* limitations under the License.
*/
-#include "kernels/UnidirectionalSequenceLSTM.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/unidirectional_lstm/FloatUnidirectionalLSTMKernel.h"
+#include "luci_interpreter/test_models/unidirectional_lstm/QuantS8UnidirectionalLSTM.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
using namespace testing;
-class UnidirectionalSequenceLSTMTest : public ::testing::Test
+class UnidirectionalLSTMTest : public ::testing::Test
{
-protected:
- void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
-
- std::unique_ptr<IMemoryManager> _memory_manager;
+ // Do nothing
};
-// NOTE from NoCifgNoPeepholeNoProjectionNoClippingUnidirectionalLstmTest
-TEST_F(UnidirectionalSequenceLSTMTest, FloatTest)
+template <typename T>
+std::vector<T> checkUnidirectionalSequenceLSTMKernel(test_kernel::TestDataBase<T> *test_data_base)
{
- const int32_t n_batch = 1;
- const int32_t n_input = 2;
- const int32_t n_cell = 4;
- const int32_t n_output = 4;
- const int32_t sequence_length = 3;
-
- std::vector<float> input_to_input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
- 0.04266912, -0.15680569, -0.34856534, 0.43890524};
-
- std::vector<float> input_to_cell_weights = {-0.50013041, 0.1370284, 0.11810488, 0.2013163,
- -0.20583314, 0.44344562, 0.22077113, -0.29909778};
-
- std::vector<float> input_to_forget_weights = {0.09701663, 0.20334584, -0.50592935, -0.31343272,
- -0.40032279, 0.44781327, 0.01387155, -0.35593212};
-
- std::vector<float> input_to_output_weights = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
- 0.44272184, 0.03897077, -0.1556896, 0.19487578};
-
- std::vector<float> input_gate_bias = {0., 0., 0., 0.};
- std::vector<float> forget_gate_bias = {1., 1., 1., 1.};
- std::vector<float> cell_gate_bias = {0., 0., 0., 0.};
- std::vector<float> output_gate_bias = {0., 0., 0., 0.};
-
- std::vector<float> recurrent_to_input_weights = {
- -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
- -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, -0.12528998,
- 0.24077177, -0.51332325, -0.33502164, 0.10629296};
-
- std::vector<float> recurrent_to_forget_weights = {
- -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
- -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
- 0.28053468, 0.01560611, -0.20127171, -0.01140004};
-
- std::vector<float> recurrent_to_cell_weights = {
- -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
- -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
- -0.46367589, 0.26016325, -0.03894562, -0.16368064};
-
- std::vector<float> recurrent_to_output_weights = {
- 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
- 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
- -0.51818722, -0.15390486, 0.0468148, 0.39922136};
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- Shape input_to_input_weights_shape{n_cell, n_input};
- Shape input_to_cell_weights_shape{n_cell, n_input};
- Shape input_to_forget_weights_shape{n_cell, n_input};
- Shape input_to_output_weights_shape{n_cell, n_input};
+ // Load model with single op
+ auto *model_data_raw = reinterpret_cast<const char *>(test_data_base->get_model_ptr());
+ ModuleLoader::load(&runtime_module, &memory_manager, model_data_raw, dealloc_input);
- Shape input_gate_bias_shape{n_cell};
- Shape forget_gate_bias_shape{n_cell};
- Shape cell_gate_bias_shape{n_cell};
- Shape output_gate_bias_shape{n_cell};
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 1);
- Shape recurrent_to_input_weights_shape{n_cell, n_output};
- Shape recurrent_to_cell_weights_shape{n_cell, n_output};
- Shape recurrent_to_forget_weights_shape{n_cell, n_output};
- Shape recurrent_to_output_weights_shape{n_cell, n_output};
+ // Set input data
+ {
+ auto *input_tensor_data = reinterpret_cast<T *>(main_runtime_graph->configureGraphInput(0));
+ std::copy(test_data_base->get_input_data_by_index(0).begin(),
+ test_data_base->get_input_data_by_index(0).end(), input_tensor_data);
+ }
