[autoparellel]add strategies constructor (#1505)

* [autoparellel]add strategies constructor

* remove duplicated strategies

* polish code

* adapt cost graph with StrategiesConstructor

* polish

colossalai/auto_parallel/solver/constants.py

@@ -0,0 +1,22 @@
+import torch
+import operator
+
+__all__ = [
+    'ELEMENTWISE_MODULE_OP', 'ELEMENTWISE_FUNC_OP', 'CONV_MODULE_OP', 'CONV_FUNC_OP', 'LINEAR_MODULE_OP',
+    'LINEAR_FUNC_OP'
+]
+
+ELEMENTWISE_MODULE_OP = [torch.nn.Dropout, torch.nn.ReLU]
+ELEMENTWISE_FUNC_OP = [
+    torch.add, operator.add, torch.abs, torch.cos, torch.exp, torch.mul, operator.mul, operator.floordiv,
+    operator.truediv, operator.neg, torch.multiply, torch.nn.functional.relu, torch.nn.functional.dropout
+]
+CONV_MODULE_OP = [
+    torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d, torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d,
+    torch.nn.ConvTranspose3d
+]
+CONV_FUNC_OP = [
+    torch.conv1d, torch.conv2d, torch.conv3d, torch.conv_transpose1d, torch.conv_transpose2d, torch.conv_transpose3d
+]
+LINEAR_MODULE_OP = [torch.nn.Linear]
+LINEAR_FUNC_OP = [torch.nn.functional.linear, torch.matmul, torch.bmm]

colossalai/auto_parallel/solver/conv_handler.py

@@ -494,3 +494,10 @@ class ConvHandler(OperatorHandler):
         self.split_1d_parallel_on_in_channel(0, 1)
 
         return self.strategies_vector
+
+
+CONV_STRATEGIES_LIST = [
+    'S0S1 = S0R x RS1', 'S1S0 = S1R x RS0', 'S0R = S0R x RR', 'S1R = S1R x RR', 'S0R = S0S1 x S1R', 'S1R = S1S0 x S0R',
+    'RS1 = RS0 x S0S1', 'RS0 = RS1 x S1S0', 'RR = RS0 x S0R', 'RR = RS1 x S1R', 'RS0 = RR x RS0', 'RS1 = RR x RS1',
+    'RR = RR x RR', 'S01R = S01R x RR', 'RR = RS01 x S01R'
+]

colossalai/auto_parallel/solver/cost_graph.py

@@ -39,11 +39,11 @@ class CostGraph:
             dst_node = strategies_vector.node
             for src_node in strategies_vector.predecessor_nodes:
                 node_pair = (src_node, dst_node)
-                src_index = strategies_vector.predecessor_nodes.index(src_node)
+                # src_index = strategies_vector.predecessor_nodes.index(src_node)
                 edge_cost = {}
                 for i in range(len(strategies_vector)):
-                    for j in range(len(src_node.stategy_vector)):
+                    for j in range(len(src_node.strategies_vector)):
-                        edge_cost[(i, j)] = strategies_vector[i].resharding_costs[src_index][j]
+                        edge_cost[(j, i)] = strategies_vector[i].resharding_costs[src_node][j]
                 self.edge_costs[node_pair] = edge_cost
             # add parents and children attribute to node
             setattr(dst_node, 'parents', strategies_vector.predecessor_nodes)
@@ -83,33 +83,19 @@ class CostGraph:
         merge_map = {}
         for dst_strate_index, strategy in enumerate(dst_node.strategies_vector):
             resharding_costs = strategy.resharding_costs
-            resharding_cost_for_src = resharding_costs[src_node_index]
+            resharding_cost_for_src = resharding_costs[src_node]
             lowest_cost_index = resharding_cost_for_src.index(min(resharding_cost_for_src))
             merge_map[dst_strate_index] = lowest_cost_index
 
         # extra_node_cost for dst node
-        extra_node_costs[dst_node] = [0.0 for _ in range(self.node_lens[dst_node])]
+        self.extra_node_costs[dst_node] = [0.0 for _ in range(self.node_lens[dst_node])]
         for dst_strate_index, strategy in enumerate(dst_node.strategies_vector):
             target_strate_index = merge_map[dst_strate_index]
-            extra_node_costs[dst_node][dst_strate_index] += strategy.resharding_costs[src_node_index][
+            self.extra_node_costs[dst_node][dst_strate_index] += strategy.resharding_costs[src_node][
                 target_strate_index]
-            if src_node in extra_node_costs:
+            if src_node in self.extra_node_costs:
-                extra_node_costs[dst_node][dst_strate_index] += extra_node_costs[src_node][target_strate_index]
+                self.extra_node_costs[dst_node][dst_strate_index] += self.extra_node_costs[src_node][
-
+                    target_strate_index]
-        # connect dst node and parents of src node
-        dst_node.parents.remove(src_node)
-        src_node.children.remove(dst_node)
-        node_pair_to_remove = [(src_node, dst_node)]
-        for parent_node in src_node.parents:
-            if parent_node not in dst_node.parents:
-                dst_node.parents.append(parent)
-            if dst_node not in parent_node.children:
-                parent_node.children.append(dst_node)
-            # remove src node from cost graph when src node has no consumer.
-            if len(src_node.children) == 0:
-                parent_node.children.remove(src_node)
-                node_pair = (parent_node, src_node)
-                self.edge_costs.pop(node_pair)
 
