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/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h
index a01e4a7..fe90b09 100644
--- a/arm_compute/runtime/CL/CLFunctions.h
+++ b/arm_compute/runtime/CL/CLFunctions.h
@@ -79,6 +79,7 @@
#include "arm_compute/runtime/CL/functions/CLHistogram.h"
#include "arm_compute/runtime/CL/functions/CLIntegralImage.h"
#include "arm_compute/runtime/CL/functions/CLL2NormalizeLayer.h"
+#include "arm_compute/runtime/CL/functions/CLLSTMLayer.h"
#include "arm_compute/runtime/CL/functions/CLLaplacianPyramid.h"
#include "arm_compute/runtime/CL/functions/CLLaplacianReconstruct.h"
#include "arm_compute/runtime/CL/functions/CLLocallyConnectedLayer.h"
diff --git a/arm_compute/runtime/CL/functions/CLLSTMLayer.h b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
new file mode 100644
index 0000000..cf47f34
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLLSTMLayer.h
@@ -0,0 +1,346 @@
+/*
+ * 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_CLLSTMLAYER_H__
+#define __ARM_COMPUTE_CLLSTMLAYER_H__
+
+#include "arm_compute/runtime/IFunction.h"
+
+#include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h"
+#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h"
+#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h"
+#include "arm_compute/core/CL/kernels/CLCopyKernel.h"
+#include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLMemoryGroup.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/functions/CLArithmeticAddition.h"
+#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
+#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+#include "arm_compute/runtime/CL/functions/CLWidthConcatenateLayer.h"
+#include "arm_compute/runtime/IMemoryManager.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+class ICLTensor;
+
+template <typename T>
+class LSTMParams
+{
+public:
+ /** Constructor */
+ LSTMParams()
+ : _input_to_input_weights(nullptr), _recurrent_to_input_weights(nullptr), _cell_to_input_weights(nullptr), _input_gate_bias(nullptr), _cell_to_forget_weights(nullptr),
+ _cell_to_output_weights(nullptr), _projection_weights(nullptr), _projection_bias(nullptr), _has_peephole_opt(false), _has_projection(false), _has_cifg_opt(true)
+ {
+ }
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ LSTMParams(const LSTMParams &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ LSTMParams &operator=(const LSTMParams &) = delete;
+ /** Default destructor */
+ ~LSTMParams() = default;
+ /** Set CIFG tensor parameters.
+ *
+ * @param[in] input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data types supported: F16/F32.
+ * @param[in] recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input_to_input_weights.
+ * @param[in] cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input_to_input_weights.
+ * @param[in] input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input_to_input_weights
+ *
+ * @return Reference to this LSTMParams object
+ */
+ LSTMParams &set_cifg_params(const T *input_to_input_weights, const T *recurrent_to_input_weights, const T *cell_to_input_weights, const T *input_gate_bias)
+ {
+ _input_to_input_weights = input_to_input_weights;
+ _recurrent_to_input_weights = recurrent_to_input_weights;
+ _cell_to_input_weights = cell_to_input_weights;
+ _input_gate_bias = input_gate_bias;
+ _has_cifg_opt = false;
+ return *this;
+ }
+ /** Set projection tensor parameters.
+ *
+ * @param[in] projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Data types supported: F16/F32.
+ * @param[in] projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p projection_weights.
+ *
+ * @return Reference to this LSTMParams object
+ */
+ LSTMParams &set_projection_params(const T *projection_weights, const T *projection_bias)
+ {
+ _projection_weights = projection_weights;
+ _projection_bias = projection_bias;
+ _has_projection = true;
+ return *this;
+ }
+ /** Set peephole tensor parameters.
+ *
+ * @param[in] cell_to_input_weights 1D weights tensor with dimensions [num_units]. Data type supported: Data types supported: F16/F32.
+ * @param[in] cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p cell_to_input_weights.
+ * @param[in] cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p cell_to_input_weights.
+ *
+ * @return Reference to this LSTMParams object
+ */
+ LSTMParams &set_peephole_params(const T *cell_to_input_weights, const T *cell_to_forget_weights, const T *cell_to_output_weights)
+ {
+ _cell_to_input_weights = cell_to_input_weights;
+ _cell_to_forget_weights = cell_to_forget_weights;
+ _cell_to_output_weights = cell_to_output_weights;
+ _has_peephole_opt = true;
+ return *this;
+ }
+
+ const T *input_to_input_weights() const
+ {
+ return _input_to_input_weights;
+ }
+
+ const T *recurrent_to_input_weights() const
+ {
+ return _recurrent_to_input_weights;
+ }
+
+ const T *cell_to_input_weights() const
+ {
+ return _cell_to_input_weights;
+ }
+
+ const T *input_gate_bias() const
+ {
+ return _input_gate_bias;
+ }
+
+ const T *cell_to_forget_weights() const
+ {
+ return _cell_to_forget_weights;
+ }
+
+ const T *cell_to_output_weights() const
+ {
+ return _cell_to_output_weights;
+ }
+
+ const T *projection_weights() const
+ {
+ return _projection_weights;
+ }
+
+ const T *projection_bias() const
+ {
+ return _projection_bias;
+ }
+
+ bool has_peephole_opt() const
+ {
+ return _has_peephole_opt;
+ }
+
+ bool has_projection() const
+ {
+ return _has_projection;
+ }
+
+ bool has_cifg_opt() const
+ {
+ return _has_cifg_opt;
+ }
+
+private:
+ const T *_input_to_input_weights;
+ const T *_recurrent_to_input_weights;
+ const T *_cell_to_input_weights;
+ const T *_input_gate_bias;
+ const T *_cell_to_forget_weights;
+ const T *_cell_to_output_weights;
+ const T *_projection_weights;
+ const T *_projection_bias;
+ bool _has_peephole_opt;
+ bool _has_projection;
+ bool _has_cifg_opt;
+};
+
+/** This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
+ *
+ */
+class CLLSTMLayer : public IFunction
+{
+public:
+ /** Default constructor */
+ CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ /** Initialize function's tensors.
