[autoparallel] implement softmax handler (#2132)

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -15,6 +15,7 @@ from .output_handler import OuputHandler
 from .placeholder_handler import PlacehodlerHandler
 from .registry import operator_registry
 from .reshape_handler import ReshapeHandler
+from .softmax_handler import SoftmaxHandler
 from .sum_handler import SumHandler
 from .tensor_constructor_handler import TensorConstructorHandler
 from .unary_elementwise_handler import UnaryElementwiseHandler
@@ -26,5 +27,5 @@ __all__ = [
     'UnaryElementwiseHandler', 'ReshapeHandler', 'PlacehodlerHandler', 'OuputHandler', 'WhereHandler',
     'NormPoolingHandler', 'BinaryElementwiseHandler', 'MatMulHandler', 'operator_registry', 'ADDMMFunctionHandler',
     'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler',
-    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler', 'SumHandler'
+    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler', 'SumHandler', 'SoftmaxHandler'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/softmax_handler.py

+from typing import Dict, List
+
+import torch
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import NodeHandler
+from .registry import operator_registry
+from .strategy import SoftmaxGenerator, StrategyGenerator
+
+__all__ = ['SoftmaxHandler']
+
+
+@operator_registry.register(torch.nn.Softmax)
+@operator_registry.register(torch.nn.functional.softmax)
+class SoftmaxHandler(NodeHandler):
+    """
+    A SoftmaxHandler which deals with the sharding strategies for
+    torch.nn.Softmax or torch.nn.functional.softmax.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(SoftmaxGenerator(op_data_mapping, self.device_mesh, self.node.args[0]))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # check if the input operand is a parameter
+        if isinstance(self.node.args[0]._meta_data, torch.nn.parameter.Parameter):
+            data_type = OperationDataType.PARAM
+        else:
+            data_type = OperationDataType.ARG
+
+        input_data = self.node.args[0]._meta_data
+        physical_input_operand = OperationData(name=str(self.node.args[0]), type=data_type, data=input_data)
+
+        softmax_dim = self.node.kwargs['dim']
+
+        num_dims = self.node.args[0]._meta_data.dim()
+        # recover negative value to positive
+        if softmax_dim < 0:
+            softmax_dim += num_dims
+
+        physical_dim_operand = OperationData(name='softmax_dim', type=OperationDataType.ARG, data=softmax_dim)
+
+        output_data = self.node._meta_data
+        physical_output_operand = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=output_data)
+
+        mapping = {
+            "input": physical_input_operand,
+            "softmax_dim": physical_dim_operand,
+            "output": physical_output_operand
+        }
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/strategy/__init__.py

@@ -15,6 +15,7 @@ from .normal_pooling_generator import NormalPoolStrategyGenerator
 from .output_generator import OutputGenerator
 from .placeholder_generator import PlaceholderGenerator
 from .reshape_generator import ReshapeGenerator
+from .softmax_generator import SoftmaxGenerator
 from .strategy_generator import StrategyGenerator
 from .sum_generator import SumGenerator
 from .tensor_constructor_generator import TensorConstructorGenerator
@@ -27,5 +28,5 @@ __all__ = [
     'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
     'LayerNormGenerator', 'ReshapeGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator',
     'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator',
-    'TensorConstructorGenerator', 'EmbeddingStrategyGenerator', 'SumGenerator'
+    'TensorConstructorGenerator', 'EmbeddingStrategyGenerator', 'SumGenerator', 'SoftmaxGenerator'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/strategy/softmax_generator.py