         # add new node pair to cost graph
         for parent_node in src_node.parents:
@@ -121,9 +107,24 @@ class CostGraph:
             for i in range(self.node_lens[dst_node]):
                 for j in range(self.node_lens[parent_node]):
                     src_strate_index = merge_map[i]
-                    edge_cost[(i, j)] = self.edge_costs[old_node_pair][(j, src_strate_index)]
+                    edge_cost[(j, i)] = self.edge_costs[old_node_pair][(j, src_strate_index)]
             self.edge_costs[new_node_pair] = edge_cost
 
+        # connect dst node and parents of src node
+        dst_node.parents.remove(src_node)
+        src_node.children.remove(dst_node)
+        self.edge_costs.pop((src_node, dst_node))
+        for parent_node in src_node.parents:
+            if parent_node not in dst_node.parents:
+                dst_node.parents.append(parent_node)
+            if dst_node not in parent_node.children:
+                parent_node.children.append(dst_node)
+            # remove src node from cost graph when src node has no consumer.
+            if len(src_node.children) == 0:
+                parent_node.children.remove(src_node)
+                node_pair = (parent_node, src_node)
+                self.edge_costs.pop(node_pair)
+
     def simplify_graph(self):
         if not self.simplify:
             return

colossalai/auto_parallel/solver/operator_handler.py

@@ -3,7 +3,7 @@ import torch
 import torch.nn as nn
 from abc import ABC, abstractmethod
 from torch.fx.node import Node
-from typing import Dict
+from typing import Dict, List
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 from colossalai.tensor.sharding_spec import ShardingSpec
@@ -56,7 +56,7 @@ class OperatorHandler(ABC):
         """
         pass
 
-    def _generate_sharding_spec(self, tensor: torch.Tensor, dim_partition_dict: Dict[int, int]) -> ShardingSpec:
+    def _generate_sharding_spec(self, tensor: torch.Tensor, dim_partition_dict: Dict[int, List[int]]) -> ShardingSpec:
         """
         Generate the sharding spec of the tensor based on the given dim_partition_dict 
         where the key is the tensor dimension and the value is the mesh dimension for sharding.
@@ -84,7 +84,9 @@ class OperatorHandler(ABC):
         for input_node, input_spec in zip(self.predecessor_node, sharding_spec_for_input):
             resharding_costs[input_node] = []
             for strategy in input_node.strategies_vector:
+                input_sharding_spec = strategy.output_sharding_spec
+                assert isinstance(input_sharding_spec, ShardingSpec), f'The input node should NOT be a tuple of tensor.'
                 _, _, resharding_cost = self.shape_consistency_manager.shape_consistency(
-                    strategy.output_sharding_spec, input_spec)
+                    input_sharding_spec, input_spec)
                 resharding_costs[input_node].append(resharding_cost)
         return resharding_costs

colossalai/auto_parallel/solver/sharding_strategy.py

@@ -1,7 +1,8 @@
 from dataclasses import dataclass
 from colossalai.tensor.sharding_spec import ShardingSpec
-from typing import Dict, List
+from typing import Dict, List, Union, Tuple
 from torch.fx.node import Node
+from .constants import *
 
 __all__ = ['ShardingStrategy', 'StrategiesVector']
 
@@ -25,12 +26,15 @@ class ShardingStrategy:
     '''
 
     name: str
-    output_sharding_spec: ShardingSpec
+    # TODO: output of fx node,such as torch.var_mean, could be a tuple, so we cannot simply suppose it is a tensor.
+    output_sharding_spec: Union[ShardingSpec, Tuple[ShardingSpec]]
     compute_cost: float = 0.
     communication_cost: float = 0.
     memory_cost: float = 0.
-    resharding_costs: Dict[int, List[float]] = None
+    resharding_costs: Dict[Node, List[float]] = None
-    input_shardings: ShardingSpec = None
+    # sometimes the input node could be a tuple of nodes, but most of op won't accept tuple of node as input.
+    # Therefore, we could process them at the specific op(operator.getitem)
+    input_shardings: List[ShardingSpec] = None
 
