Compute Library
 21.11
CLQLSTMLayer.cpp
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25 
28 #include "arm_compute/core/Utils.h"
37 
38 #include "src/common/utils/Log.h"
39 
40 namespace arm_compute
41 {
42 using namespace arm_compute::utils::info_helpers;
43 using namespace arm_compute::opencl::kernels;
44 namespace
45 {
46 Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm_input, const ITensorInfo *mm_weights, const ITensorInfo *bias,
47  float gemmlowp_scale, const TensorInfo *mm_res_info, const TensorInfo *outstage_tensor_info)
48 {
49  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(mm_input, mm_weights, nullptr, mm_res_info));
50  ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
51  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(mm_res_info, bias, outstage_tensor_info, gemmlowp_info));
52  return Status{};
53 }
54 } // namespace
55 
56 Status CLQLSTMLayer::TensorCopyKernel::validate(const ITensorInfo &src, const ITensorInfo &dst)
57 {
58  ARM_COMPUTE_RETURN_ERROR_ON(src.tensor_shape().num_dimensions() > max_dimension_supported);
59  ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().num_dimensions() > max_dimension_supported);
61  ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().y() != src.tensor_shape().y());
62  return Status{};
63 }
64 
65 void CLQLSTMLayer::TensorCopyKernel::configure(ICLTensor &src, ICLTensor &dst)
66 {
68  _src = &src;
69  _dst = &dst;
70  _row_size = std::min(_src->info()->tensor_shape().x(), _dst->info()->tensor_shape().x());
71  _window = calculate_max_window(*_src->info(), Steps());
72 }
73 
75 {
76  auto &q = CLScheduler::get().queue();
77 
78  _src->map(q, true);
79  _dst->map(q, true);
80 
81  Iterator input_iter{ _src, _window };
82  Iterator output_iter{ _dst, _window };
83 
84  execute_window_loop(_window, [&](const Coordinates &)
85  {
86  memcpy(output_iter.ptr(), input_iter.ptr(), _row_size);
87  },
88  input_iter, output_iter);
89 
90  _src->unmap(q);
91  _dst->unmap(q);
92 }
93 
94 CLQLSTMLayer::CLQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
95  : _input_to_input_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
96  _recurrent_to_input_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
97  _input_to_forget_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
98  _recurrent_to_forget_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
99  _input_to_cell_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
100  _recurrent_to_cell_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
101  _input_to_output_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
102  _recurrent_to_output_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
103  _projection_reduction(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
104  _layer_norms(),
105  _copy_output()
106 {
107  for(auto &norm : _layer_norms)
108  {
109  norm = std::make_unique<CLQLSTMLayerNormalizationKernel>();
110  }
111 
112  _memory_group = MemoryGroup(std::move(memory_manager));
113 }
114 
115 CLQLSTMLayer::~CLQLSTMLayer() = default;
116 
117 void CLQLSTMLayer::configure_layer_norm(LayerNormGate g, const ICLTensor *in)
118 {
119  ARM_COMPUTE_ERROR_ON(!_has_layer_norm);
120 
121  CLTensor *out = &get_layer_norm_output(g);
122  _memory_group.manage(out);
123  out->allocator()->init(*(in->info()));
124 
125  get_layer_norm(g).configure(in, out, get_layer_norm_weight(g), get_layer_norm_bias(g));
126 }
127 
128 Status CLQLSTMLayer::validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias)
129 {
130  // Output quantization scale will be different, but ignored here
131  // since it will be configured at configure() stage.
132  const TensorInfo out
133  {
134  in
135  };
136  return CLQLSTMLayerNormalizationKernel::validate(&in, &out, &weight, &bias);
137 }
138 
139 void CLQLSTMLayer::configure_mm(const CLCompileContext &compile_context, CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
140  const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias,
141  CLTensor *mm_res, CLTensor *outstage_res, float gemmlowp_scale,
142  const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info)
143 {
144  _memory_group.manage(mm_res);
145  _memory_group.manage(outstage_res);
146 
147  mm_res->allocator()->init(mm_res_info);
148  outstage_res->allocator()->init(outstage_tensor_info);
149 
150  // Configure matrix-multiplication
151  mm.configure(compile_context, mm_input, mm_weights, nullptr, mm_res);
152 
153  // Configure output stage
154  quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
155  outstage.configure(compile_context, mm_res, bias, outstage_res, gemmlowp_info);
156  mm_res->allocator()->allocate();
157 }
158 
162  const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
163  ICLTensor *cell_state_in, ICLTensor *output_state_in,
164  ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output,
165  const LSTMParams<ICLTensor> &lstm_params)
166 {
167  configure(CLKernelLibrary::get().get_compile_context(), input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
168  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias,
169  cell_state_in, output_state_in, cell_state_out, output_state_out, output, lstm_params);
170 }
171 
172 void CLQLSTMLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input,
175  const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
176  ICLTensor *cell_state_in, ICLTensor *output_state_in,
177  ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output,
178  const LSTMParams<ICLTensor> &lstm_params)
179 {