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
- Tensor input_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_cell_weights_shape, input_to_cell_weights, _memory_manager.get());
- Tensor input_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_forget_weights_shape, input_to_forget_weights, _memory_manager.get());
- Tensor input_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_output_weights_shape, input_to_output_weights, _memory_manager.get());
+ runtime_module.execute();
- Tensor input_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- input_gate_bias_shape, input_gate_bias, _memory_manager.get());
- Tensor forget_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- forget_gate_bias_shape, forget_gate_bias, _memory_manager.get());
- Tensor cell_gate_bias_tensor =
- makeInputTensor<DataType::FLOAT32>(cell_gate_bias_shape, cell_gate_bias, _memory_manager.get());
- Tensor output_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- output_gate_bias_shape, output_gate_bias, _memory_manager.get());
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- Tensor recurrent_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_input_weights_shape, recurrent_to_input_weights, _memory_manager.get());
- Tensor recurrent_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_cell_weights_shape, recurrent_to_cell_weights, _memory_manager.get());
- Tensor recurrent_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_forget_weights_shape, recurrent_to_forget_weights, _memory_manager.get());
- Tensor recurrent_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_output_weights_shape, recurrent_to_output_weights, _memory_manager.get());
-
- std::vector<float> input_data{2., 3., 3., 4., 1., 1.};
- Shape input_shape{sequence_length, n_batch, n_input};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
-
- Shape output_state_shape{n_batch, n_output};
- Tensor output_state_tensor = makeOutputTensor(DataType::FLOAT32);
- output_state_tensor.resize(output_state_shape);
-
- Shape cell_state_shape{n_batch, n_cell};
- Tensor cell_state_tensor = makeOutputTensor(DataType::FLOAT32);
- cell_state_tensor.resize(cell_state_shape);
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_2(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_3(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 0.0;
- params.proj_clip = 0.0;
- params.time_major = true;
- params.asymmetric_quantize_inputs = false;
-
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_forget_weights_tensor,
- &input_to_cell_weights_tensor, &input_to_output_weights_tensor,
- &recurrent_to_input_weights_tensor, &recurrent_to_forget_weights_tensor,
- &recurrent_to_cell_weights_tensor, &recurrent_to_output_weights_tensor, nullptr, nullptr,
- nullptr, &input_gate_bias_tensor, &forget_gate_bias_tensor, &cell_gate_bias_tensor,
- &output_gate_bias_tensor, nullptr, nullptr, &output_state_tensor, &cell_state_tensor, nullptr,
- nullptr, nullptr, nullptr, &output_tensor, &scratchpad_1, &scratchpad_2, &scratchpad_3, params);
-
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(output_state_tensor);
- _memory_manager->allocate_memory(cell_state_tensor);
- _memory_manager->allocate_memory(scratchpad_1);
- _memory_manager->allocate_memory(scratchpad_2);
- _memory_manager->allocate_memory(scratchpad_3);
- kernel.execute();
-
- std::vector<float> ref_output_data{-0.02973187, 0.1229473, 0.20885126, -0.15358765,
- -0.03716109, 0.12507336, 0.41193449, -0.20860538,
- -0.15053082, 0.09120187, 0.24278517, -0.12222792};
-
- std::vector<float> ref_output_shape{sequence_length, n_batch, n_output};
- const float tolerance = 1e-5;
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data, tolerance));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
+ T *output_data = reinterpret_cast<T *>(main_runtime_graph->getOutputDataByIndex(0));
+ const size_t num_elements = (main_runtime_graph->getOutputDataSizeByIndex(0) / sizeof(T));
