mirror of
https://github.com/saymrwulf/pytorch.git
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7f33ec55b2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9350 Re-apply #9270 Breaking this out of #8338 This takes care of the Eigen failure we saw on Mac CUDA builds when BUILD_CAFFE2 and BUILD_ATEN were removed. Fix is to isolate Eigen from headers included by cu files and processed by nvcc. This was worked on with smessmer. Reviewed By: mingzhe09088 Differential Revision: D8794431 fbshipit-source-id: de656334af46c697802073f8e8d9a6aeb9ca65a7
176 lines
4.4 KiB
C++
176 lines
4.4 KiB
C++
#include "caffe2/operators/minmax_ops.h"
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#include "caffe2/utils/eigen_utils.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(Max, MaxOp<float, CPUContext>);
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REGISTER_CPU_OPERATOR(Min, MinOp<float, CPUContext>);
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OPERATOR_SCHEMA(Max)
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.NumInputs(1, INT_MAX)
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.NumOutputs(1)
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.IdenticalTypeAndShapeOfInput(0)
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.AllowInplace({{0, 0}})
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.SetDoc(R"DOC(
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Element-wise max of an arbitrary number of input tensors. This operation can be
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performed in-place, by using the first input blob as the output blob. All inputs
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must have the same shape and data type, and the output will have the same shape
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as the inputs.
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Github Link:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/minmax_ops.cc
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<details>
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<summary> <b>Example</b> </summary>
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**Code**
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```
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workspace.ResetWorkspace()
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op = core.CreateOperator(
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"Max",
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["X", "Y", "Z"],
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["X"],
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)
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workspace.FeedBlob("X", (np.random.rand(3,3)).astype(np.float32))
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workspace.FeedBlob("Y", (np.random.rand(3,3)).astype(np.float32))
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workspace.FeedBlob("Z", (np.random.rand(3,3)).astype(np.float32))
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print("X:", workspace.FetchBlob("X"))
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print("Y:", workspace.FetchBlob("Y"))
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print("Z:", workspace.FetchBlob("Z"))
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workspace.RunOperatorOnce(op)
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print("Max:", workspace.FetchBlob("X"))
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```
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**Result**
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```
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X:
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[[0.4496477 0.07061381 0.7139333 ]
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[0.83203 0.05970785 0.72786295]
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[0.75988126 0.04601283 0.32820013]]
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Y:
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[[0.05683139 0.16872478 0.671098 ]
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[0.70739156 0.09878621 0.03416285]
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[0.34087983 0.94986707 0.67263436]]
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Z:
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[[0.48051122 0.07141234 0.85264146]
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[0.77086854 0.22082241 0.13154659]
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[0.42401117 0.995431 0.4263775 ]]
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Max:
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[[0.48051122 0.16872478 0.85264146]
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[0.83203 0.22082241 0.72786295]
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[0.75988126 0.995431 0.67263436]]
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```
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</details>
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)DOC")
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.Input(0, "X, Y, ...", "*(type: Tensor`<Ord>`)* List of input tensors with the same shape.")
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.Output(0, "M", "*(type: Tensor`<Ord>`)* Output tensor with same dimensions as input(s)."
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"Contains the maximum valued element at each location.")
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.InheritOnnxSchema("Max");
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OPERATOR_SCHEMA(Min)
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.NumInputs(1, INT_MAX)
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.NumOutputs(1)
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.IdenticalTypeAndShapeOfInput(0)
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.AllowInplace({{0, 0}})
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.SetDoc(R"DOC(
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Element-wise min of an arbitrary number of input tensors. This operation can be performed in-place, by using the first input blob as the output blob. All inputs must have the same shape and data type, and the output will have the same shape as the inputs.
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Github Link:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/minmax_ops.cc
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<details>
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<summary> <b>Example</b> </summary>
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**Code**
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```
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workspace.ResetWorkspace()
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op = core.CreateOperator(
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"Min",
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["X", "Y", "Z"],
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["X"],
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)
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workspace.FeedBlob("X", (np.random.rand(2,2)).astype(np.float32))
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workspace.FeedBlob("Y", (np.random.rand(2,2)).astype(np.float32))
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workspace.FeedBlob("Z", (np.random.rand(2,2)).astype(np.float32))
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print("X:", workspace.FetchBlob("X"))
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print("Y:", workspace.FetchBlob("Y"))
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print("Z:", workspace.FetchBlob("Z"))
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workspace.RunOperatorOnce(op)
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print("Min:", workspace.FetchBlob("X"))
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```
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**Result**
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```
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X:
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[[0.32731926 0.4939747 ]
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[0.29242373 0.43460014]]
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Y:
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[[0.40928316 0.916115 ]
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[0.77526504 0.29339448]]
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Z:
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[[0.7899794 0.90335774]
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[0.82599413 0.2843068 ]]
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Min:
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[[0.32731926 0.4939747 ]
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[0.29242373 0.2843068 ]]
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```
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</details>
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)DOC")
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.Input(0, "X, Y, ...", "*(type: Tensor`<Ord>`)* List of input tensors with the same shape.")
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.Output(0, "M", "*(type: Tensor`<Ord>`)* Output tensor with same dimensions as input(s)."
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"Contains the minimum valued element at each location.")
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.InheritOnnxSchema("Min");
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template <typename T, class Context>
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bool MaxOp<T, Context>::Compute() {
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auto& input0 = Input(0);
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const int N = input0.size();
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T* output_data = Output(0)->template mutable_data<T>();
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for (int i = 1; i < InputSize(); i++) {
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auto input_data = Input(i).template data<T>();
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EigenVectorMap<T> output_vec(output_data, N);
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output_vec = output_vec.cwiseMax(ConstEigenVectorMap<T>(input_data, N));
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}
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return true;
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}
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template <typename T, class Context>
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bool MinOp<T, Context>::Compute() {
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auto& input0 = Input(0);
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const int N = input0.size();
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T* output_data = Output(0)->template mutable_data<T>();
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for (int i = 1; i < InputSize(); i++) {
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auto input_data = Input(i).template data<T>();
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EigenVectorMap<T> output_vec(output_data, N);
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output_vec = output_vec.cwiseMin(ConstEigenVectorMap<T>(input_data, N));
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}
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return true;
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}
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} // namespace caffe2
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