[autoparallel] refactored shape consistency to remove redundancy (#1591)

* [autoparallel] refactored shape consistency to remove redundancy

* polish code

* polish code

* polish code

colossalai/auto_parallel/solver/_utils.py

@@ -1,8 +1,9 @@
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 import torch
 from torch.fx.node import Node
 from colossalai.tensor.sharding_spec import ShardingSpec
 from colossalai.device.device_mesh import DeviceMesh
-from typing import Union, Dict, List
+from typing import Union, Dict, List, Optional
 
 
 def generate_sharding_spec(input_: Union[Node, torch.Tensor], device_mesh: DeviceMesh,
@@ -31,3 +32,45 @@ def generate_sharding_spec(input_: Union[Node, torch.Tensor], device_mesh: Devic
 
     sharding_spec = ShardingSpec(device_mesh=device_mesh, entire_shape=shape, dim_partition_dict=dim_partition_dict)
     return sharding_spec
+
+
+def generate_resharding_costs(nodes: List[Node],
+                              sharding_specs: List[ShardingSpec],
+                              count_backward: Optional[bool] = True,
+                              dtype: Optional[torch.dtype] = None):
+    '''
+    Compute the resharding costs with this specific strategy.
+
+    Argument:
+        nodes (List[Node]): a list of nodes
+        sharding_spec_for_input(ShardingSpec): a list of ShardingSpec for the nodes.
+        count_backward (Optional[bool]): whether to include the cost of resharding in the backward pass, default is True. False can be used for inference.
+        dtype (Optional[torch.dtype]): the data type for cost calculation, default is None. 
+    '''
+    # The resharding_cost of weight is counted due to sharing weight cases.
+    resharding_costs = {}
+    size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+
+    # shape consistency manager is a singleton class
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    for input_node, input_spec in zip(nodes, 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.'
+            # compute the resharding cost during forward phase
+            _, _, resharding_cost_forward = shape_consistency_manager.shape_consistency(input_sharding_spec, input_spec)
+
+            if count_backward:
+                # In backward phase, we should convert grad with target_spec into input_sharding_spec
+                _, _, resharding_cost_backward = shape_consistency_manager.shape_consistency(
+                    input_spec, input_sharding_spec)
+                total_resharding_cost = resharding_cost_forward + resharding_cost_backward
+            else:
+                total_resharding_cost = resharding_cost_forward
+
+            # we need multiply the size of elem dtype to get correct communication cost
+            resharding_cost = total_resharding_cost * size_per_elem_bytes
+            resharding_costs[input_node].append(resharding_cost)
+    return resharding_costs

colossalai/auto_parallel/solver/op_handler/batch_norm_handler.py

@@ -4,7 +4,6 @@ import warnings
 import torch
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
 from .operator_handler import OperatorHandler
-from .._utils import generate_sharding_spec
 
 __all__ = ['BatchNormHandler']
 
@@ -115,15 +114,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'RS{mesh_dim_0} = RS{mesh_dim_0} x S{mesh_dim_0}'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_0]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -156,8 +153,7 @@ class BatchNormHandler(OperatorHandler):
         new_name = f'S{mesh_dim_1}S{mesh_dim_0} = RS{mesh_dim_0} x S{mesh_dim_0}'
 
         dim_partition_dict_for_output = {0: [mesh_dim_1], 1: [mesh_dim_0]}
-        new_sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                              dim_partition_dict_for_output)
+        new_sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
         # the computation cost is all the same
         new_compute_cost = compute_cost
 
@@ -192,15 +188,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'RS{mesh_dim_0}{mesh_dim_1} = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -234,15 +228,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'RR = RR x R'
 
         dim_partition_dict_for_input = {}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -273,8 +265,7 @@ class BatchNormHandler(OperatorHandler):
 
         def _construct_batch_sharding_strategies(mesh_dim_list, new_name):
             dim_partition_dict_for_output = {0: mesh_dim_list}
-            new_sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                                  dim_partition_dict_for_output)
+            new_sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
             # the computation cost is all the same
             new_compute_cost = compute_cost
@@ -332,15 +323,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x R WITH SYNC_BN'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -374,15 +363,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x R WITH SYNC_BN'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -416,15 +403,13 @@ class BatchNormHandler(OperatorHandler):
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1} WITH SYNC_BN'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -459,7 +444,7 @@ class BatchNormHandler(OperatorHandler):
         Generate every possible strategies for a BatchNorm node, and record all strategies into the strategies_vector.
 
