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https://github.com/zebrajr/pytorch.git
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1855b14922
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72390
This class didn't add much value and only caused more boilerplate code.
This change removes the class and updates all the use cases with
uses of `ExprHandle`.
A side effect of this change is different names in loop variables, which
caused massive mechanical changes in our tests.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D34030296
Pulled By: ZolotukhinM
fbshipit-source-id: 2ba4e313506a43ab129a10d99e72b638b7d40108
(cherry picked from commit c2ec46a058)
91 lines
2.3 KiB
C++
91 lines
2.3 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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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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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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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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int M = 4;
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int N = 20;
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Tensor producer = Compute(
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"producer", {M, N}, [&](const ExprHandle& m, const ExprHandle& n) {
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return m * n;
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});
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Tensor chunk_0 = Compute(
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"chunk_0", {M, N / 2}, [&](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_1", {M, N / 2}, [&](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", {M, N / 2}, [&](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 = LoopNest::sanitizeNames(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_2
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# CHECK: for (int j_2
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# CHECK: consumer[i_2, j_2] = i_2 * (chunk_1[i_2, j_2])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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