COMPMID-993 Implement CL LSTM function
Change-Id: Iee4ad387c41dd8ccfe31b3044d797f2d7448e552
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126655
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/fixtures/LSTMLayerFixture.h b/tests/validation/fixtures/LSTMLayerFixture.h
new file mode 100644
index 0000000..b7e43b3
--- /dev/null
+++ b/tests/validation/fixtures/LSTMLayerFixture.h
@@ -0,0 +1,404 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
+
+#include "tests/Globals.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/ArithmeticAddition.h"
+#include "tests/validation/reference/ArithmeticSubtraction.h"
+#include "tests/validation/reference/FullyConnectedLayer.h"
+#include "tests/validation/reference/GEMM.h"
+#include "tests/validation/reference/PixelWiseMultiplication.h"
+#include "tests/validation/reference/Transpose.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename FunctionParams, typename T>
+class LSTMLayerValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape input_weights_shape, TensorShape recurrent_weights_shape, TensorShape cell_bias_shape, TensorShape output_cell_shape, TensorShape output_shape,
+ TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+ {
+ _target = compute_target(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
+ data_type, projection_opt, peephole_opt);
+ _reference = compute_reference(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
+ data_type, projection_opt, peephole_opt);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ }
+ template <typename U>
+ void fill_custom_val(U &&tensor, float num, int i)
+ {
+ std::uniform_real_distribution<> distribution(num, num);
+ library->fill(tensor, distribution, i);
+ }
+ TensorType compute_target(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
+ const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
+ float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+ {
+ // Create projection bias shape
+ TensorShape projection_bias_shape{};
+ projection_bias_shape.set(0, output_shape.x());
+
+ // Create tensors
+ TensorType input = create_tensor<TensorType>(input_shape, data_type);
+ TensorType input_to_forget_w = create_tensor<TensorType>(input_weights_shape, data_type);
+ TensorType input_to_cell_w = create_tensor<TensorType>(input_weights_shape, data_type);
+ TensorType input_to_output_w = create_tensor<TensorType>(input_weights_shape, data_type);
+ TensorType recurrent_to_forget_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ TensorType recurrent_to_cell_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ TensorType recurrent_to_output_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ TensorType forget_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
+ TensorType cell_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
+ TensorType output_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
+ TensorType output_state = create_tensor<TensorType>(output_shape, data_type);
+ TensorType cell_state = create_tensor<TensorType>(output_cell_shape, data_type);
+ TensorType scratch = create_tensor<TensorType>(scratch_shape, data_type);
+ TensorType output = create_tensor<TensorType>(output_shape, data_type);
+ TensorType input_to_input_w;
+ TensorType recurrent_to_input_w;
+ TensorType cell_to_input_w;
+ TensorType cell_to_forget_w;
+ TensorType input_gate_bias;
+ TensorType cell_to_output_w;
+ TensorType projection_w;
+ TensorType projection_bias;
+
+ bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
+
+ FunctionParams lstm_params;
+
+ if(!cifg_opt)
+ {
+ input_to_input_w = create_tensor<TensorType>(input_weights_shape, data_type);
+ recurrent_to_input_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ cell_to_input_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+ input_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
+ lstm_params.set_cifg_params(&input_to_input_w, &recurrent_to_input_w, &cell_to_input_w, &input_gate_bias);
+ }
+
+ if(peephole_opt)
+ {
+ if(cifg_opt)
+ {
+ cell_to_input_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+ }
+ cell_to_forget_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+ cell_to_output_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+ lstm_params.set_peephole_params(&cell_to_input_w, &cell_to_forget_w, &cell_to_output_w);
+ }
+
+ if(projection_opt)
+ {
+ projection_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+ projection_bias = create_tensor<TensorType>(projection_bias_shape, data_type);
+ lstm_params.set_projection_params(&projection_w, &projection_bias);
+ }
+
+ // Create and configure function
+ FunctionType lstm;
+ lstm.configure(&input, &input_to_forget_w, &input_to_cell_w, &input_to_output_w, &recurrent_to_forget_w,
