard([-1], [mat2.get_tp_world_size()]))
+    mat1 = mat1.redistribute(distspec.shard([-1], [mat2.get_tp_world_size()]))
 
     # Output:P
     partial_output = torch.mm(mat1, mat2)
@@ -28,7 +28,7 @@ def colo_addmm_1Dcol(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTenso
                      alpha: Number) -> ColoTensor:
     # mat1:B x mat2:S[1] + input:S[1] = Output:S[1]
     compute_spec = mat2.compute_spec
-    mat1 = mat1.convert_to_dist_spec(distspec.replicate())
+    mat1 = mat1.redistribute(distspec.replicate())
     mat1 = reduce_grad(mat1, mat1.get_process_group())
 
     output_parallel = torch.addmm(input_tensor, mat1, mat2, beta=beta, alpha=alpha)

colossalai/nn/_ops/embedding.py

@@ -14,7 +14,7 @@ def colo_embedding_1Dcol(input_tensor: ColoTensor,
                          sparse: bool = False) -> ColoTensor:
     # embedding_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P)
     # Gather splitted lookup table
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate())
+    input_tensor = input_tensor.redistribute(distspec.replicate())
 
     output_parallel = F.embedding(input_tensor,
                                   weight,
@@ -46,7 +46,7 @@ def colo_embedding_1Drow(input_tensor: ColoTensor,
     # Find index in this shard and mask those not here
     # Reduce all
     pg = weight.get_process_group()
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate())
+    input_tensor = input_tensor.redistribute(distspec.replicate())
 
     # tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
     tensor_parallel_rank = weight.get_process_group().tp_local_rank()

colossalai/nn/_ops/embedding_bag.py

@@ -20,7 +20,7 @@ def colo_embedding_bag_1Dcol(input_tensor: ColoTensor,
     # embedding_bag_1Dcol split the weight(lookup table) to (num_embeddings, embedding_dim/P)
     # Gather splitted lookup table
     pg = weight.get_process_group()
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate())
+    input_tensor = input_tensor.redistribute(distspec.replicate())
 
     output_parallel = F.embedding_bag(input_tensor,
                                       weight,

colossalai/nn/_ops/layernorm.py

@@ -16,7 +16,7 @@ def colo_layernorm(
     assert isinstance(weight, ColoTensor)
     input_tensor = convert_to_colo_tensor(input_tensor, weight.get_process_group())
     bias = convert_to_colo_tensor(bias, weight.get_process_group())
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate())
+    input_tensor = input_tensor.redistribute(distspec.replicate())
 
     output = F.layer_norm(input_tensor, normalized_shape, weight=weight, bias=bias, eps=eps)
     output = ColoTensor.from_torch_tensor(output, ColoTensorSpec(input_tensor.get_process_group()))

colossalai/nn/_ops/linear.py

@@ -12,7 +12,7 @@ def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: Option
     # All-Reduce(Output) + bias = res
     # Input:S[1]
     pg = weight.get_process_group()
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.shard([-1], [weight.get_tp_world_size()]))
+    input_tensor = input_tensor.redistribute(distspec.shard([-1], [weight.get_tp_world_size()]))
 
     # Output:P
     partial_output = F.linear(input_tensor, weight)
@@ -33,7 +33,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: Option
     # All-Gather(Output)
     # Input:B
     compute_spec = weight.compute_spec
-    input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate())
+    input_tensor = input_tensor.redistribute(distspec.replicate())
     input_parallel = reduce_grad(input_tensor, weight.get_process_group())
 
     output_parallel = F.linear(input_parallel, weight, bias)

colossalai/tensor/colo_tensor.py

@@ -18,7 +18,7 @@ def _get_my_nowrap_functions() -> Set[Callable]:
         Tensor._base.__get__,
         Tensor.grad.__get__,
         Tensor._grad.__get__,
-        Tensor.data.__get__,  # make .data returns torch.Tensor rather than ColoTensor
+        Tensor.data.__get__,    # make .data returns torch.Tensor rather than ColoTensor
     }
 
