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
169 lines
6.1 KiB
C++
169 lines
6.1 KiB
C++
// Copyright (c) Facebook Inc. and Microsoft Corporation.
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// Licensed under the MIT license.
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#include "./schema.h"
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namespace ONNX_NAMESPACE {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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SparseLengthsSumFused8BitRowwise,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 SparseLengthsSumFused8BitRowwise operator")
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.Input(0, "DATA", "data tensor", "T1")
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.Input(1, "INDICES", "indices tensor", "T2")
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.Input(2, "LENGTHS", "lengths tensor", "T2")
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.Output(0, "output", "Output tensor", "T2")
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.TypeConstraint(
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"T1",
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{"tensor(uint8)"},
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"Constrain input data to uint8 tensors.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)",
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"tensor(int16)",
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"tensor(int32)",
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"tensor(int64)",
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"tensor(uint8)",
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"tensor(uint16)",
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"tensor(uint32)",
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"tensor(uint64)"},
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"Constrain index and length to integral tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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SparseLengthsSum,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 SparseLengthsSum operator")
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.Input(0, "DATA", "data tensor", "T1")
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.Input(1, "INDICES", "indices tensor", "T2")
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.Input(2, "LENGTHS", "lengths tensor", "T2")
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.Output(0, "output", "Output tensor", "T1")
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.TypeConstraint(
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"T1",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)",
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"tensor(int16)",
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"tensor(int32)",
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"tensor(int64)",
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"tensor(uint8)",
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"tensor(uint16)",
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"tensor(uint32)",
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"tensor(uint64)"},
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"Constrain index and length to integral tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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SparseLengthsWeightedSum,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 SparseLengthsWeightedSum operator")
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.Input(0, "DATA", "data tensor", "T1")
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.Input(1, "WEIGHTS", "data tensor", "T1")
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.Input(2, "INDICES", "indices tensor", "T2")
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.Input(3, "LENGTHS", "lengths tensor", "T2")
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.Output(0, "output", "Output tensor", "T1")
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.TypeConstraint(
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"T1",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)",
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"tensor(int16)",
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"tensor(int32)",
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"tensor(int64)",
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"tensor(uint8)",
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"tensor(uint16)",
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"tensor(uint32)",
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"tensor(uint64)"},
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"Constrain index and length to integral tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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BatchGather,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 BatchGather operator")
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.Input(0, "DATA", "data tensor", "T1")
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.Input(1, "INDICES", "indices tensor", "T2")
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.Output(0, "output", "Output tensor", "T1")
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.TypeConstraint(
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"T1",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)",
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"tensor(int16)",
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"tensor(int32)",
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"tensor(int64)",
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"tensor(uint8)",
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"tensor(uint16)",
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"tensor(uint32)",
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"tensor(uint64)"},
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"Constrain index and length to integral tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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DotProduct,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 DotProduct operator")
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.Input(0, "X", "Input 1 tensor", "T")
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.Input(1, "Y", "Input 2 tensor", "T")
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.Output(0, "Z", "Output tensor", "T")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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FCTransposed,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 FCTransposed operator")
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.Input(0, "X", "Input tensor", "T")
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.Input(1, "W", "Weight tensor", "T")
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.Input(2, "B", "Bias tensor", "T")
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.Output(0, "Z", "Output tensor", "T")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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BatchMatMul,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 BatchMatMul operator")
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.Input(0, "X", "tensor of shape (dim0, dim1 ... M, K)", "T")
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.Input(1, "Y", "tensor of shape (dim0, dim2 ... K, N)", "T")
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.Output(0, "Z", "tensor of shape (dim0, dim1 ... M, N)", "T")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors."));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,bugprone-branch-clone)
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ONNX_PYTORCH_OPERATOR_SET_SCHEMA(
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ExpandDims,
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1,
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OpSchema()
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.SetDoc("Mirror Caffe2 ExpandDims operator")
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.Input(0, "X", "Input tensor", "T")
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.Output(0, "Y", "Output tensor", "T")
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.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors."));
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} // namespace ONNX_NAMESPACE
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