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https://github.com/pytorch/pytorch.git
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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
115 lines
3.1 KiB
C++
115 lines
3.1 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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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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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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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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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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