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c2402a9257
Just a change pytorch/tree/master -> pytorch/tree/main pytorch/blob/master -> pytorch/blob/main Pull Request resolved: https://github.com/pytorch/pytorch/pull/100129 Approved by: https://github.com/huydhn
97 lines
2.2 KiB
C++
97 lines
2.2 KiB
C++
#include "caffe2/operators/mod_op.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor.h"
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namespace caffe2 {
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template <>
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template <typename T>
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bool ModOp<CPUContext>::DoRunWithType() {
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auto& data = Input(DATA);
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auto N = data.numel();
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const auto* data_ptr = data.template data<T>();
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auto* output = Output(0, Input(DATA).sizes(), at::dtype<T>());
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auto* output_ptr = output->template mutable_data<T>();
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for (auto i = 0; i < N; i++) {
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output_ptr[i] = data_ptr[i] % divisor_;
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if (output_ptr[i] && sign_follow_divisor_ &&
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((output_ptr[i] > 0) != (divisor_ > 0))) {
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output_ptr[i] += divisor_;
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}
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}
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return true;
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}
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REGISTER_CPU_OPERATOR(Mod, ModOp<CPUContext>);
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OPERATOR_SCHEMA(Mod)
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.NumInputs(1)
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.NumOutputs(1)
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.Arg("divisor", "*(type: int; default: 0)* Divisor of the modulo operation (must be >= 1).")
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.Arg(
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"sign_follow_divisor",
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"*(type: bool; default: False)* If true, sign of output matches divisor, else if false, sign follows dividend.")
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.IdenticalTypeAndShape()
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.AllowInplace({{0, 0}})
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.SetDoc(R"DOC(
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Element-wise modulo operation. Each element in the output is the modulo result
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of the corresponding element in the input data. The divisor of the modulo is
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provided by the `divisor` argument.
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Github Link:
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- https://github.com/pytorch/pytorch/blob/main/caffe2/operators/mod_op.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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"Mod",
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["X"],
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["Y"],
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divisor=10
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)
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workspace.FeedBlob("X", (np.random.randint(100, size=(5,5))))
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print("X before running op:", workspace.FetchBlob("X"))
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workspace.RunOperatorOnce(op)
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print("X after running op:", workspace.FetchBlob("Y"))
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```
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**Result**
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```
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X before running op:
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[[56 22 43 13 60]
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[ 4 55 58 10 45]
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[64 66 4 3 66]
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[10 36 47 52 78]
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[91 4 36 47 95]]
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X after running op:
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[[6 2 3 3 0]
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[4 5 8 0 5]
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[4 6 4 3 6]
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[0 6 7 2 8]
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[1 4 6 7 5]]
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```
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</details>
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)DOC")
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.Input(0, "X", "*(type: Tensor`<int>`)* Input tensor with int32 or int64 data.")
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.Output(0, "Y", "*(type: Tensor`<int>`)* Output tensor of data with modulo operation applied.");
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SHOULD_NOT_DO_GRADIENT(ModOp);
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} // namespace caffe2
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