[autoparallel] add bcast op handler (#1600)

* [autoparallel] add bcast op handler

* polish code

* add more BCAST FUNC OP

* polish code

* add exception handler

* polish

colossalai/auto_parallel/solver/_utils.py

@@ -4,6 +4,10 @@ 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, Optional
+import warnings
+from functools import reduce
+import functools
+import operator
 
 
 def generate_sharding_spec(input_: Union[Node, torch.Tensor], device_mesh: DeviceMesh,
@@ -29,6 +33,11 @@ def generate_sharding_spec(input_: Union[Node, torch.Tensor], device_mesh: Devic
         raise TypeError(
             f'We cannot generate sharding spec for {type(input_)} type, only torch.fx.Node or torch.Tensor is expected.'
         )
+    for dim_index, sharding_index_list in dim_partition_dict.items():
+        sharding_list = [device_mesh.mesh_shape[sharding_index] for sharding_index in sharding_index_list]
+        sharding_size = reduce(operator.mul, sharding_list, 1)
+        assert shape[
+            dim_index] % sharding_size == 0, f'we cannot shard the {dim_index} dimension of tensor into {sharding_size} partitions.'
 
     sharding_spec = ShardingSpec(device_mesh=device_mesh, entire_shape=shape, dim_partition_dict=dim_partition_dict)
     return sharding_spec
@@ -74,3 +83,18 @@ def generate_resharding_costs(nodes: List[Node],
             resharding_cost = total_resharding_cost * size_per_elem_bytes
             resharding_costs[input_node].append(resharding_cost)
     return resharding_costs
+
+
+def exception_handler(func):
+    """
+    A function wrapper which executes the function with a specified seed.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            func(*args, **kwargs)
+        except Exception as e:
+            warnings.warn(f'{e}')
+
+    return wrapper

colossalai/auto_parallel/solver/constants.py

@@ -3,16 +3,19 @@ import operator
 
 __all__ = [
     'ELEMENTWISE_MODULE_OP', 'ELEMENTWISE_FUNC_OP', 'RESHAPE_FUNC_OP', 'CONV_MODULE_OP', 'CONV_FUNC_OP',
-    'LINEAR_MODULE_OP', 'LINEAR_FUNC_OP', 'BATCHNORM_MODULE_OP', 'POOL_MODULE_OP', 'NON_PARAM_FUNC_OP'
+    'LINEAR_MODULE_OP', 'LINEAR_FUNC_OP', 'BATCHNORM_MODULE_OP', 'POOL_MODULE_OP', 'NON_PARAM_FUNC_OP', 'BCAST_FUNC_OP'
 ]
 
 ELEMENTWISE_MODULE_OP = [torch.nn.Dropout, torch.nn.ReLU]
-# TODO: flatten should not be added into this group
 ELEMENTWISE_FUNC_OP = [
-    torch.add, operator.add, torch.abs, torch.cos, torch.exp, torch.mul, operator.mul, operator.floordiv,
+    torch.abs, torch.cos, torch.exp, operator.neg, torch.multiply, torch.nn.functional.relu,
-    operator.truediv, operator.neg, torch.multiply, torch.nn.functional.relu, torch.nn.functional.dropout
+    torch.nn.functional.dropout, torch.flatten
 ]
 RESHAPE_FUNC_OP = [torch.flatten, torch.Tensor.view, torch.reshape]
+BCAST_FUNC_OP = [
+    torch.add, torch.sub, torch.mul, torch.div, torch.floor_divide, torch.true_divide, operator.add, operator.sub,
+    operator.mul, operator.floordiv, operator.truediv
+]
 CONV_MODULE_OP = [
     torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d, torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d,
     torch.nn.ConvTranspose3d

colossalai/auto_parallel/solver/op_handler/__init__.py

@@ -3,5 +3,6 @@ from .dot_handler import DotHandler
 from .conv_handler import ConvHandler
 from .batch_norm_handler import BatchNormHandler
 from .reshape_handler import ReshapeHandler
+from .bcast_op_handler import BcastOpHandler
 