+ *
+ * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
+ * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in, out] output_state 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
+ * @param[in, out] cell_state 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
+ * @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
+ * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
+ * Data types supported: Same as @p input.
+ * @param[in] lstm_params (Optional) Weights tensors used in peephole optimization:
+ * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
+ * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
+ * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
+ * @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled.
+ * @param[in] projection_threshold The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ */
+ void configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+ const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
+ const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *output_state, ICLTensor *cell_state, ICLTensor *scratch_buffer, ICLTensor *output,
+ const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
+
+ /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
+ *
+ * @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: F16/F32.
+ * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
+ * @param[in] output_state 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
+ * @param[in] cell_state 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
+ * @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
+ * @param[in] output Destination tensor info. Output is a 2D tensor with dimensions [output_size, batch_size].
+ * Data types supported: Same as @p input.
+ * @param[in] lstm_params (Optional) Weights tensors used in peephole optimization:
+ * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
+ * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
+ * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
+ * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
+ * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
+ * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
+ * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
+ * @param[in] cell_threshold The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled.
+ * @param[in] projection_threshold The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
+ const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
+ const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
+ const ITensorInfo *output_state, const ITensorInfo *cell_state, const ITensorInfo *scratch_buffer, const ITensorInfo *output,
+ const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ CLMemoryGroup _memory_group;
+ CLFullyConnectedLayer _fully_connected_input_gate;
+ CLGEMM _gemm_input_gate1;
+ CLGEMM _gemm_input_gate2;
+ CLTransposeKernel _transpose_input_gate1;
+ CLTransposeKernel _transpose_input_gate2;
+ CLArithmeticAdditionKernel _accum_input_gate1;
+ CLArithmeticAddition _accum_input_gate2;
+ CLArithmeticSubtractionKernel _subtract_input_gate;
+ CLActivationLayerKernel _activation_input_gate;
+ CLFullyConnectedLayer _fully_connected_forget_gate;
+ CLGEMM _gemm_forget_gate1;
+ CLGEMM _gemm_forget_gate2;
+ CLTransposeKernel _transpose_forget_gate1;
+ CLTransposeKernel _transpose_forget_gate2;
+ CLArithmeticAdditionKernel _accum_forget_gate1;
+ CLArithmeticAddition _accum_forget_gate2;
+ CLActivationLayerKernel _activation_forget_gate;
+ CLFullyConnectedLayer _fully_connected_cell_state;
+ CLGEMM _gemm_cell_state1;
+ CLGEMM _gemm_cell_state2;
+ CLTransposeKernel _transpose_cell_state1;
+ CLArithmeticAdditionKernel _accum_cell_state1;
+ CLArithmeticAdditionKernel _accum_cell_state2;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_state1;
+ CLActivationLayerKernel _activation_cell_state;
+ CLActivationLayerKernel _cell_clip;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_state2;
+ CLFullyConnectedLayer _fully_connected_output;
+ CLGEMM _gemm_output1;
+ CLGEMM _gemm_output2;
+ CLTransposeKernel _transpose_output1;
+ CLTransposeKernel _transpose_output2;
+ CLArithmeticAdditionKernel _accum_output1;
+ CLArithmeticAddition _accum_output2;
+ CLActivationLayerKernel _activation_output;
+ CLActivationLayerKernel _activation_output_state;
+ CLPixelWiseMultiplicationKernel _pixelwise_mul_output_state;
+ CLFullyConnectedLayer _fully_connected_output_state;
+ CLGEMM _gemm_output_state;
+ CLArithmeticAdditionKernel _accum_output_state;
+ CLActivationLayerKernel _projection_clip;
+ CLCopyKernel _copy_cell_state;
+ CLCopyKernel _copy_output;
+ CLWidthConcatenateLayer _concat_scratch_buffer;
+ CLTensor _input_gate_out1;
+ CLTensor _input_gate_out2;
+ CLTensor _input_gate_out3;
+ CLTensor _input_gate_out4;
+ CLTensor _input_gate_out5;
+ CLTensor _input_gate_out6;
+ CLTensor _forget_gate_out1;
+ CLTensor _forget_gate_out2;
+ CLTensor _forget_gate_out3;
+ CLTensor _forget_gate_out4;
+ CLTensor _forget_gate_out5;
+ CLTensor _forget_gate_out6;
+ CLTensor _cell_state_out1;
+ CLTensor _cell_state_out2;
+ CLTensor _cell_state_out3;
+ CLTensor _cell_state_out4;
+ CLTensor _cell_state_out5;
+ CLTensor _output1;
+ CLTensor _output2;
+ CLTensor _output3;
+ CLTensor _output4;
+ CLTensor _output5;
+ CLTensor _output6;
+ CLTensor _cell_state_activation;
+ CLTensor _output_projection1;
+ CLTensor _ones;
+ bool _run_peephole_opt;
+ bool _run_cifg_opt;
+ bool _perform_cell_clipping;
+ bool _has_projection_weights;
+ bool _perform_projection_clipping;
+};
+}
+#endif /* __ARM_COMPUTE_CLLSTMLAYER_H__ */
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
new file mode 100644
index 0000000..930d311
--- /dev/null
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -0,0 +1,508 @@
+/*
+ * 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.
+ */
+#include "arm_compute/runtime/CL/functions/CLLSTMLayer.h"
+
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <cmath>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+CLLSTMLayer::CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _gemm_input_gate1(), _gemm_input_gate2(), _transpose_input_gate1(), _transpose_input_gate2(), _accum_input_gate1(),
+ _accum_input_gate2(), _subtract_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate1(), _gemm_forget_gate2(), _transpose_forget_gate1(),
+ _transpose_forget_gate2(), _accum_forget_gate1(), _accum_forget_gate2(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _transpose_cell_state1(),
+ _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), _gemm_output1(),
+ _gemm_output2(), _transpose_output1(), _transpose_output2(), _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state(),
+ _fully_connected_output_state(), _gemm_output_state(), _accum_output_state(), _projection_clip(), _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _input_gate_out1(), _input_gate_out2(),
+ _input_gate_out3(), _input_gate_out4(), _input_gate_out5(), _input_gate_out6(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(),
+ _forget_gate_out6(), _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _output5(), _output6(),
+ _cell_state_activation(), _output_projection1(), _ones(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false),
+ _perform_projection_clipping(false)
+{
+}
+
+void CLLSTMLayer::configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+ const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
+ const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
+ ICLTensor *output_state, ICLTensor *cell_state, ICLTensor *scratch_buffer, ICLTensor *output, const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info,
+ float cell_threshold, float projection_threshold)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+ forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ LSTMParams<ITensorInfo> lstm_params_info;
+ if(lstm_params.has_peephole_opt())
+ {
+ lstm_params_info.set_peephole_params(lstm_params.cell_to_input_weights()->info(), lstm_params.cell_to_forget_weights()->info(), lstm_params.cell_to_output_weights()->info());
+ }
+ if(lstm_params.has_projection())
+ {
+ lstm_params_info.set_projection_params(lstm_params.projection_weights()->info(), lstm_params.projection_bias()->info());
+ }
+ if(!lstm_params.has_cifg_opt())
+ {
+ lstm_params_info.set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(),
+ lstm_params.cell_to_input_weights()->info(), lstm_params.input_gate_bias()->info());
+ }
+ ARM_COMPUTE_ERROR_THROW_ON(CLLSTMLayer::validate(input->info(), input_to_forget_weights->info(),
+ input_to_cell_weights->info(), input_to_output_weights->info(),
+ recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
+ forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
+ output_state->info(), cell_state->info(), scratch_buffer->info(), output->info(), lstm_params_info,
+ activation_info, cell_threshold, projection_threshold));
+
+ const TensorShape cell_state_shape = cell_state->info()->tensor_shape();
+
+ TensorShape forget_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
+ TensorShape forget_gate2_shape = compute_transposed_shape(*forget_gate_bias->info());
+ TensorShape forget_gate3_shape{ 1, output_state->info()->dimension(1) };
+ _forget_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _forget_gate_out2.allocator()->init(TensorInfo(forget_gate1_shape, 1, input->info()->data_type()));
+ _forget_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _forget_gate_out6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
+ // Configure block that calculates the forget gate
+ // forget_gate = Activation(input * input_to_forget_weights + output_state * recurrent_to_forget_weights + cell_state * cell_to_forget_weights + forget_gate_bias)
+ _memory_group.manage(&_forget_gate_out1);
+ _fully_connected_forget_gate.configure(input, input_to_forget_weights, forget_gate_bias, &_forget_gate_out1, true, false);
+ _memory_group.manage(&_forget_gate_out2);
+ _transpose_forget_gate1.configure(recurrent_to_forget_weights, &_forget_gate_out2);
+ _memory_group.manage(&_forget_gate_out3);
+ _gemm_forget_gate1.configure(output_state, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f);
+ _forget_gate_out2.allocator()->allocate();
+ _memory_group.manage(&_forget_gate_out6);
+ _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out6, ConvertPolicy::SATURATE);
+ CLTensor *forget_gate_out = &_forget_gate_out6;
+
+ if(lstm_params.has_peephole_opt())
+ {
+ _forget_gate_out4.allocator()->init(TensorInfo(forget_gate2_shape, 1, input->info()->data_type()));
+ _forget_gate_out5.allocator()->init(TensorInfo(forget_gate3_shape, 1, input->info()->data_type()));
+
+ _run_peephole_opt = true;
+ _memory_group.manage(&_forget_gate_out4);
+ _transpose_forget_gate2.configure(lstm_params.cell_to_forget_weights(), &_forget_gate_out4);
+ _memory_group.manage(&_forget_gate_out5);
+ _gemm_forget_gate2.configure(cell_state, &_forget_gate_out4, nullptr, &_forget_gate_out5, 1.f, 0.f);
+ _forget_gate_out4.allocator()->allocate();
+ _accum_forget_gate2.configure(&_forget_gate_out6, &_forget_gate_out5, &_forget_gate_out3, ConvertPolicy::SATURATE);
+ _forget_gate_out5.allocator()->allocate();
+ _forget_gate_out6.allocator()->allocate();
+ forget_gate_out = &_forget_gate_out3;
+ }
+ else
+ {
+ _forget_gate_out3.allocator()->allocate();
+ }
+ _activation_forget_gate.configure(forget_gate_out, &_forget_gate_out1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ forget_gate_out->allocator()->allocate();
+
+ TensorShape input_gate3_shape{ 1, output_state->info()->dimension(1) };
+ _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_gate_out5.allocator()->init(TensorInfo(input_gate3_shape, 1, input->info()->data_type()));
+
+ // Configure block that calculates the input gate
+ // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + cell_state * cell_to_input_weights + input_gate_bias), without CIFG
+ // input_gate = 1 - forget_gate, with CIFG
+ if(lstm_params.has_cifg_opt())
+ {
+ _memory_group.manage(&_input_gate_out1);
+ _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _subtract_input_gate.configure(&_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _ones.allocator()->allocate();
+ _run_cifg_opt = true;
+ }
+ else
+ {
+ TensorShape input_gate1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
+ TensorShape input_gate2_shape = compute_transposed_shape(*lstm_params.cell_to_input_weights()->info());
+
+ _input_gate_out2.allocator()->init(TensorInfo(input_gate1_shape, 1, input->info()->data_type()));
+ _input_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _input_gate_out4.allocator()->init(TensorInfo(input_gate2_shape, 1, input->info()->data_type()));
+ _input_gate_out6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
+ _memory_group.manage(&_input_gate_out1);
+ _fully_connected_input_gate.configure(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), &_input_gate_out1, true, false);
+ _memory_group.manage(&_input_gate_out2);
+ _transpose_input_gate1.configure(lstm_params.recurrent_to_input_weights(), &_input_gate_out2);
+ _memory_group.manage(&_input_gate_out3);
+ _gemm_input_gate1.configure(output_state, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f);
+ _input_gate_out2.allocator()->allocate();
+ _memory_group.manage(&_input_gate_out4);
+ _transpose_input_gate2.configure(lstm_params.cell_to_input_weights(), &_input_gate_out4);
+ _memory_group.manage(&_input_gate_out5);
+ _gemm_input_gate2.configure(cell_state, &_input_gate_out4, nullptr, &_input_gate_out5, 1.f, 0.f);
+ _input_gate_out4.allocator()->allocate();
+ _memory_group.manage(&_input_gate_out6);
+ _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out6, ConvertPolicy::SATURATE);
+ _input_gate_out3.allocator()->allocate();
+ _accum_input_gate2.configure(&_input_gate_out6, &_input_gate_out5, &_input_gate_out1, ConvertPolicy::SATURATE);
+ _input_gate_out5.allocator()->allocate();
+ _input_gate_out6.allocator()->allocate();
+ _activation_input_gate.configure(&_input_gate_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ }
+
+ TensorShape cell_state1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
+ _cell_state_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _cell_state_out2.allocator()->init(TensorInfo(cell_state1_shape, 1, input->info()->data_type()));
+ _cell_state_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _cell_state_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
+ // Configure block that calculates the cell state
+ // cell_state = Clip((RixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)
+ _memory_group.manage(&_cell_state_out1);
+ _fully_connected_cell_state.configure(input, input_to_cell_weights, cell_bias, &_cell_state_out1, true, false);
+ _memory_group.manage(&_cell_state_out2);
+ _transpose_cell_state1.configure(recurrent_to_cell_weights, &_cell_state_out2);
+ _memory_group.manage(&_cell_state_out3);
+ _gemm_cell_state1.configure(output_state, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f);
+ _cell_state_out2.allocator()->allocate();
+ _memory_group.manage(&_cell_state_out4);
+ _accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
+ _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info);
+ _memory_group.manage(&_cell_state_out5);
+ _pixelwise_mul_cell_state1.configure(&_cell_state_out4, &_input_gate_out1, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _input_gate_out1.allocator()->allocate();
+ _cell_state_out4.allocator()->allocate();
+ _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _forget_gate_out1.allocator()->allocate();
+ _accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
+ _cell_state_out3.allocator()->allocate();
+ _cell_state_out5.allocator()->allocate();
+
+ // Perform clipping
+ if(cell_threshold != 0.f)
+ {
+ _perform_cell_clipping = true;
+ _cell_clip.configure(&_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
+ }
+
+ TensorShape output1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
