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#include <gtest/gtest.h>
#include <torch/csrc/jit/runtime/static/impl.h>
#include <torch/torch.h>
#include <thread>
#include "test_utils.h"
using namespace torch;
using namespace torch::jit;
using namespace torch::jit::test;
TEST(CpuFusion, Simple) {
const auto simple_script = R"JIT(
def forward(self, a, b):
return (a + b).relu().tanh()
)JIT";
Module m("module");
m.define(simple_script);
StaticModuleOptions opts; // start with the defaults.
opts.enable_tensorexpr_fusion = true;
auto input1 = at::randn({2, 3});
auto input2 = at::ones({2, 3});
auto smodule = StaticModule(m, /* is_frozen */ false, opts, {input1, input2});
StaticRuntime runtime(smodule);
// Test with sample inputs
{
auto actual = runtime({input1, input2}, {});
auto expect = at::tanh(at::relu(input1 + input2));
EXPECT_TRUE(at::allclose(expect, actual.toTensor()));
}
// Test with different inputs
{
auto new_input1 = at::randn({5, 14});
auto new_input2 = at::randn({5, 14});
auto actual = runtime({new_input1, new_input2}, {});
auto expect = at::tanh(at::relu(new_input1 + new_input2));
EXPECT_TRUE(at::allclose(expect, actual.toTensor()));
}
}
TEST(CpuFusion, FallbackGraph) {
const auto simple_script = R"JIT(
def forward(self, a, b):
return (a + b).relu().tanh()
)JIT";
Module m("module");
m.define(simple_script);
StaticModuleOptions opts; // start with the defaults.
opts.enable_tensorexpr_fusion = true;
auto sample_input1 = at::randn({2, 3});
auto sample_input2 = at::ones({2, 3});
auto smodule = StaticModule(
m, /* is_frozen */ false, opts, {sample_input1, sample_input2});
StaticRuntime runtime(smodule);
// The sample inputs above were contiguous. Now, use a strided input
// to trigger running the fallback graph.
{
auto input1 = at::narrow(at::randn({2, 6}), 1, 0, 3);
auto input2 = at::ones({2, 3});
auto expect = at::tanh(at::relu(input1 + input2));
auto actual = runtime({input1, input2}, {});
EXPECT_TRUE(at::allclose(expect, actual.toTensor()));
}
// Test with strided inputs of different size.
{
auto input1 = at::narrow(at::randn({10, 30}), 1, 0, 25);
auto input2 = at::randn({10, 25});
auto expect = at::tanh(at::relu(input1 + input2));
auto actual = runtime({input1, input2}, {});
EXPECT_TRUE(at::allclose(expect, actual.toTensor()));
}
}
TEST(CpuFusion, ParallelRuntimes) {
const auto simple_script = R"JIT(
def forward(self, a, b):
return (a + b).relu().tanh()
)JIT";
Module m("module");
m.define(simple_script);
StaticModuleOptions opts; // start with the defaults.
opts.enable_tensorexpr_fusion = true;
auto sample_input1 = at::randn({2, 3});
auto sample_input2 = at::ones({2, 3});
auto smodule = StaticModule(
m, /* is_frozen */ false, opts, {sample_input1, sample_input2});
constexpr size_t kNumThreads = 2;
std::vector<std::vector<std::pair<int, int>>> all_inputs;
for (size_t id = 0; id < kNumThreads; ++id) {
std::vector<std::pair<int, int>> thread_input = {
{id, id + 1},
{id + 10, id + 11},
{id + 20, id + 21},
{id + 30, id + 31},
{id + 40, id + 41},
{id + 50, id + 51},
{id + 60, id + 61},
{id + 70, id + 71}};
all_inputs.emplace_back(std::move(thread_input));
}
auto exec_runtime = [&](size_t tid) {
const auto& inputs = all_inputs[tid];
StaticRuntime runtime(smodule);
for (const auto& inp : inputs) {
auto a = at::randn({inp.first, inp.second});
auto b = at::randn({inp.first, inp.second});
auto expect = at::tanh(at::relu(a + b));
auto actual = runtime({a, b}, {});
EXPECT_TRUE(at::allclose(expect, actual.toTensor()));
}
};
std::vector<std::thread> threads;
for (size_t id = 0; id < kNumThreads; ++id) {
threads.emplace_back(exec_runtime, id);
}
for (auto& t : threads) {
t.join();
}
}
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