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3baf99bea7
Summary: Further breakup test_misc.h. The remaining tests don't directly map to a jit file so I left them in test_misc. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18191 Differential Revision: D14533442 Pulled By: eellison fbshipit-source-id: 7f538ce0aea208b6b55a4716dfcf039548305041
36 lines
1.1 KiB
C++
36 lines
1.1 KiB
C++
#pragma once
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#include "test/cpp/jit/test_base.h"
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#include "test/cpp/jit/test_utils.h"
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namespace torch {
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namespace jit {
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namespace test {
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void testInterp() {
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constexpr int batch_size = 4;
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constexpr int input_size = 256;
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constexpr int seq_len = 32;
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int hidden_size = 2 * input_size;
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auto input = at::randn({seq_len, 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 lstm_g = build_lstm();
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Code lstm_function(lstm_g);
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InterpreterState lstm_interp(lstm_function);
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auto outputs = run(lstm_interp, {input[0], hx, cx, w_ih, w_hh});
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std::tie(hx, cx) = lstm(input[0], hx, cx, w_ih, w_hh);
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// std::cout << almostEqual(outputs[0],hx) << "\n";
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ASSERT_TRUE(exactlyEqual(outputs[0], hx));
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ASSERT_TRUE(exactlyEqual(outputs[1], cx));
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}
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} // namespace test
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} // namespace jit
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} // namespace torch
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