[autoparallel] add torch.nn.ReLU metainfo (#1868)

* [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

colossalai/auto_parallel/meta_profiler/constants.py

+import torch
+import torch.nn as nn
+
+# list of inplace operations
+INPLACE_MODULE = [nn.ReLU]

colossalai/auto_parallel/meta_profiler/meta_registry/__init__.py

+from .activation import *
 from .conv import *
 from .linear import *
 from .norm import *

colossalai/auto_parallel/meta_profiler/meta_registry/activation.py

+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__ = ["relu_meta_info"]
+
+
+@meta_register.register(torch.nn.ReLU)
+def relu_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+    """torch.nn.ReLU metainfo generator
+    The aten graph of torch.nn.ReLU is
+    graph():
+    %input_2 : [#users=1] = placeholder[target=placeholder](default=)
+    %relu_default : [#users=2] = call_function[target=torch.ops.aten.relu.default](args = (%input_2,), kwargs = {})
+    %zeros_like_default : [#users=1] = call_function[target=torch.ops.aten.zeros_like.default](args = (%relu_default,), kwargs = {dtype: None, layout: None, device: None, pin_memory: None})
+    %detach_default : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%relu_default,), kwargs = {})
+    %threshold_backward_default : [#users=1] = call_function[target=torch.ops.aten.threshold_backward.default](args = (%zeros_like_default, %detach_default, None), kwargs = {})
+    %detach_default_1 : [#users=1] = call_function[target=torch.ops.aten.detach.default](args = (%threshold_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
+    inplace = kwargs.get("inplace", False)
+
+    # construct input args for forward
+    fwd_in_args = [input_tensor]
+
+    # construct input args for backward
+    bwd_in_args = [output_tensor]
+
+    # calculate cost
+    # the fwd op with compute cost is relu.default
+    # the bwd op with compute cost is threshold_backward
+
+    # calculate compute cost
+    fwd_compute_cost = flop_mapping[torch.ops.aten.relu.default](fwd_in_args, (output_tensor,))
+    bwd_compute_cost = flop_mapping[torch.ops.aten.threshold_backward.default](bwd_in_args, (input_tensor,))
+    compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
+
+    # calculate memory cost
+    # NOTE: the inplace ReLU don't have forward memory cost
+    fwd_memory_cost = MemoryCost(activation=0 if inplace else activation_size(output_tensor),
+                                 parameter=0,
+                                 temp=0,
+                                 buffer=0)
+
+    bwd_memory_cost = MemoryCost(activation=activation_size(input_tensor), parameter=0, temp=0, buffer=0)
+
+    # total cost is the sum of forward and backward cost
+    total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation,
+                            parameter=fwd_memory_cost.parameter + bwd_memory_cost.parameter)
+
+    memory_cost = TrainCycleItem(fwd=fwd_memory_cost, bwd=bwd_memory_cost, total=total_cost)
+
+    # store fwd_in
+    fwd_in = [input_tensor]
+
+    return compute_cost, memory_cost, fwd_in

colossalai/auto_parallel/meta_profiler/meta_registry/conv.py

@@ -22,7 +22,7 @@ __all__ = ['convnd_meta_info']
 @meta_register.register(torch.nn.Conv1d)
 @meta_register.register(torch.nn.Conv2d)
 @meta_register.register(torch.nn.Conv3d)
-def convnd_meta_info(*args) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d meta info generator
     The atens graph of torch.nn.Convnd with bias is
     graph():

colossalai/auto_parallel/meta_profiler/meta_registry/linear.py

@@ -20,7 +20,7 @@ __all__ = ['linear_meta_info']
 
 
 @meta_register.register(torch.nn.Linear)
-def linear_meta_info(*args) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """torch.nn.Linear meta info generator
     The atens graph of torch.nn.Linear with bias is
     graph():

colossalai/auto_parallel/meta_profiler/meta_registry/norm.py

@@ -22,7 +22,7 @@ __all__ = ['batchnormnd_meta_info']
 @meta_register.register(torch.nn.BatchNorm1d)
 @meta_register.register(torch.nn.BatchNorm2d)
 @meta_register.register(torch.nn.BatchNorm3d)
-def batchnormnd_meta_info(*args) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+def batchnormnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """BatchNorm1d, BatchNorm2d, BatchNorm3d, meta info generator
     The aten graph of BatchNorm2d is like
 

colossalai/auto_parallel/meta_profiler/metainfo.py

@@ -13,6 +13,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
 )
 from colossalai.tensor.sharding_spec import ShardingSpec
 
+from .constants import INPLACE_MODULE
 from .registry import meta_register
 
 __all__ = ['MetaInfo']
@@ -91,11 +92,17 @@ class MetaInfo:
         Compute meta info based on sharding strategy and the given target function.
         """
 
-        assert meta_register.has(self._target), f'{self._target} not found in the meta registry'
-        meta_func = meta_register.get(self._target)
+        assert meta_register.has(self._target.__class__), f'{self._target.__class__} not found in the meta registry'
+        meta_func = meta_register.get(self._target.__class__)
 
         # construct args for meta_func
         args = [self.compute_sharded_tensor(k, v) for k, v in self._strategy.sharding_specs.items()]
 
+        # construct kwargs
+        if self.target in INPLACE_MODULE:
+            kwargs = {'inplace': self.target.inplace}
+        else:
+            kwargs = {'inplace': False}
+
         # compute metainfo with meta_func
-        self.compute_cost, self.memory_cost, self.fwd_in = meta_func(*args)
+        self.compute_cost, self.memory_cost, self.fwd_in = meta_func(*args, **kwargs)

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py

+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 _ReLU_module_mem_test(rank, world_size, port):
+    """This function is for conv 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
+        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.ReLU()).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_ReLU_meta_concrete_info_match():
+    world_size = 4
+    run_func_module = partial(_ReLU_module_mem_test, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_ReLU_meta_concrete_info_match()

tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py

@@ -60,9 +60,10 @@ def mem_test_for_node_strategy(rank: int,
         gm.recompile()
         gm: GraphModule
 
+        num_of_strategies = len(target_node.strategies_vector)
         if rank == 0:
             print("=======================")
-            print(f"#strategy_index: {strategy_index}")
+            print(f"#strategy_index: {strategy_index + 1}/{num_of_strategies}")
             pprint(target_node.strategies_vector[strategy_index])
 
         # warmup
@@ -104,7 +105,7 @@ def mem_test_for_node_strategy(rank: int,
 
             # estimated memory
             metainfo = MetaInfo(target_node.strategies_vector[strategy_index],
-                                target_node.graph.owning_module.get_submodule(target_node.target).__class__)
+                                target_node.graph.owning_module.get_submodule(target_node.target))
             print("estimated memory:")
             print(
                 f"forward activation: {metainfo.memory_cost.fwd.activation / 1024} kb, forward param: {metainfo.memory_cost.fwd.parameter / 1024} kb"