[autoparallel] add pooling metainfo (#1968)

* [fx] metainfo class for auto parallel

* [fx] add unit test for linear metainfo

* [fx] fix bwd param for linear

* [fx] modify unit test

* [fx] modify unit test

* [fx] modify import

* [fx] modify import

* [fx] modify import

* [fx] move meta profiler to auto parallel

* [fx] add conv metainfo class

* [fx] restore profiler

* [fx] restore meta profiler

* [autoparallel] modify unit test

* [fx] modify unit test

* [autoparallel] add batchnorm metainfo class

* [autoparallel] fix batchnorm unit test function declaration

* [fx] restore profiler

* [fx] add relu metainfo class

* [fx] restore profiler

* [autoparallel] modify metainfo input

* [autoparallel] add pooling metainfo

colossalai/auto_parallel/meta_profiler/meta_registry/__init__.py

@@ -2,3 +2,4 @@ from .activation import *
 from .conv import *
 from .linear import *
 from .norm import *
+from .pooling import *

colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py

@@ -0,0 +1,127 @@
+from typing import List, Tuple
+
+import torch
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
+from colossalai.fx.profiler.memory_utils import activation_size
+from colossalai.fx.profiler.opcount import flop_mapping
+
+from ..registry import meta_register
+
+__all__ = ["avgpool_meta_info", "maxpool_meta_info"]
+
+
+@meta_register.register(torch.nn.AdaptiveAvgPool1d)
+@meta_register.register(torch.nn.AdaptiveAvgPool2d)
+@meta_register.register(torch.nn.AdaptiveAvgPool3d)
+def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+    """Meta info for AdaptiveAvgPool
+    The aten graph of AdaptiveAvgPool is
+    graph():
+    %input_2 : [#users=2] = placeholder[target=placeholder](default=)
+    %_adaptive_avg_pool2d_default : [#users=1] = call_function[target=torch.ops.aten._adaptive_avg_pool2d.default](args = (%input_2, [None, None]), kwargs = {})
+    %zeros_like_default : [#users=1] = call_function[target=torch.ops.aten.zeros_like.default](args = (%_adaptive_avg_pool2d_default,), kwargs = {dtype: None, layout: None, device: None, pin_memory: None})
+    %detach_default : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%input_2,), kwargs = {})
+    %_adaptive_avg_pool2d_backward_default : [#users=1] = call_function[target=torch.ops.aten._adaptive_avg_pool2d_backward.default](args = (%zeros_like_default, %detach_default), kwargs = {})
+    %detach_default_1 : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%_adaptive_avg_pool2d_backward_default,), kwargs = {})
+    %detach_default_2 : [#users=0] = call_function[target=torch.ops.aten.detach.default](args = (%detach_default_1,), kwargs = {})
+
+    Returns:
+        Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
+    """
+
+    input_tensor = next(filter(lambda x: x.type == OperationDataType.ARG, args)).data
+    output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
+
+    # construct forward args for flop mapping
+    fwd_in_args = [input_tensor]
+    fwd_out_args = [output_tensor]
+
+    # construct backward args for flop mapping
+    bwd_in_args = [output_tensor]
+    bwd_out_args = [input_tensor]
+
+    # calculate cost
+    # the fwd op with compute cost is _adaptive_avg_pool2d.default
+    # the bwd op with compute cost is _adaptive_avg_pool2d_backward.default
+
+    # calculate compute cost
+    fwd_compute_cost = flop_mapping[torch.ops.aten._adaptive_avg_pool2d.default](fwd_in_args, fwd_out_args)
+    bwd_compute_cost = flop_mapping[torch.ops.aten._adaptive_avg_pool2d_backward.default](bwd_in_args, bwd_out_args)
+    compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
+
+    # calculate memory cost
+    fwd_mem_cost = MemoryCost(activation=activation_size(output_tensor))
+    bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor))
+
+    # total cost
+    total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation)
+
+    mem_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+
+    # store_fwd_in
+    fwd_in = [input_tensor]
+
+    return compute_cost, mem_cost, fwd_in
+
+
+@meta_register.register(torch.nn.MaxPool1d)
+@meta_register.register(torch.nn.MaxPool2d)
+@meta_register.register(torch.nn.MaxPool3d)
+def maxpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+    """Meta info for MaxPool
+    The aten graph of MaxPool is
+    graph():
+    %input_2 : [#users=2] = placeholder[target=placeholder](default=)
+    %max_pool2d_with_indices_default : [#users=2] = call_function[target=torch.ops.aten.max_pool2d_with_indices.default](args = (%input_2, [None, None], [None, None]), kwargs = {})
+    %zeros_like_default : [#users=1] = call_function[target=torch.ops.aten.zeros_like.default](args = (%max_pool2d_with_indices_default,), kwargs = {dtype: None, layout: None, device: None, pin_memory: None})
+    %detach_default : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%input_2,), kwargs = {})
+    %detach_default_1 : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%max_pool2d_with_indices_default,), kwargs = {})
+    %max_pool2d_with_indices_backward_default : [#users=1] = call_function[target=torch.ops.aten.max_pool2d_with_indices_backward.default](args = (%zeros_like_default, %detach_default, [None, None], [None, None], [None, None], [None, None], None, %detach_default_1), kwargs = {})
+    %detach_default_2 : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%max_pool2d_with_indices_backward_default,), kwargs = {})
+    %detach_default_3 : [#users=0] = call_function[target=torch.ops.aten.detach.default](args = (%detach_default_2,), kwargs = {})
+
+    Returns:
+        Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
+    """
+
+    input_tensor = next(filter(lambda x: x.type == OperationDataType.ARG, args)).data
+    output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
+
+    # construct forward args for flop mapping
+    fwd_in_args = [input_tensor]
+    fwd_out_args = [output_tensor]
+
+    # construct backward args for flop mapping
+    bwd_in_args = [output_tensor]
+    bwd_out_args = [input_tensor]
+
+    # construct index matrix
+    index_matrix = torch.zeros_like(output_tensor, device="meta", dtype=torch.int64)
+
+    # calculate cost
+    # the fwd op with compute cost is max_pool2d_with_indices.default
+    # the bwd op with compute cost is max_pool2d_with_indices_backward.default
+
+    # calculate compute cost
+    fwd_compute_cost = flop_mapping[torch.ops.aten.max_pool2d_with_indices.default](fwd_in_args, fwd_out_args)
+    bwd_compute_cost = flop_mapping[torch.ops.aten.max_pool2d_with_indices_backward.default](bwd_in_args, bwd_out_args)
+    compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
+
+    # calculate memory cost
+    # NOTE: the index matrix will be discarded in backward phase
+    fwd_mem_cost = MemoryCost(activation=activation_size(output_tensor) + activation_size(index_matrix))
+
+    # temp memory for backward is the index matrix to be discarded
+    bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor) - activation_size(index_matrix),
+                              temp=activation_size(index_matrix))
+
+    # total cost
+    total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation, temp=bwd_mem_cost.temp)
+
+    mem_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+
+    # store_fwd_in
+    fwd_in = [input_tensor]
+
+    return compute_cost, mem_cost, fwd_in