-__all__ = ['OperatorHandler', 'DotHandler', 'ConvHandler', 'BatchNormHandler', 'ReshapeHandler']
+__all__ = ['OperatorHandler', 'DotHandler', 'ConvHandler', 'BatchNormHandler', 'ReshapeHandler', 'BcastOpHandler']

colossalai/auto_parallel/solver/op_handler/batch_norm_handler.py

@@ -1,9 +1,9 @@
 import operator
 from functools import reduce
-import warnings
 import torch
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
 from .operator_handler import OperatorHandler
+from colossalai.auto_parallel.solver._utils import exception_handler
 
 __all__ = ['BatchNormHandler']
 
@@ -110,6 +110,7 @@ class BatchNormHandler(OperatorHandler):
 
         return memory_cost, memory_cost_forward_activation, memory_cost_backward_activation
 
+    @exception_handler
     def split_input_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'RS{mesh_dim_0} = RS{mesh_dim_0} x S{mesh_dim_0}'
 
@@ -184,6 +185,7 @@ class BatchNormHandler(OperatorHandler):
 
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_channel_1d(self, mesh_dim_0, mesh_dim_1):
         name = f'RS{mesh_dim_0}{mesh_dim_1} = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}'
 
@@ -224,6 +226,7 @@ class BatchNormHandler(OperatorHandler):
 
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def non_split(self, mesh_dim_0, mesh_dim_1):
         name = f'RR = RR x R'
 
@@ -319,6 +322,7 @@ class BatchNormHandler(OperatorHandler):
         new_sharding_strategy = _construct_batch_sharding_strategies(mesh_dim_list, new_name)
         self.strategies_vector.append(new_sharding_strategy)
 
+    @exception_handler
     def split_input_batch(self, mesh_dim_0):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x R WITH SYNC_BN'
 
@@ -359,6 +363,7 @@ class BatchNormHandler(OperatorHandler):
 
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_batch_1d(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x R WITH SYNC_BN'
 
@@ -399,6 +404,7 @@ class BatchNormHandler(OperatorHandler):
 
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_both_dim(self, mesh_dim_0, mesh_dim_1):
         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'
 