+ std::vector<T> output_data_vector(output_data, output_data + num_elements);
+ return output_data_vector;
}
-TEST_F(UnidirectionalSequenceLSTMTest, FloatTest_batch)
+TEST_F(UnidirectionalLSTMTest, Float_P)
{
- const int32_t n_batch = 1;
- const int32_t n_input = 2;
- const int32_t n_cell = 4;
- const int32_t n_output = 4;
- const int32_t sequence_length = 3;
-
- std::vector<float> input_to_input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
- 0.04266912, -0.15680569, -0.34856534, 0.43890524};
-
- std::vector<float> input_to_cell_weights = {-0.50013041, 0.1370284, 0.11810488, 0.2013163,
- -0.20583314, 0.44344562, 0.22077113, -0.29909778};
-
- std::vector<float> input_to_forget_weights = {0.09701663, 0.20334584, -0.50592935, -0.31343272,
- -0.40032279, 0.44781327, 0.01387155, -0.35593212};
-
- std::vector<float> input_to_output_weights = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
- 0.44272184, 0.03897077, -0.1556896, 0.19487578};
-
- std::vector<float> input_gate_bias = {0., 0., 0., 0.};
- std::vector<float> forget_gate_bias = {1., 1., 1., 1.};
- std::vector<float> cell_gate_bias = {0., 0., 0., 0.};
- std::vector<float> output_gate_bias = {0., 0., 0., 0.};
-
- std::vector<float> recurrent_to_input_weights = {
- -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
- -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322, -0.12528998,
- 0.24077177, -0.51332325, -0.33502164, 0.10629296};
-
- std::vector<float> recurrent_to_forget_weights = {
- -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
- -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
- 0.28053468, 0.01560611, -0.20127171, -0.01140004};
-
- std::vector<float> recurrent_to_cell_weights = {
- -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
- -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
- -0.46367589, 0.26016325, -0.03894562, -0.16368064};
-
- std::vector<float> recurrent_to_output_weights = {
- 0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
- 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
- -0.51818722, -0.15390486, 0.0468148, 0.39922136};
-
- Shape input_to_input_weights_shape{n_cell, n_input};
- Shape input_to_cell_weights_shape{n_cell, n_input};
- Shape input_to_forget_weights_shape{n_cell, n_input};
- Shape input_to_output_weights_shape{n_cell, n_input};
-
- Shape input_gate_bias_shape{n_cell};
- Shape forget_gate_bias_shape{n_cell};
- Shape cell_gate_bias_shape{n_cell};
- Shape output_gate_bias_shape{n_cell};
-
- Shape recurrent_to_input_weights_shape{n_cell, n_output};
- Shape recurrent_to_cell_weights_shape{n_cell, n_output};
- Shape recurrent_to_forget_weights_shape{n_cell, n_output};
- Shape recurrent_to_output_weights_shape{n_cell, n_output};
-
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
- Tensor input_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_cell_weights_shape, input_to_cell_weights, _memory_manager.get());
- Tensor input_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_forget_weights_shape, input_to_forget_weights, _memory_manager.get());
- Tensor input_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_output_weights_shape, input_to_output_weights, _memory_manager.get());
-
- Tensor input_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- input_gate_bias_shape, input_gate_bias, _memory_manager.get());
- Tensor forget_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- forget_gate_bias_shape, forget_gate_bias, _memory_manager.get());
- Tensor cell_gate_bias_tensor =
- makeInputTensor<DataType::FLOAT32>(cell_gate_bias_shape, cell_gate_bias, _memory_manager.get());
- Tensor output_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- output_gate_bias_shape, output_gate_bias, _memory_manager.get());
-
- Tensor recurrent_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_input_weights_shape, recurrent_to_input_weights, _memory_manager.get());
- Tensor recurrent_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_cell_weights_shape, recurrent_to_cell_weights, _memory_manager.get());