180  ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
181  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
182  forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
183  cell_state_out, output_state_out, output);
184 
185  ARM_COMPUTE_LOG_PARAMS(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
186  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
187  forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
188  cell_state_out, output_state_out, output, lstm_params);
189  // Set lstm parameters
190  LSTMParams<ITensorInfo> lstm_params_info{};
191  build_lstm_params_tensor_info(lstm_params, &lstm_params_info);
192 
193  // Validate
194  ARM_COMPUTE_ERROR_THROW_ON(CLQLSTMLayer::validate(input->info(), input_to_forget_weights->info(), input_to_cell_weights->info(), input_to_output_weights->info(),
195  recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
196  forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
197  cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(), output->info(),
198  lstm_params_info));
199 
200  const int batch_size = input->info()->dimension(1);
201  const int num_units = input_to_output_weights->info()->dimension(1);
202  const int output_size = output_state_out->info()->dimension(_out_state_output_size_dimension_idx);
203 
204  const UniformQuantizationInfo qinput = input->info()->quantization_info().uniform();
205  const UniformQuantizationInfo qcell_state_in = cell_state_in->info()->quantization_info().uniform();
206  const UniformQuantizationInfo qoutput_state_in = output_state_in->info()->quantization_info().uniform();
207 
208  _projection_bias = lstm_params.projection_bias();
209  _input_to_forget_weights = input_to_forget_weights;
210  _input_to_cell_weights = input_to_cell_weights;
211  _input_to_output_weights = input_to_output_weights;
212  _recurrent_to_forget_weights = recurrent_to_forget_weights;
213  _recurrent_to_cell_weights = recurrent_to_cell_weights;
214  _recurrent_to_output_weights = recurrent_to_output_weights;
215  _projection_weights = lstm_params.projection_weights();
216 
217  // Layer normalization
218  _has_layer_norm = lstm_params.use_layer_norm();
219  if(_has_layer_norm)
220  {
221  set_layer_norm_weight(lstm_params.forget_layer_norm_weights(), LayerNormGate::Forget);
222  set_layer_norm_weight(lstm_params.cell_layer_norm_weights(), LayerNormGate::Cell);
223  set_layer_norm_weight(lstm_params.input_layer_norm_weights(), LayerNormGate::Input);
224  set_layer_norm_weight(lstm_params.output_layer_norm_weights(), LayerNormGate::Output);
225 
226  set_layer_norm_bias(forget_gate_bias, LayerNormGate::Forget);
227  set_layer_norm_bias(cell_bias, LayerNormGate::Cell);
228  set_layer_norm_bias(lstm_params.input_gate_bias(), LayerNormGate::Input);
229  set_layer_norm_bias(output_gate_bias, LayerNormGate::Output);
230  }
231 
232  _has_cifg = lstm_params.has_cifg_opt();
233  _has_projection = lstm_params.has_projection();
234  _has_peephole = lstm_params.has_peephole_opt();
235 
236  // Calculate and decompose effective scales for optimizing matmul calculation
237  const int32_t cell_shift = log2(qcell_state_in.scale);
238 
239  // Calculate quantized parameters for clipping.
240  int16_t quantized_cell_clip = 0;
241  if(lstm_params.cell_clip() > 0.0f)
242  {
243  quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
244  }
245  _has_cell_clipping = quantized_cell_clip > 0;
246 
247  // Precompute effective bias for optimizing the matmul computations.
248  if(!_has_cifg)
249  {
250  _input_to_input_weights = lstm_params.input_to_input_weights();
251  _recurrent_to_input_weights = lstm_params.recurrent_to_input_weights();
252 
253  _input_to_input_reduction->configure(compile_context, _input_to_input_weights->info(), _input_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
254  _recurrent_to_input_reduction->configure(compile_context, _recurrent_to_input_weights->info(), _recurrent_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false,
255  -qoutput_state_in.offset, true));
256  }
257  _input_to_forget_reduction->configure(compile_context, input_to_forget_weights->info(), _input_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
258  _recurrent_to_forget_reduction->configure(compile_context, recurrent_to_forget_weights->info(), _recurrent_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false,
259  -qoutput_state_in.offset, true));
260  _input_to_cell_reduction->configure(compile_context, input_to_cell_weights->info(), _input_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
261  _recurrent_to_cell_reduction->configure(compile_context, recurrent_to_cell_weights->info(), _recurrent_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset,
262  true));
263  _input_to_output_reduction->configure(compile_context, input_to_output_weights->info(), _input_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
264  _recurrent_to_output_reduction->configure(compile_context, recurrent_to_output_weights->info(), _recurrent_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false,
265  -qoutput_state_in.offset, true));
266  if(_has_projection)
267  {
268  _projection_reduction->configure(compile_context, _projection_weights->info(), _projection_eff_bias.info(), GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
269  if(_projection_bias != nullptr)
270  {
271  _projection_bias_add.configure(compile_context, _projection_bias, &_projection_eff_bias, &_projection_eff_bias, ConvertPolicy::SATURATE);
272  }
273  }
274 
275  // Pre-transpose weights to be used in GEMM.