colossalai/auto_parallel/solver/op_handler/bcast_op_handler.py

@@ -0,0 +1,164 @@
+import operator
+from functools import reduce
+import warnings
+import torch
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from .operator_handler import OperatorHandler
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.tensor.sharding_spec import ShardingSpec
+from copy import deepcopy
+from typing import Dict, List
+from colossalai.auto_parallel.solver._utils import exception_handler
+
+__all__ = ['BcastOpHandler']
+
+
+class BcastOpHandler(OperatorHandler):
+    """
+    An OperatorHandler which deals with the sharding strategies of broadcast operators(such as operator.add).
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert len(self.predecessor_node) == 2
+        self.lhs_data = self.predecessor_node[0]._meta_data
+        self.rhs_data = self.predecessor_node[1]._meta_data
+        self.lhs = self.predecessor_node[0]
+        self.rhs = self.predecessor_node[1]
+        self.output_data = self.node._meta_data
+
+    def _generate_sharding_spec(self, input_: torch.Tensor, dim_partition_dict: Dict[int, List[int]]) -> ShardingSpec:
+        shape = list(input_.shape)
+
+        # padding the shape to the same length as output_data
+        while len(shape) < self.output_data.dim():
+            shape.insert(0, 1)
+        shape = torch.Size(shape)
+
+        # if the sharding happens on a size one dimension, we should record it as R.
+        processed_dim_partition_dict = deepcopy(dim_partition_dict)
+        for dim_index, _ in dim_partition_dict.items():
+            if shape[dim_index] == 1:
+                processed_dim_partition_dict.pop(dim_index)
+        sharding_spec = ShardingSpec(device_mesh=self.device_mesh,
+                                     entire_shape=shape,
+                                     dim_partition_dict=processed_dim_partition_dict)
+
+        return sharding_spec
+
+    def _generate_resharding_costs(self, sharding_specs):
+        # The resharding_cost of weight is counted due to sharing weight cases.
+        dtype = self.node._meta_data.dtype
+        nodes = self.predecessor_node
+        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.'
+                # if the input shape is smaller than the target input, we will fill the input to the same length as target.
+                # Then, use the padded input sharding spec to compute the resharding cost.
+                if len(input_sharding_spec.entire_shape) < len(input_spec.entire_shape):
+                    new_entire_shape = list(input_sharding_spec.entire_shape)
+                    while len(new_entire_shape) < len(input_spec.entire_shape):
+                        new_entire_shape.insert(0, 1)
+                    new_entire_shape = torch.Size(new_entire_shape)
+                    new_device_mesh = input_sharding_spec.device_mesh
+                    new_dim_partition_dict = input_sharding_spec.dim_partition_dict
+                    input_sharding_spec = ShardingSpec(device_mesh=new_device_mesh,
+                                                       entire_shape=new_entire_shape,
+                                                       dim_partition_dict=new_dim_partition_dict)
+
+                # compute the resharding cost during forward phase
+                _, _, resharding_cost_forward = shape_consistency_manager.shape_consistency(
+                    input_sharding_spec, input_spec)
+
+                _, _, resharding_cost_backward = shape_consistency_manager.shape_consistency(
+                    input_spec, input_sharding_spec)
+                total_resharding_cost = resharding_cost_forward + resharding_cost_backward
+
+                # 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
+
+    def _enumerate_all_possible_output(self, mesh_dim_0, mesh_dim_1):
+        # use mesh_dim_0, mesh_dim_1 instead of constant 0, 1 in here for N-D device mesh scaliablity.
+
+        output_sharding_spec_list = []
+        output_dim_partition_list = []
+
+        # enumerate all the 2D sharding cases
+        for i in range(self.output_data.dim()):
+            for j in range(i + 1, self.output_data.dim()):
+                dim_partition_dict_0 = {i: [mesh_dim_0], j: [mesh_dim_1]}
+                dim_partition_dict_1 = {i: [mesh_dim_1], j: [mesh_dim_0]}
+                output_dim_partition_list.append(dim_partition_dict_0)
+                output_dim_partition_list.append(dim_partition_dict_1)
+
+        # enumerate all the 1D sharding cases
+        for i in range(self.output_data.dim()):
+            dim_partition_dict_0 = {i: [mesh_dim_0]}
+            dim_partition_dict_1 = {i: [mesh_dim_1]}
+            dim_partition_dict_flatten = {i: [mesh_dim_0, mesh_dim_1]}
+            output_dim_partition_list.append(dim_partition_dict_0)
+            output_dim_partition_list.append(dim_partition_dict_1)
+            output_dim_partition_list.append(dim_partition_dict_flatten)
+
+        # add empty dict for fully replicated case
+        output_dim_partition_list.append({})
+        check_duplicated_list = []
+        for output_dim_partition_dict in output_dim_partition_list:
+            output_sharding_spec = self._generate_sharding_spec(self.output_data, output_dim_partition_dict)
+            sharding_seq = output_sharding_spec.sharding_sequence
+            if sharding_seq not in check_duplicated_list:
+                check_duplicated_list.append(sharding_seq)
+                output_sharding_spec_list.append(output_sharding_spec)
+
+        return output_sharding_spec_list
+
+    def _generate_compute_cost(self, *args, **kwargs):
+        return super()._generate_compute_cost(*args, **kwargs)
+
+    @exception_handler
+    def _register_strategy(self, output_sharding_spec):
+        dim_partition_dict_for_input = output_sharding_spec.dim_partition_dict
+        sharding_spec_for_lhs = self._generate_sharding_spec(self.lhs_data, dim_partition_dict_for_input)
+        sharding_spec_for_rhs = self._generate_sharding_spec(self.rhs_data, dim_partition_dict_for_input)
+
+        name = f'{output_sharding_spec.sharding_sequence} = {sharding_spec_for_lhs.sharding_sequence} x {sharding_spec_for_rhs.sharding_sequence}'
+        dim_partition_dict_for_output = output_sharding_spec.dim_partition_dict
+
+        # generate resharding cost for this strategy
+        resharding_costs = self._generate_resharding_costs([sharding_spec_for_lhs, sharding_spec_for_rhs])
+
+        # compute the computation cost of this strategy
+        sharding_dims = []
+        for mesh_dims in dim_partition_dict_for_output.values():
+            for mesh_dim in mesh_dims:
+                sharding_dims.append(self.device_mesh.shape[mesh_dim])
+        sharding_size = reduce(operator.mul, sharding_dims, 1)
+        memory_cost = self.output_data.numel() / sharding_size
+        compute_cost = memory_cost
+        communication_cost = 0
+
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=output_sharding_spec,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_lhs, sharding_spec_for_rhs))
+
+        self.strategies_vector.append(sharding_strategies)
+
+    def register_strategy(self) -> StrategiesVector:
+        output_sharding_specs = self._enumerate_all_possible_output(0, 1)
+        for output_sharding_spec in output_sharding_specs:
+            self._register_strategy(output_sharding_spec)

colossalai/auto_parallel/solver/op_handler/conv_handler.py

@@ -4,13 +4,14 @@ import warnings
 import torch
 from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
 from .operator_handler import OperatorHandler
+from colossalai.auto_parallel.solver._utils import exception_handler
 