+ &recurrent_to_cell_w, &recurrent_to_output_w, &forget_gate_bias, &cell_bias, &output_gate_bias, &output_state, &cell_state,
+ &scratch, &output, lstm_params, info, cell_threshold, projection_threshold);
+
+ ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ input.allocator()->allocate();
+ input_to_forget_w.allocator()->allocate();
+ input_to_cell_w.allocator()->allocate();
+ input_to_output_w.allocator()->allocate();
+ recurrent_to_forget_w.allocator()->allocate();
+ recurrent_to_cell_w.allocator()->allocate();
+ recurrent_to_output_w.allocator()->allocate();
+ forget_gate_bias.allocator()->allocate();
+ cell_bias.allocator()->allocate();
+ output_gate_bias.allocator()->allocate();
+ output_state.allocator()->allocate();
+ cell_state.allocator()->allocate();
+ scratch.allocator()->allocate();
+ output.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(input), 0);
+ fill(AccessorType(input_to_forget_w), 1);
+ fill(AccessorType(input_to_cell_w), 2);
+ fill(AccessorType(input_to_output_w), 3);
+ fill(AccessorType(recurrent_to_forget_w), 4);
+ fill(AccessorType(recurrent_to_cell_w), 5);
+ fill(AccessorType(recurrent_to_output_w), 6);
+ fill(AccessorType(forget_gate_bias), 7);
+ fill(AccessorType(cell_bias), 8);
+ fill(AccessorType(output_gate_bias), 9);
+ fill(AccessorType(output_state), 10);
+ fill(AccessorType(cell_state), 11);
+ fill(AccessorType(scratch), 12);
+
+ if(!cifg_opt)
+ {
+ ARM_COMPUTE_EXPECT(input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ input_to_input_w.allocator()->allocate();
+ recurrent_to_input_w.allocator()->allocate();
+ cell_to_input_w.allocator()->allocate();
+ input_gate_bias.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(input_to_input_w), 13);
+ fill(AccessorType(recurrent_to_input_w), 14);
+ fill(AccessorType(cell_to_input_w), 15);
+ fill(AccessorType(recurrent_to_input_w), 16);
+ fill(AccessorType(input_gate_bias), 17);
+ }
+
+ if(peephole_opt)
+ {
+ if(cifg_opt)
+ {
+ ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ cell_to_input_w.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(cell_to_input_w), 15);
+ }
+ ARM_COMPUTE_EXPECT(cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ cell_to_forget_w.allocator()->allocate();
+ cell_to_output_w.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ fill(AccessorType(cell_to_output_w), 18);
+ }
+
+ if(projection_opt)
+ {
+ ARM_COMPUTE_EXPECT(projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ projection_w.allocator()->allocate();
+ projection_bias.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ fill(AccessorType(projection_w), 19);
+ fill(AccessorType(projection_bias), 20);
+ }
+
+ // Compute function
+ lstm.run();
+
+ return output;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
+ const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
+ float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+ {
+ // Create projection bias shape
+ TensorShape projection_bias_shape{};
+ projection_bias_shape.set(0, output_shape.x());
+
+ TensorShape gemm_shape{ 1, output_shape.y() };
+ SimpleTensor<T> gemm_out{ gemm_shape, data_type };
+
+ // Create reference
+ SimpleTensor<T> input{ input_shape, data_type };
+ SimpleTensor<T> input_to_input_w{ input_weights_shape, data_type };
+ SimpleTensor<T> input_to_forget_w{ input_weights_shape, data_type };
+ SimpleTensor<T> input_to_cell_w{ input_weights_shape, data_type };
+ SimpleTensor<T> input_to_output_w{ input_weights_shape, data_type };
+ SimpleTensor<T> recurrent_to_input_w{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> recurrent_to_forget_w{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> recurrent_to_cell_w{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> recurrent_to_output_w{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> cell_to_input_w{ cell_bias_shape, data_type };
+ SimpleTensor<T> cell_to_forget_w{ cell_bias_shape, data_type };
+ SimpleTensor<T> cell_to_output_w{ cell_bias_shape, data_type };
+ SimpleTensor<T> input_gate_bias{ cell_bias_shape, data_type };
+ SimpleTensor<T> forget_gate_bias{ cell_bias_shape, data_type };
+ SimpleTensor<T> cell_bias{ cell_bias_shape, data_type };
+ SimpleTensor<T> output_gate_bias{ cell_bias_shape, data_type };
+ SimpleTensor<T> projection_w{ recurrent_weights_shape, data_type };
+ SimpleTensor<T> projection_bias{ projection_bias_shape, data_type };
+ SimpleTensor<T> output_state{ output_shape, data_type };
+ SimpleTensor<T> cell_state{ output_cell_shape, data_type };
+ SimpleTensor<T> scratch{ scratch_shape, data_type };
+ SimpleTensor<T> output{ output_shape, data_type };
+
+ // Fill reference
+ fill(input, 0);
+ fill(input_to_forget_w, 1);
+ fill(input_to_cell_w, 2);
+ fill(input_to_output_w, 3);