+ TensorShape output2_shape = compute_transposed_shape(*cell_bias->info());
+ TensorShape output3_shape{ 1, output_state->info()->dimension(1) };
+ _output1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output2.allocator()->init(TensorInfo(output1_shape, 1, input->info()->data_type()));
+ _output3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _output6.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
+ // Configure block that calculates the output
+ // output_gate = Activation(input * input_to_output_weights + output_state * recurrent_to_output_weights + cell_state * cell_to_output_weights + output_gate_bias)
+ _memory_group.manage(&_output1);
+ _fully_connected_output.configure(input, input_to_output_weights, output_gate_bias, &_output1, true, false);
+ _memory_group.manage(&_output2);
+ _transpose_output1.configure(recurrent_to_output_weights, &_output2);
+ _memory_group.manage(&_output3);
+ _gemm_output1.configure(output_state, &_output2, nullptr, &_output3, 1.f, 0.f);
+ _output2.allocator()->allocate();
+ _memory_group.manage(&_output6);
+ _accum_output1.configure(&_output1, &_output3, &_output6, ConvertPolicy::SATURATE);
+ _output3.allocator()->allocate();
+ CLTensor *output_gate_out = &_output6;
+ if(lstm_params.has_peephole_opt())
+ {
+ _output4.allocator()->init(TensorInfo(output2_shape, 1, input->info()->data_type()));
+ _output5.allocator()->init(TensorInfo(output3_shape, 1, input->info()->data_type()));
+
+ _memory_group.manage(&_output4);
+ _transpose_output2.configure(lstm_params.cell_to_output_weights(), &_output4);
+ _memory_group.manage(&_output5);
+ _gemm_output2.configure(&_cell_state_out1, &_output4, nullptr, &_output5, 1.f, 0.f);
+ _accum_output2.configure(&_output6, &_output5, &_output1, ConvertPolicy::SATURATE);
+ _output6.allocator()->allocate();
+ output_gate_out = &_output1;
+
+ // Allocate intermediate buffers
+ _output4.allocator()->allocate();
+ _output5.allocator()->allocate();
+ }
+ else
+ {
+ _output1.allocator()->allocate();
+ }
+ _activation_output.configure(output_gate_out, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ output_gate_out->allocator()->allocate();
+
+ _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+
+ // Configure block that calculates the output state
+ /** lstm_res = PixelwiseMul(output, Activation(cell_state))
+ *
+ * -- Clip(lstm_res * projection_weights + projection_bias, projection_threshold) , if there is a projection
+ * /
+ * output_state = --
+ * \
+ * -- lstm_res , otherwise
+ */
+ _memory_group.manage(&_cell_state_activation);
+ _activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info);
+ _pixelwise_mul_output_state.configure(&_cell_state_activation, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+ _cell_state_activation.allocator()->allocate();
+
+ if(lstm_params.has_projection())
+ {
+ _has_projection_weights = true;
+ _output_projection1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_output_projection1);
+ _fully_connected_output_state.configure(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), &_output_projection1, true, false);
+ // Perform clipping
+ if(projection_threshold != 0.f)
+ {
+ _perform_projection_clipping = true;
+ _projection_clip.configure(&_output_projection1, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
+ }
+
+ // Allocate intermediate buffer
+ _output_projection1.allocator()->allocate();
+ }
+
+ // Copy cell state and output
+ _copy_cell_state.configure(&_cell_state_out1, cell_state);
+ _cell_state_out1.allocator()->allocate();
+ _copy_output.configure(output_state, output);
+
+ // Vector for holding the tensors to store in scratch buffer
+ std::vector<ICLTensor *> scratch_inputs;
+ if(lstm_params.has_cifg_opt())
+ {
+ scratch_inputs.emplace_back(&_input_gate_out1);
+ }
+ scratch_inputs.emplace_back(&_cell_state_out1);
+ scratch_inputs.emplace_back(forget_gate_out);
+ scratch_inputs.emplace_back(output_gate_out);
+ _concat_scratch_buffer.configure(scratch_inputs, scratch_buffer);
+}
+
+Status CLLSTMLayer::validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
+ const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
+ const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
+ const ITensorInfo *output_state, const ITensorInfo *cell_state, const ITensorInfo *scratch_buffer, const ITensorInfo *output,
+ const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+ forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
+ recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, output_state, cell_state);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_to_forget_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_to_cell_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_forget_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_cell_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_gate_bias->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_state->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_state->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0) && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0));