 
@@ -140,7 +140,7 @@ class ColoTensor(torch.Tensor):
         """
         assert isinstance(dist_spec, _DistSpec)
         assert self.process_group is not None
-        self._convert_to_dist_spec(dist_spec)
+        self._redistribute(dist_spec)
 
     def set_tensor_spec(self, dist_spec, compute_spec):
         if dist_spec:
@@ -174,8 +174,8 @@ class ColoTensor(torch.Tensor):
     def __repr__(self):
         return f'ColoTensor:\n{super().__repr__()}\n{self.dist_spec}\n{self.process_group}'
 
-    def _convert_to_dist_spec(self, dist_spec: _DistSpec) -> None:
+    def _redistribute(self, dist_spec: _DistSpec) -> None:
-        """_convert_to_dist_spec 
+        """_redistribute 
         Note the function will not handle the logic of backward propagation!
         It is used during model tensor initializations as an internal function.
         Args:
@@ -186,7 +186,7 @@ class ColoTensor(torch.Tensor):
             self.data = DistSpecManager.handle_trans_spec(self.data, self.dist_spec, dist_spec, self.process_group)
         self.dist_spec = dist_spec
 
-    def convert_to_dist_spec(self, dist_spec: _DistSpec) -> 'ColoTensor':
+    def redistribute(self, dist_spec: _DistSpec) -> 'ColoTensor':
         ret = DistSpecManager.handle_trans_spec(self, self.dist_spec, dist_spec, self.process_group)
         return ColoTensor.from_torch_tensor(ret, ColoTensorSpec(self.process_group, dist_attr=dist_spec))
 
@@ -194,13 +194,13 @@ class ColoTensor(torch.Tensor):
         """to_replicate_ 
         an inline member function, converting dist spec of the tensor to REPLICATE
         """
-        self._convert_to_dist_spec(dist_spec=distspec.replicate())
+        self._redistribute(dist_spec=distspec.replicate())
 
     def to_replicate(self) -> 'ColoTensor':
         """to_replicate
         converting dist spec of the tensor to REPLICATE
         """
-        return self.convert_to_dist_spec(distspec.replicate())
+        return self.redistribute(distspec.replicate())
 
     @staticmethod
     def from_torch_tensor(tensor: torch.Tensor, spec: Optional[ColoTensorSpec] = None) -> 'ColoTensor':
@@ -234,7 +234,7 @@ class ColoTensor(torch.Tensor):
         """
         if self.is_replicate():
             return super().view(*args)
-        replicated_t = self.convert_to_dist_spec(dist_spec=distspec.replicate())
+        replicated_t = self.redistribute(dist_spec=distspec.replicate())
         return replicated_t.view(*args)
 
     def size_global(self, args: Optional[int] = None):
@@ -280,4 +280,4 @@ class ColoTensor(torch.Tensor):
             and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
 
     def is_sharded(self):
-        return self.dist_spec.placement == DistPlacementPattern.SHARD
+        return self.dist_spec.placement == DistPlacementPattern.SHARD

tests/test_tensor/test_loss_func.py

@@ -22,7 +22,7 @@ def check_cross_entropy():
     world_size = torch.distributed.get_world_size()
     pg = ProcessGroup(tp_degree=world_size)
     input_t_colo = ColoTensor.from_torch_tensor(tensor=input_ct, spec=ColoTensorSpec(pg))
-    input_shard = input_t_colo.convert_to_dist_spec(distspec.shard([-1], [pg.tp_world_size()]))
+    input_shard = input_t_colo.redistribute(distspec.shard([-1], [pg.tp_world_size()]))
     input_shard.set_tensor_spec(dist_spec=None, compute_spec=ComputeSpec(ComputePattern.TP1D))
 
     output = F.cross_entropy(input_t, target)