mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
4cb534f92e
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
122 lines
2.8 KiB
C++
122 lines
2.8 KiB
C++
#include "caffe2/operators/cosh_op.h"
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#include <algorithm>
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#include <functional>
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namespace caffe2 {
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template <>
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template <typename T>
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bool CoshGradientFunctor<CPUContext>::Forward(
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const std::vector<int>& /* dY_dims */,
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const std::vector<int>& X_dims,
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const T* dY,
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const T* X,
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T* dX,
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CPUContext* /* context */) const {
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const int size = std::accumulate(
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// NOLINTNEXTLINE(modernize-use-transparent-functors)
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X_dims.cbegin(), X_dims.cend(), 1, std::multiplies<int>());
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ConstEigenVectorArrayMap<T> dY_arr(dY, size);
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ConstEigenVectorArrayMap<T> X_arr(X, size);
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EigenVectorMap<T>(dX, size) = dY_arr * (X_arr.exp() - (-X_arr).exp()) / 2;
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return true;
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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REGISTER_CPU_OPERATOR(
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Cosh,
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UnaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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CoshFunctor<CPUContext>>);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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REGISTER_CPU_OPERATOR(
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CoshGradient,
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BinaryElementwiseOp<
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TensorTypes<float>,
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CPUContext,
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CoshGradientFunctor<CPUContext>>);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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OPERATOR_SCHEMA(Cosh)
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.NumInputs(1)
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.NumOutputs(1)
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.IdenticalTypeAndShape()
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.SetDoc(R"DOC(
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Calculates the hyperbolic cosine of the given input tensor, element-wise.
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Github Links:
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- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/cosh_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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"Cosh",
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["X"],
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["Y"]
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)
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workspace.FeedBlob("X", np.random.rand(5).astype(np.float32))
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print("X:", workspace.FetchBlob("X"))
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workspace.RunOperatorOnce(op)
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print("Y:", workspace.FetchBlob("Y"))
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```
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**Result**
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```
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X: [0.66423494 0.32074615 0.81523746 0.90423071 0.39275789]
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Y: [1.22883528 1.05188156 1.35112322 1.43744212 1.07812598]
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```
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</details>
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)DOC")
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.Input(0, "input", "Input tensor")
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.Output(
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0,
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"output",
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"The hyperbolic cosine values of the input tensor, computed "
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"element-wise")
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.InheritOnnxSchema();
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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OPERATOR_SCHEMA(CoshGradient)
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.NumInputs(2)
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.NumOutputs(1)
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.IdenticalTypeAndShapeOfInput(0);
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namespace {
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class GetCoshGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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std::vector<OperatorDef> GetGradientDefs() override {
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return SingleGradientDef(
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"CoshGradient",
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"",
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std::vector<std::string>{GO(0), I(0)},
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std::vector<std::string>{GI(0)});
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}
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};
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} // namespace
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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REGISTER_GRADIENT(Cosh, GetCoshGradient);
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} // namespace caffe2
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