+
+ if(lstm_params.has_peephole_opt())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights(), lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_output_weights()->num_dimensions() != 1);
+ }
+
+ TensorShape units_out_transposed_shape = compute_transposed_shape(*recurrent_to_output_weights);
+ TensorShape gemmv_shape{ 1, output_state->dimension(1) };
+ TensorShape num_units_transposed_shape = compute_transposed_shape(*forget_gate_bias);
+ const TensorInfo units_out_transposed_info = TensorInfo(units_out_transposed_shape, 1, input->data_type());
+ const TensorInfo gemmv_shape_info = TensorInfo(gemmv_shape, 1, input->data_type());
+ const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type());
+
+ // Validate forget gate
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, cell_state, true, false));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state, &units_out_transposed_info, nullptr, cell_state, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ if(lstm_params.has_peephole_opt())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE));
+ }
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+
+ // Validate input gate
+ if(!lstm_params.has_cifg_opt())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.cell_to_input_weights(), lstm_params.input_gate_bias());
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_to_input_weights()->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.recurrent_to_input_weights()->num_dimensions() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), lstm_params.input_gate_bias(), cell_state, true, false));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(cell_state, &num_units_transposed_info, nullptr, &gemmv_shape_info, 1.f, 0.f, GEMMInfo()));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, &gemmv_shape_info, cell_state, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ }
+
+ // Validate cell state
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, cell_bias, cell_state, true, false));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, activation_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, cell_state, cell_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
+
+ if(cell_threshold != 0.f)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)));
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, output_gate_bias, cell_state, true, false));
+ if(lstm_params.has_peephole_opt())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(cell_state, cell_state, cell_state, ConvertPolicy::SATURATE));
+ }
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+
+ // Validate output state
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, cell_state, activation_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state, output, output_state, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
+ if(lstm_params.has_projection())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(output_state, lstm_params.projection_weights(), lstm_params.projection_bias(), cell_state, true, false));
+ if(projection_threshold != 0.f)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(cell_state, output_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold,
+ projection_threshold)));
+ }
+ }
+
+ std::vector<TensorInfo> inputs_vector_info;
+ if(lstm_params.has_cifg_opt())
+ {
+ inputs_vector_info.emplace_back(*cell_state);
+ }
+ inputs_vector_info.emplace_back(*cell_state);
+ inputs_vector_info.emplace_back(*cell_state);
+ inputs_vector_info.emplace_back(*cell_state);
+
+ std::vector<ITensorInfo *> inputs_vector_info_raw;
+ for(auto &input : inputs_vector_info)
+ {
+ inputs_vector_info_raw.emplace_back(&input);
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer));
+ return Status{};
+}
+
+void CLLSTMLayer::run()
+{
+ _memory_group.acquire();
+
+ _fully_connected_forget_gate.run();
+ CLScheduler::get().enqueue(_transpose_forget_gate1);
+ _gemm_forget_gate1.run();
+ CLScheduler::get().enqueue(_accum_forget_gate1);
+
+ if(_run_peephole_opt)
+ {
+ CLScheduler::get().enqueue(_transpose_forget_gate2);
+ _gemm_forget_gate2.run();
+ _accum_forget_gate2.run();
+ }
+ CLScheduler::get().enqueue(_activation_forget_gate);
+
+ if(_run_cifg_opt)
+ {
+ _ones.map(true);
+ std::fill_n(_ones.buffer(), _ones.info()->total_size(), 1);
+ _ones.unmap();
+ CLScheduler::get().enqueue(_subtract_input_gate);
+ }
+ else
+ {
+ _fully_connected_input_gate.run();
+ CLScheduler::get().enqueue(_transpose_input_gate1);
+ _gemm_input_gate1.run();
+ CLScheduler::get().enqueue(_transpose_input_gate2);
+ _gemm_input_gate2.run();
+ CLScheduler::get().enqueue(_accum_input_gate1);
+ _accum_input_gate2.run();
+ CLScheduler::get().enqueue(_activation_input_gate);
+ }
+
+ _fully_connected_cell_state.run();
+ CLScheduler::get().enqueue(_transpose_cell_state1);
+ _gemm_cell_state1.run();
+ CLScheduler::get().enqueue(_accum_cell_state1);
+ CLScheduler::get().enqueue(_activation_cell_state);
+ CLScheduler::get().enqueue(_pixelwise_mul_cell_state1);
+ CLScheduler::get().enqueue(_pixelwise_mul_cell_state2);
+ CLScheduler::get().enqueue(_accum_cell_state2);
