1 /*
2 * Copyright (c) 2018-2021 Arm Limited.
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 */
24 #include "arm_compute/runtime/CL/functions/CLLSTMLayer.h"
25
26 #include "arm_compute/core/Utils.h"
27 #include "arm_compute/core/Validate.h"
28 #include "arm_compute/core/utils/misc/InfoHelpers.h"
29 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
30 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
31 #include "arm_compute/runtime/CL/CLScheduler.h"
32 #include "src/core/CL/kernels/CLFillBorderKernel.h"
33 #include "src/gpu/cl/kernels/ClTransposeKernel.h"
34
35 #include "src/common/utils/Log.h"
36
37 namespace arm_compute
38 {
39 using namespace arm_compute::misc::shape_calculator;
40 using namespace arm_compute::utils::info_helpers;
41
CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)42 CLLSTMLayer::CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
43 : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _accum_input_gate1(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(),
44 _fully_connected_forget_gate(), _accum_forget_gate1(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(),
45 _transpose_cell_state(std::make_unique<opencl::kernels::ClTransposeKernel>()), _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(),
46 _pixelwise_mul_cell_state2(), _fully_connected_output(), _pixelwise_mul_output_state1(), _accum_output1(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(),
47 _fully_connected_output_state(), _projection_clip(), _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(),
48 _concat_weights_input_gate(), _concat_weights_output(), _ones_fill(), _mean_std_norm_input_gate(), _pixelwise_mul_input_gate_coeff(), _accum_input_gate_bias(), _mean_std_norm_forget_gate(),
49 _pixelwise_mul_forget_gate_coeff(), _accum_forget_gate_bias(), _mean_std_norm_cell_gate(), _pixelwise_mul_cell_gate_coeff(), _accum_cell_gate_bias(), _mean_std_norm_output_gate(),
50 _pixelwise_mul_output_gate_coeff(), _accum_output_gate_bias(), _input_gate_out1(), _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(),
51 _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(),
52 _output2(), _output3(), _output4(), _cell_state_activation(), _output_state1(), _ones(), _input_layer_norm_out1(), _input_layer_norm_out2(), _forget_layer_norm_out1(), _forget_layer_norm_out2(),
53 _cell_layer_norm_out1(), _cell_layer_norm_out2(), _output_layer_norm_out1(), _output_layer_norm_out2(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false),
54 _has_projection_weights(false), _perform_projection_clipping(false), _is_prepared(false), _is_layer_norm_lstm(false)
55 {
56 }
57
58 CLLSTMLayer::~CLLSTMLayer() = default;
59
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,const ICLTensor * output_state_in,ICLTensor * cell_state_in,ICLTensor * scratch_buffer,ICLTensor * output_state_out,ICLTensor * cell_state_out,ICLTensor * output,const LSTMParams<ICLTensor> & lstm_params,const ActivationLayerInfo & activation_info,float cell_threshold,float projection_threshold)60 void CLLSTMLayer::configure(const ICLTensor *input,
61 const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
62 const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
63 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
64 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
65 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
66 const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
67 {
68 configure(CLKernelLibrary::get().get_compile_context(), input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
69 recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, output_state_in, cell_state_in, scratch_buffer, output_state_out, cell_state_out, output, lstm_params, activation_info,
70 cell_threshold, projection_threshold);
71 }
72
configure(const CLCompileContext & compile_context,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,const ICLTensor * output_state_in,ICLTensor * cell_state_in,ICLTensor * scratch_buffer,ICLTensor * output_state_out,ICLTensor * cell_state_out,ICLTensor * output,const LSTMParams<ICLTensor> & lstm_params,const ActivationLayerInfo & activation_info,float cell_threshold,float projection_threshold)73 void CLLSTMLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input,
74 const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
75 const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
76 const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
77 const ICLTensor *output_state_in, ICLTensor *cell_state_in,
