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Commit 94649220 authored by Adam Rogowiec's avatar Adam Rogowiec Committed by arogowie-intel
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Unit tests.

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test/backend_fused_op.in.cpp
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......@@ -934,3 +934,315 @@ NGRAPH_TEST(${BACKEND_NAME}, split_var_len_parts)
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_no_bias_no_peepholes)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 4;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto lstm_cell = make_shared<op::LSTMCell>(X, W, R, H_t, C_t, hidden_size);
auto function =
make_shared<Function>(lstm_cell->decompose_op(), ParameterVector{X, W, R, H_t, C_t});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f});
// W
test_case.add_input<float>({3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f,
7.3950343e-02f, 3.8063636e-01f, 9.6921772e-01f, 9.6897459e-01f,
6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f,
4.0472135e-01f, 6.8342745e-01f, 8.3432144e-01f, 4.4928190e-01f,
7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f,
2.1716513e-01f, 2.7473119e-01f, 3.3999152e-02f, 9.6835363e-01f,
3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f});
// R
test_case.add_input<float>(
{0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f});
// Ht
test_case.add_input<float>(
{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f});
// Ct
test_case.add_input<float>(
{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_bias_peepholes)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 4;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto P = make_shared<op::Parameter>(element::f32, Shape{3 * hidden_size});
const auto lstm_cell = make_shared<op::LSTMCell>(X, W, R, H_t, C_t, hidden_size, B, P);
auto function =
make_shared<Function>(lstm_cell->decompose_op(), ParameterVector{X, W, R, H_t, C_t, B, P});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f});
// W
test_case.add_input<float>({3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f,
7.3950343e-02f, 3.8063636e-01f, 9.6921772e-01f, 9.6897459e-01f,
6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f,
4.0472135e-01f, 6.8342745e-01f, 8.3432144e-01f, 4.4928190e-01f,
7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f,
2.1716513e-01f, 2.7473119e-01f, 3.3999152e-02f, 9.6835363e-01f,
3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f});
// R
test_case.add_input<float>(
{0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f});
// Ht
test_case.add_input<float>(
{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f});
// Ct
test_case.add_input<float>(
{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f});
// B
test_case.add_input<float>({0.81130236f, 0.31332242f, 0.6423671f, 0.09981899f, 0.7847627f,
0.8405669f, 0.0330242f, 0.45014873f, 0.5599519f, 0.31807426f,
0.7356558f, 0.6298691f, 0.26263478f, 0.8391581f, 0.52434635f,
0.11468413f, 0.4533051f, 0.67632145f, 0.43415946f, 0.46795473f,
0.5674715f, 0.19214648f, 0.37824264f, 0.11187395f});
// P
test_case.add_input<float>({0.38557124f,
0.9482306f,
0.6808912f,
0.93585867f,
0.74540526f,
0.10507805f,
0.8180733f,
0.13840231f,
0.24175227f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.9218244f, 0.78787273f, 0.8754273f, 0.7361462f, 0.70927656f, 0.83522964f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{1.7094649f, 1.1259761f, 1.444019f, 1.086587f, 0.9762144f, 1.3066899f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_bias_peepholes_clip_input_forget)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 4;
const float clip_threshold = 3.5f;
bool input_forget = true;
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto P = make_shared<op::Parameter>(element::f32, Shape{3 * hidden_size});
const auto lstm_cell = make_shared<op::LSTMCell>(X,
W,
R,
H_t,
C_t,
hidden_size,
B,
P,
vector<string>{"sigmoid", "tanh", "tanh"},
vector<float>{},
vector<float>{},
clip_threshold,
input_forget);
auto function =
make_shared<Function>(lstm_cell->decompose_op(), ParameterVector{X, W, R, H_t, C_t, B, P});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f});
// W
test_case.add_input<float>({3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f,
7.3950343e-02f, 3.8063636e-01f, 9.6921772e-01f, 9.6897459e-01f,
6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f,