 __all__ = ['ConvHandler']
 
 
 class ConvHandler(OperatorHandler):
     """
-    A OperatorHandler which deals with the sharding strategies of Convolution.
+    An OperatorHandler which deals with the sharding strategies of Convolution.
     """
 
     def __init__(self, *args, **kwargs):
@@ -104,6 +105,7 @@ class ConvHandler(OperatorHandler):
 
         return memory_cost, memory_cost_forward_activation, memory_cost_backward_activation, memory_cost_backward_weight
 
+    @exception_handler
     def split_input_batch_weight_out_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
 
@@ -151,6 +153,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_batch(self, mesh_dim_0):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x RR'
 
@@ -196,6 +199,7 @@ class ConvHandler(OperatorHandler):
 
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_both_dim_weight_in_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
 
@@ -241,6 +245,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_in_channel_weight_both_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
 
@@ -283,6 +288,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_input_in_channel_weight_in_channel(self, mesh_dim_0):
         name = f'RR = RS{mesh_dim_0} x S{mesh_dim_0}R'
 
@@ -325,6 +331,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_weight_out_channel(self, mesh_dim_0):
         name = f'RS{mesh_dim_0} = RR x RS{mesh_dim_0}'
 
@@ -367,6 +374,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def non_split(self):
         name = f'RR = RR x RR'
 
@@ -407,6 +415,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_1d_parallel_on_input_batch(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x RR'
 
@@ -454,6 +463,7 @@ class ConvHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_1d_parallel_on_in_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'RR = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}R'
 
@@ -554,48 +564,24 @@ class ConvHandler(OperatorHandler):
             RR = RS01 x S01R: compute_cost is 8856576, communication_cost is 0, memory_cost is 1968128, resharding_costs is {mul: [0, 0, 262148.4, 65538.002, 196614.402, 262148.4, 65538.2]}
         '''
         # SS = SR x RS
-        try:
+        self.split_input_batch_weight_out_channel(0, 1)
-            self.split_input_batch_weight_out_channel(0, 1)
+        self.split_input_batch_weight_out_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
-        try:
-            self.split_input_batch_weight_out_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
 
         # SR = SR x RR
         self.split_input_batch(0)
         self.split_input_batch(1)
 
         # SR = SS x SR
-        try:
+        self.split_input_both_dim_weight_in_channel(0, 1)
-            self.split_input_both_dim_weight_in_channel(0, 1)
+        self.split_input_both_dim_weight_in_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
-        try:
-            self.split_input_both_dim_weight_in_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
 
         # RS = RS x SS
-        try:
+        self.split_input_in_channel_weight_both_channel(0, 1)
-            self.split_input_in_channel_weight_both_channel(0, 1)
+        self.split_input_in_channel_weight_both_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
-        try:
-            self.split_input_in_channel_weight_both_channel(1, 0)
-        except Exception as e:
-            warnings.warn(f'{e}')
 
         # RR = RS x SR
-        try:
+        self.split_input_in_channel_weight_in_channel(0)
-            self.split_input_in_channel_weight_in_channel(0)
+        self.split_input_in_channel_weight_in_channel(1)
-        except Exception as e:
-            warnings.warn(f'{e}')
-        try:
-            self.split_input_in_channel_weight_in_channel(1)
-        except Exception as e:
-            warnings.warn(f'{e}')
 
         # RS = RR x RS
         self.split_weight_out_channel(0)
@@ -608,12 +594,7 @@ class ConvHandler(OperatorHandler):
         self.split_1d_parallel_on_input_batch(0, 1)
 
