[autoparallel] added dot handler (#1475)

colossalai/auto_parallel/solver/conv_handler.py

-from lib2to3.pytree import Base
 import operator
 from functools import reduce
 import torch
-from colossalai.tensor.sharding_spec import ShardingSpec
-from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy
 from .operator_handler import OperatorHanlder
 
 
@@ -26,25 +24,6 @@ class ConvHandler(OperatorHanlder):
         assert self.input_data.dim() in (3, 4,
                                          5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
 
-    def _generate_resharding_costs(self, resharding_costs, 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.
-        '''
-        # The resharding_cost of weight is counted due to sharing weight cases.
-        resharding_costs[self.input_index] = []
-        for stategy in self.input_node.strategies_vector.strategies:
-            _, _, resharding_cost = self.shape_consistency_manager.shape_consistency(stategy, sharding_spec_for_input)
-            resharding_costs[self.input_index].append(resharding_cost)
-
     def _generate_compute_cost(self, bs, channel_in, channel_out):
         '''
         Compute the computation cost per device with this specific strategy.

colossalai/auto_parallel/solver/dot_handler.py

+import operator
+import torch
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy
 from .operator_handler import OperatorHanlder
+from functools import reduce
 
 
 class DotHandler(OperatorHanlder):
@@ -6,7 +10,226 @@ class DotHandler(OperatorHanlder):
     A OperatorHandler which deals with the sharding strategies of linear matrix multiplication.
     """
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    def _generate_compute_cost(self, input_shape, weight_shape):
+        # TODO: consider bias addition
+        compute_cost = reduce(operator.mul, input_shape) * weight_shape[0] * 2
+        return compute_cost
 
-        # TODO: refactor the dot handler in my local branch to align with the latest main branch
+    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}'
+
+        dim_partition_dict_for_input = {0: [mesh_dim_0]}
+        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 = 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 = self._generate_sharding_spec(self.output, dim_partition_dict_for_input)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute computation cost
+        compute_cost = self._generate_compute_cost(self.input_data.shape, self.weight.shape)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost
+        # no all-reduce required for this case
+        communication_cost = 0
+
+        # create and register strategy
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    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'
+
+        dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
+        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 = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {0: [mesh_dim_0]}
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output, dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        compute_cost = self._generate_compute_cost(self.input_data.shape, self.weight.shape)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    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}'
+
+        dim_partition_dict_for_input = {1: [mesh_dim_0]}
+        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 = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {1: [mesh_dim_1]}
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output, dim_partition_dict_for_input)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        compute_cost = self._generate_compute_cost(self.input_data.shape, self.weight.shape)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def recompute_split_both_contract(self, mesh_dim):
+        name = f'RR = RS{mesh_dim} x S{mesh_dim}R'
+
+        dim_partition_dict_for_input = {1: [mesh_dim]}
+        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 = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {}
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output, dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        compute_cost = self._generate_compute_cost(self.input_data.shape, self.weight.shape)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        memory_cost = numel * size_per_elem_bytes
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def split_rhs_space_only(self, mesh_dim):
+        name = f'RS{mesh_dim} = RR x RS{mesh_dim}'
+
+        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 = self._generate_sharding_spec(self.weight, dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {1: [mesh_dim]}
+        sharding_spec_for_ouput = self._generate_sharding_spec(self.output, dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        compute_cost = self._generate_compute_cost(self.input_data.shape, self.weight.shape)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def register_strategy_into_strategies_vector(self):
+        '''
+        Generate every possible strategies for a Conv node, and record all strategies into the strategies_vector.
+
+        Output:
+
+        '''
+        # SS = SR x RS
+        self.split_lhs_space_rhs_space(0, 1)
+        self.split_lhs_space_rhs_space(1, 0)
+
+        # SR = SS x SR
+        self.split_lhs_space_both_contract(0, 1)
+        self.split_lhs_space_both_contract(1, 0)
+
+        # RS = RS x SS
+        self.split_rhs_space_both_contract(0, 1)
+        self.split_rhs_space_both_contract(1, 0)
+
+        # RR= RS x SR
+        self.recompute_split_both_contract(0)
+        self.recompute_split_both_contract(1)
+
+        # RS = RR x RS
+        self.split_rhs_space_only(0)
+        self.split_rhs_space_only(1)

