mirror of
https://github.com/pytorch/pytorch.git
synced 2026-10-28 18:56:51 +08:00
Aaron Orenstein
25337f5e93
C++ JIT tests use __FILE__ to locate test data files (.ptl, .pt), but __FILE__ contains the build-time source path which may not exist when running from the install tree. This became a problem because of the build-osdc / test split: build-osdc jobs run directly inside a container where the workspace is /__w/pytorch/ pytorch/, so __FILE__ bakes in that path. But the test jobs run inside Docker with the workspace mounted at /var/lib/jenkins/workspace/. When reuse-old-whl grabs a wheel from a build-osdc run and feeds it to a regular test job, the baked-in __FILE__ paths point to a nonexistent directory, and the tests fail. Add a resolveTestDataFile() helper that tries the source-relative path first, then falls back to the executable-relative path. Install the test data files alongside the test_jit binary. This subsumes and generalizes the fix from #179998, which used per-site inline lambdas for 3 of the 25 affected call sites. Authored with Claude. Pull Request resolved: https://github.com/pytorch/pytorch/pull/180160 Approved by: https://github.com/atalman
73 lines
2.3 KiB
C++
73 lines
2.3 KiB
C++
#include <gtest/gtest.h>
|
|
|
|
#include <filesystem>
|
|
|
|
#include "test/cpp/jit/test_utils.h"
|
|
#include "torch/csrc/jit/runtime/graph_executor.h"
|
|
#include "torch/jit.h"
|
|
#include "torch/script.h"
|
|
#include "torch/torch.h"
|
|
|
|
namespace torch {
|
|
namespace jit {
|
|
|
|
TEST(GraphExecutorTest, Basic_CUDA) {
|
|
constexpr int batch_size = 4;
|
|
constexpr int input_size = 256;
|
|
|
|
int hidden_size = 2 * input_size;
|
|
|
|
auto input = at::randn({batch_size, input_size}, at::kCUDA);
|
|
auto hx = at::randn({batch_size, hidden_size}, at::kCUDA);
|
|
auto cx = at::randn({batch_size, hidden_size}, at::kCUDA);
|
|
auto w_ih = t_def(at::randn({4 * hidden_size, input_size}, at::kCUDA));
|
|
auto w_hh = t_def(at::randn({4 * hidden_size, hidden_size}, at::kCUDA));
|
|
|
|
auto g = build_lstm();
|
|
GraphExecutor executor(g, "");
|
|
auto stack = createStack({input, hx, cx, w_ih, w_hh});
|
|
executor.run(stack);
|
|
ASSERT_EQ(stack.size(), 2);
|
|
auto [r0, r1] = lstm(input, hx, cx, w_ih, w_hh);
|
|
ASSERT_TRUE(almostEqual(stack[0].toTensor(), r0));
|
|
ASSERT_TRUE(almostEqual(stack[1].toTensor(), r1));
|
|
}
|
|
|
|
TEST(GraphExecutorTest, runAsync_executor) {
|
|
/*
|
|
TODO: there are some problem with C++ parsing script program involving
|
|
fork. Use the test module below for now.
|
|
issue about this: github.com/pytorch/pytorch/issues/46368
|
|
The test module file is generated by following:
|
|
class DemoModule(torch.nn.Module):
|
|
def forward(self):
|
|
r1 = torch.jit.fork(torch.mm, torch.rand(100,100),torch.rand(100,100))
|
|
r2 = torch.jit.fork(torch.mm, torch.rand(100,100),torch.rand(100,100))
|
|
return r1.wait() + r2.wait()
|
|
demo = DemoModule()
|
|
torch.jit.save(torch.jit.script(demo), 'test_interpreter_async.pt')
|
|
*/
|
|
auto testModelFile =
|
|
resolveTestDataFile(__FILE__, "test_interpreter_async.pt");
|
|
auto module = load(testModelFile);
|
|
auto graph = module.get_method("forward").graph();
|
|
GraphExecutor graphExecutor(graph, "");
|
|
auto asyncCounter = 0;
|
|
std::mutex mtx;
|
|
// a dummy executor which actually use at::launch, but add up a counter
|
|
auto launcher = [&](std::function<void()> f) {
|
|
mtx.lock();
|
|
++asyncCounter;
|
|
mtx.unlock();
|
|
at::launch(std::move(f));
|
|
};
|
|
std::vector<IValue> stack;
|
|
// NOLINTNEXTLINE(modernize-use-emplace)
|
|
stack.push_back(module._ivalue());
|
|
graphExecutor.runAsync(stack, launcher)->wait();
|
|
ASSERT_TRUE(asyncCounter > 0);
|
|
}
|
|
|
|
} // namespace jit
|
|
} // namespace torch
|