[autoparallel] process size nodes in runtime pass (#2130)

* [autoparallel] process size nodes in runtime pass

* polish code

colossalai/auto_parallel/passes/runtime_preparation_pass.py

+import operator
 from copy import deepcopy
-from typing import List
+from typing import Dict, List, Union
 
 import torch
 from torch.fx import symbolic_trace
@@ -20,6 +21,35 @@ from colossalai.tensor.sharding_spec import ShardingSpec
 shape_consistency_manager = ShapeConsistencyManager()
 
 
+def size_processing(size: Union[int, torch.Size],
+                    dim_partition_dict: Dict[int, List[int]],
+                    device_mesh_info: Dict[int, int],
+                    target_dim: int = None,
+                    node_name: str = None):
+    """
+    This method will be invoked during runtime to convert size node value depending on distributed information.
+    """
+    if target_dim is not None:
+        assert isinstance(size, int)
+        if target_dim in dim_partition_dict:
+            total_shard_size = 1
+            for shard_dim in dim_partition_dict[target_dim]:
+                total_shard_size *= device_mesh_info[shard_dim]
+            size = size * total_shard_size
+
+    else:
+        size = list(size)
+        for dim, dim_size in enumerate(size):
+            if dim in dim_partition_dict:
+                total_shard_size = 1
+                for shard_dim in dim_partition_dict[dim]:
+                    total_shard_size *= device_mesh_info[shard_dim]
+                size[dim] = dim_size * total_shard_size
+        size = torch.Size(size)
+
+    return size
+
+
 def _solution_annotatation(gm: torch.fx.GraphModule,
                            solution: List[int],
                            strategies_constructor: StrategiesConstructor = None):
@@ -103,6 +133,119 @@ def _solution_annotatation(gm: torch.fx.GraphModule,
     return gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict
 
 
+def _size_value_converting(gm: torch.fx.GraphModule, device_mesh: DeviceMesh):
+    """
+    In the auto parallel system, tensors may get shard on different devices, so the size of tensors
+    need to be converted to the size of original tensor and managed by the users, such as torch.view,
+    torch.reshape, etc. These nodes have enough information like input sharding_spec and
+    output sharding_spec to decide how to convert the size value.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+    node_pairs = {}
+
+    for node in nodes:
+
+        if node.op == 'call_method' and node.target == 'size':
+            # extract useful information from size node
+            # dim_partition_dict will instruct the size value on which
+            # dimension should be enlarged.
+            sharding_spec = node.args[0].sharding_spec
+            dim_partition_dict = sharding_spec.dim_partition_dict
+
+            # there are two usages of torch.Tensor.size:
+            #   tensor.size()
+            #   tensor.size(dim)
+            # if a target_dim is assigned, then the output will be
+            # in type of int, instead of torch.Size
+            target_dim = None
+            if len(node.args) > 1:
+                target_dim = node.args[1]
+                if target_dim < 0:
+                    target_dim += node.args[0]._meta_data.dim()
+
+            # DeviceMesh information instructs the scaling of the size value
+            device_mesh_info = {}
+            for dim, dim_size in enumerate(device_mesh.mesh_shape):
+                device_mesh_info[dim] = dim_size
+
+            with mod_graph.inserting_after(node):
+                size_processing_node = mod_graph.create_node('call_function',
+                                                             size_processing,
+                                                             args=(node, dim_partition_dict, device_mesh_info,
+                                                                   target_dim, node.name))
+                # store original node and processing node pair in node_pairs dictioanry
+                # It will be used to replace the original node with processing node in slice object
+                node_pairs[node] = size_processing_node
+                size_processing_node._meta_data = node._meta_data
+
+            user_list = list(node.users.keys())
+            for user in user_list:
+                if user == size_processing_node:
+                    continue
+                new_args = list(user.args)
+                new_kwargs = dict(user.kwargs)
+                # the origin node may be a positional argument or key word argument of user node
+                if node in new_args:
+                    # substitute the origin node with size_processing_node
+                    new_args[new_args.index(node)] = size_processing_node
+                    user.args = tuple(new_args)
+                elif str(node) in new_kwargs:
+                    # substitute the origin node with size_processing_node
+                    new_kwargs[str(node)] = size_processing_node
+                    user.kwargs = new_kwargs
+
+        if node.op == 'call_function' and node.target == operator.getitem:
+
+            getitem_index = node.args[1]
+            # slice object is quite special in torch.fx graph,
+            # On one side, we treat slice object same as type of int,
+            # so we do not create a node for slice object. On the other side,
+            # slice object could take fx.Node as its argument. And the user
+            # relationship cannot be tracked in fx graph.
+            # Therefore, I record the node_pairs in this pass, and use the it
+            # to replace the original node argument inside the slice object if
+            # it has been processed in above pass.
+
+            # There are three main usages of operator.getitem:
+            #   getitem(input, int)
+            #   getitem(input, slice)
+            #   getitem(input, Tuple[slice])
+            # In this pass, we need process the last two cases because
+            # node arguments may potentially appear in these cases.
+            if isinstance(getitem_index, slice):
+                new_start, new_stop, new_step = getitem_index.start, getitem_index.stop, getitem_index.step
+                if getitem_index.start in node_pairs:
+                    new_start = node_pairs[getitem_index.start]
+                elif getitem_index.stop in node_pairs:
+                    new_stop = node_pairs[getitem_index.stop]
+                elif getitem_index.step in node_pairs:
+                    new_step = node_pairs[getitem_index.step]
+                new_slice_item = slice(new_start, new_stop, new_step)
+                new_args = (node.args[0], new_slice_item)
+                node.args = new_args
+
+            elif isinstance(getitem_index, (tuple, list)):
+                assert isinstance(getitem_index[0], slice)
+                new_slice_items = []
+
+                for slice_item in getitem_index:
+                    new_start, new_stop, new_step = slice_item.start, slice_item.stop, slice_item.step
+                    if slice_item.start in node_pairs:
+                        new_start = node_pairs[slice_item.start]
+                    elif slice_item.stop in node_pairs:
+                        new_stop = node_pairs[slice_item.stop]
+                    elif slice_item.step in node_pairs:
+                        new_step = node_pairs[slice_item.step]
+                    new_slice_item = slice(new_start, new_stop, new_step)
+                    new_slice_items.append(new_slice_item)
+
+                new_args = (node.args[0], tuple(new_slice_items))
+                node.args = new_args
+
+    return gm
+
+
 def _node_args_converting(gm: torch.fx.GraphModule, device_mesh: DeviceMesh):
     """
     This pass will process node args to adapt the distributed tensor layout.
@@ -138,6 +281,7 @@ def _node_args_converting(gm: torch.fx.GraphModule, device_mesh: DeviceMesh):
             method = getattr(node.args[0]._meta_data.__class__, node.target)
             # process the node with (input, *shape) style args
             if method in (torch.Tensor.view, torch.Tensor.reshape):
+
                 for arg in node.args:
                     if isinstance(arg, Node):
                         if isinstance(arg._meta_data, (int, tuple, list)):
@@ -157,9 +301,17 @@ def _node_args_converting(gm: torch.fx.GraphModule, device_mesh: DeviceMesh):
                     # 1. torch.view(input, *shape)
                     # 2. torch.view(input, shape)
                     if isinstance(new_args[1], int):
+                        # we will skip the dim with -1 value
+                        if new_args[dim + 1] == -1:
+                            continue
+                        else:
                             new_args[dim + 1] //= total_shard_size
                     else:
                         new_args[1] = list(new_args[1])
+                        # we will skip the dim with -1 value
+                        if new_args[1][dim] == -1:
+                            continue
+                        else:
                             new_args[1][dim] //= total_shard_size
                 node.args = tuple(new_args)
 
@@ -298,6 +450,7 @@ def runtime_preparation_pass(gm: torch.fx.GraphModule,
                              strategies_constructor: StrategiesConstructor = None):
     gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict = _solution_annotatation(
         gm, solution, strategies_constructor)
+    gm = _size_value_converting(gm, device_mesh)
     gm = _node_args_converting(gm, device_mesh)
     # TODO: the pass below should be uncommented after the implementation of implicit_comm_action_apply_pass completed.
     # gm = implicit_comm_action_apply(gm)

colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py

@@ -28,8 +28,9 @@ def _update_sharding_spec_for_transposed_weight_for_linear(strategy: ShardingStr
     # switch the dimensions of the transposed weight
     sharding_spec = strategy.get_sharding_spec_by_name(weight_name)
     op_data = strategy.get_op_data_by_name(weight_name)
-    assert op_data.logical_shape != op_data.data.shape, \
-        "Expected the logical and physical shape of the linear operator's weight to be different, but found them to be the same"
+    assert op_data.logical_shape[0] == op_data.data.shape[1] and \
+           op_data.logical_shape[1] == op_data.data.shape[0], \
+           "Expected the logical shape  of the linear operator's weight is equal to transposed physical shape"
     dim_size = len(op_data.logical_shape)
     transpose_partition_dim(sharding_spec, 0, dim_size - 1)
     return strategy