78 ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
79 const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
80 {
81 ARM_COMPUTE_ERROR_ON_NULLPTR(input,
82 input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
83 recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
84 forget_gate_bias, cell_bias, output_gate_bias,
85 output_state_in, cell_state_in,
86 scratch_buffer, output_state_out, cell_state_out, output);
87
88 ARM_COMPUTE_LOG_PARAMS(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
89 recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, output_state_in, cell_state_in, scratch_buffer, output_state_out, cell_state_out,
90 output, lstm_params, activation_info, cell_threshold, projection_threshold);
91
92 _is_layer_norm_lstm = lstm_params.use_layer_norm();
93
94 // Set lstm parameters
95 LSTMParams<ITensorInfo> lstm_params_info{};
96 build_lstm_params_tensor_info(lstm_params, &lstm_params_info);
97
98 // Validate
99 ARM_COMPUTE_ERROR_THROW_ON(CLLSTMLayer::validate(input->info(), input_to_forget_weights->info(),
100 input_to_cell_weights->info(), input_to_output_weights->info(),
101 recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
102 forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
103 output_state_in->info(), cell_state_in->info(),
104 scratch_buffer->info(), output_state_out->info(), cell_state_out->info(), output->info(),
105 lstm_params_info, activation_info, cell_threshold, projection_threshold));
106
107 const TensorShape cell_state_shape = cell_state_in->info()->tensor_shape();
108 // Configure block that calculates the forget gate
109 // forget_gate = Activation(input * input_to_forget_weights + output_state_in * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias)
110 // We optimize this as follows:
111 // forget_gate = Activation( (input,output_state_in) * (input_to_forget_weights,recurrent_to_forget_weights) + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias
112 _forget_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
113 _forget_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
114 _forget_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
115
116 std::vector<const ICLTensor *> inputs_vector;
117 inputs_vector.emplace_back(input);
118 inputs_vector.emplace_back(output_state_in);
119 const TensorShape concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, 0);
120 _forget_gate_out2.allocator()->init(TensorInfo(concat_shape, 1, input->info()->data_type()));
121
122 _memory_group.manage(&_forget_gate_out2);
123 _concat_inputs_forget_gate.configure(compile_context, inputs_vector, &_forget_gate_out2, Window::DimX);
124
125 std::vector<const ICLTensor *> weights_vector;
126
127 weights_vector.emplace_back(input_to_forget_weights);
128 weights_vector.emplace_back(recurrent_to_forget_weights);
129 const TensorShape weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(weights_vector, 0);
130 _forget_gate_out6.allocator()->init(TensorInfo(weights_concat_shape, 1, input->info()->data_type()));
131
132 _concat_weights_forget_gate.configure(compile_context, weights_vector, &_forget_gate_out6, Window::DimX);
133
134 _memory_group.manage(&_forget_gate_out5);
135 _fully_connected_forget_gate.configure(compile_context, &_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5);
136 _memory_group.manage(&_forget_gate_out1);
137 _memory_group.manage(&_forget_gate_out3);
138 _forget_gate_out6.allocator()->allocate();
139
140 CLTensor *forget_gate_out = &_forget_gate_out5;
141 if(lstm_params.has_peephole_opt())
142 {
143 _forget_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
144
145 _run_peephole_opt = true;
146 _memory_group.manage(&_forget_gate_out4);
147 _pixelwise_mul_forget_gate.configure(compile_context, cell_state_in, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
148 _accum_forget_gate1.configure(compile_context, &_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE);
149 _forget_gate_out4.allocator()->allocate();
150 _forget_gate_out5.allocator()->allocate();
151 forget_gate_out = &_forget_gate_out3;
152 }
153 else
154 {
155 _forget_gate_out3.allocator()->allocate();
156 }
157 if(_is_layer_norm_lstm)
158 {
159 _forget_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
160 _forget_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
161 _memory_group.manage(&_forget_layer_norm_out1);
162 _memory_group.manage(&_forget_layer_norm_out2);
163 _mean_std_norm_forget_gate.configure(compile_context, forget_gate_out);
164 _pixelwise_mul_forget_gate_coeff.configure(compile_context, forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE,
