Compute Library
 22.02
CLLSTMLayer.cpp
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25 
26 #include "arm_compute/core/Utils.h"
34 
35 #include "src/common/utils/Log.h"
36 
37 namespace arm_compute
38 {
40 using namespace arm_compute::utils::info_helpers;
41 
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 
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 
73 void CLLSTMLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input,
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 {
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);
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,
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,
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,
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,
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 
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 {
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
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
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);
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);
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  {
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);
460  }
461 
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  {
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);
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  {
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  {
511  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE));
512  }
514 
515  // Validate input gate
516  if(!lstm_params.has_cifg_opt())
517  {
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  {
537  ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1);
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  {
547  }
549  }
550  else
551  {
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  {
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));
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  {
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  {
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  {
596  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE));
597  }
599 
600  // Validate output state
601  ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_state_tmp, &cell_state_tmp, activation_info));
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 
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();
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 
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
OpenCL kernel to transpose a 2D tensor.
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Class describing the value of a pixel for any image format.
Definition: PixelValue.h:34
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)
Static function to check if given info will lead to a valid configuration of CLActivationLayer.
const T * projection_weights() const
Definition: LSTMParams.h:225
const T * input_to_input_weights() const
Definition: LSTMParams.h:195
Shape of a tensor.
Definition: TensorShape.h:39
void configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, 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=0.f, float projection_threshold=0.f)
Initialize function&#39;s tensors.
Definition: CLLSTMLayer.cpp:60
bool use_layer_norm() const
Definition: LSTMParams.h:310
TensorInfo * info() const override
Interface to be implemented by the child class to return the tensor&#39;s metadata.
Definition: CLTensor.cpp:41
void run() override
Run the kernels contained in the function.
static Status validate(const ITensorInfo *input, const ITensorInfo *output=nullptr, float epsilon=1e-8f)
Static function to check if given info will lead to a valid configuration of CLMeanStdDevNormalizatio...
void run() override
Run the kernels contained in the function.
Definition: CLGEMM.cpp:100
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
bool has_peephole_opt() const
Definition: LSTMParams.h:295
static CLScheduler & get()
Access the scheduler singleton.
T * forget_layer_norm_weights() const
Definition: LSTMParams.h:240
void build_lstm_params_tensor_info(const LSTMParams< T > &lstm_params, LSTMParams< ITensorInfo > *lstm_params_info)
Build LSTMParams<ITensorInfo> object by extracting the metadata from each tensor. ...
Definition: InfoHelpers.h:71
void run() override
Run the kernels contained in the function.
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
virtual DataType data_type() const =0
Data type used for each element of the tensor.
void run() override
Run the kernels contained in the function.
Definition: CLCopy.cpp:76
1 channel, 1 F32 per channel
void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Initialise the kernel&#39;s inputs, output and convertion policy.
bool has_cifg_opt() const
Definition: LSTMParams.h:305
static CLKernelLibrary & get()
Access the KernelLibrary singleton.
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
void run() override
Run the kernels contained in the function.
CLTensorAllocator * allocator()
Return a pointer to the tensor&#39;s allocator.
Definition: CLTensor.cpp:61
void configure(ICLTensor *input, ICLTensor *output=nullptr, float epsilon=1e-8f)
Initialise the function&#39;s input and outputs.
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
CLLSTMLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
Definition: CLLSTMLayer.cpp:42
T * cell_to_input_weights() const
Definition: LSTMParams.h:205
Status class.
Definition: Error.h:52
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
Activation Layer Information class.
Definition: Types.h:1509
void configure(std::vector< const ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis)
Initialise the kernel&#39;s inputs vector and output.
void run() override
Run the kernels contained in the function.
void init(const TensorInfo &input, size_t alignment=0)
Initialize a tensor based on the passed TensorInfo.
Copyright (c) 2017-2021 Arm Limited.
~CLLSTMLayer()
Default destructor.
1 channel, 1 F16 per channel
TensorShape compute_transposed_shape(const ITensorInfo &input)
Calculate the transposed shape of a tensor.