+
+ if(_perform_cell_clipping)
+ {
+ CLScheduler::get().enqueue(_cell_clip);
+ }
+
+ _fully_connected_output.run();
+ CLScheduler::get().enqueue(_transpose_output1);
+ _gemm_output1.run();
+ CLScheduler::get().enqueue(_accum_output1);
+ CLScheduler::get().enqueue(_pixelwise_mul_output_state);
+
+ if(_run_peephole_opt)
+ {
+ CLScheduler::get().enqueue(_transpose_output2);
+ _gemm_output2.run();
+ _accum_output2.run();
+ }
+ CLScheduler::get().enqueue(_activation_output);
+
+ CLScheduler::get().enqueue(_activation_output_state);
+ CLScheduler::get().enqueue(_pixelwise_mul_output_state);
+
+ if(_has_projection_weights)
+ {
+ _fully_connected_output_state.run();
+ if(_perform_projection_clipping)
+ {
+ CLScheduler::get().enqueue(_projection_clip);
+ }
+ }
+
+ CLScheduler::get().enqueue(_copy_cell_state);
+ CLScheduler::get().enqueue(_copy_output);
+
+ _concat_scratch_buffer.run();
+
+ _memory_group.release();
+}
\ No newline at end of file
diff --git a/tests/datasets/LSTMLayerDataset.h b/tests/datasets/LSTMLayerDataset.h
new file mode 100644
index 0000000..51802fd
--- /dev/null
+++ b/tests/datasets/LSTMLayerDataset.h
@@ -0,0 +1,171 @@
+/*
+ * 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_DATASET
+#define ARM_COMPUTE_TEST_LSTM_LAYER_DATASET
+
+#include "utils/TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+class LSTMLayerDataset
+{
+public:
+ using type = std::tuple<TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, ActivationLayerInfo, float, float>;
+
+ struct iterator
+ {
+ iterator(std::vector<TensorShape>::const_iterator src_it,
+ std::vector<TensorShape>::const_iterator input_weights_it,
+ std::vector<TensorShape>::const_iterator recurrent_weights_it,
+ std::vector<TensorShape>::const_iterator cells_bias_it,
+ std::vector<TensorShape>::const_iterator output_cell_it,
+ std::vector<TensorShape>::const_iterator dst_it,
+ std::vector<TensorShape>::const_iterator scratch_it,
+ std::vector<ActivationLayerInfo>::const_iterator infos_it,
+ std::vector<float>::const_iterator cell_threshold_it,
+ std::vector<float>::const_iterator projection_threshold_it)
+ : _src_it{ std::move(src_it) },
+ _input_weights_it{ std::move(input_weights_it) },
+ _recurrent_weights_it{ std::move(recurrent_weights_it) },
+ _cells_bias_it{ std::move(cells_bias_it) },
+ _output_cell_it{ std::move(output_cell_it) },
+ _dst_it{ std::move(dst_it) },
+ _scratch_it{ std::move(scratch_it) },
+ _infos_it{ std::move(infos_it) },
+ _cell_threshold_it{ std::move(cell_threshold_it) },
+ _projection_threshold_it{ std::move(projection_threshold_it) }
+ {
+ }
+
+ std::string description() const
+ {
+ std::stringstream description;
+ description << "In=" << *_src_it << ":";
+ description << "InputWeights=" << *_input_weights_it << ":";
+ description << "RecurrentWeights=" << *_recurrent_weights_it << ":";
+ description << "Biases=" << *_cells_bias_it << ":";
+ description << "Scratch=" << *_scratch_it << ":";
+ description << "Out=" << *_dst_it;
+ return description.str();
+ }
+
+ LSTMLayerDataset::type operator*() const
+ {
+ return std::make_tuple(*_src_it, *_input_weights_it, *_recurrent_weights_it, *_cells_bias_it, *_output_cell_it, *_dst_it, *_scratch_it, *_infos_it, *_cell_threshold_it, *_projection_threshold_it);
+ }
+
+ iterator &operator++()
+ {
+ ++_src_it;
+ ++_input_weights_it;
+ ++_recurrent_weights_it;
+ ++_cells_bias_it;
+ ++_output_cell_it;
+ ++_dst_it;
+ ++_scratch_it;
+ ++_infos_it;
+ ++_cell_threshold_it;
+ ++_projection_threshold_it;
+
+ return *this;
+ }
+
+ private:
+ std::vector<TensorShape>::const_iterator _src_it;
+ std::vector<TensorShape>::const_iterator _input_weights_it;
+ std::vector<TensorShape>::const_iterator _recurrent_weights_it;
+ std::vector<TensorShape>::const_iterator _cells_bias_it;
+ std::vector<TensorShape>::const_iterator _output_cell_it;
+ std::vector<TensorShape>::const_iterator _dst_it;
+ std::vector<TensorShape>::const_iterator _scratch_it;
+ std::vector<ActivationLayerInfo>::const_iterator _infos_it;
+ std::vector<float>::const_iterator _cell_threshold_it;
+ std::vector<float>::const_iterator _projection_threshold_it;
+ };
+
+ iterator begin() const
+ {
+ return iterator(_src_shapes.begin(), _input_weights_shapes.begin(), _recurrent_weights_shapes.begin(), _cell_bias_shapes.begin(), _output_cell_shapes.begin(), _dst_shapes.begin(),
+ _scratch_shapes.begin(), _infos.begin(), _cell_threshold.begin(), _projection_threshold.begin());
+ }
+
+ int size() const
+ {
+ return std::min(_src_shapes.size(), std::min(_input_weights_shapes.size(), std::min(_recurrent_weights_shapes.size(), std::min(_cell_bias_shapes.size(), std::min(_output_cell_shapes.size(),
+ std::min(_dst_shapes.size(), std::min(_scratch_shapes.size(), std::min(_cell_threshold.size(), std::min(_projection_threshold.size(), _infos.size())))))))));
+ }
+
+ void add_config(TensorShape src, TensorShape input_weights, TensorShape recurrent_weights, TensorShape cell_bias_weights, TensorShape output_cell_state, TensorShape dst, TensorShape scratch,
+ ActivationLayerInfo info, float cell_threshold, float projection_threshold)