165 RoundingPolicy::TO_NEAREST_EVEN);
166 // forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
167 forget_gate_out->allocator()->allocate();
168 _accum_forget_gate_bias.configure(compile_context, &_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE);
169 _forget_layer_norm_out1.allocator()->allocate();
170 forget_gate_out = &_forget_layer_norm_out2;
171 }
172 _activation_forget_gate.configure(compile_context, forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
173
174 // Configure block that calculates the input gate
175 // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG
176 // input_gate = 1 - forget_gate, with CIFG
177 // We optimize this as follows:
178 // input_gate = Activation((input,output_state) * (input_to_input_weights,recurrent_to_input_weights) + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG
179 _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
180 CLTensor *input_gate_out = &_input_gate_out1;
181 if(lstm_params.has_cifg_opt())
182 {
183 _memory_group.manage(&_input_gate_out1);
184 _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
185 _ones_fill.configure(compile_context, &_ones, PixelValue(1, _ones.info()->data_type()));
186 _subtract_input_gate.configure(compile_context, &_ones, forget_gate_out, &_input_gate_out1, ConvertPolicy::SATURATE);
187 _ones.allocator()->allocate();
188 _run_cifg_opt = true;
189 }
190 else
191 {
192 _input_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
193 _input_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
194
195 std::vector<const ICLTensor *> lstm_weights;
196 lstm_weights.emplace_back(lstm_params.input_to_input_weights());
197 lstm_weights.emplace_back(lstm_params.recurrent_to_input_weights());
198 TensorShape lstm_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(lstm_weights, 0);
199 _input_gate_out2.allocator()->init(TensorInfo(lstm_weights_concat_shape, 1, input->info()->data_type()));
200
201 _concat_weights_input_gate.configure(compile_context, lstm_weights, &_input_gate_out2, Window::DimX);
202
203 _memory_group.manage(&_input_gate_out1);
204
205 _memory_group.manage(&_input_gate_out3);
206 _fully_connected_input_gate.configure(compile_context, &_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3);
207 _input_gate_out2.allocator()->allocate();
208
209 input_gate_out = &_input_gate_out3;
210 if(_run_peephole_opt)
211 {
212 _memory_group.manage(&_input_gate_out4);
213 _pixelwise_mul_input_gate.configure(compile_context, cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
214 _accum_input_gate1.configure(compile_context, &_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE);
215 _input_gate_out3.allocator()->allocate();
216 _input_gate_out4.allocator()->allocate();
217 input_gate_out = &_input_gate_out1;
218 }
219 else
220 {
221 _input_gate_out1.allocator()->allocate();
222 }
223
224 if(_is_layer_norm_lstm)
225 {
226 _input_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
227 _input_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
228 _memory_group.manage(&_input_layer_norm_out1);
229 _memory_group.manage(&_input_layer_norm_out2);
230 _mean_std_norm_input_gate.configure(compile_context, input_gate_out);
231 _pixelwise_mul_input_gate_coeff.configure(compile_context, input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE,
232 RoundingPolicy::TO_NEAREST_EVEN);
233 // input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
234 input_gate_out->allocator()->allocate();
235 _accum_input_gate_bias.configure(compile_context, &_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE);
236 _input_layer_norm_out1.allocator()->allocate();
237 input_gate_out = &_input_layer_norm_out2;
238 }
239 _activation_input_gate.configure(compile_context, input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
240 }
241
242 // Configure block that calculates the cell state
243 // cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state_in * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold)
244 TensorShape cell_state1_shape = compute_transposed_shape(*recurrent_to_output_weights->info());
245 _cell_state_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
246 _cell_state_out2.allocator()->init(TensorInfo(cell_state1_shape, 1, input->info()->data_type()));
247 _cell_state_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
248 _cell_state_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
249 _cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
250
251 _memory_group.manage(&_cell_state_out1);
