pytorch/test/cpp/jit/test_graph_executor.cpp

#include <gtest/gtest.h>

#include "test/cpp/jit/test_utils.h"
#include "torch/csrc/jit/runtime/graph_executor.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);
  at::Tensor r0, r1;
  std::tie(r0, r1) = lstm(input, hx, cx, w_ih, w_hh);
  ASSERT_TRUE(almostEqual(stack[0].toTensor(), r0));
  ASSERT_TRUE(almostEqual(stack[1].toTensor(), r1));
}

} // namespace jit
} // namespace torch