[fx] temporarily used (#1215)

colossalai/fx/passes/shard_1d_pass.py

@@ -4,75 +4,37 @@ from torch.fx.node import Node
 from torch.fx.passes.split_module import split_module
 
 import colossalai
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
+from colossalai.tensor import ColoTensor, TensorSpec, distspec, ProcessGroup, ComputeSpec, ComputePattern
 
 
-def all_gather_function(input_):
-    world_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
-    rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
-    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
-    tensor_list[rank] = input_
-    group = gpc.get_group(ParallelMode.PARALLEL_1D)
-    torch.distributed.all_gather(tensor_list, input_, group=group)
-    output = torch.cat(tensor_list, dim=-1).contiguous()
-    return output
+def weight_split(weight: torch.nn.parameter.Parameter, dim: int) -> torch.nn.parameter.Parameter:
+    """weight_split 
+    split a nn.Parameter
 
+    Args:
+        weight (torch.nn.parameter.Parameter): a torch Parameter instance
+        dim (int): the dimension to be sharded along with
 
-def all_reduce_function(input_):
-    if gpc.get_world_size(ParallelMode.PARALLEL_1D) == 1:
-        return input_
-    torch.distributed.all_reduce(input_, group=gpc.get_group(ParallelMode.PARALLEL_1D))
-    return input_
-
-
-def weight_split(weight, dim):
-    #TODO: this function will be refactored by using ColoTensor dist_spec when a stable reshaper feature is ready to use.
-    num_partition = gpc.get_world_size(ParallelMode.TENSOR)
-    shape = weight.shape
-    length = shape[dim] // num_partition
-    sharded_weight_list = []
-    for i in range(num_partition):
-        sharded_weight_list.append(weight.narrow(dim, i * length, length))
-    return sharded_weight_list[gpc.get_local_rank(ParallelMode.PARALLEL_1D)]
-
-
-def replace_all_uses_except_replaced(node, replace_node):
+    Returns:
+        _type_: _description_
     """
-        Replace all uses of ``node`` in the Graph with the Node ``replace_node``,
-        except the user of ``node`` is ``replace_node``.
+    #TODO: This func temporarily works with no materialization
+    # Append a Tensor spec to target_module.weight.shard
+    # Convert to ColoTensor: colo_tensor = ColoTensor.from_torch_tensor(tensor, spec)
+    # assert isinstance(weight, torch.nn.parameter.Parameter), \
+    #     f'The type of the input tensor should be torch.nn.parameter' \
+    #     f'Your Input tensor is {type(weight)}'
 
-        Args:
+    # FIXME() I initialized a PG for this tensor. Only has TP comm group.
+    # we only consider the TP-only caes.
+    world_size = torch.distributed.get_world_size()
+    pg = ProcessGroup(tp_degree=world_size)
 
-            replace_node (Node): The node to replace all uses of ``node`` with.
-
-        Returns:
-
-            The list of Nodes on which this change was made.
-    """
-    to_process = list(node.users)
-    for use_node in to_process:
-        if use_node == replace_node:
-            continue
-
-        def may_replace_node(n):
-            if n == node:
-                return replace_node
-            else:
-                return n
-
-        new_args = map_arg(use_node.args, may_replace_node)
-        new_kwargs = map_arg(use_node.kwargs, may_replace_node)
-        use_node._args = new_args
-        use_node._kwargs = new_kwargs
-        for old_use in use_node._input_nodes.keys():
-            old_use.users.pop(use_node)
-        use_node._input_nodes = {}
-        map_arg(use_node._args, lambda n: use_node._input_nodes.setdefault(n))
-        map_arg(use_node._kwargs, lambda n: use_node._input_nodes.setdefault(n))
-        for new_use in use_node._input_nodes.keys():
-            new_use.users.setdefault(use_node)
-    return to_process
+    spec = TensorSpec(distspec.shard(pg, [dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
+    # As you has constructed a Spec, why not directly convert the tensor to ColoTensor.
+    # setattr(weight, "fx_attr", spec)
+    weight.data = ColoTensor(data=weight.data, spec=spec)
+    return weight
 
 
 def column_shard_linear_pass(gm: torch.fx.GraphModule):
@@ -81,14 +43,10 @@ def column_shard_linear_pass(gm: torch.fx.GraphModule):
         if node.op == "call_module":
             target_module = node.graph.owning_module.get_submodule(node.target)
             if isinstance(target_module, torch.nn.Linear):
-                target_module.weight.data = weight_split(target_module.weight.data, dim=0)
+                target_module.weight = weight_split(target_module.weight, dim=0)
                 if target_module.bias is not None:
                     target_module.bias.data = weight_split(target_module.bias.data, dim=0)
 
-                # inserting communication node after the sharded linear node
-                with mod_graph.inserting_after(node):
-                    new_node = mod_graph.create_node('call_function', all_gather_function, args=(node,))
-                    replace_all_uses_except_replaced(node, new_node)
     gm.recompile()
     return gm
 
@@ -99,20 +57,7 @@ def row_shard_linear_pass(gm: torch.fx.GraphModule):
         if node.op == "call_module":
             target_module = node.graph.owning_module.get_submodule(node.target)
             if isinstance(target_module, torch.nn.Linear):
-                target_module.weight.data = weight_split(target_module.weight.data, dim=-1)
-
-                # insert input sharding node before the sharded linear node
-                with mod_graph.inserting_before(node):
-                    input_node_list = list(node._input_nodes.keys())
-                    assert len(input_node_list) == 1, 'linear forward must have and only have one input tensor.'
-                    input_node = input_node_list[0]
-                    new_input_node = mod_graph.create_node('call_function', weight_split, args=(input_node, -1))
-                    replace_all_uses_except_replaced(input_node, new_input_node)
-
-                # inserting communication node after the sharded linear node
-                with mod_graph.inserting_after(node):
-                    new_node = mod_graph.create_node('call_function', all_reduce_function, args=(node,))
-                    replace_all_uses_except_replaced(node, new_node)
+                target_module.weight = weight_split(target_module.weight, dim=-1)
     gm.recompile()
     return gm