+import copy
+import operator
+from functools import reduce
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.node_handler.strategy.strategy_generator import FollowingStrategyGenerator
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import (
+    check_keep_sharding_status,
+    detect_reshape_mapping,
+    infer_output_dim_partition_dict,
+)
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+
+__all__ = ['SoftmaxGenerator']
+
+
+class SoftmaxGenerator(FollowingStrategyGenerator):
+    """
+    SoftmaxGenerator is used to generate strategies for torch.nn.Softmax or F.softmax.
+    """
+
+    def validate(self) -> bool:
+        return super().validate()
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        '''
+        Compute the computation cost per device with this specific strategy.
+        '''
+        sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+        sharded_output_shape = strategy.sharding_specs[self.op_data['output']].get_sharded_shape_per_device()
+        input_size_product = reduce(operator.mul, sharded_input_shape)
+        output_size_product = reduce(operator.mul, sharded_output_shape)
+
+        forward_compute_cost = output_size_product * 2
+        backward_compute_cost = input_size_product
+        total_compute_cost = forward_compute_cost + backward_compute_cost
+        compute_cost = TrainCycleItem(fwd=forward_compute_cost, bwd=backward_compute_cost, total=total_compute_cost)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        '''
+        Compute the memory cost per device with this specific strategy.
+        '''
+        forward_size_mapping = {
+            'input': self._compute_size_in_bytes(strategy, "input"),
+            'output': self._compute_size_in_bytes(strategy, "output")
+        }
+
+        backward_size_mapping = copy.deepcopy(forward_size_mapping)
+        backward_size_mapping.pop("output")
+        # compute fwd cost incurred
+        # fwd_cost = input + output
+        fwd_activation_cost = sum([v for k, v in forward_size_mapping.items() if not self.is_param(k)])
+        fwd_parameter_cost = sum([v for k, v in forward_size_mapping.items() if self.is_param(k)])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=fwd_parameter_cost)
+
+        # compute bwd cost incurred
+        # bwd_cost = input_grad
+        bwd_activation_cost = sum([v for k, v in backward_size_mapping.items() if not self.is_param(k)])
+        bwd_parameter_cost = sum([v for k, v in backward_size_mapping.items() if self.is_param(k)])
+        bwd_mem_cost = MemoryCost(activation=bwd_activation_cost, parameter=bwd_parameter_cost)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost + bwd_activation_cost,
+                                    parameter=fwd_parameter_cost + bwd_parameter_cost)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategy_list = []
+        for index, strategy in enumerate(self.predecessor_node.strategies_vector):
+            dim_partition_dict_mapping = {}
+            communication_action_mapping = {}
+            input_sharding_spec = strategy.output_sharding_specs[self.op_data["input"]]
+            dim_partition_dict_for_input = copy.deepcopy(input_sharding_spec.dim_partition_dict)
+            softmax_dim = self.op_data['softmax_dim'].data
+
+            if softmax_dim in dim_partition_dict_for_input:
+                recover_dims = dim_partition_dict_for_input.pop(softmax_dim)
+
+            dim_partition_dict_for_output = copy.deepcopy(dim_partition_dict_for_input)
+            dim_partition_dict_mapping = {
+                "input": dim_partition_dict_for_input,
+                "output": dim_partition_dict_for_output,
+            }
+            sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+            # add index into name to pass the duplicated check
+            # we keep same strategies with different name for node merging, and it will not increase the searching space,
+            # because in solver, this node will be merged into other nodes, and solver will not create a new variable for this node.
+            name = f'{sharding_spec_mapping["input"].sharding_sequence} -> {sharding_spec_mapping["output"].sharding_sequence}_{index}'
+
+            strategy = self.get_sharding_strategy(name=name,
+                                                  sharding_spec_mapping=sharding_spec_mapping,
+                                                  communication_action_mapping=communication_action_mapping)
+            strategy_list.append(strategy)
+
+        return strategy_list

colossalai/auto_parallel/tensor_shard/node_handler/unary_elementwise_handler.py

@@ -16,8 +16,6 @@ __all__ = ['UnaryElementwiseHandler']
 @operator_registry.register(torch.nn.ReLU)
 @operator_registry.register(torch.nn.Tanh)
 @operator_registry.register(torch.tanh)
-# TODO: softmax need to be relocated
-@operator_registry.register(torch.nn.functional.softmax)
 @operator_registry.register(torch.nn.modules.dropout.Dropout)
 @operator_registry.register(torch.Tensor.contiguous)
 @operator_registry.register(torch.nn.functional.dropout)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py