         Example:
-            norm_handler = BatchNormHandler(node, self.device_mesh, strategies_vector,
+            norm_handler = BatchNormHandler(node,  strategies_vector,
                                                self.shape_consistency_manager)
             norm_handler.register_strategy()
             for strategy in norm_handler.strategies_vector:

colossalai/auto_parallel/solver/op_handler/conv_handler.py

@@ -4,7 +4,6 @@ import warnings
 import torch
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
 from .operator_handler import OperatorHandler
-from .._utils import generate_sharding_spec
 
 __all__ = ['ConvHandler']
 
@@ -109,15 +108,13 @@ class ConvHandler(OperatorHandler):
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {1: [mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -158,15 +155,13 @@ class ConvHandler(OperatorHandler):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x RR'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -205,15 +200,13 @@ class ConvHandler(OperatorHandler):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -252,15 +245,13 @@ class ConvHandler(OperatorHandler):
         name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -296,15 +287,13 @@ class ConvHandler(OperatorHandler):
         name = f'RR = RS{mesh_dim_0} x S{mesh_dim_0}R'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -340,15 +329,13 @@ class ConvHandler(OperatorHandler):
         name = f'RS{mesh_dim_0} = RR x RS{mesh_dim_0}'
 
         dim_partition_dict_for_input = {}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {1: [mesh_dim_0]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_0]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -384,15 +371,13 @@ class ConvHandler(OperatorHandler):
         name = f'RR = RR x RR'
 
         dim_partition_dict_for_input = {}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -426,15 +411,13 @@ class ConvHandler(OperatorHandler):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x RR'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])
@@ -475,15 +458,13 @@ class ConvHandler(OperatorHandler):
         name = f'RR = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}R'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_output = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                          dim_partition_dict_for_output)
+        sharding_spec_for_output = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input, sharding_spec_for_weight])

colossalai/auto_parallel/solver/op_handler/dot_handler.py

@@ -3,7 +3,6 @@ import torch
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
 from .operator_handler import OperatorHandler
 from functools import reduce
-from .._utils import generate_sharding_spec
 
 __all__ = ['DotHandler']
 
@@ -29,16 +28,14 @@ class DotHandler(OperatorHandler):
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         # linear layer weight is transposed during init
         dim_partition_dict_for_weight = {0: [mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_input)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -69,17 +66,15 @@ class DotHandler(OperatorHandler):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         # since weight of the linear layer is transposed
         # the actual dim to be sharded is 1
         dim_partition_dict_for_weight = {1: [mesh_dim_0]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -106,15 +101,13 @@ class DotHandler(OperatorHandler):
         name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0], 1: [mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_1]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_input)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -141,15 +134,13 @@ class DotHandler(OperatorHandler):
         name = f'RR = RS{mesh_dim} x S{mesh_dim}R'
 
         dim_partition_dict_for_input = {1: [mesh_dim]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {1: [mesh_dim]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -176,15 +167,13 @@ class DotHandler(OperatorHandler):
         name = f'RS{mesh_dim} = RR x RS{mesh_dim}'
 
         dim_partition_dict_for_input = {}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -211,15 +200,13 @@ class DotHandler(OperatorHandler):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x RR'
 
         dim_partition_dict_for_input = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -246,15 +233,13 @@ class DotHandler(OperatorHandler):
         name = f'RR = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}R'
 
         dim_partition_dict_for_input = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {0: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])
@@ -281,15 +266,13 @@ class DotHandler(OperatorHandler):
         name = f'RS{mesh_dim_0}{mesh_dim_1} = RR x RS{mesh_dim_0}{mesh_dim_1}'
 
