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review.mlplatform.org / ml / ComputeLibrary / 205ba243309baaec4bccfc82229139978d1a354e / . / arm_compute / runtime / CL / functions / CLLSTMLayer.h
blob: d26b4c5595b23ef646aafdd12dba18d4ea11ea66 [file] [log] [blame]
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]1/*
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]2 * Copyright (c) 2018-2021 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"
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]33#include "arm_compute/runtime/CL/functions/CLCopy.h"
giuros01164a2722018-11-20 18:34:46 +0000[diff] [blame]34#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]35#include "arm_compute/runtime/CL/functions/CLFill.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]36#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
37#include "arm_compute/runtime/CL/functions/CLGEMM.h"
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]38#include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h"
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]39#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]40#include "arm_compute/runtime/IMemoryManager.h"
Georgios Pinitas26014cf2019-09-09 19:00:57 +0100[diff] [blame]41#include "arm_compute/runtime/MemoryGroup.h"
Michalis Spyrou25f45a42018-08-08 12:53:05 +0100[diff] [blame]42#include "arm_compute/runtime/common/LSTMParams.h"
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]43
44#include <memory>
45
46namespace arm_compute
47{
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]48class CLCompileContext;
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]49class ICLTensor;
Teresa Charlin27886092021-02-25 20:15:01 +0000[diff] [blame]50namespace opencl
51{
52namespace kernels
53{
54class ClTransposeKernel;
55}
56}
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]57
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]58/** This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
59 *
60 */
61class CLLSTMLayer : public IFunction
62{
63public:
64 /** Default constructor */
65 CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]66 /** Prevent instances of this class from being copied */
67 CLLSTMLayer(const CLLSTMLayer &) = delete;
68 /** Prevent instances of this class from being copied */
69 CLLSTMLayer &operator=(const CLLSTMLayer &) = delete;
70 /** Prevent instances of this class to be moved */
71 CLLSTMLayer(CLLSTMLayer &&) = delete;
72 /** Prevent instances of this class to be moved */
73 CLLSTMLayer &operator=(CLLSTMLayer &&) = delete;
74 /** Default destructor */
75 ~CLLSTMLayer();
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]76 /** Initialize function's tensors.
77 *
Teresa Charlin62687422021-04-28 10:58:49 +0100[diff] [blame]78 * Valid data layouts:
79 * - All
80 *
81 * Valid data type configurations:
82 * |src0 - src13 | dst0 - dst3 |
83 * |:------------|:------------|
84 * |F16 |F16 |
85 * |F32 |F32 |
86 *
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]87 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
88 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
89 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
90 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
91 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
92 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
93 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
94 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
95 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
96 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
97 * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
98 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
99 * @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.
100 * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
101 * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
102 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
103 * Data types supported: Same as @p input.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]104 * @param[in] lstm_params Weights tensors used in peephole optimization:
105 * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
106 * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
107 * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
108 * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
109 * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
110 * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
111 * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
112 * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
113 * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
114 * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
115 * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
116 * 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]117 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]118 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
119 * If set to 0.0f then clipping is disabled.
120 * @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]121 * If set to 0.0f then clipping is disabled.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]122 */
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]123 void configure(const ICLTensor *input,
124 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]125 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]126 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]127 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]128 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]129 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]130 /** Initialize function's tensors.
131 *
132 * @param[in] compile_context The compile context to be used.
133 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
134 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
135 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
136 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
137 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
138 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
139 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
140 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
141 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
142 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
143 * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
144 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
145 * @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.
146 * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
147 * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
148 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
149 * Data types supported: Same as @p input.
150 * @param[in] lstm_params Weights tensors used in peephole optimization:
151 * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
152 * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
153 * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
154 * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
155 * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
156 * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
157 * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
158 * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
159 * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
160 * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
161 * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
162 * output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
163 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
164 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
165 * If set to 0.0f then clipping is disabled.
166 * @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].
167 * If set to 0.0f then clipping is disabled.
168 */
169 void configure(const CLCompileContext &compile_context, const ICLTensor *input,
170 const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
171 const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
172 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]173 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]174 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
175 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]176
177 /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
178 *
Michele Di Giorgio39438b42019-06-04 12:41:45 +0100[diff] [blame]179 * @param[in] input Source tensor info. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
180 * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
181 * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
182 * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
183 * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
184 * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
185 * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
186 * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
187 * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
188 * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
189 * @param[in] output_state_in 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
190 * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
191 * @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF.
192 * Data type supported: Same as @p input.
193 * @param[in] output_state_out 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
194 * @param[in] cell_state_out 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
195 * @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]196 * @param[in] lstm_params Weights tensors info used in peephole optimization:
197 * input_to_input_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
198 * recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
199 * cell_to_input_weights 1D weights tensor info with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
200 * cell_to_forget_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
201 * cell_to_output_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
202 * input_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input
203 * projection_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
204 * projection_bias 1D weights tensor info with dimensions [output_size]. Data type supported: Same as @p input.
205 * input_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
206 * forget_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
207 * cell_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
208 * 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]209 * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
Michele Di Giorgio25d97752020-03-04 18:08:47 +0000[diff] [blame]210 * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
211 * If set to 0.0f then clipping is disabled.
212 * @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]213 * If set to 0.0f then clipping is disabled.