 
 class StrategiesVector(list):
@@ -46,8 +50,23 @@ class StrategiesVector(list):
         super().__init__()
         self.node = node
         # fetch its input and output nodes
+        # TODO: placeholder input nodes
         self.predecessor_nodes = list(node._input_nodes.keys())
         self.successor_nodes = list(node.users.keys())
 
     def check_merge(self):
-        pass
+        merge_label = False
+        if self.node.op == 'call_module':
+            target = self.node.target
+            root_module = self.node.graph.owning_module
+            submod = root_module.get_submodule(target)
+            submod_type = type(submod)
+            # merge elementwise module node into following nodes
+            if submod_type in ELEMENTWISE_MODULE_OP:
+                merge_label = True
+
+        if self.node.op == 'call_function':
+            if self.node.target in ELEMENTWISE_FUNC_OP:
+                merge_label = True
+
+        return merge_label

colossalai/auto_parallel/solver/strategies_constructor.py

@@ -0,0 +1,355 @@
+from torch.fx import Graph, Node
+from colossalai.tensor.sharding_spec import ShardingSpec
+from .sharding_strategy import ShardingStrategy, StrategiesVector
+from .conv_handler import ConvHandler
+from .constants import *
+from copy import deepcopy
+import math
+import torch
+import operator
+from typing import Dict, List
+
+
+class StrategiesConstructor:
+
+    def __init__(self, graph, device_mesh, shape_consistency_manager, solver_options):
+        self.graph = graph
+        self.root_module = self.graph.owning_module
+        self.nodes = list(graph.nodes)
+        self.device_mesh = device_mesh
+        self.leaf_strategies = []
+        self.strategy_map = {}
+        self.shape_consistency_manager = shape_consistency_manager
+        self.solver_options = solver_options
+
+    def _generate_sharding_spec(self, node: Node, dim_partition_dict: Dict[int, List[int]]) -> ShardingSpec:
+        """
+        Generate the sharding spec of the tensor based on the given dim_partition_dict 
+        where the key is the tensor dimension and the value is the mesh dimension for sharding.
+        """
+        meta_tensor = node._meta_data
+        sharding_spec = ShardingSpec(device_mesh=self.device_mesh,
+                                     entire_shape=meta_tensor.shape,
+                                     dim_partition_dict=dim_partition_dict)
+        return sharding_spec
+
+    def _generate_resharding_costs(self, input_nodes, target_sharding_specs):
+        '''
+        Compute the resharding costs with this specific strategy.
+
+        Argument:
+            sharding_spec_for_input(ShardingSpec): ShardingSpec of the input node.
+        '''
+        resharding_costs = {}
+        for input_node, target_sharding_spec in zip(input_nodes, target_sharding_specs):
+            resharding_costs[input_node] = []
+            for strategy in input_node.strategies_vector:
+                input_sharding_spec = strategy.output_sharding_spec
+                assert isinstance(input_sharding_spec, ShardingSpec), f'The input node should NOT be a tuple of tensor.'
+                _, _, resharding_cost = self.shape_consistency_manager.shape_consistency(
+                    input_sharding_spec, target_sharding_spec)
+                resharding_costs[input_node].append(resharding_cost)
+        return resharding_costs
+
+    def remove_duplicated_strategy(self, strategies_vector):
+        '''
+        In build_strategies_and_cost method, we may produce some duplicated strategies.
+        In this method, we will remove the duplicated strategies depending on the strategies name.
+        '''
+        name_checklist = []
+        remove_list = []
+        for strategy in strategies_vector:
+            if strategy.name not in name_checklist:
+                name_checklist.append(strategy.name)
+            else:
+                remove_list.append(strategy)
+        for strategy in remove_list:
+            strategies_vector.remove(strategy)
+
+    def build_strategies_and_cost(self):
+        for node in self.nodes:
+            strategies_vector = StrategiesVector(node)
+            # placeholder node
+            if node.op == 'placeholder':
+                # For placeholder nodes, if solver_options['fast_mode'] is True, we just let them in
+                # fully replicate status, then strategies of following node will be treated equally due
+                # to replicate status has no resharding cost to other status. At the same time, the searching
+                # space is smaller than enumerating all the possible sharding spec for the placeholder node.
+                # Otherwise, all the possible sharding spec for the placeholder node will be enumerated.
+
+                if self.solver_options['fast_mode']:
+                    # create sharding strategy for placeholder
+                    name = 'Replica Placeholder'
+                    dim_partition_dict = {}
+                    output_sharding_spec = self._generate_sharding_spec(node, dim_partition_dict)
+                    # TODO: use meta_info_prop to profile memory cost
+                    memory_cost = 0
+                    sharding_strategy_placeholder = ShardingStrategy(name,
+                                                                     output_sharding_spec,
+                                                                     memory_cost=memory_cost)
+                    strategies_vector.append(sharding_strategy_placeholder)
+
+            # get_attr node
+            if node.op == 'get_attr':
+                # Same as placeholder nodes, if solver_options['fast_mode'] is True, we just let them in
+                # fully replicate status, then strategies of following node will be treated equally due
+                # to replicate status has no resharding cost to other status. At the same time, the searching
+                # space is smaller than enumerating all the possible sharding spec for the get_attr node.
+                # Otherwise, all the possible sharding spec for the get_attr node will be enumerated.
+                if self.solver_options['fast_mode']:
+                    # create sharding strategy for get_attr
+                    name = 'Replica Attribute'
+                    dim_partition_dict = {}
+                    output_sharding_spec = self._generate_sharding_spec(node, dim_partition_dict)
+                    # TODO: use meta_info_prop to profile memory cost
+                    memory_cost = 0
+                    sharding_strategy_attribute = ShardingStrategy(name, output_sharding_spec, memory_cost=memory_cost)
+                    strategies_vector.append(sharding_strategy_attribute)
+
+            # call_module node
+            if node.op == 'call_module':
+
+                target = node.target
+                submod = self.root_module.get_submodule(target)
+                submod_type = type(submod)
+
+                # conv module
+                if submod_type in CONV_MODULE_OP:
+                    # use ConvHandler to create sharding strategies for conv module node
+                    conv_handler = ConvHandler(node, self.device_mesh, strategies_vector,
+                                               self.shape_consistency_manager)
+                    conv_handler.register_strategy()
+
+                # linear module
+                elif submod_type in LINEAR_MODULE_OP:
+                    # use DotHandler to create sharding strategies for linear module node
+                    dot_handler = DotHandler(node, self.device_mesh, strategies_vector, self.shape_consistency_manager)
+                    dot_handler.register_strategy()
+
+                # element-wise module
+                elif submod_type in ELEMENTWISE_MODULE_OP:
+                    # create sharding strategy for element-wise module
+                    assert len(strategies_vector.predecessor_nodes
+                              ) == 1, f'Temporally, we just support single input element-wise op.'
+                    input_node = strategies_vector.predecessor_nodes[0]
+                    # For element-wise module, we keep the sharding spec of output node same as
+                    # the input. Therefore, the different strategies of input node with same
+                    # output sharding spec will generate same strategy for element-wise module.
+                    sharding_spec_checklist = []
+                    for strategy in input_node.strategies_vector:
+                        # It looks a little bit confusing, the input of the processing node
+                        # is the output of the input_node.
+                        input_sharding_spec = strategy.output_sharding_spec
+                        assert isinstance(input_sharding_spec,
+                                          ShardingSpec), f'The input node should NOT be a tuple of tensor.'
+                        if input_sharding_spec in sharding_spec_checklist:
+                            continue
+
+                        sharding_spec_checklist.append(input_sharding_spec)
+                        dim_partition_dict = deepcopy(input_sharding_spec.dim_partition_dict)
+                        output_sharding_spec = self._generate_sharding_spec(node, dim_partition_dict)
+
+                        name = f'{input_sharding_spec.sharding_sequence} -> {output_sharding_spec.sharding_sequence}'
+
+                        # TODO: use meta_info_prop to profile memory cost and compute cost
+                        compute_cost = node._meta_data.numel()
+                        memory_cost = 0
+                        resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                           [input_sharding_spec])
+
+                        # to prevent the resharding happening, set their resharding cost to inf.
+                        resharding_costs[input_node] = [
+                            cost if cost == 0 else math.inf for cost in resharding_costs[input_node]
+                        ]
+                        sharding_strategy = ShardingStrategy(name,
+                                                             output_sharding_spec,
+                                                             compute_cost=compute_cost,
+                                                             memory_cost=memory_cost,
+                                                             resharding_costs=resharding_costs,
+                                                             input_shardings=[input_sharding_spec])
+                        strategies_vector.append(sharding_strategy)
+
+                # other module
+                else:
+                    raise RuntimeError(f'{submod_type} module is NOT supported now.')
+
+            # call_function node
+            if node.op == 'call_function':
+                target = node.target
+                # conv function
+                if target in CONV_FUNC_OP:
+                    # use ConvHandler to create sharding strategies for conv node
+                    # TODO: the operator_handler does NOT support function node processing now.
+                    conv_handler = ConvHandler(node, self.device_mesh, strategies_vector,
+                                               self.shape_consistency_manager)
+                    conv_handler.register_strategy()
+
+                # linear function
+                elif target in LINEAR_FUNC_OP:
+                    # use DotHandler to create sharding strategies for linear node
+                    # TODO: the operator_handler does NOT support function node processing now.
+                    linear_handler = DotHandler(node, self.device_mesh, strategies_vector,
+                                                self.shape_consistency_manager)
+                    linear_handler.register_strategy()
+
+                # element-wise function
+                elif target in ELEMENTWISE_FUNC_OP:
+                    # TODO: integrate element-wise func and module together
+                    # create sharding strategy for element-wise function
+                    assert len(strategies_vector.predecessor_nodes
+                              ) == 1, f'Temporally, we just support single input element-wise op.'
+                    input_node = strategies_vector.predecessor_nodes[0]
+                    # For element-wise function, we keep the sharding spec of output node same as
+                    # the input. Therefore, the different strategies of input node with same
+                    # output sharding spec will generate same strategy for element-wise function.
+                    sharding_spec_checklist = []
+                    for strategy in input_node.strategies_vector:
+                        # It looks a little bit confusing, the input of the processing node
+                        # is the output of the input_node.
+                        input_sharding_spec = strategy.output_sharding_spec
+                        assert isinstance(input_sharding_spec,
+                                          ShardingSpec), f'The input node should NOT be a tuple of tensor.'
+                        if input_sharding_spec in sharding_spec_checklist:
+                            continue
+                        sharding_spec_checklist.append(input_sharding_spec)
+                        dim_partition_dict = deepcopy(input_sharding_spec.dim_partition_dict)
+                        output_sharding_spec = self._generate_sharding_spec(node, dim_partition_dict)
+                        name = f'{input_sharding_spec.sharding_sequence} -> {output_sharding_spec.sharding_sequence}'
+                        # TODO: use meta_info_prop to profile memory cost and compute cost
+                        compute_cost = node._meta_data.numel()
+                        memory_cost = 0
+
+                        resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                           [input_sharding_spec])
+
+                        # to prevent the resharding happening, set their resharding cost to inf.
+                        resharding_costs[input_node] = [
+                            0 if cost == 0 else math.inf for cost in resharding_costs[input_node]
+                        ]
+                        sharding_strategy = ShardingStrategy(name,
+                                                             output_sharding_spec,
+                                                             compute_cost=compute_cost,
+                                                             memory_cost=memory_cost,
+                                                             resharding_costs=resharding_costs,
+                                                             input_shardings=[input_sharding_spec])
+                        strategies_vector.append(sharding_strategy)
+
+                # torch.var_mean
+                elif target == torch.var_mean:
+                    dim = node.kwargs['dim']
+                    input_tensor_node = strategies_vector.predecessor_nodes[0]
+                    for strategy in input_tensor_node.strategies_vector:
+                        input_sharding_spec = strategy.output_sharding_spec
+                        assert isinstance(input_sharding_spec,
+                                          ShardingSpec), f'The input node should NOT be a tuple of tensor.'
+                        entire_shape_input = input_sharding_spec.entire_shape
+                        dim_partition_dict_input = input_sharding_spec.dim_partition_dict