252 _fully_connected_cell_state.configure(compile_context, input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1);
253 _memory_group.manage(&_cell_state_out2);
254 _transpose_cell_state->configure(compile_context, recurrent_to_cell_weights->info(), _cell_state_out2.info());
255 _recurrent_to_cell_weights = recurrent_to_cell_weights;
256 _memory_group.manage(&_cell_state_out3);
257 _gemm_cell_state1.configure(compile_context, output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f);
258 _cell_state_out2.allocator()->allocate();
259 _memory_group.manage(&_cell_state_out4);
260 _accum_cell_state1.configure(compile_context, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE);
261 CLTensor *cell_state_out_ptr = &_cell_state_out4;
262 if(_is_layer_norm_lstm)
263 {
264 _cell_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
265 _cell_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
266 _memory_group.manage(&_cell_layer_norm_out1);
267 _memory_group.manage(&_cell_layer_norm_out2);
268 _mean_std_norm_cell_gate.configure(compile_context, cell_state_out_ptr);
269 _pixelwise_mul_cell_gate_coeff.configure(compile_context, cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE,
270 RoundingPolicy::TO_NEAREST_EVEN);
271 // cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before
272 cell_state_out_ptr->allocator()->allocate();
273 _accum_cell_gate_bias.configure(compile_context, &_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE);
274 _cell_layer_norm_out1.allocator()->allocate();
275 cell_state_out_ptr = &_cell_layer_norm_out2;
276 }
277 _activation_cell_state.configure(compile_context, cell_state_out_ptr, nullptr, activation_info);
278 _memory_group.manage(&_cell_state_out5);
279 _pixelwise_mul_cell_state1.configure(compile_context, cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
280 cell_state_out_ptr->allocator()->allocate();
281 _pixelwise_mul_cell_state2.configure(compile_context, forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
282 _accum_cell_state2.configure(compile_context, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE);
283 _cell_state_out3.allocator()->allocate();
284 _cell_state_out5.allocator()->allocate();
285 // Perform clipping
286 if(cell_threshold != 0.f)
287 {
288 _perform_cell_clipping = true;
289 _cell_clip.configure(compile_context, &_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, cell_threshold, -cell_threshold));
290 }
291
292 // Configure block that calculates the output
293 // output_state_out = Activation(input * input_to_output_weights + output_state_in * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)
294 // We optimize this as follows:
295 // output_state_out = Activation( (input,output_state_in) * (input_to_output_weights, recurrent_to_output_weights) + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias)
296 _output1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
297 _output4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
298 std::vector<const ICLTensor *> in_out_weights;
299 in_out_weights.emplace_back(input_to_output_weights);
300 in_out_weights.emplace_back(recurrent_to_output_weights);
301 TensorShape in_out_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(in_out_weights, 0);
302 _output2.allocator()->init(TensorInfo(in_out_weights_concat_shape, 1, input->info()->data_type()));
303
304 _concat_weights_output.configure(compile_context, in_out_weights, &_output2, Window::DimX);
305
306 _memory_group.manage(&_output1);
307 _memory_group.manage(&_output4);
308
309 _fully_connected_output.configure(compile_context, &_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4);
310
311 _output2.allocator()->allocate();
312 _forget_gate_out2.allocator()->allocate();
313
314 CLTensor *output_gate_out = &_output4;
315 if(lstm_params.has_peephole_opt())
316 {
317 _output3.allocator()->init(TensorInfo(_cell_state_out1.info()->tensor_shape(), 1, input->info()->data_type()));
318
319 _memory_group.manage(&_output3);
320 _pixelwise_mul_output_state1.configure(compile_context, &_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
321 _accum_output1.configure(compile_context, &_output4, &_output3, &_output1, ConvertPolicy::SATURATE);
322 _output4.allocator()->allocate();
323 output_gate_out = &_output1;
324
325 // Allocate intermediate buffers
326 _output3.allocator()->allocate();
327 }
328 else
329 {
330 _output1.allocator()->allocate();
331 }
332 if(_is_layer_norm_lstm)
333 {
334 _output_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
335 _output_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
336 _memory_group.manage(&_output_layer_norm_out1);
337 _memory_group.manage(&_output_layer_norm_out2);
338 _mean_std_norm_output_gate.configure(compile_context, output_gate_out);
339 _pixelwise_mul_output_gate_coeff.configure(compile_context, output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE,
340 RoundingPolicy::TO_NEAREST_EVEN);
341 // output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before
342 output_gate_out->allocator()->allocate();
343 _accum_output_gate_bias.configure(compile_context, &_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE);
344 _output_layer_norm_out1.allocator()->allocate();
345 output_gate_out = &_output_layer_norm_out2;
346 }
347 _activation_output.configure(compile_context, output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
348
349 // Configure block that calculates the output state
350 /** lstm_res = PixelwiseMul(output, Activation(cell_state))
351 *
352 * -- Clip(lstm_res * projection_weights + projection_bias, projection_threshold) , if there is a projection
353 * /
354 * output_state = --
355 * \
356 * -- lstm_res , otherwise
357 */
358 ICLTensor *output_state_out_tmp = lstm_params.has_projection() ? &_output_state1 : output_state_out;
359 _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
360 _output_state1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type()));
361
362 _memory_group.manage(&_cell_state_activation);
363 _activation_output_state.configure(compile_context, &_cell_state_out1, &_cell_state_activation, activation_info);
364 _pixelwise_mul_output_state2.configure(compile_context, &_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
365 _cell_state_activation.allocator()->allocate();
366
367 if(lstm_params.has_projection())
368 {
369 _has_projection_weights = true;
370 _fully_connected_output_state.configure(compile_context, output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out);
371 _output_state1.allocator()->allocate();
372 // Perform clipping
373 if(projection_threshold != 0.f)
374 {
375 _perform_projection_clipping = true;
376 _projection_clip.configure(compile_context, output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
377 }
378 }
379
380 // Copy cell state and output
381 _copy_cell_state.configure(compile_context, &_cell_state_out1, cell_state_out);
382 _copy_output.configure(compile_context, output_state_out, output);
383
384 // Vector for holding the tensors to store in scratch buffer
385 std::vector<const ICLTensor *> scratch_inputs;
386 if(!lstm_params.has_cifg_opt())
387 {
388 scratch_inputs.emplace_back(input_gate_out);
389 }
390 scratch_inputs.emplace_back(&_cell_state_out1);
391 scratch_inputs.emplace_back(forget_gate_out);
392 scratch_inputs.emplace_back(output_gate_out);
393 _concat_scratch_buffer.configure(compile_context, scratch_inputs, scratch_buffer, Window::DimX);
394 input_gate_out->allocator()->allocate();
395 _cell_state_out1.allocator()->allocate();
396 forget_gate_out->allocator()->allocate();
397 output_gate_out->allocator()->allocate();
398 }
399
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_in,const ITensorInfo * cell_state_in,const ITensorInfo * scratch_buffer,const ITensorInfo * output_state_out,const ITensorInfo * cell_state_out,const ITensorInfo * output,const LSTMParams<ITensorInfo> & lstm_params,const ActivationLayerInfo & activation_info,float cell_threshold,float projection_threshold)400 Status CLLSTMLayer::validate(const ITensorInfo *input,
401 const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
402 const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
403 const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
404 const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in,
405 const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output,
406 const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold)
407 {
408 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input,
409 input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
410 recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
411 forget_gate_bias, cell_bias, output_gate_bias,
412 output_state_in, cell_state_in,
413 scratch_buffer, output_state_out, cell_state_out, output);
414
415 // Check data types
416 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
417 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input,
418 input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
419 recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