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]214 *
215 * @return a status
216 */
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]217 static Status validate(const ITensorInfo *input,
218 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]219 const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
220 const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
Georgios Pinitas8bc745d2018-07-18 19:51:24 +0100[diff] [blame]221 const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in,
222 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]223 const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
224
225 // Inherited methods overridden:
226 void run() override;
John Kesapidescafec8f2019-02-19 15:53:59 +0000[diff] [blame]227 void prepare() override;
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]228
229private:
Teresa Charlin27886092021-02-25 20:15:01 +0000[diff] [blame]230 MemoryGroup _memory_group;
231 CLFullyConnectedLayer _fully_connected_input_gate;
232 CLArithmeticAddition _accum_input_gate1;
233 CLArithmeticSubtraction _subtract_input_gate;
234 CLPixelWiseMultiplication _pixelwise_mul_input_gate;
235 CLActivationLayer _activation_input_gate;
236 CLFullyConnectedLayer _fully_connected_forget_gate;
237 CLArithmeticAddition _accum_forget_gate1;
238 CLPixelWiseMultiplication _pixelwise_mul_forget_gate;
239 CLActivationLayer _activation_forget_gate;
240 CLFullyConnectedLayer _fully_connected_cell_state;
241 CLGEMM _gemm_cell_state1;
242 std::unique_ptr<opencl::kernels::ClTransposeKernel> _transpose_cell_state;
243 CLArithmeticAddition _accum_cell_state1;
244 CLArithmeticAddition _accum_cell_state2;
245 CLPixelWiseMultiplication _pixelwise_mul_cell_state1;
246 CLActivationLayer _activation_cell_state;
247 CLActivationLayer _cell_clip;
248 CLPixelWiseMultiplication _pixelwise_mul_cell_state2;
249 CLFullyConnectedLayer _fully_connected_output;
250 CLPixelWiseMultiplication _pixelwise_mul_output_state1;
251 CLArithmeticAddition _accum_output1;
252 CLActivationLayer _activation_output;
253 CLActivationLayer _activation_output_state;
254 CLPixelWiseMultiplication _pixelwise_mul_output_state2;
255 CLFullyConnectedLayer _fully_connected_output_state;
256 CLActivationLayer _projection_clip;
257 CLCopy _copy_cell_state;
258 CLCopy _copy_output;
259 CLConcatenateLayer _concat_scratch_buffer;
260 CLConcatenateLayer _concat_inputs_forget_gate;
261 CLConcatenateLayer _concat_weights_forget_gate;
262 CLConcatenateLayer _concat_weights_input_gate;
263 CLConcatenateLayer _concat_weights_output;
264 CLFill _ones_fill;
265 CLMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
266 CLPixelWiseMultiplication _pixelwise_mul_input_gate_coeff;
267 CLArithmeticAddition _accum_input_gate_bias;
268 CLMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
269 CLPixelWiseMultiplication _pixelwise_mul_forget_gate_coeff;
270 CLArithmeticAddition _accum_forget_gate_bias;
271 CLMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
272 CLPixelWiseMultiplication _pixelwise_mul_cell_gate_coeff;
273 CLArithmeticAddition _accum_cell_gate_bias;
274 CLMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
275 CLPixelWiseMultiplication _pixelwise_mul_output_gate_coeff;
276 CLArithmeticAddition _accum_output_gate_bias;
277 CLTensor _input_gate_out1;
278 CLTensor _input_gate_out2;
279 CLTensor _input_gate_out3;
280 CLTensor _input_gate_out4;
281 CLTensor _forget_gate_out1;
282 CLTensor _forget_gate_out2;
283 CLTensor _forget_gate_out3;
284 CLTensor _forget_gate_out4;
285 CLTensor _forget_gate_out5;
286 CLTensor _forget_gate_out6;
287 CLTensor _cell_state_out1;
288 CLTensor _cell_state_out2;
289 CLTensor _cell_state_out3;
290 CLTensor _cell_state_out4;
291 CLTensor _cell_state_out5;
292 CLTensor _output1;
293 CLTensor _output2;
294 CLTensor _output3;
295 CLTensor _output4;
296 CLTensor _cell_state_activation;
297 CLTensor _output_state1;
298 CLTensor _ones;
299 CLTensor _input_layer_norm_out1;
300 CLTensor _input_layer_norm_out2;
301 CLTensor _forget_layer_norm_out1;
302 CLTensor _forget_layer_norm_out2;
303 CLTensor _cell_layer_norm_out1;
304 CLTensor _cell_layer_norm_out2;
305 CLTensor _output_layer_norm_out1;
306 CLTensor _output_layer_norm_out2;
307 bool _run_peephole_opt;
308 bool _run_cifg_opt;
309 bool _perform_cell_clipping;
310 bool _has_projection_weights;
311 bool _perform_projection_clipping;
312 bool _is_prepared;
313 bool _is_layer_norm_lstm;
314 const ICLTensor *_recurrent_to_cell_weights{ nullptr };
Michalis Spyroubcedf512018-03-22 14:55:08 +0000[diff] [blame]315};
John Kesapidescafec8f2019-02-19 15:53:59 +0000[diff] [blame]316} // namespace arm_compute
Michalis Spyrouf4643372019-11-29 16:17:13 +0000[diff] [blame]317#endif /* ARM_COMPUTE_CLLSTMLAYER_H */