+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.nn.functional as F
+
+from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.softmax_handler import SoftmaxHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
+
+
+class LinearSplitModel(nn.Module):
+
+    def __init__(self, softmax_dim):
+        super().__init__()
+        self.softmax_dim = softmax_dim
+
+    def forward(self, input, other):
+        linear_node = F.linear(input, other, bias=None)
+        softmax_node = F.softmax(linear_node, self.softmax_dim)
+        return softmax_node
+
+
+def check_split_handler(rank, softmax_dim, model_cls, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = model_cls(softmax_dim=softmax_dim).cuda()
+
+    input = torch.rand(8, 16, 64, 32).to('cuda')
+    other = torch.rand(64, 32).to('cuda')
+    # index of linear node in computation graph
+    node_index = 2
+    # total number of linear strategies
+    strategy_number = 23
+
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=[input, other],
+                                     meta_arg_names=['input', 'other'],
+                                     node_type='following')
+    tracer = ColoTracer()
+
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %other : torch.Tensor [#users=1] = placeholder[target=other]
+    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None})
+    #     %softmax : [#users=1] = call_method[target=split](args = (%linear,), kwargs = {})
+    #     return split
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(8, 16, 64, 32).to('meta'),
+                             "other": torch.rand(64, 32).to('meta'),
+                         })
+
+    gm = ColoGraphModule(model, graph)
+
+    previous_mod_node = list(graph.nodes)[2]
+    split_node = list(graph.nodes)[3]
+    split_strategies_vector = StrategiesVector(split_node)
+    previous_strategies_vector = StrategiesVector(previous_mod_node)
+
+    # build handler
+    assert len(previous_strategies_vector) == 0
+    linear_handler = LinearFunctionHandler(node=previous_mod_node,
+                                           device_mesh=device_mesh,
+                                           strategies_vector=previous_strategies_vector)
+    linear_handler.register_strategy(compute_resharding_cost=False)
+    setattr(previous_mod_node, 'strategies_vector', previous_strategies_vector)
+
+    softmax_handler = SoftmaxHandler(node=split_node,
+                                     device_mesh=device_mesh,
+                                     strategies_vector=split_strategies_vector)
+
+    softmax_handler.register_strategy(compute_resharding_cost=False)
+
+    # check operation data mapping
+    mapping = softmax_handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "linear"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([8, 16, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([8, 16, 64, 64])
+
+    assert mapping['softmax_dim'].name == "softmax_dim"
+    assert mapping['softmax_dim'].data == softmax_dim
+    assert mapping['softmax_dim'].type == OperationDataType.ARG
+
+    assert mapping['output'].name == "softmax"
+    assert mapping['output'].data.shape == torch.Size([8, 16, 64, 64])
+    assert mapping['output'].logical_shape == torch.Size([8, 16, 64, 64])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    # reshape handler is a following strategy handler, so the number of strategies is equal to the predecessor node.
+    assert len(split_strategies_vector) == len(previous_strategies_vector)
+    strategy_name_list = [strategy.name for strategy in split_strategies_vector]
+
+    if softmax_dim == 0:
+        assert '[R, R, R, S1] -> [R, R, R, S1]_0' in strategy_name_list
+        assert '[R, S0, R, S1] -> [R, S0, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_3' in strategy_name_list
+        assert '[R, S1, R, S0] -> [R, S1, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_6' in strategy_name_list
+        assert '[R, S0, R, R] -> [R, S0, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_9' in strategy_name_list
+        assert '[R, S1, R, R] -> [R, S1, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list
+        assert '[R, S01, R, R] -> [R, S01, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list
+
+    if softmax_dim == 1:
+        assert '[S0, R, R, S1] -> [S0, R, R, S1]_0' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list
+        assert '[S1, R, R, S0] -> [S1, R, R, S0]_3' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list
+        assert '[S0, R, R, R] -> [S0, R, R, R]_6' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list
+        assert '[S1, R, R, R] -> [S1, R, R, R]_9' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[S01, R, R, R] -> [S01, R, R, R]_18' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@parameterize('softmax_dim', [0, 1, 2, 3])
+@parameterize('model_cls', [LinearSplitModel])
+def test_split_handler(softmax_dim, model_cls):
+    world_size = 4
+    run_func = partial(check_split_handler,
+                       softmax_dim=softmax_dim,
+                       model_cls=model_cls,
+                       world_size=world_size,
+                       port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_split_handler()