         # RR = RS01 x S01R
-        try:
+        self.split_1d_parallel_on_in_channel(0, 1)
-            self.split_1d_parallel_on_in_channel(0, 1)
-        except Exception as e:
-            warnings.warn(f'{e}')
-
-        # print(f'strategies num is :{len(self.strategies_vector)}')
 
         return self.strategies_vector
 

colossalai/auto_parallel/solver/op_handler/dot_handler.py

@@ -6,10 +6,12 @@ from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy,
 from .operator_handler import OperatorHandler
 from ..constants import LINEAR_FUNC_OP, LINEAR_MODULE_OP
 from functools import reduce
+from colossalai.auto_parallel.solver._utils import exception_handler
 from enum import Enum
 from .strategy_generator import StrategyGenerator, IntermediateStrategy
 from typing import List
 
+
 __all__ = ['DotHandler']
 
 
@@ -414,6 +416,7 @@ class DotHandler(OperatorHandler):
         compute_cost = reduce(operator.mul, input_shape) * weight_shape[0] * 2
         return compute_cost
 
+    @exception_handler
     def split_lhs_space_rhs_space(self, mesh_dim_0, mesh_dim_1):
         # handle case SS = SR x RS
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
@@ -452,6 +455,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_lhs_space_both_contract(self, mesh_dim_0, mesh_dim_1):
         # handle the case SR = SS x SR
         name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
@@ -488,6 +492,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_rhs_space_both_contract(self, mesh_dim_0, mesh_dim_1):
         name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
 
@@ -521,6 +526,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def recompute_split_both_contract(self, mesh_dim):
         name = f'RR = RS{mesh_dim} x S{mesh_dim}R'
 
@@ -554,6 +560,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_rhs_space_only(self, mesh_dim):
         name = f'RS{mesh_dim} = RR x RS{mesh_dim}'
 
@@ -587,6 +594,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_lhs_1st_dim_1d(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x RR'
 
@@ -620,6 +628,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_lhs_2nd_dim_1d(self, mesh_dim_0, mesh_dim_1):
         name = f'RR = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}R'
 
@@ -653,6 +662,7 @@ class DotHandler(OperatorHandler):
                                                input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
         self.strategies_vector.append(sharding_strategies)
 
+    @exception_handler
     def split_rhs_2nd_dim_1d(self, mesh_dim_0, mesh_dim_1):
         name = f'RS{mesh_dim_0}{mesh_dim_1} = RR x RS{mesh_dim_0}{mesh_dim_1}'
 

colossalai/auto_parallel/solver/op_handler/reshape_handler.py

@@ -20,6 +20,7 @@ class ReshapeHandler(OperatorHandler):
         return super()._generate_compute_cost(*args, **kwargs)
 
     def register_strategy(self):
+        # TODO: add strategies with more output sharding specs other than only fully replicated.
         input_node = self.strategies_vector.predecessor_nodes[0]
         # For reshape function, to keep the computing correctness we keep the sharding
         # spec of input is fully replicated. In addition, we will keep the output in

colossalai/auto_parallel/solver/sharding_strategy.py

@@ -70,6 +70,9 @@ class StrategiesVector(list):
             # we could merge element-wise op, because the output sharding spec is always same as the input sharding spec.
             if self.node.target in ELEMENTWISE_FUNC_OP:
                 merge_label = True
+            # we could merge bcast op if the rhs is a scalar, because it will fall back to the element-wise case.
+            if self.node.target in BCAST_FUNC_OP and len(self.predecessor_nodes) == 1:
+                merge_label = True
             # we could merge reshape op, because the output sharding spec of reshape op is always fully replicated.
             if self.node.target in RESHAPE_FUNC_OP:
                 merge_label = True

colossalai/auto_parallel/solver/strategies_constructor.py

@@ -1,4 +1,5 @@
 from torch.fx import Graph, Node
+from colossalai.auto_parallel.solver.op_handler.bcast_op_handler import BcastOpHandler
 from colossalai.tensor.sharding_spec import ShardingSpec
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.shape_consistency import ShapeConsistencyManager
@@ -52,6 +53,7 @@ class StrategiesConstructor:
     def build_strategies_and_cost(self):
         for node in self.nodes:
             strategies_vector = StrategiesVector(node)
+            input_nodes_len = len(strategies_vector.predecessor_nodes)
             # placeholder node
             if node.op == 'placeholder':
                 # For placeholder nodes, if solver_options.fast is True, we just let them in
@@ -165,6 +167,9 @@ class StrategiesConstructor:
 