         dim_partition_dict_for_input = {}
-        sharding_spec_for_input = generate_sharding_spec(self.input_data, self.device_mesh,
-                                                         dim_partition_dict_for_input)
+        sharding_spec_for_input = self._generate_sharding_spec(self.input_data, dim_partition_dict_for_input)
 
         dim_partition_dict_for_weight = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_weight = generate_sharding_spec(self.weight, self.device_mesh, dim_partition_dict_for_weight)
+        sharding_spec_for_weight = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
 
         dim_partition_dict_for_output = {1: [mesh_dim_0, mesh_dim_1]}
-        sharding_spec_for_ouput = generate_sharding_spec(self.output_data, self.device_mesh,
-                                                         dim_partition_dict_for_output)
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output_data, dim_partition_dict_for_output)
 
         # generate resharding cost for this strategy
         resharding_costs = self._generate_resharding_costs([sharding_spec_for_input])

colossalai/auto_parallel/solver/op_handler/operator_handler.py

@@ -7,6 +7,7 @@ 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
+from .._utils import generate_resharding_costs, generate_sharding_spec
 
 from ..sharding_strategy import StrategiesVector
 
@@ -17,24 +18,24 @@ class OperatorHandler(ABC):
     '''
     The OperatorHandler is an abstract class used to generate every possible strategies for an operator node.
 
-    Argument:
-        input_node(Node): the input node in node argument list.
-        input_index(int): the index of input node in the node argument list.
-        weight(torch.Tensor): Weight of the node.
-        output_node(Node): Output_node is the output of the node.
-        device_mesh(DeviceMesh): A logical view of a physical mesh.
-        strategies_vector(StrategiesVector): all the strategies generated in this handler will be recorded into the strategies_vector.
-        shape_consistency_manager(ShapeConsistencyManager): ShapeConsistencyManager will give the resharding costs of the different sharding specs. 
+    Args:
+        node (Node): the input node in node argument list.
+        device_mesh (DeviceMesh): A logical view of a physical mesh.
+        strategies_vector (StrategiesVector): all the strategies generated in this handler will be recorded into the strategies_vector.
+        handle_backward (Optional[bool]): whether to consider the backward pass. The default value is True. False can be used for inference.
     '''
 
-    def __init__(self, node: Node, device_mesh: DeviceMesh, strategies_vector: StrategiesVector,
-                 shape_consistency_manager: ShapeConsistencyManager):
+    def __init__(self,
+                 node: Node,
+                 device_mesh: DeviceMesh,
+                 strategies_vector: StrategiesVector,
+                 handle_backward: bool = True):
         self.node = node
         self.predecessor_node = list(node._input_nodes.keys())
         self.successor_node = list(node.users.keys())
         self.device_mesh = device_mesh
         self.strategies_vector = strategies_vector
-        self.shape_consistency_manager = shape_consistency_manager
+        self.handle_backward = handle_backward
 
         # find the module and its parameters associated with this node
         # this can be used to compute the compute/communication/sharding cost
@@ -102,35 +103,23 @@ class OperatorHandler(ABC):
 