- Tensor recurrent_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_forget_weights_shape, recurrent_to_forget_weights, _memory_manager.get());
- Tensor recurrent_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_output_weights_shape, recurrent_to_output_weights, _memory_manager.get());
-
- std::vector<float> input_data{2., 3., 3., 4., 1., 1.};
- Shape input_shape{n_batch, sequence_length, n_input};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
-
- Shape output_state_shape{n_batch, n_output};
- Tensor output_state_tensor = makeOutputTensor(DataType::FLOAT32);
- output_state_tensor.resize(output_state_shape);
-
- Shape cell_state_shape{n_batch, n_cell};
- Tensor cell_state_tensor = makeOutputTensor(DataType::FLOAT32);
- cell_state_tensor.resize(cell_state_shape);
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 0.0;
- params.proj_clip = 0.0;
- params.time_major = false;
- params.asymmetric_quantize_inputs = false;
-
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_forget_weights_tensor,
- &input_to_cell_weights_tensor, &input_to_output_weights_tensor,
- &recurrent_to_input_weights_tensor, &recurrent_to_forget_weights_tensor,
- &recurrent_to_cell_weights_tensor, &recurrent_to_output_weights_tensor, nullptr, nullptr,
- nullptr, &input_gate_bias_tensor, &forget_gate_bias_tensor, &cell_gate_bias_tensor,
- &output_gate_bias_tensor, nullptr, nullptr, &output_state_tensor, &cell_state_tensor, nullptr,
- nullptr, nullptr, nullptr, &output_tensor, &output_state_tensor, &cell_state_tensor,
- &scratchpad_1, params);
-
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(output_state_tensor);
- _memory_manager->allocate_memory(cell_state_tensor);
- _memory_manager->allocate_memory(scratchpad_1);
- kernel.execute();
-
- std::vector<float> ref_output_data{-0.02973187, 0.1229473, 0.20885126, -0.15358765,
- -0.03716109, 0.12507336, 0.41193449, -0.20860538,
- -0.15053082, 0.09120187, 0.24278517, -0.12222792};
-
- std::vector<float> ref_output_shape{n_batch, sequence_length, n_output};
- const float tolerance = 1e-5;
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data, tolerance));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
+ test_kernel::TestDataFloatUnidirectionalLSTM test_data_kernel;
+ std::vector<float> output_data_vector = checkUnidirectionalSequenceLSTMKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.0001f));
}
-TEST_F(UnidirectionalSequenceLSTMTest, FloatTest_simple)
+TEST_F(UnidirectionalLSTMTest, Int8_P)
{
- const int32_t n_batch = 1;
- const int32_t n_input = 1;
- const int32_t n_cell = 1;
- const int32_t n_output = 1;
- const int32_t sequence_length = 1;
-
- std::vector<float> input_to_input_weights = {0.329067};
- std::vector<float> input_to_forget_weights = {0.308059};
- std::vector<float> input_to_cell_weights = {0.152916};
- std::vector<float> input_to_output_weights = {-0.476033};
-
- std::vector<float> input_gate_bias = {0.};
- std::vector<float> forget_gate_bias = {1.};
- std::vector<float> cell_gate_bias = {0.};
- std::vector<float> output_gate_bias = {0.};
-
- std::vector<float> recurrent_to_input_weights = {0.207806};
- std::vector<float> recurrent_to_forget_weights = {0.028718};
- std::vector<float> recurrent_to_cell_weights = {-0.182756};
- std::vector<float> recurrent_to_output_weights = {-0.960517};
-
- Shape input_to_input_weights_shape{n_cell, n_input};
- Shape input_to_cell_weights_shape{n_cell, n_input};
- Shape input_to_forget_weights_shape{n_cell, n_input};
- Shape input_to_output_weights_shape{n_cell, n_input};
-
- Shape input_gate_bias_shape{n_cell};
- Shape forget_gate_bias_shape{n_cell};
- Shape cell_gate_bias_shape{n_cell};
- Shape output_gate_bias_shape{n_cell};
-
- Shape recurrent_to_input_weights_shape{n_cell, n_output};
- Shape recurrent_to_cell_weights_shape{n_cell, n_output};
- Shape recurrent_to_forget_weights_shape{n_cell, n_output};
- Shape recurrent_to_output_weights_shape{n_cell, n_output};