+ fill(recurrent_to_forget_w, 4);
+ fill(recurrent_to_cell_w, 5);
+ fill(recurrent_to_output_w, 6);
+ fill(forget_gate_bias, 7);
+ fill(cell_bias, 8);
+ fill(output_gate_bias, 9);
+ fill(output_state, 10);
+ fill(cell_state, 11);
+ fill(scratch, 12);
+ fill(input_to_input_w, 13);
+ fill(recurrent_to_input_w, 14);
+ fill(cell_to_input_w, 15);
+ fill(recurrent_to_input_w, 16);
+ fill(input_gate_bias, 17);
+ fill(cell_to_output_w, 18);
+ fill(projection_w, 19);
+ fill(projection_bias, 20);
+
+ bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
+
+ // Compute forget_gate
+ SimpleTensor<T> fully_connected_forget = reference::fully_connected_layer(input, input_to_forget_w, forget_gate_bias, output_cell_shape);
+ SimpleTensor<T> transposed_weights = reference::transpose(recurrent_to_forget_w);
+ SimpleTensor<T> gemm = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+ SimpleTensor<T> forget_gate = reference::arithmetic_addition(fully_connected_forget, gemm, data_type, ConvertPolicy::SATURATE);
+
+ if(peephole_opt)
+ {
+ transposed_weights = reference::transpose(cell_to_forget_w);
+ gemm = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+ forget_gate = reference::arithmetic_addition(forget_gate, gemm, data_type, ConvertPolicy::SATURATE);
+ }
+
+ forget_gate = reference::activation_layer(forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+
+ // Compute input_gate
+ SimpleTensor<T> input_gate;
+ if(cifg_opt)
+ {
+ SimpleTensor<T> ones{ cell_bias_shape, data_type };
+ fill_custom_val(ones, 1.f, 0);
+ input_gate = reference::arithmetic_subtraction<T, T, T>(ones, forget_gate, data_type, ConvertPolicy::SATURATE);
+ }
+ else
+ {
+ SimpleTensor<T> fully_connected_input = reference::fully_connected_layer(input, input_to_input_w, input_gate_bias, output_cell_shape);
+ transposed_weights = reference::transpose(recurrent_to_input_w);
+ gemm = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+ input_gate = reference::arithmetic_addition(fully_connected_input, gemm, data_type, ConvertPolicy::SATURATE);
+ transposed_weights = reference::transpose(cell_to_input_w);
+ gemm = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+ input_gate = reference::arithmetic_addition(input_gate, gemm, data_type, ConvertPolicy::SATURATE);
+ input_gate = reference::activation_layer(input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ }
+
+ // Compute cell_state
+ SimpleTensor<T> fully_connected_cell_state = reference::fully_connected_layer(input, input_to_cell_w, cell_bias, output_cell_shape);
+ transposed_weights = reference::transpose(recurrent_to_cell_w);
+ gemm = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+ SimpleTensor<T> pixelwise_mul = reference::pixel_wise_multiplication(cell_state, forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ cell_state = reference::arithmetic_addition(fully_connected_cell_state, gemm, data_type, ConvertPolicy::SATURATE);
+ cell_state = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ cell_state = reference::pixel_wise_multiplication(cell_state, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ cell_state = reference::arithmetic_addition(cell_state, pixelwise_mul, data_type, ConvertPolicy::SATURATE);
+ if(cell_threshold != 0.f)
+ {
+ cell_state = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
+ }
+
+ // Compute output
+ SimpleTensor<T> fully_connected_output = reference::fully_connected_layer(input, input_to_output_w, output_gate_bias, output_cell_shape);
+ transposed_weights = reference::transpose(recurrent_to_output_w);
+ gemm = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+ output = reference::arithmetic_addition(fully_connected_output, gemm, data_type, ConvertPolicy::SATURATE);
+ if(peephole_opt)
+ {
+ transposed_weights = reference::transpose(cell_to_output_w);
+ gemm = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+ output = reference::arithmetic_addition(output, gemm, data_type, ConvertPolicy::SATURATE);
+ }
+ output = reference::activation_layer(output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+
+ // Compute output state
+ SimpleTensor<T> cell_state_activation = reference::activation_layer(cell_state, info);
+ output_state = reference::pixel_wise_multiplication(output, cell_state_activation, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+
+ if(projection_opt)
+ {
+ SimpleTensor<T> fully_connected_projection = reference::fully_connected_layer(output_state, projection_w, projection_bias, output_cell_shape);
+ if(projection_threshold != 0.f)
+ {
+ output_state = reference::activation_layer(fully_connected_projection, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
+ }
+ }
+ return output_state;
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE */