mirror of
https://github.com/saymrwulf/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
100 lines
2.4 KiB
C++
100 lines
2.4 KiB
C++
#include <gtest/gtest.h>
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#include <stdexcept>
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#include "test/cpp/tensorexpr/test_base.h"
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#include <torch/csrc/jit/tensorexpr/expr.h>
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#include <torch/csrc/jit/tensorexpr/ir.h>
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#include <torch/csrc/jit/tensorexpr/ir_printer.h>
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#include <torch/csrc/jit/tensorexpr/loopnest.h>
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#include <torch/csrc/jit/tensorexpr/tensor.h>
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#include <torch/csrc/jit/testing/file_check.h>
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#include <sstream>
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namespace torch {
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namespace jit {
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using namespace torch::jit::tensorexpr;
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TEST(IRPrinter, BasicValueTest) {
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KernelScope kernel_scope;
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ExprHandle a = IntImm::make(2), b = IntImm::make(3);
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ExprHandle c = Add::make(a, b);
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std::stringstream ss;
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ss << c;
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ASSERT_EQ(ss.str(), "2 + 3");
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}
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TEST(IRPrinter, BasicValueTest02) {
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KernelScope kernel_scope;
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ExprHandle a(2.0f);
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ExprHandle b(3.0f);
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ExprHandle c(4.0f);
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ExprHandle d(5.0f);
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ExprHandle f = (a + b) - (c + d);
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std::stringstream ss;
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ss << f;
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ASSERT_EQ(ss.str(), "(2.f + 3.f) - (4.f + 5.f)");
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}
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TEST(IRPrinter, CastTest) {
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KernelScope kernel_scope;
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VarHandle x("x", kHalf);
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VarHandle y("y", kFloat);
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ExprHandle body = ExprHandle(2.f) +
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(Cast::make(kFloat, x) * ExprHandle(3.f) + ExprHandle(4.f) * y);
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std::stringstream ss;
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ss << body;
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ASSERT_EQ(ss.str(), "2.f + (float(x) * 3.f + 4.f * y)");
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}
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TEST(IRPrinter, FunctionName) {
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KernelScope kernel_scope;
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int M = 4;
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int N = 20;
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Tensor* producer = Compute(
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"producer",
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{{M, "m"}, {N, "n"}},
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[&](const ExprHandle& m, const ExprHandle& n) { return m * n; });
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Tensor* chunk_0 = Compute(
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"chunk",
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{{M, "m"}, {N / 2, "n"}},
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[&](const ExprHandle& m, const ExprHandle& n) {
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return producer->load(m, n);
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});
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Tensor* chunk_1 = Compute(
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"chunk",
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{{M, "m"}, {N / 2, "n"}},
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[&](const ExprHandle& m, const ExprHandle& n) {
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return producer->load(m, n + ExprHandle(N / 2));
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});
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Tensor* consumer = Compute(
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"consumer",
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{{M, "i"}, {N / 2, "j"}},
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[&](const ExprHandle& i, const ExprHandle& j) {
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return i * chunk_1->load(i, j);
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});
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LoopNest l({chunk_0, chunk_1, consumer});
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auto* body = l.root_stmt();
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std::stringstream ss;
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ss << *body;
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const std::string& verification_pattern =
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R"IR(
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# CHECK: for (int i
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# CHECK: for (int j
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# CHECK: consumer[i, j] = i * (chunk_1[i, j])IR";
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torch::jit::testing::FileCheck().run(verification_pattern, ss.str());
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}
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} // namespace jit
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} // namespace torch
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