Gitiles
Code Review Sign In
review.mlplatform.org / ml / ComputeLibrary / ea7de7babc319e2fa31c5e1c986e48d6c5370689 / . / arm_compute / runtime / CL / functions / CLLSTMLayer.h
blob: 017f26aa1e143f73f51846365a18aa09533c99be [file] [log] [blame]
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]1/*
Michele Di Giorgiod9eaf612020-07-08 11:12:57 +0100[diff] [blame]2 * Copyright (c) 2018-2020 Arm Limited.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
Michalis Spyrouf4643372019-11-29 16:17:13 +0000[diff] [blame]24#ifndef ARM_COMPUTE_CLLSTMLAYER_H
25#define ARM_COMPUTE_CLLSTMLAYER_H
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]26
27#include "arm_compute/runtime/IFunction.h"
28
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]29#include "arm_compute/core/Types.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]30#include "arm_compute/runtime/CL/CLTensor.h"
Georgios Pinitasab23dd02020-07-06 14:57:36 +0100[diff] [blame]31#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
Georgios Pinitas09f24972019-05-17 18:14:40 +0100[diff] [blame]32#include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
giuros01164a2722018-11-20 18:34:46 +0000[diff] [blame]33#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]34#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
35#include "arm_compute/runtime/CL/functions/CLGEMM.h"
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]36#include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h"
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]37#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]38#include "arm_compute/runtime/IMemoryManager.h"
Georgios Pinitas26014cf2019-09-09 19:00:57 +0100[diff] [blame]39#include "arm_compute/runtime/MemoryGroup.h"
Michalis Spyrou25f45a42018-08-08 12:53:05 +0100[diff] [blame]40#include "arm_compute/runtime/common/LSTMParams.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]41
42#include <memory>
43
44namespace arm_compute
45{
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]46class CLCompileContext;
47class CLCopyKernel;
48class CLMemsetKernel;
49class CLTransposeKernel;
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]50class ICLTensor;
51
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]52/** This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
53 *
54 */
55class CLLSTMLayer : public IFunction
56{
57public:
58 /** Default constructor */
59 CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]60 /** Prevent instances of this class from being copied */
61 CLLSTMLayer(const CLLSTMLayer &) = delete;
62 /** Prevent instances of this class from being copied */
63 CLLSTMLayer &operator=(const CLLSTMLayer &) = delete;
64 /** Prevent instances of this class to be moved */
65 CLLSTMLayer(CLLSTMLayer &&) = delete;
66 /** Prevent instances of this class to be moved */
67 CLLSTMLayer &operator=(CLLSTMLayer &&) = delete;
68 /** Default destructor */
69 ~CLLSTMLayer();
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]70 /** Initialize function's tensors.
71 *
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]72 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
73 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
74 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
75 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
76 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
77 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
78 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
79 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
80 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
81 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
82 * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
83 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
84 * @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.
85 * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
86 * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
87 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
88 * Data types supported: Same as @p input.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]89 * @param[in] lstm_params Weights tensors used in peephole optimization:
90 * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
91 * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
92 * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
93 * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
94 * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
95 * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
96 * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
97 * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
98 * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
99 * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
100 * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
101 * output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]102 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]103 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
104 * If set to 0.0f then clipping is disabled.
105 * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]106 * If set to 0.0f then clipping is disabled.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]107 */
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]108 void configure(const ICLTensor *input,
109 const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]110 const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]111 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]112 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]113 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]114 const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]115 /** Initialize function's tensors.
116 *
117 * @param[in] compile_context The compile context to be used.
118 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
119 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
120 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
121 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
122 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
123 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
124 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
125 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
126 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
127 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
128 * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
129 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
130 * @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.
131 * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
132 * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
133 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
134 * Data types supported: Same as @p input.
135 * @param[in] lstm_params Weights tensors used in peephole optimization:
136 * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
137 * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
138 * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
139 * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
140 * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
141 * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
142 * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
143 * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
144 * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
145 * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
146 * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
147 * output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
148 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
149 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
150 * If set to 0.0f then clipping is disabled.
151 * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
152 * If set to 0.0f then clipping is disabled.
153 */
154 void configure(const CLCompileContext &compile_context, const ICLTensor *input,
155 const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
156 const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
157 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]158 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]159 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
160 const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]161
162 /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
163 *
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]164 * @param[in] input Source tensor info. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
165 * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
166 * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
167 * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
168 * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
169 * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
170 * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
171 * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
172 * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
173 * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
174 * @param[in] output_state_in 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
175 * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
176 * @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF.
177 * Data type supported: Same as @p input.
178 * @param[in] output_state_out 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
179 * @param[in] cell_state_out 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
180 * @param[in] output Destination tensor info. Output is a 2D tensor with dimensions [output_size, batch_size]. Data types supported: Same as @p input.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]181 * @param[in] lstm_params Weights tensors info used in peephole optimization:
182 * input_to_input_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
183 * recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
184 * cell_to_input_weights 1D weights tensor info with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
185 * cell_to_forget_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
186 * cell_to_output_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
187 * input_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input
188 * projection_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
189 * projection_bias 1D weights tensor info with dimensions [output_size]. Data type supported: Same as @p input.