         return total_memory_cost, activation_memory_cost, weight_memory_cost
 
-    def _generate_resharding_costs(self, sharding_spec_for_input):
-        '''
-        Compute the resharding costs with this specific strategy.
-
-        Note: The resharding_cost of weight is NOT counted.
-
-        Argument:
-            resharding_costs(Dict[int, List[float]]): The resharding cost generated in this method will be appended into this dictionary. 
-                                                      Resharding_cost[i][j] means the cost of i-th argument in the output node argument list
-                                                      with j-th strategy in its strategies_vector transforms to sharding spec wanted in this
-                                                      strategy.
-            sharding_spec_for_input(ShardingSpec): ShardingSpec of the input node.
-        '''
+    def _generate_resharding_costs(self, sharding_specs):
         # The resharding_cost of weight is counted due to sharing weight cases.
-        resharding_costs = {}
         dtype = self.node._meta_data.dtype
-        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
-        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.'
-                # compute the resharding cost during forward phase
-                _, _, resharding_cost_forward = self.shape_consistency_manager.shape_consistency(
-                    input_sharding_spec, input_spec)
-                # In backward phase, we should convert grad with target_spec into input_sharding_spec
-                _, _, resharding_cost_backward = self.shape_consistency_manager.shape_consistency(
-                    input_spec, input_sharding_spec)
-                # we need multiply the size of elem dtype to get correct communication cost
-                resharding_cost = (resharding_cost_forward + resharding_cost_backward) * size_per_elem_bytes
-                resharding_costs[input_node].append(resharding_cost)
-        return resharding_costs
+        nodes = self.predecessor_node
+        return generate_resharding_costs(nodes=nodes,
+                                         sharding_specs=sharding_specs,
+                                         count_backward=self.handle_backward,
+                                         dtype=dtype)
+
+    def _generate_sharding_spec(self, input_: torch.Tensor, dim_partition_dict: Dict[int, List[int]]) -> ShardingSpec:
+        return generate_sharding_spec(input_=input_,
+                                      device_mesh=self.device_mesh,
+                                      dim_partition_dict=dim_partition_dict)
+
+    @abstractmethod
+    def _generate_compute_cost(self, *args, **kwargs):
+        """
+        Compute the flops involved in the node.
+        """
+        pass

colossalai/auto_parallel/solver/strategies_constructor.py

@@ -11,7 +11,7 @@ import math
 import torch
 import operator
 from typing import Dict, List
-from ._utils import generate_sharding_spec
+from ._utils import generate_sharding_spec, generate_resharding_costs
 
 
 class StrategiesConstructor:
@@ -21,12 +21,10 @@ class StrategiesConstructor:
     Args:
         graph (Graph): a Graph object used for analysis and strategy generation.
         device_mesh (DeviceMesh): a DeviceMesh object which contains the meta information about the cluster.
-        shape_consistency_manager (ShapeConsistencyManager): a ShapeConsistencyManager object to make sure the sharding specs are consistent.
         solver_options (SolverOptions): a SolverOptions object which specifies the preferences for plan searching.
     """
 
-    def __init__(self, graph: Graph, device_mesh: DeviceMesh, shape_consistency_manager: ShapeConsistencyManager,
-                 solver_options: SolverOptions):
+    def __init__(self, graph: Graph, device_mesh: DeviceMesh, solver_options: SolverOptions):
         self.graph = graph
         assert graph.owning_module is not None, 'The given graph is not associated with a owning_module'
         self.root_module = self.graph.owning_module
@@ -34,27 +32,8 @@ class StrategiesConstructor:
         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_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.
@@ -120,14 +99,13 @@ class StrategiesConstructor:
                 # 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 = ConvHandler(node, self.device_mesh, strategies_vector)
                     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 = DotHandler(node, self.device_mesh, strategies_vector)
                     dot_handler.register_strategy()
 
                 # element-wise module
@@ -158,8 +136,8 @@ class StrategiesConstructor:
                         # 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])
+                        resharding_costs = 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] = [
@@ -214,8 +192,8 @@ class StrategiesConstructor:
                         # 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])
+                        resharding_costs = generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                     [input_sharding_spec])
 
                         sharding_strategy = ShardingStrategy(name,
                                                              output_sharding_spec,
@@ -275,8 +253,8 @@ class StrategiesConstructor:
                         compute_cost = node._meta_data.numel()
                         memory_cost = 0
 
-                        resharding_costs = self._generate_resharding_costs(strategies_vector.predecessor_nodes,
-                                                                           [input_sharding_spec])
+                        resharding_costs = 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] = [
@@ -317,8 +295,8 @@ class StrategiesConstructor:
                             # 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])
+                            resharding_costs = 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,
@@ -335,8 +313,8 @@ class StrategiesConstructor:
                             # 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])
+                            resharding_costs = 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,
@@ -360,8 +338,8 @@ class StrategiesConstructor:
                         # 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])
+                        resharding_costs = 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]
@@ -397,8 +375,8 @@ class StrategiesConstructor:
                     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)
+                    resharding_costs = generate_resharding_costs(strategies_vector.predecessor_nodes,
+                                                                 input_sharding_specs)
 