420 forget_gate_bias, cell_bias, output_gate_bias,
421 output_state_in, cell_state_in,
422 scratch_buffer, output_state_out, cell_state_out, output);
423
424 // Check dimensions
425 ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2);
426 ARM_COMPUTE_RETURN_ERROR_ON(input_to_forget_weights->num_dimensions() > 2);
427 ARM_COMPUTE_RETURN_ERROR_ON(input_to_cell_weights->num_dimensions() > 2);
428 ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() > 2);
429 ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_forget_weights->num_dimensions() > 2);
430 ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_cell_weights->num_dimensions() > 2);
431 ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() > 2);
432 ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() > 1);
433 ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->num_dimensions() > 1);
434 ARM_COMPUTE_RETURN_ERROR_ON(output_gate_bias->num_dimensions() > 1);
435 ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2);
436 ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() > 2);
437 ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->num_dimensions() > 2);
438 ARM_COMPUTE_RETURN_ERROR_ON(output_state_out->num_dimensions() > 2);
439 ARM_COMPUTE_RETURN_ERROR_ON(cell_state_out->num_dimensions() > 2);
440 ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() > 2);
441 ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0)
442 && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0));
443
444 const unsigned int num_batches = input->dimension(1);
445 const unsigned int num_cells = input_to_output_weights->dimension(1);
446
447 if(lstm_params.use_layer_norm())
448 {
449 // If CIFG is used, input layer normalization weights tensor is omitted
450 if(lstm_params.has_cifg_opt())
451 {
452 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights() != nullptr);
453 }
454 else
455 {
456 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
457 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1);
458 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_cells);
459 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights());
460 }
461
462 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
463 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights());
464 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1);
465 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1);
466 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1);
467 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_cells);
468 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_cells);
469 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_cells);
470 }
471
472 // Check peephole optimization
473 if(lstm_params.has_peephole_opt())
474 {
475 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights());
476 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() > 1);
477 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_output_weights()->num_dimensions() > 1);
478 }
479
480 TensorShape units_out_transposed_shape = compute_transposed_shape(*recurrent_to_output_weights);
481 TensorShape num_units_transposed_shape = compute_transposed_shape(*forget_gate_bias);
482 const TensorInfo units_out_transposed_info = TensorInfo(units_out_transposed_shape, 1, input->data_type());
483 const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type());
484
485 TensorInfo input_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
486 TensorInfo forget_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
487 TensorInfo output_gate_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
488 TensorInfo cell_state_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type());
489
490 // Validate forget gate
491 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, (lstm_params.use_layer_norm()) ? nullptr : forget_gate_bias, &forget_gate));
492
493 std::vector<const ITensorInfo *> inputs_vector;
494 inputs_vector.emplace_back(input);
495 inputs_vector.emplace_back(output_state_in);
496 const TensorShape concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, 0);
497 TensorInfo forget_gate_concat = TensorInfo(concat_shape, 1, input->data_type());
498
499 ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(inputs_vector, &forget_gate_concat, Window::DimX));
500
501 if(lstm_params.has_peephole_opt())
502 {
503 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
504 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
505 }
506 if(lstm_params.use_layer_norm())
507 {
508 ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&forget_gate));