                 # MaxPool module
                 elif submod_type in POOL_MODULE_OP:
+                    # TODO: add sharding constraints on image dimension
+                    # e.g.: for a 2D pooling input NCHW, we should promise no sharding happens on H and W dimension
+
                     # create sharding strategy for element-wise module
                     assert len(strategies_vector.predecessor_nodes
                               ) == 1, f'Temporally, we just support single input element-wise op.'
@@ -230,7 +235,7 @@ class StrategiesConstructor:
                     reshape_handler.register_strategy()
 
                 # element-wise function
-                elif target in ELEMENTWISE_FUNC_OP:
+                elif target in ELEMENTWISE_FUNC_OP or (target in BCAST_FUNC_OP and input_nodes_len == 1):
                     # TODO: integrate element-wise func and module together
                     # create sharding strategy for element-wise function
                     assert len(strategies_vector.predecessor_nodes
@@ -271,6 +276,11 @@ class StrategiesConstructor:
                                                              input_shardings=[input_sharding_spec])
                         strategies_vector.append(sharding_strategy)
 
+                # bcast op
+                elif target in BCAST_FUNC_OP:
+                    bcast_op_handler = BcastOpHandler(node, self.device_mesh, strategies_vector)
+                    bcast_op_handler.register_strategy()
+
                 # torch.var_mean
                 elif target == torch.var_mean:
                     dim = node.kwargs['dim']
@@ -383,9 +393,8 @@ class StrategiesConstructor:
 
                     # clear the resharding cost for the output node
                     # TODO: we may remove this in final version
-                    if True:
+                    for prev_node, resharding_cost_list in resharding_costs.items():
-                        for prev_node, resharding_cost_list in resharding_costs.items():
+                        resharding_costs[prev_node] = [0] * len(resharding_cost_list)
-                            resharding_costs[prev_node] = [0] * len(resharding_cost_list)
 
                     sharding_strategy_attribute = ShardingStrategy(name,
                                                                    output_sharding_spec,

tests/test_auto_parallel/test_bcast_handler.py

@@ -0,0 +1,71 @@
+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.auto_parallel.solver.options import SolverOptions
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.device.device_mesh import DeviceMesh
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, stride=2)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = x1 + 1
+        x1 = torch.reshape(x1, [1, -1, 64, 1])
+        x3 = self.conv2(x1)
+        x3 = torch.reshape(x3, [4, 1, 64, -1])
+        x = x1 + x3
+
+        return x
+
+
+def test_conv_handler():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+
+    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]
+    #     %conv1 : [#users=2] = call_module[target=conv1](args = (%x,), kwargs = {})
+    #     %add : [#users=0] = call_function[target=operator.add](args = (%conv1, 1), kwargs = {})
+    #     %reshape : [#users=2] = call_function[target=torch.reshape](args = (%conv1, [1, -1, 64, 1]), kwargs = {})
+    #     %conv2 : [#users=1] = call_module[target=conv2](args = (%reshape,), kwargs = {})
+    #     %reshape_1 : [#users=1] = call_function[target=torch.reshape](args = (%conv2, [4, 1, 64, -1]), kwargs = {})
+    #     %add_1 : [#users=1] = call_function[target=operator.add](args = (%reshape, %reshape_1), kwargs = {})
+    #     return add_1
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    # [x, conv1, add, reshape, conv2, reshape_1, add_1, output]
+    nodes = [node for node in gm.graph.nodes]
+    solver_options = SolverOptions(fast=True)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+
+    strategies_constructor.build_strategies_and_cost()
+    strategy_map = strategies_constructor.strategy_map
+    # check a tensor add with a scalar case
+    conv1_strategies = strategy_map[nodes[1]]
+    add_strategies = strategy_map[nodes[2]]
+    add_strategies_cover_list = [strategy.input_shardings[0].sharding_sequence for strategy in add_strategies]
+    for strategy in conv1_strategies:
+        assert strategy.output_sharding_spec.sharding_sequence in add_strategies_cover_list
+
+    # check two tensors element-wise add case
+    add_1_strategies = strategy_map[nodes[6]]
+    assert len(add_1_strategies) == 25
+
+
+if __name__ == '__main__':
+    test_conv_handler()