                     # clear the resharding cost for the output node
                     # TODO: we may remove this in final version

colossalai/context/singleton_meta.py

@@ -15,4 +15,7 @@ class SingletonMeta(type):
         if cls not in cls._instances:
             instance = super().__call__(*args, **kwargs)
             cls._instances[cls] = instance
+        else:
+            assert len(args) == 0 and len(
+                kwargs) == 0, f'{cls.__name__} is a singleton class and a instance has been created.'
         return cls._instances[cls]

colossalai/tensor/shape_consistency.py

@@ -1,15 +1,22 @@
 import torch
+from dataclasses import dataclass
 from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
 from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator
 from enum import Enum
 from copy import deepcopy
 from typing import Dict, List, Optional, Tuple, Union
+from colossalai.context.singleton_meta import SingletonMeta
 import torch.distributed as dist
 import math
 from functools import reduce
 import operator
 from torch.distributed import ReduceOp
 
+__all__ = [
+    'CollectiveCommPattern', 'CommSpec', 'ShapeConsistencyManager', 'ShapeConsistencyOptions',
+    'set_shape_consistency_options'
+]
+
 
 class CollectiveCommPattern(Enum):
     ALLGATHER = 'all_gather'
@@ -152,14 +159,40 @@ class CommSpec:
             tensor.data = tensor
 
 
-class ShapeConsistencyManager:
+@dataclass
+class ShapeConsistencyOptions:
+    """
+    ShapeConsistencyOptions is a dataclass which specifies the preferences for shape consistency.
+    """
+    # TODO: shape consistency option is not implemented yet
+    pass
+
+
+def set_shape_consistency_options(options: ShapeConsistencyOptions):
+    """
+    Configure the shape consistency manager via function call.
+    """
+    manager = ShapeConsistencyManager()
+    manager.options = options
+
 
-    def __init__(self, consistency_option=None):
-        self.consistency_option = consistency_option
+class ShapeConsistencyManager(metaclass=SingletonMeta):
+
+    def __init__(self):
+        self._options = None
         self.total_communication_cost = 0
         self.total_transform_steps = 0
         self.cached_spec_pairs_transform_path = {}
 
+    @property
+    def options(self):
+        return self._options
+
+    @options.setter
+    def options(self, options_: ShapeConsistencyOptions):
+        assert isinstance(options_, ShapeConsistencyOptions)
+        self._options = options_
+
     def get_all_all_gather_spec(self, source_spec, orig_cost):
         '''
         Get all valid sharding specs from source_spec with single all-gather operation, and 

tests/test_auto_parallel/test_batch_norm_handler.py

@@ -8,7 +8,6 @@ from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
 from colossalai.auto_parallel.solver.op_handler.batch_norm_handler import BatchNormHandler
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 from colossalai.device.device_mesh import DeviceMesh
 
 
@@ -31,7 +30,6 @@ def test_bn_handler():
     #  [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 = BNModel(16)
@@ -77,10 +75,11 @@ def test_bn_handler():
 
     # generate bn strategy
     strategies_vector = StrategiesVector(node=nodes[2])
-    bn_handler = BatchNormHandler(node=nodes[2],
-                                  device_mesh=device_mesh,
-                                  strategies_vector=strategies_vector,
-                                  shape_consistency_manager=shape_consistency_manager)
+    bn_handler = BatchNormHandler(
+        node=nodes[2],
+        device_mesh=device_mesh,
+        strategies_vector=strategies_vector,
+    )
     bn_handler.register_strategy()
     # ['RS0 = RS0 x S0', 'S1S0 = RS0 x S0', 'RS1 = RS1 x S1', 'S0S1 = RS1 x S1', 'RR = RR x R', 'S0R = RR x R', 'S1R = RR x R', 'S01R = RR x R', 'RS01 = RS01 x S01',
     # 'S0R = S0R x R WITH SYNC_BN', 'S1R = S1R x R WITH SYNC_BN', 'S0S1 = S0S1 x S1 WITH SYNC_BN', 'S1S0 = S1S0 x S0 WITH SYNC_BN', 'S01R = S01R x R WITH SYNC_BN']