-
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
- Tensor input_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_cell_weights_shape, input_to_cell_weights, _memory_manager.get());
- Tensor input_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_forget_weights_shape, input_to_forget_weights, _memory_manager.get());
- Tensor input_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_output_weights_shape, input_to_output_weights, _memory_manager.get());
-
- Tensor input_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- input_gate_bias_shape, input_gate_bias, _memory_manager.get());
- Tensor forget_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- forget_gate_bias_shape, forget_gate_bias, _memory_manager.get());
- Tensor cell_gate_bias_tensor =
- makeInputTensor<DataType::FLOAT32>(cell_gate_bias_shape, cell_gate_bias, _memory_manager.get());
- Tensor output_gate_bias_tensor = makeInputTensor<DataType::FLOAT32>(
- output_gate_bias_shape, output_gate_bias, _memory_manager.get());
-
- Tensor recurrent_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_input_weights_shape, recurrent_to_input_weights, _memory_manager.get());
- Tensor recurrent_to_cell_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_cell_weights_shape, recurrent_to_cell_weights, _memory_manager.get());
- Tensor recurrent_to_forget_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_forget_weights_shape, recurrent_to_forget_weights, _memory_manager.get());
- Tensor recurrent_to_output_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- recurrent_to_output_weights_shape, recurrent_to_output_weights, _memory_manager.get());
-
- std::vector<float> input_data{0.03653763};
- Shape input_shape{n_batch, sequence_length, n_input};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
-
- Shape output_state_shape{n_batch, n_output};
- Tensor output_state_tensor = makeOutputTensor(DataType::FLOAT32);
- output_state_tensor.resize(output_state_shape);
-
- Shape cell_state_shape{n_batch, n_cell};
- Tensor cell_state_tensor = makeOutputTensor(DataType::FLOAT32);
- cell_state_tensor.resize(cell_state_shape);
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 10.0;
- params.proj_clip = 0.0;
- params.time_major = false;
- params.asymmetric_quantize_inputs = false;
-
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_forget_weights_tensor,
- &input_to_cell_weights_tensor, &input_to_output_weights_tensor,
- &recurrent_to_input_weights_tensor, &recurrent_to_forget_weights_tensor,
- &recurrent_to_cell_weights_tensor, &recurrent_to_output_weights_tensor, nullptr, nullptr,
- nullptr, &input_gate_bias_tensor, &forget_gate_bias_tensor, &cell_gate_bias_tensor,
- &output_gate_bias_tensor, nullptr, nullptr, &output_state_tensor, &cell_state_tensor, nullptr,
- nullptr, nullptr, nullptr, &output_tensor, &output_state_tensor, &cell_state_tensor,
- &scratchpad_1, params);
-
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(output_state_tensor);
- _memory_manager->allocate_memory(cell_state_tensor);
- _memory_manager->allocate_memory(scratchpad_1);
- kernel.execute();
-
- std::vector<float> ref_output_data{0.00139296};
- std::vector<float> ref_output_shape{n_batch, sequence_length, n_output};
- const float tolerance = 1e-5;
- auto aa = extractTensorData<float>(output_tensor);
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data, tolerance));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
+ test_kernel::TestDataInt8UnidirectionalLSTM test_data_kernel;
+ std::vector<int8_t> output_data_vector = checkUnidirectionalSequenceLSTMKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, test_data_kernel.get_output_data_by_index(0));
}
-TEST_F(UnidirectionalSequenceLSTMTest, Unsupported_Type_Configure_NEG)
-{
- const int32_t n_batch = 1;
- const int32_t n_input = 2;
- const int32_t n_cell = 4;
- const int32_t n_output = 4;
- const int32_t sequence_length = 3;
-