colossalai/auto_parallel/solver/operator_handler.py

@@ -43,3 +43,23 @@ class OperatorHanlder(ABC):
                                      entire_shape=tensor.shape,
                                      dim_partition_dict=dim_partition_dict)
         return sharding_spec
+
+    def _generate_resharding_costs(self, resharding_costs, 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.
+        '''
+        # The resharding_cost of weight is counted due to sharing weight cases.
+        resharding_costs[self.input_index] = []
+        for stategy in self.input_node.strategies_vector.strategies:
+            _, _, resharding_cost = self.shape_consistency_manager.shape_consistency(stategy, sharding_spec_for_input)
+            resharding_costs[self.input_index].append(resharding_cost)
+        return resharding_cost

colossalai/auto_parallel/solver/sharding_strategy.py

@@ -42,10 +42,13 @@ class StrategiesVector:
         strategies(List[ShardingStrategy]): enumerate all the possible sharding strategies of the node.
     '''
 
-    def __init__(self, node, in_nodes, following_nodes=None, strategies=[]):
+    def __init__(self, node, in_nodes, following_nodes=None, strategies=None):
         self.node = node
         self.in_nodes = in_nodes
         self.following_nodes = following_nodes
+
+        if strategies is None:
+            strategies = []
         self.strategies = strategies
 
     def check_merge(self):

tests/test_auto_parallel/test_dot_handler.py

+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.proxy import ColoProxy
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
+from colossalai.auto_parallel.solver.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
+
+
+class LinearModel(nn.Module):
+
+    def __init__(self, in_features, out_features):
+        super().__init__()
+        self.linear = nn.Linear(in_features, out_features)
+
+    def forward(self, x):
+        x = x * 2
+        x = self.linear(x)
+        return x
+
+
+def test_dot_handler():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    entire_shape = torch.Size((4, 8))
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    model = LinearModel(8, 16)
+    input_sample = {'x': torch.rand(4, 8).to('meta')}
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
+    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+    #     return conv
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+    # [x, mul, linear, output]
+    nodes = [node for node in gm.graph.nodes]
+
+    strategies_for_input = []
+    sharding_option = (None, 0, 1)
+    for first_sharding_index in sharding_option:
+        for second_sharding_index in sharding_option:
+            if first_sharding_index is not None and second_sharding_index == first_sharding_index:
+                continue
+            if first_sharding_index is None:
+                first_dim_spec = _DimSpec([])
+            else:
+                first_dim_spec = _DimSpec([first_sharding_index])
+
+            if second_sharding_index is None:
+                second_dim_spec = _DimSpec([])
+            else:
+                second_dim_spec = _DimSpec([second_sharding_index])
+
+            sharding_sequence = [first_dim_spec, second_dim_spec]
+            sharding_spec = ShardingSpec(device_mesh=device_mesh,
+                                         entire_shape=entire_shape,
+                                         sharding_sequence=sharding_sequence)
+            strategies_for_input.append(sharding_spec)
+
+    # strategies_for_input = [[R, R, R, R], [R, S0, R, R], [R, S1, R, R], [S0, R, R, R], [S0, S1, R, R], [S1, R, R, R], [S1, S0, R, R]]
+    strategies_vector_for_input = StrategiesVector(node=nodes[1], in_nodes=nodes[0], strategies=strategies_for_input)
+    setattr(nodes[1], 'strategies_vector', strategies_vector_for_input)
+
+    strategies_vector = StrategiesVector(node=nodes[2], in_nodes=[
+        nodes[1],
+    ])
+    dot_handler = DotHandler(input_node=nodes[1],
+                             input_index=0,
+                             weight=dict(gm.named_modules())[nodes[2].name].weight,
+                             output_node=nodes[2],
+                             device_mesh=device_mesh,
+                             strategies_vector=strategies_vector,
+                             shape_consistency_manager=shape_consistency_manager)
+    dot_handler.register_strategy_into_strategies_vector()
+
+    # ['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']
+    strategy_name_list = [strategy.name for strategy in dot_handler.strategies_vector.strategies]
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R' in strategy_name_list
+    assert 'S1R = S1S0 x S0R' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # RR = RS x SR
+    assert 'RR = RS0 x S0R' in strategy_name_list
+    assert 'RR = RS1 x S1R' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = RR x RS0' in strategy_name_list
+    assert 'RS1 = RR x RS1' in strategy_name_list
+
+
+if __name__ == '__main__':
+    test_dot_handler()