[autoparallel] add pooling handler

colossalai/auto_parallel/solver/op_handler/normal_pooling_handler.py

@@ -0,0 +1,40 @@
+import torch
+import torch.nn.functional as F
+from .node_handler import ModuleHandler, NodeHandler
+from ..sharding_strategy import ShardingStrategy_V2, OperationDataType, OperationData
+from ..strategy import NormalPoolStrategyGenerator, StrategyGenerator_V2
+from typing import List, Dict
+from .registry import operator_registry
+
+__all__ = ['LinearModuleHandler', 'LinearFunctionHandler']
+
+
+@operator_registry.register(torch.nn.MaxPool1d)
+@operator_registry.register(torch.nn.MaxPool2d)
+@operator_registry.register(torch.nn.MaxPool1d)
+@operator_registry.register(torch.nn.AvgPool1d)
+@operator_registry.register(torch.nn.AvgPool2d)
+@operator_registry.register(torch.nn.AvgPool3d)
+class NormPoolingHandler(ModuleHandler):
+    """
+    A NormPoolingHandler which deals with the sharding strategies for nn.MaxPoolxd module.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator_V2]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(NormalPoolStrategyGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data)
+        physical_weight_operand = OperationData(name="kernel", type=OperationDataType.ARG, data=self.module.kernel_size)
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"input": physical_input_operand, "other": physical_weight_operand, "output": physical_output}
+
+        return mapping

colossalai/auto_parallel/solver/strategy/__init__.py

@@ -7,10 +7,11 @@
 from .layer_norm_generator import LayerNormGenerator
 from .where_generator import WhereGenerator
 from .reshape_generator import ReshapeGenerator
+from .normal_pooling_generator import NormalPoolStrategyGenerator
 
 __all__ = [
     'StrategyGenerator_V2', 'DotProductStrategyGenerator', 'MatVecStrategyGenerator',
     'LinearProjectionStrategyGenerator', 'BatchedMatMulStrategyGenerator', 'ConvStrategyGenerator',
     'UnaryElementwiseGenerator', 'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator',
-    'TensorTupleStrategyGenerator', 'LayerNormGenerator', "WhereGenerator", 'ReshapeGenerator'
+    'TensorTupleStrategyGenerator', 'LayerNormGenerator', "WhereGenerator", 'ReshapeGenerator', 'NormalPoolStrategyGenerator'
 ]

