[autoparallel] refactor the runtime apply pass and add docstring to passes (#1757)

* [autoparallel] refactor the runtime apply pass and add doc string to passes

* fix unit test

* polish

colossalai/auto_parallel/passes/runtime_apply_pass.py

@@ -0,0 +1,151 @@
+from copy import deepcopy
+from typing import Dict, List
+
+import torch
+from torch.fx.node import Node
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    OperationData,
+    OperationDataType,
+)
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.tensor.comm_spec import CommSpec
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+
+shape_consistency_manager = ShapeConsistencyManager()
+
+
+def runtime_apply(node: Node, origin_dict: Dict, input_dict: Dict, node_index: int, user_node_index: int):
+    """
+    This method will be invoked during runtime to do the shape consistency, which make sure the activations is converted into
+    the user node expected form.
+    """
+    origin_sharding_spec = origin_dict[node_index]
+    target_sharding_spec = input_dict[node_index][user_node_index]
+
+    return shape_consistency_manager.apply_for_autoparallel_runtime(node, origin_sharding_spec, target_sharding_spec)
+
+
+def runtime_comm_spec_apply(tensor: torch.Tensor, comm_actions_dict: Dict, node_index: int, op_data_name: str):
+    """
+    This method will be invoked during runtime to apply the comm action following the instruction of comm spec.
+    """
+    comm_action = comm_actions_dict[node_index][op_data_name]
+    if isinstance(comm_action.comm_spec, CommSpec):
+        rst = comm_action.comm_spec.covert_spec_to_action(tensor)
+    else:
+        origin_sharding_spec = comm_action.comm_spec['src_spec']
+        tgt_sharding_spec = comm_action.comm_spec['tgt_spec']
+        rst = shape_consistency_manager.apply_for_autoparallel_runtime(tensor, origin_sharding_spec, tgt_sharding_spec)
+    return rst
+
+
+def _preprocess_graph(nodes: List[Node]):
+    """
+    This method is used to extract all the placeholders with sharding information,
+    and mapping the nodes into the index of the origin graph.
+    """
+    # mapping the node into the origin graph index
+    node_to_index_dict = {}
+    index = 0
+    for node in nodes:
+        if node.target == 'sharding_spec_convert_dict':
+            input_dict_node = node
+            continue
+        if node.target == 'origin_node_sharding_spec_dict':
+            origin_dict_node = node
+            continue
+        if node.target == 'comm_actions_dict':
+            comm_actions_dict_node = node
+            continue
+        if not hasattr(node, 'best_strategy'):
+            continue
+        node_to_index_dict[node] = index
+        index += 1
+
+    return input_dict_node, origin_dict_node, comm_actions_dict_node, node_to_index_dict
+
+
+def _shape_consistency_apply(gm: torch.fx.GraphModule):
+    """
+    This pass is used to add the shape consistency node to the origin graph.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    input_dict_node, origin_dict_node, _, node_to_index_dict = _preprocess_graph(nodes)
+
+    for node in nodes:
+        if not hasattr(node, 'best_strategy') or node.op == 'output':
+            continue
+
+        for user_node in node.strategies_vector.successor_nodes:
+            user_node_index = user_node.strategies_vector.predecessor_nodes.index(node)
+            with mod_graph.inserting_before(user_node):
+                shape_consistency_node = mod_graph.create_node('call_function',
+                                                               runtime_apply,
+                                                               args=(node, origin_dict_node, input_dict_node,
+                                                                     node_to_index_dict[node], user_node_index))
+
+            origin_index_args = user_node.args.index(node)
+            new_args = list(user_node.args)
+            new_args[origin_index_args] = shape_consistency_node
+            user_node.args = new_args
+
+    return gm
+
+
+def _comm_spec_apply(gm: torch.fx.GraphModule):
+    """
+    This pass is used to add the comm spec apply node to the origin graph.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    _, _, comm_actions_dict_node, node_to_index_dict = _preprocess_graph(nodes)
+
+    for node in nodes:
+        if not hasattr(node, 'best_strategy') or node.op == 'output':
+            continue
+
+        comm_actions = node.best_strategy.communication_actions
+        for op_data, comm_action in comm_actions.items():
+            comm_object = node.args[comm_action.arg_index]
+            if op_data.type == OperationDataType.PARAM:
+                continue
+            if comm_action.comm_type == CommType.BEFORE:
+                with mod_graph.inserting_before(node):
+                    comm_spec_apply_node = mod_graph.create_node('call_function',
+                                                                 runtime_comm_spec_apply,
+                                                                 args=(comm_object, comm_actions_dict_node,
+                                                                       node_to_index_dict[node], op_data.name))
+                new_args = list(node.args)
+                new_args[comm_action.arg_index] = comm_spec_apply_node
+                node.args = new_args
+            elif comm_action.comm_type == CommType.AFTER:
+                with mod_graph.inserting_after(node):
+                    comm_spec_apply_node = mod_graph.create_node('call_function',
+                                                                 runtime_comm_spec_apply,
+                                                                 args=(node, comm_actions_dict_node,
+                                                                       node_to_index_dict[node], op_data.name))
+                user_list = list(node.users.keys())
+                for user in user_list:
+                    if user == comm_spec_apply_node:
+                        continue
+                    new_args = list(user.args)
+                    new_args[new_args.index(node)] = comm_spec_apply_node
+                    user.args = tuple(new_args)
+
+    return gm
+
+
+def runtime_apply_pass(gm: torch.fx.GraphModule):
+    """
+    The method manages all the passes acting on the distributed training runtime.
+    """
+    gm = _shape_consistency_apply(gm)
+    gm = _comm_spec_apply(gm)
+
+    return gm

colossalai/auto_parallel/passes/runtime_preparation_pass.py

@@ -0,0 +1,130 @@
+from copy import deepcopy
+from typing import List
+
+import torch
+from torch.fx import symbolic_trace
+from torch.fx.node import Node
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction, CommType, OperationDataType
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.tensor.comm_spec import _all_reduce
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.tensor.sharding_spec import ShardingSpec
+
+shape_consistency_manager = ShapeConsistencyManager()
+
+
+def _solution_annotatation(gm: torch.fx.GraphModule, solution: List[int]):
+    """
+    This method is used to stick the solution strategy to the nodes and add the information
+    required in runtime into graph as placeholder nodes.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    # the dict to get origin sharding spec of node
+    origin_node_sharding_spec_dict = {}
+    for node_index, (node, strategy_index) in enumerate(zip(nodes, solution)):
+        strategies_vector = node.strategies_vector
+        # stick the solution strategy to the corresponding node
+        setattr(node, 'best_strategy', strategies_vector[strategy_index])
+        setattr(node, 'sharding_spec', strategies_vector[strategy_index].get_sharding_spec_by_name(str(node)))
+        origin_node_sharding_spec_dict[node_index] = strategies_vector[strategy_index].get_sharding_spec_by_name(
+            str(node))
+
+    # the dict to get input sharding specs of user node
+    sharding_spec_convert_dict = {}
+    # the dict to record comm actions of nodes
+    comm_actions_dict = {}
+    for index, node in enumerate(nodes):
+        target_sharding_specs = []
+        for user_node in node.strategies_vector.successor_nodes:
+            target_sharding_spec = user_node.best_strategy.get_sharding_spec_by_name(str(node.name))
+            target_sharding_specs.append(target_sharding_spec)
+        sharding_spec_convert_dict[index] = target_sharding_specs
+
+        comm_action_dict = {}
+        for op_data, comm_action in node.best_strategy.communication_actions.items():
+            comm_action_dict[op_data.name] = comm_action
+        comm_actions_dict[index] = comm_action_dict
+
+    # add above dicts into graph
+    for node in nodes:
+        if node.op != 'placeholder':
+            with mod_graph.inserting_before(node):
+                input_specs_node = mod_graph.create_node('placeholder', target='sharding_spec_convert_dict')
+                origin_specs_node = mod_graph.create_node('placeholder', target='origin_node_sharding_spec_dict')
+                comm_actions_dict_node = mod_graph.create_node('placeholder', target='comm_actions_dict')
+            break
+    return gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict
+
+
+def _module_params_sharding(gm: torch.fx.GraphModule, device_mesh):
+    """
+    Apply the sharding action to the module parameters and buffers following the
+    instructions of solver solution.
+    """
+    mod_graph = gm.graph
+    nodes = tuple(mod_graph.nodes)
+
+    for node in nodes:
+        if node.op == 'call_module':
+            target_module = node.graph.owning_module.get_submodule(node.target)
+
+            for name, param in target_module.named_parameters():
+                target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
+                # apply the sharding spec of parameters
+                if target_sharding_spec.dim_partition_dict != {}:
+                    origin_sharding_spec = ShardingSpec(device_mesh, param.shape, {})
+                    setattr(param, 'sharding_spec', origin_sharding_spec)
+                    param_sharded = torch.nn.Parameter(
+                        shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
+                                                                                 target_sharding_spec).detach().clone())
+                else:
+                    param_sharded = param
+                setattr(target_module, name, param_sharded)
+                comm_actions = node.best_strategy.communication_actions
+                for operation_data, comm_action in comm_actions.items():
+                    comm_spec_to_use = comm_action.comm_spec
+                    # register hook to the parameters
+                    if operation_data.type == OperationDataType.PARAM and operation_data.name == name and comm_action.comm_type == CommType.HOOK:
+
+                        def wrapper(param, comm_spec):
+
+                            def hook_fn(grad):
+                                _all_reduce(grad, comm_spec)
+
+                            param.register_hook(hook_fn)
+
+                        wrapper(param_sharded, comm_spec_to_use)
+
+            sharded_buffer_dict = {}
+            # apply the sharding spec of buffers
+            for name, buffer in target_module.named_buffers():
+                origin_sharding_spec = ShardingSpec(device_mesh, buffer.shape, {})
+                setattr(buffer, 'sharding_spec', origin_sharding_spec)
+                target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
+                buffer_sharded = shape_consistency_manager.apply(buffer, target_sharding_spec)
+                sharded_buffer_dict[name] = buffer_sharded
+
+            for name, buffer_sharded in sharded_buffer_dict.items():
+                setattr(target_module, name, buffer_sharded.detach().clone())
+
+    return gm
+
+
+def implicit_comm_action_apply(gm: torch.fx.GraphModule):
+    """
+    replace the origin kernel into kernel with implicit communication inside.
+    """
+    pass
+
+
+def runtime_preparation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh: DeviceMesh):
+    gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict = _solution_annotatation(
+        gm, solution)
+    # TODO: the pass below should be uncommented after the implementation of implicit_comm_action_apply_pass completed.
+    # gm = implicit_comm_action_apply(gm)
+    gm = _module_params_sharding(gm, device_mesh)
+
+    return gm, sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict

colossalai/fx/passes/experimental/adding_shape_consistency_pass_v2.py

@@ -1,193 +0,0 @@
-import builtins
-import copy
-import operator
-from ast import NodeTransformer
-from copy import deepcopy
-from typing import List
-
-import torch
-from torch.fx import symbolic_trace
-from torch.fx.node import Node
-
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction, CommType, OperationDataType
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.passes.split_module import split_module
-from colossalai.tensor.comm_spec import CollectiveCommPattern, CommSpec, _all_reduce, pattern_to_func_dict
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager
-from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
-
-shape_consistency_manager = ShapeConsistencyManager()
-
-
-def runtime_apply(node, origin_dict, input_dict, node_index, user_node_index):
-    origin_sharding_spec = origin_dict[node_index]
-    target_sharding_spec = input_dict[node_index][user_node_index]
-    return shape_consistency_manager.apply_for_autoparallel_runtime(node, origin_sharding_spec, target_sharding_spec)
-
-
-def runtime_comm_spec_apply(tensor, comm_actions_dict, node_index, op_data):
-
-    comm_action = comm_actions_dict[node_index][op_data]
-    if isinstance(comm_action.comm_spec, CommSpec):
-        rst = comm_action.comm_spec.covert_spec_to_action(tensor)
-    else:
-        origin_sharding_spec = comm_action.comm_spec['src_spec']
-        tgt_sharding_spec = comm_action.comm_spec['tgt_spec']
-        rst = shape_consistency_manager.apply_for_autoparallel_runtime(tensor, origin_sharding_spec, tgt_sharding_spec)
-    return rst
-
-
-def solution_annotatation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh):
-    mod_graph = gm.graph
-    nodes = tuple(mod_graph.nodes)
-
-    # the dict to get origin sharding spec of node
-    origin_node_sharding_spec_dict = {}
-    for node_index, (node, strategy_index) in enumerate(zip(nodes, solution)):
-        strategies_vector = node.strategies_vector
-        setattr(node, 'best_strategy', strategies_vector[strategy_index])
-        setattr(node, 'sharding_spec', strategies_vector[strategy_index].get_sharding_spec_by_name(str(node)))
-        origin_node_sharding_spec_dict[node_index] = strategies_vector[strategy_index].get_sharding_spec_by_name(
-            str(node))
-
-    # apply the sharding spec of parameters
-    for node in nodes:
-        if node.op == 'call_module':
-            target_module = node.graph.owning_module.get_submodule(node.target)
-            for name, param in target_module.named_parameters():
-                target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
-                if target_sharding_spec.dim_partition_dict != {}:
-                    origin_sharding_spec = ShardingSpec(device_mesh, param.shape, {})
-                    setattr(param, 'sharding_spec', origin_sharding_spec)
-                    param_sharded = torch.nn.Parameter(
-                        shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
-                                                                                 target_sharding_spec).detach().clone())
-                else:
-                    param_sharded = param
-                setattr(target_module, name, param_sharded)
-                comm_actions = node.best_strategy.communication_actions
-                for operation_data, comm_action in comm_actions.items():
-                    comm_spec_to_use = comm_action.comm_spec
-                    if operation_data.type == OperationDataType.PARAM and operation_data.name == name and comm_action.comm_type == CommType.HOOK:
-
-                        def wrapper(param, comm_spec):
-
-                            def hook_fn(grad):
-                                _all_reduce(grad, comm_spec)
-
-                            param.register_hook(hook_fn)
-
-                        wrapper(param_sharded, comm_spec_to_use)
-
-            sharded_buffer_dict = {}
-            for name, buffer in target_module.named_buffers():
-                origin_sharding_spec = ShardingSpec(device_mesh, buffer.shape, {})
-                setattr(buffer, 'sharding_spec', origin_sharding_spec)
-                target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
-                buffer_sharded = shape_consistency_manager.apply(buffer, target_sharding_spec)
-                sharded_buffer_dict[name] = buffer_sharded
-
-            for name, buffer_sharded in sharded_buffer_dict.items():
-                setattr(target_module, name, buffer_sharded.detach().clone())
-
-    # the dict to get input sharding specs of user node
-    sharding_spec_convert_dict = {}
-    for index, node in enumerate(nodes):
-        target_sharding_specs = []
-        for user_node in node.strategies_vector.successor_nodes:
-            target_sharding_spec = user_node.best_strategy.get_sharding_spec_by_name(str(node.name))
-            target_sharding_specs.append(target_sharding_spec)
-        sharding_spec_convert_dict[index] = target_sharding_specs
-
-    # the dict to record comm actions of nodes
-    comm_actions_dict = {}
-    for index, node in enumerate(nodes):
-        comm_action_dict = {}
-        for op_data, comm_action in node.best_strategy.communication_actions.items():
-            comm_action_dict[op_data.name] = comm_action
-        comm_actions_dict[index] = comm_action_dict
-
-    # add above dicts into graph
-    for node in nodes:
-        if node.op != 'placeholder':
-            with mod_graph.inserting_before(node):
-                input_specs_node = mod_graph.create_node('placeholder', target='sharding_spec_convert_dict')
-                origin_specs_node = mod_graph.create_node('placeholder', target='origin_node_sharding_spec_dict')
-                comm_actions_dict_node = mod_graph.create_node('placeholder', target='comm_actions_dict')
-            break
-
-    return sharding_spec_convert_dict, origin_node_sharding_spec_dict, comm_actions_dict
-
-
-def shape_consistency_pass(gm: torch.fx.GraphModule):
-    mod_graph = gm.graph
-    nodes = tuple(mod_graph.nodes)
-    input_dict_node = None
-    origin_dict_node = None
-
-    # mapping the node into the origin graph index
-    node_to_index_dict = {}
-    index = 0
-    for node in nodes:
-        if node.target == 'sharding_spec_convert_dict':
-            input_dict_node = node
-            continue
-        if node.target == 'origin_node_sharding_spec_dict':
-            origin_dict_node = node
-            continue
-        if node.target == 'comm_actions_dict':
-            comm_actions_dict_node = node
-            continue
-        if not hasattr(node, 'best_strategy'):
-            continue
-        node_to_index_dict[node] = index
-        index += 1
-    assert input_dict_node is not None
-
-    # add shape consistency apply function into graph
-    for node in nodes:
-        if not hasattr(node, 'best_strategy') or node.op == 'output':
-            continue
-
-        for user_node in node.strategies_vector.successor_nodes:
-            user_node_index = user_node.strategies_vector.predecessor_nodes.index(node)
-            with mod_graph.inserting_before(user_node):
-                shape_consistency_node = mod_graph.create_node('call_function',
-                                                               runtime_apply,
-                                                               args=(node, origin_dict_node, input_dict_node,
-                                                                     node_to_index_dict[node], user_node_index))
-
-            origin_index_args = user_node.args.index(node)
-            new_args = list(user_node.args)
-            new_args[origin_index_args] = shape_consistency_node
-            user_node.args = new_args
-
-        comm_actions = node.best_strategy.communication_actions
-        for op_data, comm_action in comm_actions.items():
-            comm_object = node.args[comm_action.arg_index]
-            if op_data.type == OperationDataType.PARAM:
-                continue
-            if comm_action.comm_type == CommType.BEFORE:
-                with mod_graph.inserting_before(node):
-                    comm_spec_apply_node = mod_graph.create_node('call_function',
-                                                                 runtime_comm_spec_apply,
-                                                                 args=(comm_object, comm_actions_dict_node,
-                                                                       node_to_index_dict[node], op_data.name))
-                new_args = list(node.args)
-                new_args[comm_action.arg_index] = comm_spec_apply_node
-                node.args = new_args
-            elif comm_action.comm_type == CommType.AFTER:
-                with mod_graph.inserting_after(node):
-                    comm_spec_apply_node = mod_graph.create_node('call_function',
-                                                                 runtime_comm_spec_apply,
-                                                                 args=(node, comm_actions_dict_node,
-                                                                       node_to_index_dict[node], op_data.name))
-                user_list = list(node.users.keys())
-                for user in user_list:
-                    if user == comm_spec_apply_node:
-                        continue
-                    new_args = list(user.args)
-                    new_args[new_args.index(node)] = comm_spec_apply_node
-                    user.args = tuple(new_args)
-            # TODO: consider other OperationDataType, such as OperationDataType.OUTPUT
-    return gm

