Compute Library
 22.05
CLLSTMLayer.h
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24 #ifndef ARM_COMPUTE_CLLSTMLAYER_H
25 #define ARM_COMPUTE_CLLSTMLAYER_H
26 
27 #include "arm_compute/runtime/IFunction.h"
28 
29 #include "arm_compute/core/Types.h"
30 #include "arm_compute/runtime/CL/CLTensor.h"
31 #include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
32 #include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
33 #include "arm_compute/runtime/CL/functions/CLCopy.h"
34 #include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
35 #include "arm_compute/runtime/CL/functions/CLFill.h"
36 #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
37 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
38 #include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h"
39 #include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
40 #include "arm_compute/runtime/IMemoryManager.h"
41 #include "arm_compute/runtime/MemoryGroup.h"
42 #include "arm_compute/runtime/common/LSTMParams.h"
43 
44 #include <memory>
45 
46 namespace arm_compute
47 {
48 class CLCompileContext;
49 class ICLTensor;
50 namespace opencl
51 {
52 namespace kernels
53 {
54 class ClTransposeKernel;
55 }
56 }
57 
58 /** This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
59  *
60  */
61 class CLLSTMLayer : public IFunction
62 {
63 public:
64  /** Default constructor */
65  CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
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();
76  /** Initialize function's tensors.
77  *
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  *
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.
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.
117  * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
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].
121  * If set to 0.0f then clipping is disabled.
122  */
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,
125  const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
126  const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
127  const ICLTensor *output_state_in, ICLTensor *cell_state_in,
128  ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
129  const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
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,
173  const ICLTensor *output_state_in, ICLTensor *cell_state_in,
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);
176 
177  /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
178  *
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.
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.
209  * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
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].
213  * If set to 0.0f then clipping is disabled.
214  *
215  * @return a status
216  */
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,
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,
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,
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;
227  void prepare() override;
228 
229 private:
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 };
315 };
316 } // namespace arm_compute
317 #endif /* ARM_COMPUTE_CLLSTMLAYER_H */
arm_compute::opencl::kernels::ClTransposeKernel
OpenCL kernel to transpose a 2D tensor.
Definition: ClTransposeKernel.h:38
CLCopy.h
arm_compute::MemoryGroup
Memory group.
Definition: MemoryGroup.h:43
arm_compute::test::validation::output_gate_bias
auto output_gate_bias
Definition: LSTMLayerQuantized.cpp:483
arm_compute::CLFullyConnectedLayer
Basic function to compute a Fully Connected layer on OpenCL.
Definition: CLFullyConnectedLayer.h:43
arm_compute::IFunction
Base class for all functions.
Definition: IFunction.h:30
arm_compute::CLCopy
Basic function to run opencl::kernels::ClCopyKernel.
Definition: CLCopy.h:39
CLMeanStdDevNormalizationLayer.h
arm_compute::CLMeanStdDevNormalizationLayer
Basic function to execute mean and standard deviation normalization by calling CLMeanStdDevNormalizat...
Definition: CLMeanStdDevNormalizationLayer.h:37
arm_compute::test::validation::forget_gate_bias
auto forget_gate_bias
Definition: LSTMLayerQuantized.cpp:481
CLConcatenateLayer.h
CLGEMM.h
IMemoryManager.h
arm_compute::CLArithmeticAddition
Basic function to run opencl::kernels::ClSaturatedArithmeticKernel for addition.
Definition: CLElementwiseOperations.h:41
arm_compute::test::validation::recurrent_to_forget_weights
auto recurrent_to_forget_weights
Definition: LSTMLayerQuantized.cpp:477
arm_compute::ITensorInfo
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
arm_compute::CLActivationLayer
Basic function to run opencl::kernels::ClActivationKernel.
Definition: CLActivationLayer.h:42
arm_compute::Status
Status class.
Definition: Error.h:52
arm_compute::CLConcatenateLayer
Basic function to execute concatenate tensors along a given axis.
Definition: CLConcatenateLayer.h:50
arm_compute::ActivationLayerInfo
Activation Layer Information class.
Definition: Types.h:1625
arm_compute
Copyright (c) 2017-2022 Arm Limited.
Definition: introduction.dox:24
arm_compute::test::validation::input_to_cell_weights
auto input_to_cell_weights
Definition: LSTMLayerQuantized.cpp:474
arm_compute::test::validation::input
auto input
Definition: LSTMLayerQuantized.cpp:486
arm_compute::test::validation::recurrent_to_output_weights
auto recurrent_to_output_weights
Definition: LSTMLayerQuantized.cpp:479
arm_compute::test::validation::input_to_output_weights
auto input_to_output_weights
Definition: LSTMLayerQuantized.cpp:475
CLFill.h
arm_compute::CLGEMM
Basic function to execute GEMM on OpenCL.
Definition: CLGEMM.h:44
arm_compute::CLLSTMLayer
This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
Definition: CLLSTMLayer.h:61
LSTMParams.h
arm_compute::test::validation::recurrent_to_cell_weights
auto recurrent_to_cell_weights
Definition: LSTMLayerQuantized.cpp:478
arm_compute::CLPixelWiseMultiplication
Basic function to run opencl::ClMul.
Definition: CLPixelWiseMultiplication.h:38
MemoryGroup.h
arm_compute::CLCompileContext
CLCompileContext class.
Definition: CLCompileContext.h:204
arm_compute::test::validation::run
lstmq run()
CLElementwiseOperations.h
CLTensor.h
arm_compute::ICLTensor
Interface for OpenCL tensor.
Definition: ICLTensor.h:42
CLFullyConnectedLayer.h
IFunction.h
CLPixelWiseMultiplication.h
arm_compute::CLFill
Basic function to run opencl::kernels::ClFillKernel.
Definition: CLFill.h:38
arm_compute::test::validation::configure
im2col_func configure(src_target.info(), dst_target.info(), spatial_kernel, conv_info, has_bias)
arm_compute::CLArithmeticSubtraction
Basic function to run opencl::kernels::ClSaturatedArithmeticKernel for subtraction.
Definition: CLElementwiseOperations.h:151
Types.h
arm_compute::test::validation::input_to_forget_weights
auto input_to_forget_weights
Definition: LSTMLayerQuantized.cpp:473
CLActivationLayer.h
arm_compute::validate
Status validate(const ITensorInfo *scores_in, const ITensorInfo *boxes_in, const ITensorInfo *batch_splits_in, const ITensorInfo *scores_out, const ITensorInfo *boxes_out, const ITensorInfo *classes, const ITensorInfo *batch_splits_out, const ITensorInfo *keeps, const ITensorInfo *keeps_size, const BoxNMSLimitInfo info)
Definition: CPPBoxWithNonMaximaSuppressionLimit.cpp:214
arm_compute::LSTMParams
Definition: LSTMParams.h:36
arm_compute::CLTensor
Basic implementation of the OpenCL tensor interface.
Definition: CLTensor.h:41