509 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&forget_gate, lstm_params.forget_layer_norm_weights(), &forget_gate, 1, ConvertPolicy::SATURATE,
510 RoundingPolicy::TO_NEAREST_EVEN));
511 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE));
512 }
513 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
514
515 // Validate input gate
516 if(!lstm_params.has_cifg_opt())
517 {
518 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(),
519 lstm_params.recurrent_to_input_weights(),
520 lstm_params.input_gate_bias());
521 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_to_input_weights()->num_dimensions() > 2);
522 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.recurrent_to_input_weights()->num_dimensions() > 2);
523 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() > 1);
524
525 std::vector<const ITensorInfo *> lstm_weights;
526 lstm_weights.emplace_back(lstm_params.input_to_input_weights());
527 lstm_weights.emplace_back(lstm_params.recurrent_to_input_weights());
528 TensorShape lstm_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(lstm_weights, 0);
529 TensorInfo lstm_gate_concat = TensorInfo(lstm_weights_concat_shape, 1, input->data_type());
530 ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(lstm_weights, &lstm_gate_concat, Window::DimX));
531
532 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), (lstm_params.use_layer_norm()) ? nullptr : lstm_params.input_gate_bias(), &input_gate));
533
534 if(lstm_params.has_peephole_opt())
535 {
536 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights());
537 ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1);
538 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
539 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE));
540 }
541
542 if(lstm_params.use_layer_norm())
543 {
544 ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&input_gate));
545 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&input_gate, lstm_params.input_layer_norm_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
546 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_gate, lstm_params.input_gate_bias(), &input_gate, ConvertPolicy::SATURATE));
547 }
548 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
549 }
550 else
551 {
552 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtraction::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE));
553 }
554
555 // Validate cell state
556 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_cell_weights, (lstm_params.use_layer_norm()) ? nullptr : cell_bias, &cell_state_tmp));
557 ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &cell_state_tmp, 1.f, 0.f, GEMMInfo()));
558 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE));
559 if(lstm_params.use_layer_norm())
560 {
561 ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&cell_state_tmp));
562 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&cell_state_tmp, lstm_params.cell_layer_norm_weights(), &cell_state_tmp, 1, ConvertPolicy::SATURATE,
563 RoundingPolicy::TO_NEAREST_EVEN));
564 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, cell_bias, &cell_state_tmp, ConvertPolicy::SATURATE));
565 }
566 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_state_tmp, nullptr, activation_info));
567 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
568 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
569 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE));
570 if(cell_threshold != 0.f)
571 {
572 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_state_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, cell_threshold,
573 -cell_threshold)));
574 }
575
576 std::vector<const ITensorInfo *> in_out_weights;
577 in_out_weights.emplace_back(input_to_output_weights);
578 in_out_weights.emplace_back(recurrent_to_output_weights);
579 TensorShape in_out_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(in_out_weights, 0);
580 TensorInfo in_out_gate_concat = TensorInfo(in_out_weights_concat_shape, 1, input->data_type());
581 ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(in_out_weights, &in_out_gate_concat, Window::DimX));
582 // Validate output gate tmp
583 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_output_weights, (lstm_params.use_layer_norm()) ? nullptr : output_gate_bias, &output_gate_tmp));
584
585 if(lstm_params.has_peephole_opt())
586 {
587 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&cell_state_tmp, lstm_params.cell_to_output_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE,