190 * input_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
191 * forget_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
192 * cell_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
193 * output_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]194 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]195 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
196 * If set to 0.0f then clipping is disabled.
197 * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]198 * If set to 0.0f then clipping is disabled.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]199 *
200 * @return a status
201 */
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]202 static Status validate(const ITensorInfo *input,
203 const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]204 const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
205 const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]206 const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in,
207 const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output,
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]208 const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
209
210 // Inherited methods overridden:
211 void run() override;
John Kesapidescafec8f2019-02-19 15:53:59 +0000[diff] [blame]212 void prepare() override;
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]213
214private:
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]215 MemoryGroup _memory_group;
216 CLFullyConnectedLayer _fully_connected_input_gate;
217 CLArithmeticAddition _accum_input_gate1;
218 CLArithmeticSubtraction _subtract_input_gate;
219 CLPixelWiseMultiplication _pixelwise_mul_input_gate;
220 CLActivationLayer _activation_input_gate;
221 CLFullyConnectedLayer _fully_connected_forget_gate;
222 CLArithmeticAddition _accum_forget_gate1;
223 CLPixelWiseMultiplication _pixelwise_mul_forget_gate;
224 CLActivationLayer _activation_forget_gate;
225 CLFullyConnectedLayer _fully_connected_cell_state;
226 CLGEMM _gemm_cell_state1;
227 std::unique_ptr<CLTransposeKernel> _transpose_cell_state;
228 CLArithmeticAddition _accum_cell_state1;
229 CLArithmeticAddition _accum_cell_state2;
230 CLPixelWiseMultiplication _pixelwise_mul_cell_state1;
231 CLActivationLayer _activation_cell_state;
232 CLActivationLayer _cell_clip;
233 CLPixelWiseMultiplication _pixelwise_mul_cell_state2;
234 CLFullyConnectedLayer _fully_connected_output;
235 CLPixelWiseMultiplication _pixelwise_mul_output_state1;
236 CLArithmeticAddition _accum_output1;
237 CLActivationLayer _activation_output;
238 CLActivationLayer _activation_output_state;
239 CLPixelWiseMultiplication _pixelwise_mul_output_state2;
240 CLFullyConnectedLayer _fully_connected_output_state;
241 CLActivationLayer _projection_clip;
242 std::unique_ptr<CLCopyKernel> _copy_cell_state;
243 std::unique_ptr<CLCopyKernel> _copy_output;
244 CLConcatenateLayer _concat_scratch_buffer;
245 CLConcatenateLayer _concat_inputs_forget_gate;
246 CLConcatenateLayer _concat_weights_forget_gate;
247 CLConcatenateLayer _concat_weights_input_gate;
248 CLConcatenateLayer _concat_weights_output;
249 std::unique_ptr<CLMemsetKernel> _ones_memset_kernel;
250 CLMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
251 CLPixelWiseMultiplication _pixelwise_mul_input_gate_coeff;
252 CLArithmeticAddition _accum_input_gate_bias;
253 CLMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
254 CLPixelWiseMultiplication _pixelwise_mul_forget_gate_coeff;
255 CLArithmeticAddition _accum_forget_gate_bias;
256 CLMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
257 CLPixelWiseMultiplication _pixelwise_mul_cell_gate_coeff;
258 CLArithmeticAddition _accum_cell_gate_bias;
259 CLMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
260 CLPixelWiseMultiplication _pixelwise_mul_output_gate_coeff;
261 CLArithmeticAddition _accum_output_gate_bias;
262 CLTensor _input_gate_out1;
263 CLTensor _input_gate_out2;
264 CLTensor _input_gate_out3;
265 CLTensor _input_gate_out4;
266 CLTensor _forget_gate_out1;
267 CLTensor _forget_gate_out2;
268 CLTensor _forget_gate_out3;
269 CLTensor _forget_gate_out4;
270 CLTensor _forget_gate_out5;
271 CLTensor _forget_gate_out6;
272 CLTensor _cell_state_out1;
273 CLTensor _cell_state_out2;
274 CLTensor _cell_state_out3;
275 CLTensor _cell_state_out4;
276 CLTensor _cell_state_out5;
277 CLTensor _output1;
278 CLTensor _output2;
279 CLTensor _output3;
280 CLTensor _output4;
281 CLTensor _cell_state_activation;
282 CLTensor _output_state1;
283 CLTensor _ones;
284 CLTensor _input_layer_norm_out1;
285 CLTensor _input_layer_norm_out2;
286 CLTensor _forget_layer_norm_out1;
287 CLTensor _forget_layer_norm_out2;
288 CLTensor _cell_layer_norm_out1;
289 CLTensor _cell_layer_norm_out2;
290 CLTensor _output_layer_norm_out1;
291 CLTensor _output_layer_norm_out2;
292 bool _run_peephole_opt;
293 bool _run_cifg_opt;
294 bool _perform_cell_clipping;
295 bool _has_projection_weights;
296 bool _perform_projection_clipping;
297 bool _is_prepared;
298 bool _is_layer_norm_lstm;
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]299};
John Kesapidescafec8f2019-02-19 15:53:59 +0000[diff] [blame]300} // namespace arm_compute
Michalis Spyrouf4643372019-11-29 16:17:13 +0000[diff] [blame]301#endif /* ARM_COMPUTE_CLLSTMLAYER_H */