- std::vector<int8_t> input_data{2, 3, 3, 4, 1, 1}; // int8 is not support as of now
- Shape input_shape{sequence_length, n_batch, n_input};
- Tensor input_tensor =
- makeInputTensor<DataType::S8>(input_shape, input_data, _memory_manager.get());
-
- std::vector<float> input_to_input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
- 0.04266912, -0.15680569, -0.34856534, 0.43890524};
- Shape input_to_input_weights_shape{n_cell, n_input};
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_2(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_3(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 0.0;
- params.proj_clip = 0.0;
- params.time_major = true;
- params.asymmetric_quantize_inputs = false;
-
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- nullptr, nullptr, nullptr, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr, nullptr,
- nullptr, &output_tensor, &scratchpad_1, &scratchpad_2, &scratchpad_3, params);
-
- EXPECT_ANY_THROW(kernel.configure());
-}
-
-TEST_F(UnidirectionalSequenceLSTMTest, Invalid_Input_Shape_NEG)
-{
- const int32_t n_batch = 1;
- const int32_t n_input = 2;
- const int32_t n_cell = 4;
- const int32_t n_output = 4;
- const int32_t sequence_length = 3;
-
- std::vector<float> input_data{2., 3., 3., 4., 1., 1.};
- Shape input_shape{sequence_length, n_input}; // this is wrong
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
-
- std::vector<float> input_to_input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
- 0.04266912, -0.15680569, -0.34856534, 0.43890524};
- Shape input_to_input_weights_shape{n_cell, n_input};
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_2(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_3(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 0.0;
- params.proj_clip = 0.0;
- params.time_major = true;
- params.asymmetric_quantize_inputs = false;
-
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- nullptr, nullptr, nullptr, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr, nullptr,
- nullptr, &output_tensor, &scratchpad_1, &scratchpad_2, &scratchpad_3, params);
-
- EXPECT_ANY_THROW(kernel.configure());
-}
-
-TEST_F(UnidirectionalSequenceLSTMTest, Invalid_Input_Shape_2_NEG)
-{
- const int32_t n_batch = 1;
- const int32_t n_input = 2;
- const int32_t n_cell = 4;
- const int32_t n_output = 4;
- const int32_t sequence_length = 3;
-
- std::vector<float> input_data{2., 3., 3., 4., 1., 1.};
- Shape input_shape{sequence_length, n_batch, n_input};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
-
- std::vector<float> input_to_input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
- 0.04266912, -0.15680569, -0.34856534, 0.43890524};
- Shape input_to_input_weights_shape{n_cell, n_input};
- Tensor input_to_input_weights_tensor = makeInputTensor<DataType::FLOAT32>(
- input_to_input_weights_shape, input_to_input_weights, _memory_manager.get());
-
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor scratchpad_1(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_2(DataType::FLOAT32, Shape({}), {}, "");
- Tensor scratchpad_3(DataType::FLOAT32, Shape({}), {}, "");
-
- UnidirectionalSequenceLSTMParams params{};
- params.activation = Activation::TANH;
- params.cell_clip = 0.0;
- params.proj_clip = 0.0;
- params.time_major = true;
- params.asymmetric_quantize_inputs = false;
-
- // NOTE provide wrong shaped inputs
- UnidirectionalSequenceLSTM kernel(
- &input_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- nullptr, nullptr, nullptr, &input_to_input_weights_tensor, &input_to_input_weights_tensor,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr,
- &input_to_input_weights_tensor, &input_to_input_weights_tensor, nullptr, nullptr, nullptr,
- nullptr, &output_tensor, &scratchpad_1, &scratchpad_2, &scratchpad_3, params);
-
- EXPECT_ANY_THROW(kernel.configure());
-}
+// TODO: add negative tests?
} // namespace
-} // namespace kernels
} // namespace luci_interpreter
projects / platform / core / ml / nnfw.git / blobdiff