DataType data_type() const override
Data type used for each element of the tensor.
Definition: TensorInfo.h:242
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:159
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
Definition: MemoryGroup.h:79
const T * recurrent_to_input_weights() const
Definition: LSTMParams.h:200
const T * projection_bias() const
Definition: LSTMParams.h:230
Interface to enqueue OpenCL kernels and get/set the OpenCL CommandQueue and ICLTuner.
void run() override
Run the kernels contained in the function.
Definition: CLFill.cpp:72
T * output_layer_norm_weights() const
Definition: LSTMParams.h:250
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
void configure(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())
Initialise the kernel&#39;s inputs and output.
Definition: CLGEMM.cpp:69
void run() override final
Run the kernels contained in the function.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
void configure(ICLTensor *input, ICLTensor *output, Window *dst_window=nullptr)
Initialise the function&#39;s source and destination.
Definition: CLCopy.cpp:54
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())
Static function to check if given info will lead to a valid configuration of CLFullyConnectedLayer.
static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Static function to check if given info will lead to a valid configuration of opencl::kernels::ClSatur...
void enqueue_op(ICLKernel &kernel, ITensorPack &tensors, bool flush=true)
Schedule the execution of the passed kernel if possible.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
void configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Initialise the kernel&#39;s inputs, output and conversion policy.
static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *output_state_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=0.f, float projection_threshold=0.f)
Static function to check if given info will lead to a valid configuration of CLLSTMLayer.
void run() override
Run the kernels contained in the function.
static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Static function to check if given info will lead to a valid configuration of opencl::kernels::ClSatur...
void configure(ICLTensor *tensor, const PixelValue &constant_value, Window *window=nullptr)
Initialize the kernel&#39;s tensor and filling value.
Definition: CLFill.cpp:52
CLCompileContext class.
T * cell_to_forget_weights() const
Definition: LSTMParams.h:215
static Status validate(const ITensorInfo *input, const ITensorInfo *output, Window *dst_window=nullptr)
Static function to check if given info will lead to a valid configuration of CLCopy.
Definition: CLCopy.cpp:71
void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())
Set the input and output tensors.
bool has_projection() const
Definition: LSTMParams.h:300
static Status validate(const std::vector< const ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis)
Static function to check if given info will lead to a valid configuration of CLConcatenateLayer.
void configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Initialise the kernel&#39;s inputs, output and conversion policy.
void allocate() override
Allocate size specified by TensorInfo of OpenCL memory.
T * cell_to_output_weights() const
Definition: LSTMParams.h:220
Rounds to nearest value; half rounds to nearest even.
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
Interface for OpenCL tensor.
Definition: ICLTensor.h:42
T * input_layer_norm_weights() const
Definition: LSTMParams.h:235
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:541
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:788
const T * input_gate_bias() const
Definition: LSTMParams.h:210
void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)
Set the input and output tensor.
static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info=GEMMInfo())
Static function to check if given info will lead to a valid configuration of CLGEMM.
Definition: CLGEMM.cpp:95
Tensor packing service.
Definition: ITensorPack.h:39
#define ARM_COMPUTE_LOG_PARAMS(...)
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
GEMM information class.
Definition: Types.h:1974
T * cell_layer_norm_weights() const
Definition: LSTMParams.h:245
const TensorShape & tensor_shape() const override
Size for each dimension of the tensor.
Definition: TensorInfo.h:234
void run() override
Run the kernels contained in the function.
TensorShape calculate_concatenate_shape(const std::vector< T *> &input, size_t axis)
Calculate the concatenate output shape of the concatenate operation along a single axis...
void run() override
Run the kernels contained in the function.
static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info=ActivationLayerInfo())
Static function to check if given info will lead to a valid configuration of CLPixelWiseMultiplicatio...
void add_tensor(int id, ITensor *tensor)
Add tensor to the pack.
Definition: ITensorPack.cpp:39
void prepare() override
Prepare the function for executing.
Basic implementation of the OpenCL tensor interface.
Definition: CLTensor.h:41