tests/test_auto_parallel/test_tensor_shard/test_resnet_block_runtime.py

@@ -10,6 +10,8 @@ from torch.fx import GraphModule
 from torchvision.models import resnet34, resnet50
 
 from colossalai import device
+from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
+from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
 from colossalai.auto_parallel.tensor_shard.constants import *
 from colossalai.auto_parallel.tensor_shard.solver.cost_graph import CostGraph
 from colossalai.auto_parallel.tensor_shard.solver.graph_analysis import GraphAnalyser
@@ -17,10 +19,6 @@ from colossalai.auto_parallel.tensor_shard.solver.options import SolverOptions
 from colossalai.auto_parallel.tensor_shard.solver.solver import Solver
 from colossalai.auto_parallel.tensor_shard.solver.strategies_constructor import StrategiesConstructor
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.passes.experimental.adding_shape_consistency_pass_v2 import (
-    shape_consistency_pass,
-    solution_annotatation_pass,
-)
 from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
@@ -153,8 +151,8 @@ def check_apply_bottleneck(rank, world_size, port):
     print(solution)
     for index, node in enumerate(graph.nodes):
         print(node.name, node.strategies_vector[solution[index]].name)
-    sharding_spec_dict, origin_spec_dict, comm_actions_dict = solution_annotatation_pass(gm, solution, device_mesh)
-    shape_consistency_pass(gm)
+    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
+    gm = runtime_apply_pass(gm)
     gm.recompile()
     nodes = [node for node in gm.graph.nodes]
     # TODO: wrap the gm to avoid the influence of the user training code

tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py

@@ -7,6 +7,8 @@ import torch.multiprocessing as mp
 import torch.nn as nn
 from torch.fx import GraphModule
 
+from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
+from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
 from colossalai.auto_parallel.tensor_shard.solver import (
     CostGraph,
     GraphAnalyser,
@@ -15,10 +17,6 @@ from colossalai.auto_parallel.tensor_shard.solver import (
     StrategiesConstructor,
 )
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.passes.experimental.adding_shape_consistency_pass_v2 import (
-    shape_consistency_pass,
-    solution_annotatation_pass,
-)
 from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
@@ -72,8 +70,8 @@ def check_apply(rank, world_size, port):
     solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
     ret = solver.call_solver_serialized_args()
     solution = list(ret[0])
-    sharding_spec_dict, origin_spec_dict, comm_actions_dict = solution_annotatation_pass(gm, solution, device_mesh)
-    shape_consistency_pass(gm)
+    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
+    gm = runtime_apply_pass(gm)
     gm.recompile()
     nodes = [node for node in gm.graph.nodes]
     # TODO: wrap the gm to avoid the influence of the user training code