+                        name = f'{new_input_sharding_spec.sharding_sequence} -> ({output_sharding_spec.sharding_sequence}, {output_sharding_spec.sharding_sequence})'
+                        if dim in dim_partition_dict_input:
+                            # We need to make the action dimension in replicate status
+                            dim_partition_dict_for_input = deepcopy(dim_partition_dict_input)
+                            dim_partition_dict_for_input.pop(dim)
+                            new_input_sharding_spec = ShardingSpec(self.device_mesh,
+                                                                   entire_shape_input,
+                                                                   dim_partition_dict=dim_partition_dict_for_input)
+                            entire_shape_output = deepcopy(entire_shape_input)
+                            entire_shape_output.pop(dim)
+                            dim_partition_dict_for_output = deepcopy(dim_partition_dict_for_input)
+                            output_sharding_spec = ShardingSpec(self.device_mesh,
+                                                                entire_shape_output,
+                                                                dim_partition_dict=dim_partition_dict_for_input)
+                            # TODO: use meta_info_prop to profile origin memory cost and compute cost, then divide them depending on sharding spec.
+                            compute_cost = 0
+                            memory_cost = 0
+                            resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                               [new_input_sharding_spec])
+                            sharding_strategy = ShardingStrategy(name, (output_sharding_spec, output_sharding_spec),
+                                                                 compute_cost=compute_cost,
+                                                                 memory_cost=memory_cost,
+                                                                 resharding_costs=resharding_costs,
+                                                                 input_shardings=[new_input_sharding_spec])
+
+                        else:
+                            entire_shape_output = deepcopy(entire_shape_input)
+                            entire_shape_output.pop(dim)
+                            dim_partition_dict_for_output = deepcopy(dim_partition_dict_input)
+                            output_sharding_spec = ShardingSpec(self.device_mesh,
+                                                                entire_shape_output,
+                                                                dim_partion_dict=dim_partition_dict_input)
+                            # TODO: use meta_info_prop to profile origin memory cost and compute cost, then divide them depending on sharding spec.
+                            compute_cost = 0
+                            memory_cost = 0
+                            resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                               [input_sharding_spec])
+                            sharding_strategy = ShardingStrategy(name, (output_sharding_spec, output_sharding_spec),
+                                                                 compute_cost=compute_cost,
+                                                                 memory_cost=memory_cost,
+                                                                 resharding_costs=resharding_costs,
+                                                                 input_shardings=[input_sharding_spec])
+
+                        strategies_vector.append(sharding_strategy)
+
+                # operator.getitem
+                elif target == operator.getitem:
+                    index = node.args[1]
+                    input_tensor_node = strategies_vector.predecessor_nodes[0]
+                    for strategy in input_tensor_node.strategies_vector:
+                        input_sharding_spec = input_tensor_node.output_sharding_spec[index]
+                        assert isinstance(input_sharding_spec, ShardingSpec), f'This assertion is used to debug.'
+                        dim_partition_dict_for_output = deepcopy(input_sharding_spec.dim_partition_dict)
+                        entire_shape_output = deepcopy(input_sharding_spec.entire_shape)
+                        output_sharding_spec = ShardingSpec(self.device_mesh,
+                                                            entire_shape_output,
+                                                            dim_partition_dict=dim_partition_dict_for_output)
+                        # TODO: use meta_info_prop to profile origin memory cost and compute cost, then divide them depending on sharding spec.
+                        compute_cost = 0
+                        memory_cost = 0
+                        resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                           [input_sharding_spec])
+                        # to prevent the resharding happening, set their resharding cost to inf.
+                        resharding_costs[input_tensor_node] = [
+                            cost if cost == 0 else math.inf for cost in resharding_costs[input_tensor_node]
+                        ]
+                        sharding_strategy = ShardingStrategy(name,
+                                                             output_sharding_spec,
+                                                             compute_cost=compute_cost,
+                                                             memory_cost=memory_cost,
+                                                             resharding_costs=resharding_costs,
+                                                             input_shardings=[input_tensor_node.output_sharding_spec])
+                        strategies_vector.append(sharding_strategy)
+
+                # other function
+                else:
+                    raise RuntimeError(f'{target} function is NOT supported now.')
+
+            # output node
+            if node.op == 'output':
+                if self.solver_options['fast_mode']:
+                    # create sharding strategy for output
+                    name = 'Replica Output'
+                    input_nodes = strategies_vector.predecessor_nodes
+                    input_sharding_specs = []
+                    for input_node in input_nodes:
+                        dim_partition_dict_for_input = {}
+                        entire_shape = input_node._meta_data.shape
+                        sharding_spec = ShardingSpec(self.device_mesh,
+                                                     entire_shape,
+                                                     dim_partition_dict=dim_partition_dict_for_input)
+                        input_sharding_specs.append(sharding_spec)
+
+                    dim_partition_dict = {}
+                    output_sharding_spec = input_sharding_specs
+                    # TODO: use meta_info_prop to profile memory cost
+                    memory_cost = 0
+                    resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                       input_sharding_specs)
+                    sharding_strategy_attribute = ShardingStrategy(name,
+                                                                   output_sharding_spec,
+                                                                   memory_cost=memory_cost,
+                                                                   resharding_costs=resharding_costs)
+                    strategies_vector.append(sharding_strategy_attribute)
+
+            self.remove_duplicated_strategy(strategies_vector)
+            setattr(node, 'strategies_vector', strategies_vector)
+            self.leaf_strategies.append(strategies_vector)
+            self.strategy_map[node] = strategies_vector

tests/test_auto_parallel/test_cost_graph.py

@@ -0,0 +1,97 @@
+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.proxy import ColoProxy
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
+from colossalai.auto_parallel.solver.conv_handler import ConvHandler, CONV_STRATEGIES_LIST
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from colossalai.auto_parallel.solver.cost_graph import CostGraph
+from copy import deepcopy
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = x * 2
+        x = self.conv1(x)
+        x = x / 2
+        x = self.relu(x)
+        return x
+
+
+def test_cost_graph():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    entire_shape = torch.Size((4, 16, 64, 64))
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    model = ConvModel(16, 32)
+    input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
+    #     %conv1 : [#users=1] = call_module[target=conv1](args = (%mul,), kwargs = {})
+    #     %truediv : [#users=1] = call_function[target=operator.truediv](args = (%conv1, 2), kwargs = {})
+    #     %relu : [#users=1] = call_module[target=relu](args = (%truediv,), kwargs = {})
+    #     return relu
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+
+    solver_options = {'fast_mode': True}
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+
+    # (x, mul): {(0, 0): 0}
+    # (mul, conv1): {(0, 0): 0, (0, 1): 0, (0, 2): 0, (0, 3): 0, (0, 4): 0, (0, 5): 0, (0, 6): 0, (0, 7): 0, (0, 8): 0, (0, 9): 0, (0, 10): 0, (0, 11): 0, (0, 12): 0, (0, 13): 0, (0, 14): 0}
+    # (conv1, truediv): {(0, 0): 0, (1, 0): inf, (2, 0): 0, (3, 0): inf, (4, 0): 0, (5, 0): inf, (6, 0): inf, (7, 0): 0, (8, 0): inf, (9, 0): 0, (10, 0): 0, (11, 0): 0, (12, 0): 0, (13, 0): inf, (14, 0): inf, (0, 1): inf, (1, 1): 0, (2, 1): inf, (3, 1): 0, (4, 1): inf, (5, 1): 0, (6, 1): 0, (7, 1): inf, (8, 1): 0, (9, 1): inf, (10, 1): 0, (11, 1): 0, (12, 1): 0, (13, 1): inf, (14, 1): inf, (0, 2): inf, (1, 2): inf, (2, 2): 0, (3, 2): inf, (4, 2): inf, (5, 2): inf, (6, 2): inf, (7, 2): inf, (8, 2): inf, (9, 2): inf, (10, 2): 0, (11, 2): 0, (12, 2): 0, (13, 2): inf, (14, 2): inf, (0, 3): inf, (1, 3): inf, (2, 3): inf, (3, 3): 0, (4, 3): inf, (5, 3): inf, (6, 3): inf, (7, 3): inf, (8, 3): inf, (9, 3): inf, (10, 3): 0, (11, 3): 0, (12, 3): 0, (13, 3): inf, (14, 3): inf, (0, 4): inf, (1, 4): inf, (2, 4): inf, (3, 4): inf, (4, 4): inf, (5, 4): inf, (6, 4): 0, (7, 4): inf, (8, 4): 0, (9, 4): inf, (10, 4): 0, (11, 4): 0, (12, 4): 0, (13, 4): inf, (14, 4): inf, (0, 5): inf, (1, 5): inf, (2, 5): inf, (3, 5): inf, (4, 5): inf, (5, 5): inf, (6, 5): inf, (7, 5): 0, (8, 5): inf, (9, 5): 0, (10, 5): 0, (11, 5): 0, (12, 5): 0, (13, 5): inf, (14, 5): inf, (0, 6): inf, (1, 6): inf, (2, 6): inf, (3, 6): inf, (4, 6): inf, (5, 6): inf, (6, 6): inf, (7, 6): inf, (8, 6): inf, (9, 6): inf, (10, 6): 0, (11, 6): 0, (12, 6): 0, (13, 6): inf, (14, 6): inf, (0, 7): inf, (1, 7): inf, (2, 7): 0, (3, 7): inf, (4, 7): inf, (5, 7): inf, (6, 7): inf, (7, 7): inf, (8, 7): inf, (9, 7): inf, (10, 7): 0, (11, 7): 0, (12, 7): 0, (13, 7): 0, (14, 7): inf, (0, 8): inf, (1, 8): inf, (2, 8): inf, (3, 8): inf, (4, 8): inf, (5, 8): inf, (6, 8): 0, (7, 8): inf, (8, 8): 0, (9, 8): inf, (10, 8): 0, (11, 8): 0, (12, 8): 0, (13, 8): inf, (14, 8): 0}
+    # (truediv, relu): {(0, 0): 0, (1, 0): inf, (2, 0): 0, (3, 0): inf, (4, 0): inf, (5, 0): 0, (6, 0): 0, (7, 0): inf, (8, 0): inf, (0, 1): inf, (1, 1): 0, (2, 1): inf, (3, 1): 0, (4, 1): 0, (5, 1): inf, (6, 1): 0, (7, 1): inf, (8, 1): inf, (0, 2): inf, (1, 2): inf, (2, 2): 0, (3, 2): inf, (4, 2): inf, (5, 2): inf, (6, 2): 0, (7, 2): inf, (8, 2): inf, (0, 3): inf, (1, 3): inf, (2, 3): inf, (3, 3): 0, (4, 3): inf, (5, 3): inf, (6, 3): 0, (7, 3): inf, (8, 3): inf, (0, 4): inf, (1, 4): inf, (2, 4): inf, (3, 4): inf, (4, 4): 0, (5, 4): inf, (6, 4): 0, (7, 4): inf, (8, 4): inf, (0, 5): inf, (1, 5): inf, (2, 5): inf, (3, 5): inf, (4, 5): inf, (5, 5): 0, (6, 5): 0, (7, 5): inf, (8, 5): inf, (0, 6): inf, (1, 6): inf, (2, 6): inf, (3, 6): inf, (4, 6): inf, (5, 6): inf, (6, 6): 0, (7, 6): inf, (8, 6): inf, (0, 7): inf, (1, 7): inf, (2, 7): 0, (3, 7): inf, (4, 7): inf, (5, 7): inf, (6, 7): 0, (7, 7): 0, (8, 7): inf, (0, 8): inf, (1, 8): inf, (2, 8): inf, (3, 8): inf, (4, 8): 0, (5, 8): inf, (6, 8): 0, (7, 8): inf, (8, 8): 0}
+    # (relu, output): {(0, 0): 246019.30000000002, (1, 0): 246019.30000000002, (2, 0): 123009.1, (3, 0): 123009.1, (4, 0): 123009.1, (5, 0): 123009.1, (6, 0): 0, (7, 0): 246019.30000000002, (8, 0): 246019.30000000002}
+    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
+
+    # construct all node pairs
+    all_node_pairs = []
+
+    for node in graph.nodes:
+        if node.op == 'output':
+            continue
+        all_node_pairs.append((node, node.next))
+
+    for node_pair in all_node_pairs:
+        assert node_pair in cost_graph.edge_costs
+
+    # construct merged node pairs
+    merged_node_pairs = []
+    node_list = list(graph.nodes)
+
+    # add (x, conv) and (conv, output) into check node pairs
+    merged_node_pairs.append((node_list[0], node_list[2]))
+    merged_node_pairs.append((node_list[2], node_list[-1]))
+    # (x, conv1): {(0, 0): 0, (0, 1): 0, (0, 2): 0, (0, 3): 0, (0, 4): 0, (0, 5): 0, (0, 6): 0, (0, 7): 0, (0, 8): 0, (0, 9): 0, (0, 10): 0, (0, 11): 0, (0, 12): 0, (0, 13): 0, (0, 14): 0}
+    # (conv1, output): {(0, 0): inf, (1, 0): inf, (2, 0): inf, (3, 0): inf, (4, 0): inf, (5, 0): inf, (6, 0): inf, (7, 0): inf, (8, 0): inf, (9, 0): inf, (10, 0): 0, (11, 0): 0, (12, 0): 0, (13, 0): inf, (14, 0): inf}
+    cost_graph.simplify_graph()
+    for node_pair in all_node_pairs:
+        if node_pair in merged_node_pairs:
+            assert node_pair in cost_graph.edge_costs
+        else:
+            assert node_pair not in cost_graph.edge_costs
+
+
+if __name__ == '__main__':
+    test_cost_graph()