4.0472135e-01f, 6.8342745e-01f, 8.3432144e-01f, 4.4928190e-01f,
7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f,
2.1716513e-01f, 2.7473119e-01f, 3.3999152e-02f, 9.6835363e-01f,
3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f});
// R
test_case.add_input<float>(
{0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f});
// Ht
test_case.add_input<float>(
{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f});
// Ct
test_case.add_input<float>(
{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f});
// B
test_case.add_input<float>({0.81130236f, 0.31332242f, 0.6423671f, 0.09981899f, 0.7847627f,
0.8405669f, 0.0330242f, 0.45014873f, 0.5599519f, 0.31807426f,
0.7356558f, 0.6298691f, 0.26263478f, 0.8391581f, 0.52434635f,
0.11468413f, 0.4533051f, 0.67632145f, 0.43415946f, 0.46795473f,
0.5674715f, 0.19214648f, 0.37824264f, 0.11187395f});
// P
test_case.add_input<float>({0.38557124f,
0.9482306f,
0.6808912f,
0.93585867f,
0.74540526f,
0.10507805f,
0.8180733f,
0.13840231f,
0.24175227f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.71485436f, 0.71844107f, 0.72704613f, 0.6235602f, 0.68306124f, 0.6978715f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.94656503f, 0.9527454f, 0.9706756f, 0.84206575f, 0.91898793f, 0.9127192f});
test_case.run();
}
NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_activaction_functions)
{
const size_t batch_size = 2;
const size_t input_size = 3;
const size_t hidden_size = 3;
const size_t gates_count = 4;
const float clip_threshold = 3.5f;
bool input_forget = true;
vector<string> activations{"sigmoid", "tanh", "hardsigmoid"};
vector<float> activation_alpha{0.f, 0.f, 1.8345f};
vector<float> activation_beta{0.f, 0.f, 3.05f};
const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
const auto W =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
const auto R =
make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
const auto B = make_shared<op::Parameter>(element::f32, Shape{2 * gates_count * hidden_size});
const auto P = make_shared<op::Parameter>(element::f32, Shape{3 * hidden_size});
const auto lstm_cell = make_shared<op::LSTMCell>(X,
W,
R,
H_t,
C_t,
hidden_size,
B,
P,
activations,
activation_alpha,
activation_beta,
clip_threshold,
input_forget);
auto function =
make_shared<Function>(lstm_cell->decompose_op(), ParameterVector{X, W, R, H_t, C_t, B, P});
auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
// X
test_case.add_input<float>(
{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f});
// W
test_case.add_input<float>({3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f,
7.3950343e-02f, 3.8063636e-01f, 9.6921772e-01f, 9.6897459e-01f,
6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f,
4.0472135e-01f, 6.8342745e-01f, 8.3432144e-01f, 4.4928190e-01f,
7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f,
2.1716513e-01f, 2.7473119e-01f, 3.3999152e-02f, 9.6835363e-01f,
3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f});
// R
test_case.add_input<float>(
{0.0987983f, 0.52032113f, 0.5848073f, 0.5356095f, 0.74497133f, 0.73260087f,
0.1700787f, 0.45684233f, 0.1495722f, 0.42734373f, 0.4433832f, 0.25906256f,
0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
0.6881257f, 0.8170279f, 0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
0.86843914f, 0.45967972f, 0.6237719f, 0.11074839f, 0.6029616f, 0.3149305f,
0.46504205f, 0.5843412f, 0.8733427f, 0.7687243f, 0.07074859f, 0.39188156f});
// Ht
test_case.add_input<float>(
{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f});
// Ct
test_case.add_input<float>(
{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f});
// B
test_case.add_input<float>({0.81130236f, 0.31332242f, 0.6423671f, 0.09981899f, 0.7847627f,
0.8405669f, 0.0330242f, 0.45014873f, 0.5599519f, 0.31807426f,
0.7356558f, 0.6298691f, 0.26263478f, 0.8391581f, 0.52434635f,
0.11468413f, 0.4533051f, 0.67632145f, 0.43415946f, 0.46795473f,
0.5674715f, 0.19214648f, 0.37824264f, 0.11187395f});
// P
test_case.add_input<float>({0.38557124f,
0.9482306f,
0.6808912f,
0.93585867f,
0.74540526f,
0.10507805f,
0.8180733f,
0.13840231f,
0.24175227f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.96834344f, 0.9695254f, 0.97068775f, 0.9077866f, 0.94161016f, 0.96599925f});
test_case.add_expected_output<float>(
Shape{batch_size, hidden_size},
{0.94656503f, 0.9527454f, 0.9706756f, 0.84206575f, 0.91898793f, 0.9127192f});
test_case.run();
}
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