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dbe850af5b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33851 Rationale and context described in #33828. Script to reproduce the move: https://gist.github.com/suo/16cbefaaeb67ca5a7c6caffd49b7f6e9 ghstack-source-id: 99079645 Test Plan: Make sure CI passes Reviewed By: jamesr66a Differential Revision: D20133869 fbshipit-source-id: 390e9241a9c85366d9005c492ac31f10aa96488e
33 lines
1014 B
C++
33 lines
1014 B
C++
#include "test/cpp/jit/test_base.h"
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#include "test/cpp/jit/test_utils.h"
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#include "torch/csrc/jit/runtime/graph_executor.h"
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namespace torch {
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namespace jit {
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void testGraphExecutor() {
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constexpr int batch_size = 4;
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constexpr int input_size = 256;
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int hidden_size = 2 * input_size;
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auto input = at::randn({batch_size, input_size}, at::kCUDA);
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auto hx = at::randn({batch_size, hidden_size}, at::kCUDA);
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auto cx = at::randn({batch_size, hidden_size}, at::kCUDA);
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auto w_ih = t_def(at::randn({4 * hidden_size, input_size}, at::kCUDA));
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auto w_hh = t_def(at::randn({4 * hidden_size, hidden_size}, at::kCUDA));
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auto g = build_lstm();
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GraphExecutor executor(g);
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auto stack = createStack({input, hx, cx, w_ih, w_hh});
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executor.run(stack);
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ASSERT_EQ(stack.size(), 2);
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at::Tensor r0, r1;
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std::tie(r0, r1) = lstm(input, hx, cx, w_ih, w_hh);
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ASSERT_TRUE(almostEqual(stack[0].toTensor(), r0));
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ASSERT_TRUE(almostEqual(stack[1].toTensor(), r1));
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}
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} // namespace jit
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} // namespace torch
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