+ {
+ _src_shapes.emplace_back(std::move(src));
+ _input_weights_shapes.emplace_back(std::move(input_weights));
+ _recurrent_weights_shapes.emplace_back(std::move(recurrent_weights));
+ _cell_bias_shapes.emplace_back(std::move(cell_bias_weights));
+ _output_cell_shapes.emplace_back(std::move(output_cell_state));
+ _dst_shapes.emplace_back(std::move(dst));
+ _scratch_shapes.emplace_back(std::move(scratch));
+ _infos.emplace_back(std::move(info));
+ _cell_threshold.emplace_back(std::move(cell_threshold));
+ _projection_threshold.emplace_back(std::move(projection_threshold));
+ }
+
+protected:
+ LSTMLayerDataset() = default;
+ LSTMLayerDataset(LSTMLayerDataset &&) = default;
+
+private:
+ std::vector<TensorShape> _src_shapes{};
+ std::vector<TensorShape> _input_weights_shapes{};
+ std::vector<TensorShape> _recurrent_weights_shapes{};
+ std::vector<TensorShape> _cell_bias_shapes{};
+ std::vector<TensorShape> _output_cell_shapes{};
+ std::vector<TensorShape> _dst_shapes{};
+ std::vector<TensorShape> _scratch_shapes{};
+ std::vector<ActivationLayerInfo> _infos{};
+ std::vector<float> _cell_threshold{};
+ std::vector<float> _projection_threshold{};
+};
+
+class SmallLSTMLayerDataset final : public LSTMLayerDataset
+{
+public:
+ SmallLSTMLayerDataset()
+ {
+ add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), TensorShape(16U, 2U), TensorShape(64U, 2U), ActivationLayerInfo(), 0.05f, 0.93f);
+ add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), TensorShape(16U, 2U), TensorShape(48U, 2U), ActivationLayerInfo(), 0.05f, 0.93f);
+ }
+};
+
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_LSTM_LAYER_DATASET */
diff --git a/tests/validation/CL/LSTMLayer.cpp b/tests/validation/CL/LSTMLayer.cpp
new file mode 100644
index 0000000..bd43678
--- /dev/null
+++ b/tests/validation/CL/LSTMLayer.cpp
@@ -0,0 +1,177 @@
+/*
+ * 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.
+ */
+#include "arm_compute/runtime/CL/functions/CLLSTMLayer.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/LSTMLayerDataset.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/LSTMLayerFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f);
+RelativeTolerance<half> tolerance_f16(half(0.1));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(LSTMLayer)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(zip(
+ framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 2U), 1, DataType::U8, 0), // Wrong data type
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong input weights size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong recurrent weights size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong cell bias size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong cell state size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong output size
+ TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0), // Wrong scratch size
+ }),
+ framework::dataset::make("InputWeightsInfo", { TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("CellBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ProjectionBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("CellStateInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ScratchInfo", { TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+ TensorInfo(TensorShape(12U, 2U), 1, DataType::F32, 0),
+ })),
+ framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ })),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false })),
+ input_info, input_weights_info, recurrent_weights_info, cell_bias_info, projection_bias_info, cell_state_info, output_info, scratch_info, info, expected)
+{
+ LSTMParams<ITensorInfo> lstm_params_info;
+ lstm_params_info.set_peephole_params(&cell_bias_info, &cell_bias_info, &cell_bias_info)
+ .set_projection_params(&recurrent_weights_info, &projection_bias_info)
+ .set_cifg_params(&input_weights_info, &recurrent_weights_info, &cell_bias_info, &cell_bias_info);
+
+ ARM_COMPUTE_EXPECT(bool(CLLSTMLayer::validate(&input_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false),
+ &input_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false),
+ &recurrent_weights_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false),
+ &cell_bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &cell_state_info.clone()->set_is_resizable(false),
+ &scratch_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), lstm_params_info, info, 0.05, 0.9)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLLSTMLayerFixture = LSTMLayerValidationFixture<CLTensor, CLAccessor, CLLSTMLayer, LSTMParams<ICLTensor>, T>;
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("ProjectionOpt", { true, false })),
+ framework::dataset::make("PeepholeOpt", { true, false })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("ProjectionOpt", { true, false })),
+ framework::dataset::make("PeepholeOpt", { true, false })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // LSTMLayer
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
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 */