tests/test_auto_parallel/test_conv_handler.py

@@ -8,7 +8,6 @@ from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
 from colossalai.auto_parallel.solver.op_handler.conv_handler import ConvHandler
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 from colossalai.device.device_mesh import DeviceMesh
 
 
@@ -31,7 +30,6 @@ def test_conv_handler():
     #  [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)
@@ -77,10 +75,11 @@ def test_conv_handler():
 
     # generate conv strategy
     strategies_vector = StrategiesVector(node=nodes[2])
-    conv_handler = ConvHandler(node=nodes[2],
-                               device_mesh=device_mesh,
-                               strategies_vector=strategies_vector,
-                               shape_consistency_manager=shape_consistency_manager)
+    conv_handler = ConvHandler(
+        node=nodes[2],
+        device_mesh=device_mesh,
+        strategies_vector=strategies_vector,
+    )
     conv_handler.register_strategy()
     # ['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']
     strategy_name_list = [strategy.name for strategy in conv_handler.strategies_vector]

tests/test_auto_parallel/test_cost_graph.py

@@ -4,10 +4,7 @@ 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.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
@@ -37,7 +34,6 @@ def test_cost_graph():
     #  [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)
@@ -55,7 +51,7 @@ def test_cost_graph():
     gm.recompile()
 
     solver_options = SolverOptions(fast=True)
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
     strategies_constructor.build_strategies_and_cost()
 
     # (x, mul):{(0, 0): 0}

tests/test_auto_parallel/test_dot_handler.py

@@ -8,7 +8,6 @@ from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
 from colossalai.auto_parallel.solver.op_handler.dot_handler import DotHandler
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 from colossalai.device.device_mesh import DeviceMesh
 
 
@@ -31,7 +30,6 @@ def test_dot_handler():
     #  [2, 3]]
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
     entire_shape = torch.Size((4, 8))
-    shape_consistency_manager = ShapeConsistencyManager()
 
     tracer = ColoTracer()
     model = LinearModel(8, 16)
@@ -76,10 +74,11 @@ def test_dot_handler():
 
     # generate dot strategy
     strategies_vector = StrategiesVector(node=nodes[2])
-    dot_handler = DotHandler(node=nodes[2],
-                             device_mesh=device_mesh,
-                             strategies_vector=strategies_vector,
-                             shape_consistency_manager=shape_consistency_manager)
+    dot_handler = DotHandler(
+        node=nodes[2],
+        device_mesh=device_mesh,
+        strategies_vector=strategies_vector,
+    )
     strategies_vector = dot_handler.register_strategy()
 
     # ['S0S1 = S0R x RS1', 'S1S0 = S1R x RS0', 'S0R = S0S1 x S1R', 'S1R = S1S0 x S0R', 'RS1 = RS0 x S0S1', 'RS0 = RS1 x S1S0', 'RS0 = RR x RS0', 'RS1 = RR x RS1', 'RR = RR x RR']

tests/test_auto_parallel/test_strategies_constructor.py

@@ -8,7 +8,6 @@ from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
 from colossalai.auto_parallel.solver.op_handler.conv_handler import 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.options import SolverOptions
@@ -34,7 +33,6 @@ def test_strategies_constructor():
     #  [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)
@@ -49,7 +47,7 @@ def test_strategies_constructor():
     gm.recompile()
 
     solver_options = SolverOptions(fast=True)
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
 
     assert strategies_constructor.leaf_strategies == []
     assert strategies_constructor.strategy_map == {}