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
119 lines
3.4 KiB
C++
119 lines
3.4 KiB
C++
#include <gtest/gtest.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/ir/irparser.h>
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#include <torch/csrc/jit/passes/constant_pooling.h>
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#include <torch/csrc/jit/passes/constant_propagation.h>
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#include <torch/csrc/jit/testing/file_check.h>
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#include <sstream>
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#include <string>
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namespace torch {
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namespace jit {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(ConstantPoolingTest, Int) {
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auto graph = std::make_shared<Graph>();
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parseIR(
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R"IR(
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graph():
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%8 : int = prim::Constant[value=1]()
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%10 : int = prim::Constant[value=1]()
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return (%8, %10)
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)IR",
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&*graph);
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ConstantPooling(graph);
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testing::FileCheck()
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.check_count("prim::Constant", 1, /*exactly*/ true)
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->run(*graph);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(ConstantPoolingTest, PoolingAcrossBlocks) {
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auto graph = std::make_shared<Graph>();
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parseIR(
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R"IR(
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graph(%cond : Tensor):
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%a : str = prim::Constant[value="bcd"]()
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%3 : bool = aten::Bool(%cond)
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%b : str = prim::If(%3)
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block0():
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%b.1 : str = prim::Constant[value="abc"]()
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-> (%b.1)
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block1():
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%b.2 : str = prim::Constant[value="abc"]()
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-> (%b.2)
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%7 : (str, str) = prim::TupleConstruct(%a, %b)
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return (%7)
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)IR",
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&*graph);
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ConstantPooling(graph);
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testing::FileCheck()
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.check_count("prim::Constant[value=\"abc\"]", 1, /*exactly*/ true)
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->check_count("prim::Constant[value=\"bcd\"]", 1, /*exactly*/ true)
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->run(*graph);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(ConstantPoolingTest, PoolingDifferentDevices) {
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auto graph = std::make_shared<Graph>();
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parseIR(
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R"IR(
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graph():
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%2 : int = prim::Constant[value=2]()
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%1 : int = prim::Constant[value=1]()
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%5 : int? = prim::Constant()
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%7 : Device? = prim::Constant()
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%15: bool = prim::Constant[value=0]()
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%10 : int = prim::Constant[value=6]()
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%3 : int[] = prim::ListConstruct(%1, %2)
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%x : Tensor = aten::tensor(%3, %5, %7, %15)
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%y : Tensor = aten::tensor(%3, %10, %7, %15)
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%9 : int[] = prim::ListConstruct(%1, %2)
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%z : Tensor = aten::tensor(%9, %10, %7, %15)
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prim::Print(%x, %y, %z)
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return (%1)
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)IR",
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&*graph);
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// three tensors created - two different devices among the three
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// don't have good support for parsing tensor constants
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ConstantPropagation(graph);
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ConstantPooling(graph);
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testing::FileCheck()
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.check_count(
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"Float(2, strides=[1], requires_grad=0, device=cpu) = prim::Constant",
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1,
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/*exactly*/ true)
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->check_count(
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"Long(2, strides=[1], requires_grad=0, device=cpu) = prim::Constant",
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1,
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/*exactly*/ true)
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->run(*graph);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(ConstantPoolingTest, DictConstantPooling) {
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auto graph = std::make_shared<Graph>();
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parseIR(
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R"IR(
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graph():
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%0 : int = prim::Constant[value=1]() # test/elias.py:6:9
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%1 : int = prim::Constant[value=2]() # test/elias.py:6:12
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%a.1 : Dict(int, int) = prim::DictConstruct(%0, %1)
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%b.1 : Dict(int, int) = prim::DictConstruct(%1, %1)
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return (%a.1, %b.1)
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)IR",
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&*graph);
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ConstantPropagation(graph);
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ConstantPooling(graph);
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testing::FileCheck()
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.check_count(
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"Dict(int, int) = prim::Constant",
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2,
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/*exactly*/ true)
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->run(*graph);
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}
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} // namespace jit
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} // namespace torch
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