588 RoundingPolicy::TO_NEAREST_EVEN));
589 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE));
590 }
591 if(lstm_params.use_layer_norm())
592 {
593 ARM_COMPUTE_RETURN_ON_ERROR(CLMeanStdDevNormalizationLayer::validate(&output_gate_tmp));
594 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&output_gate_tmp, lstm_params.output_layer_norm_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE,
595 RoundingPolicy::TO_NEAREST_EVEN));
596 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE));
597 }
598 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
599
600 // Validate output state
601 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_state_tmp, &cell_state_tmp, activation_info));
602 ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&cell_state_tmp, &output_gate_tmp, &output_gate_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN));
603 if(lstm_params.has_projection())
604 {
605 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(&output_gate_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out));
606 if(projection_threshold != 0.f)
607 {
608 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output_state_out, output_state_out,
609 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)));
610 }
611 }
612
613 // Validate copy kernel
614 ARM_COMPUTE_RETURN_ON_ERROR(CLCopy::validate(&cell_state_tmp, cell_state_out));
615 ARM_COMPUTE_RETURN_ON_ERROR(CLCopy::validate(output_state_out, output));
616
617 // Validate scratch concatenation
618 std::vector<const ITensorInfo *> inputs_vector_info_raw;
619 if(!lstm_params.has_cifg_opt())
620 {
621 inputs_vector_info_raw.push_back(&input_gate);
622 }
623 inputs_vector_info_raw.push_back(&cell_state_tmp);
624 inputs_vector_info_raw.push_back(&forget_gate);
625 inputs_vector_info_raw.push_back(&output_gate_tmp);
626
627 ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer, Window::DimX));
628 return Status{};
629 }
630
run()631 void CLLSTMLayer::run()
632 {
633 prepare();
634
635 MemoryGroupResourceScope scope_mg(_memory_group);
636
637 _concat_inputs_forget_gate.run();
638
639 _fully_connected_forget_gate.run();
640
641 if(_run_peephole_opt)
642 {
643 _pixelwise_mul_forget_gate.run();
644 _accum_forget_gate1.run();
645 }
646 if(_is_layer_norm_lstm)
647 {
648 _mean_std_norm_forget_gate.run();
649 _pixelwise_mul_forget_gate_coeff.run();
650 _accum_forget_gate_bias.run();
651 }
652 _activation_forget_gate.run();
653
654 if(_run_cifg_opt)
655 {
656 _ones_fill.run();
657 _subtract_input_gate.run();
658 }
659 else
660 {
661 _fully_connected_input_gate.run();
662
663 if(_run_peephole_opt)
664 {
665 _pixelwise_mul_input_gate.run();
666 _accum_input_gate1.run();
667 }
668
669 if(_is_layer_norm_lstm)
670 {
671 _mean_std_norm_input_gate.run();
672 _pixelwise_mul_input_gate_coeff.run();
673 _accum_input_gate_bias.run();
674 }
675 _activation_input_gate.run();
676 }
677
678 _fully_connected_cell_state.run();
679 ITensorPack pack;
680 pack.add_tensor(TensorType::ACL_SRC, _recurrent_to_cell_weights);
681 pack.add_tensor(TensorType::ACL_DST, &_cell_state_out2);
682 CLScheduler::get().enqueue_op(*_transpose_cell_state,
683 pack,
684 false);
685 _gemm_cell_state1.run();
686 _accum_cell_state1.run();
687 if(_is_layer_norm_lstm)
688 {
689 _mean_std_norm_cell_gate.run();
690 _pixelwise_mul_cell_gate_coeff.run();
691 _accum_cell_gate_bias.run();
692 }
693 _activation_cell_state.run();
694 _pixelwise_mul_cell_state1.run();
695 _pixelwise_mul_cell_state2.run();
696 _accum_cell_state2.run();
697
698 if(_perform_cell_clipping)
699 {
700 _cell_clip.run();
701 }
702
703 _fully_connected_output.run();
704
705 if(_run_peephole_opt)
706 {
707 _pixelwise_mul_output_state1.run();
708 _accum_output1.run();
709 }
710 if(_is_layer_norm_lstm)
711 {
712 _mean_std_norm_output_gate.run();
713 _pixelwise_mul_output_gate_coeff.run();
714 _accum_output_gate_bias.run();
715 }
716 _activation_output.run();
717
718 _activation_output_state.run();
719 _pixelwise_mul_output_state2.run();
720
721 if(_has_projection_weights)
722 {
723 _fully_connected_output_state.run();
724 if(_perform_projection_clipping)
725 {
726 _projection_clip.run();
727 }
728 }
729
730 _copy_cell_state.run();
731 _copy_output.run();
732
733 _concat_scratch_buffer.run();
734 }
735
prepare()736 void CLLSTMLayer::prepare()
737 {
738 if(!_is_prepared)
739 {
740 _concat_weights_forget_gate.run();
741 if(!_run_cifg_opt)
742 {
743 _concat_weights_input_gate.run();
744 }
745 _concat_weights_output.run();
746 _is_prepared = true;
747 }
748 }
749 } // namespace arm_compute
750