tests/test_auto_parallel/test_strategies_constructor.py

@@ -0,0 +1,98 @@
+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.proxy import ColoProxy
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
+from colossalai.auto_parallel.solver.conv_handler import ConvHandler, CONV_STRATEGIES_LIST
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from copy import deepcopy
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv = nn.Conv2d(c_in, c_out, kernel_size=3)
+
+    def forward(self, x):
+        x = x * 2
+        x = self.conv(x)
+        return x
+
+
+def test_strategies_constructor():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    entire_shape = torch.Size((4, 16, 64, 64))
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    model = ConvModel(16, 32)
+    input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
+    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+    #     return conv
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+
+    solver_options = {'fast_mode': True}
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
+
+    assert strategies_constructor.leaf_strategies == []
+    assert strategies_constructor.strategy_map == {}
+    strategies_constructor.build_strategies_and_cost()
+
+    # check leaf_strategies
+
+    # In fast mode, placeholder node only has replica strategy.
+    assert strategies_constructor.leaf_strategies[0][0].name == 'Replica Placeholder'
+
+    # Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
+    assert strategies_constructor.leaf_strategies[1][0].name == '[R, R, R, R] -> [R, R, R, R]'
+
+    # Third node is conv.
+    conv_check_list = deepcopy(CONV_STRATEGIES_LIST)
+    for strategy in strategies_constructor.leaf_strategies[2]:
+        conv_check_list.remove(strategy.name)
+    assert len(conv_check_list) == 0
+
+    # In fast mode, output node only has replica strategy.
+    assert strategies_constructor.leaf_strategies[3][0].name == 'Replica Output'
+
+    # check strategy_map
+
+    nodes = [node for node in graph.nodes]
+    # In fast mode, placeholder node only has replica strategy.
+    x = nodes[0]
+    assert strategies_constructor.strategy_map[x][0].name == 'Replica Placeholder'
+
+    # Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
+    mul = nodes[1]
+    assert strategies_constructor.strategy_map[mul][0].name == '[R, R, R, R] -> [R, R, R, R]'
+
+    # Third node is conv.
+    conv = nodes[2]
+    conv_check_list = deepcopy(CONV_STRATEGIES_LIST)
+    for strategy in strategies_constructor.strategy_map[conv]:
+        conv_check_list.remove(strategy.name)
+    assert len(conv_check_list) == 0
+
+    # In fast mode, output node only has replica strategy.
+    output = nodes[3]
+    assert strategies_constructor.strategy_map[output][0].name == 'Replica Output'
+
+
+if __name__ == '__main__':
+    test_strategies_constructor()