colossalai/auto_parallel/solver/strategy/normal_pooling_generator.py

@@ -0,0 +1,117 @@
+import operator
+from functools import reduce
+from ..sharding_strategy import ShardingStrategy_V2, TrainCycleItem, MemoryCost
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+from .strategy_generator import StrategyGenerator_V2
+from typing import List
+from .._utils import exception_handler, enumerate_all_possible_1d_sharding, enumerate_all_possible_2d_sharding
+import copy
+
+
+class NormalPoolStrategyGenerator(StrategyGenerator_V2):
+    """
+    NormalPoolStrategyGenerator is a generic class to generate strategies for pool operation like MaxPoolxd.
+    The reason we call this normal pool is AvgPoolxd and MaxPoolxd are taking the kernel size element from image,
+    and reduce them depening on the operation type.
+    """
+
+    def validate(self) -> bool:
+        '''
+        In sanity check, we need make sure the input data having correct dimension size.
+        For Pool1d, the dim of input data should be 3([N, C, L]).
+        For Pool2d, the dim of input data should be 4([N, C, H, W]).
+        For Pool3d, the dim of input data should be 5([N, C, H, W, D]).
+        '''
+        input_op_data = self.op_data['input']
+        assert input_op_data.dim() in (3, 4,
+                                       5), f'We suppose the dim of input fed into Pool op should in range of [3, 5].'
+
+    def update_compute_cost(self, strategy: ShardingStrategy_V2) -> TrainCycleItem:
+        '''
+        Compute the computation cost per device with this specific strategy.
+
+        Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
+        '''
+        # TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
+        # 1D: (Lout) * N * C * kernel
+        # 2D: (H * W) * N * Cout * Cin * kernel
+        # 3D: (H * W  * D) * N * Cout * Cin * kernel
+        sharded_output_shape = strategy.sharding_specs[self.op_data['output']].get_sharded_shape_per_device()
+        sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+
+        kernel_size = self.op_data["other"].data
+        if isinstance(kernel_size, int):
+            kernel_size = [kernel_size] * (len(sharded_output_shape) - 2)
+        kernel_size_product = reduce(operator.mul, kernel_size)
+        output_size_product = reduce(operator.mul, sharded_output_shape)
+        input_size_product = reduce(operator.mul, sharded_input_shape)
+
+        forward_compute_cost = output_size_product * kernel_size_product
+        backward_compute_cost = input_size_product * kernel_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)
+        return compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
+        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()])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=0)
+
+        # compute bwd cost incurred
+        # bwd_cost = input_grad
+        bwd_activation_cost = sum([v for k, v in backward_size_mapping.items()])
+        bwd_mem_cost = MemoryCost(activation=bwd_activation_cost, parameter=0)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost + bwd_activation_cost, parameter=0)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    def _generate_strategy_with_dim_partition(self, dim_partition):
+        dim_partition_dict_mapping = {"input": dim_partition, "output": dim_partition}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}'
+        communication_action_mapping = {}
+
+        strategy = self.get_sharding_strategy(name=name,
+                                              sharding_spec_mapping=sharding_spec_mapping,
+                                              communication_action_mapping=communication_action_mapping)
+
+        return strategy
+
+    def enumerate_all_possible_batch_dimensions_dim_partition(self, mesh_dim_0, mesh_dim_1):
+        dim_partition_list = []
+        dim_partition_list.extend(enumerate_all_possible_1d_sharding(mesh_dim_0, 2))
+        dim_partition_list.extend(enumerate_all_possible_1d_sharding(mesh_dim_1, 2))
+        dim_partition_list.extend(enumerate_all_possible_2d_sharding(mesh_dim_0, mesh_dim_1, 2))
+        # append {} for non_split case
+        dim_partition_list.append({})
+
+        return dim_partition_list
+
+    def generate(self) -> List[ShardingStrategy_V2]:
+        strategy_list = []
+
+        dim_partition_list = self.enumerate_all_possible_batch_dimensions_dim_partition(0, 1)
+        for dim_partition in dim_partition_list:
+            strategy = self._generate_strategy_with_dim_partition(dim_partition)
+            strategy_list.append(strategy)
+
+        for strategy in strategy_list:
+            self.update_communication_cost(strategy)
+            self.update_compute_cost(strategy)
+            self.update_memory_cost(strategy)
+
+        return strategy_list

tests/test_auto_parallel/test_node_handler/test_norm_pooling_handler.py

@@ -0,0 +1,54 @@
+from colossalai.fx.tracer.meta_patch.patched_module import linear
+import torch
+import torch.nn as nn
+from colossalai.fx import ColoTracer, ColoGraphModule
+from colossalai.auto_parallel.solver.op_handler.normal_pooling_handler import NormPoolingHandler
+from colossalai.auto_parallel.solver.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+
+
+def test_norm_pool_handler():
+    model = nn.Sequential(nn.MaxPool2d(4, padding=1).to('meta'))
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {})
+    #     return _0
+    graph = tracer.trace(model, meta_args={"input": torch.rand(4, 4, 64, 64).to('meta')})
+
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    conv_mod_node = list(graph.nodes)[1]
+    strategies_vector = StrategiesVector(conv_mod_node)
+
+    # build handler
+    handler = NormPoolingHandler(node=conv_mod_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+    # check operation data mapping
+    mapping = 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 == "input_1"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 4, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['output'].name == "_0"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 4, 16, 16])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    strategies_vector = handler.register_strategy()
+    strategy_name_list = [val.name for val in strategies_vector]
+    assert len(strategy_name_list) == 9
+
+
+if __name__ == '__main__':
+    test_norm_pool_handler()