[autoparallel] Patch meta information of torch.where (#2822)

* [autoparallel] patch meta information of torch.where

* [autoparallel] pre-commit modified

colossalai/auto_parallel/meta_profiler/meta_registry/__init__.py

@@ -6,3 +6,4 @@ from .linear import *
 from .norm import *
 from .pooling import *
 from .tensor import *
+from .where import *

colossalai/auto_parallel/meta_profiler/meta_registry/where.py

@@ -0,0 +1,60 @@
+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__ = ["where_meta_info"]
+
+
+@meta_register.register(torch.where)
+def where_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
+    """torch.where meta information generator
+
+    Returns:
+        Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
+    """
+
+    condition_tensor, x_tensor, y_tensor, output_tensor = [arg.data for arg in args]
+
+    # compute cost
+    fwd_compute_cost = 0
+
+    # if we need to broadcast the condition tensor, during backward we need to do a reduce_sum
+    bwd_compute_cost = 0
+    if x_tensor.shape != output_tensor.shape:
+        bwd_compute_cost += flop_mapping[torch.ops.aten.sum.dim_IntList]([output_tensor], [x_tensor])
+    if y_tensor.shape != output_tensor.shape:
+        bwd_compute_cost += flop_mapping[torch.ops.aten.sum.dim_IntList]([output_tensor], [y_tensor])
+
+    compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
+
+    # memory cost
+    # during the forward phase, torch.where will allocate memory for output tensor and condition tensor
+    # during the backward phase, torch.where will allocate temp memory which is 3 times as output tensor, then generate
+    # gradient matrix for input x and input y, remove the temp memory and condition tensor generated in forward phase
+    # NOTE: currently in SPMD solver we always believe that there will be a new input tensor created in forward
+    fwd_mem_cost = MemoryCost(activation=activation_size([condition_tensor, x_tensor, y_tensor, output_tensor]))
+    bwd_mem_cost = MemoryCost(activation=activation_size([x_tensor, y_tensor]) - activation_size([condition_tensor]),
+                              parameter=0,
+                              temp=activation_size([output_tensor]) * 3 + activation_size([condition_tensor]) -
+                              activation_size([x_tensor, y_tensor]),
+                              buffer=0)
+
+    total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation,
+                                parameter=fwd_mem_cost.parameter + bwd_mem_cost.parameter,
+                                temp=fwd_mem_cost.temp + bwd_mem_cost.temp,
+                                buffer=fwd_mem_cost.buffer + bwd_mem_cost.buffer)
+
+    memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+
+    # store fwd_in, fwd_buffer, fwd_out
+    fwd_in = [condition_tensor]
+    fwd_buffer = []
+    fwd_out = [output_tensor]
+
+    return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py

@@ -0,0 +1,104 @@
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    MemoryCost,
+    OperationData,
+    OperationDataType,
+    ShardingStrategy,
+    StrategiesVector,
+    TrainCycleItem,
+)
+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 print_results
+
+if torch.__version__ >= '1.12.0':
+    from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register
+
+
+@pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+def test_where_meta_info():
+    meta_func = meta_register.get(torch.where)
+
+    # construct meta tensors
+    condition_tensor = torch.rand(1, 1, 1024, 1024) > 0.5
+    condition_tensor = condition_tensor.to(device="meta")
+    x_tensor = torch.rand(8, 16, 1024, 1024, device="meta")
+    y_tensor = torch.tensor(0, device="meta")
+    output_tensor = torch.rand(8, 16, 1024, 1024)
+
+    # construct operation data
+    condition_data = OperationData(
+        name="condition",
+        data=condition_tensor,
+        type=OperationDataType.ARG,
+        logical_shape=condition_tensor.shape,
+    )
+    x_data = OperationData(
+        name="x",
+        data=x_tensor,
+        type=OperationDataType.ARG,
+        logical_shape=x_tensor.shape,
+    )
+    y_data = OperationData(
+        name="y",
+        data=y_tensor,
+        type=OperationDataType.ARG,
+        logical_shape=y_tensor.shape,
+    )
+    output_data = OperationData(
+        name="output",
+        data=output_tensor,
+        type=OperationDataType.OUTPUT,
+        logical_shape=output_tensor.shape,
+    )
+
+    # construct args and kwargs
+    args = [condition_data, x_data, y_data, output_data]
+    kwargs = {'inplace': False}
+
+    # estimated results
+    compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out = meta_func(*args, **kwargs)
+
+    # actual results
+    condition_real_tensor = torch.rand(1, 1, 1024, 1024) > 0.5
+    condition_real_tensor = condition_real_tensor.to(device="cuda")
+    x_real_tensor = torch.rand(8, 16, 1024, 1024, device="cuda")
+    y_real_tensor = torch.tensor(0.0, device="cuda")
+
+    x_real_tensor.requires_grad = True
+    y_real_tensor.requires_grad = True
+
+    # fwd
+    torch.cuda.reset_peak_memory_stats()
+    mem_stamp0 = torch.cuda.memory_allocated()
+    output_real_tensor = torch.where(condition_real_tensor, x_real_tensor, y_real_tensor)
+    fwd_allocated = torch.cuda.memory_allocated() - mem_stamp0
+    fwd_peak = torch.cuda.max_memory_allocated() - mem_stamp0
+
+    # bwd
+    upstream_grad = torch.rand_like(output_real_tensor)
+    torch.cuda.reset_peak_memory_stats()
+    mem_stamp0 = torch.cuda.memory_allocated()
+    torch.autograd.backward(output_real_tensor, upstream_grad)
+    bwd_allocated = torch.cuda.memory_allocated() - mem_stamp0
+    bwd_peak = torch.cuda.max_memory_allocated() - mem_stamp0
+
+    compute_cost: TrainCycleItem
+    memory_cost: TrainCycleItem
+
+    print_results([condition_real_tensor, x_real_tensor, y_real_tensor], [output_real_tensor], compute_cost,
+                  memory_cost, fwd_allocated, fwd_peak, bwd_allocated, bwd_peak)
+
+
+if __name__ == '__main__':
+    test_where_meta_info()