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py

@@ -16,7 +16,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_t
 
 
 def _ReLU_module_mem_test(rank, world_size, port):
-    """This function is for conv memory test
+    """This function is for ReLU memory test
     Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
 
     Args:

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_batchnorm_metainfo.py

@@ -16,10 +16,9 @@ from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_t
 
 
 def _batchnorm_module_mem_test(rank, world_size, port):
-    """This function is for conv memory test
+    """This function is for batchnorm memory test
     Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
 
-    Args:
     Args:
         rank: device rank
         bias: indicate whether conv module need bias

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py

@@ -0,0 +1,102 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+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.pytest_wrapper import run_on_environment_flag
+from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
+
+
+def _adaptiveavgpool_module_mem_test(rank, world_size, port):
+    """This function is for AdaptiveAvgPool memory test
+    Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
+
+    Args:
+        rank: device rank
+        bias: indicate whether conv module need bias
+        world_size: number of devices
+        port: port for initializing process group
+    """
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = nn.Sequential(nn.AdaptiveAvgPool2d((16, 16))).cuda()
+    input = torch.rand(4, 128, 64, 64).cuda()
+    input.requires_grad = True
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    # index of conv node in computation graph
+    node_index = 1
+    # total number of conv strategies
+    strategy_number = 1
+    mem_test_for_node_strategy(rank=rank,
+                               model=model,
+                               device_mesh=device_mesh,
+                               node_index=node_index,
+                               strategy_number=strategy_number,
+                               input_args=[input],
+                               meta_arg_names=['input'])
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_adaptiveavgpool_meta_concrete_info_match():
+    world_size = 4
+    run_func_module = partial(_adaptiveavgpool_module_mem_test, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+def _maxpool_module_mem_test(rank, world_size, port):
+    """This function is for MaxPool memory test
+    Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
+
+    Args:
+        rank: device rank
+        bias: indicate whether conv module need bias
+        world_size: number of devices
+        port: port for initializing process group
+    """
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = nn.Sequential(nn.MaxPool2d((16, 16))).cuda()
+    input = torch.rand(4, 128, 64, 64).cuda()
+    input.requires_grad = True
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    # index of conv node in computation graph
+    node_index = 1
+    # total number of conv strategies
+    strategy_number = 9
+    mem_test_for_node_strategy(rank=rank,
+                               model=model,
+                               device_mesh=device_mesh,
+                               node_index=node_index,
+                               strategy_number=strategy_number,
+                               input_args=[input],
+                               meta_arg_names=['input'])
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_maxpool_meta_concrete_info_match():
+    world_size = 4
+    run_func_module = partial(_maxpool_module_mem_test, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_adaptiveavgpool_meta_concrete_info_match()
+    test_maxpool_meta_concrete_info_match()