276  _transpose_input_to_forget_weights.configure(compile_context, input_to_forget_weights, &_input_to_forget_weights_transposed);
277  _transpose_input_to_cell_weights.configure(compile_context, input_to_cell_weights, &_input_to_cell_weights_transposed);
278  _transpose_input_to_output_weights.configure(compile_context, input_to_output_weights, &_input_to_output_weights_transposed);
279  _transpose_recurrent_to_forget_weights.configure(compile_context, recurrent_to_forget_weights, &_recurrent_to_forget_weights_transposed);
280  _transpose_recurrent_to_cell_weights.configure(compile_context, recurrent_to_cell_weights, &_recurrent_to_cell_weights_transposed);
281  _transpose_recurrent_to_output_weights.configure(compile_context, recurrent_to_output_weights, &_recurrent_to_output_weights_transposed);
282  if(!_has_cifg)
283  {
284  _transpose_input_to_input_weights.configure(compile_context, lstm_params.input_to_input_weights(), &_input_to_input_weights_transposed);
285  _transpose_recurrent_to_input_weights.configure(compile_context, lstm_params.recurrent_to_input_weights(), &_recurrent_to_input_weights_transposed);
286  }
287  if(_has_projection)
288  {
289  _transpose_projection_weights.configure(compile_context, _projection_weights, &_projection_weights_transposed);
290  }
291 
292  GEMMLowpOutputStageInfo gemmlowp_info;
295  gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max();
296  gemmlowp_info.output_data_type = DataType::QSYMM16;
297 
298  const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
299  // Forget gate.
300  const TensorInfo forget_gate_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
301  const float input_to_forget_scale = input_to_forget_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
302  configure_mm(compile_context, _mm_input_to_forget, _input_to_forget_outstage, gemmlowp_info,
303  input, &_input_to_forget_weights_transposed, &_input_to_forget_eff_bias,
304  &_mm_input_to_forget_res, &_input_to_forget_outstage_res, input_to_forget_scale,
305  mm_out_info, forget_gate_outstage_info);
306 
307  const float recurrent_to_forget_scale = recurrent_to_forget_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
308  configure_mm(compile_context, _mm_recurrent_to_forget, _recurrent_to_forget_outstage, gemmlowp_info,
309  output_state_in, &_recurrent_to_forget_weights_transposed, &_recurrent_to_forget_eff_bias,
310  &_mm_recurrent_to_forget_res, &_recurrent_to_forget_outstage_res, recurrent_to_forget_scale,
311  mm_out_info, forget_gate_outstage_info);
312 
313  _accumulate_input_recurrent_forget.configure(compile_context, &_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, &_recurrent_to_forget_outstage_res,
315  _input_to_forget_outstage_res.allocator()->allocate();
316 
317  if(_has_peephole)
318  {
319  _mul_cell_to_forget_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
320  _memory_group.manage(&_mul_cell_to_forget_res);
321  _pixelwise_mul_cell_to_forget.configure(compile_context, cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
322  _cell_to_forget_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)));
323  _memory_group.manage(&_cell_to_forget_outstage_res);
324  const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->info()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
325  quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
326  _cell_to_forget_outstage.configure(compile_context, &_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res, gemmlowp_info);
327  _mul_cell_to_forget_res.allocator()->allocate();
328  _accumulate_cell_forget.configure(compile_context, &_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res,
330  _cell_to_forget_outstage_res.allocator()->allocate();
331  }
332 
333  CLTensor *forget_activation_input = &_recurrent_to_forget_outstage_res;
334 
335  if(_has_layer_norm)
336  {
337  configure_layer_norm(LayerNormGate::Forget, &_recurrent_to_forget_outstage_res);
338  _recurrent_to_forget_outstage_res.allocator()->allocate();
339  forget_activation_input = &get_layer_norm_output(LayerNormGate::Forget);
340  }
341 
342  // Output quantization info of Sigmoid and Tanh activations
343  const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
344 
345  const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
346  _memory_group.manage(&_forget_gate);
347  _forget_gate.allocator()->init(forget_gate_info);
348  _forget_gate_sigmoid.configure(compile_context, forget_activation_input, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
349  forget_activation_input->allocator()->allocate();
350 
351  // Modulation gate.
352  const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
353  const float input_to_cell_scale = input_to_cell_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
354  configure_mm(compile_context, _mm_input_to_cell, _input_to_cell_outstage, gemmlowp_info,
355  input, &_input_to_cell_weights_transposed, &_input_to_cell_eff_bias,
356  &_mm_input_to_cell_res, &_input_to_cell_outstage_res, input_to_cell_scale,
357  mm_out_info, cell_outstage_info);
358 
359  const float recurrent_to_cell_scale = recurrent_to_cell_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
360  configure_mm(compile_context, _mm_recurrent_to_cell, _recurrent_to_cell_outstage, gemmlowp_info,
361  output_state_in, &_recurrent_to_cell_weights_transposed, &_recurrent_to_cell_eff_bias,
362  &_mm_recurrent_to_cell_res, &_recurrent_to_cell_outstage_res, recurrent_to_cell_scale,
363  mm_out_info, cell_outstage_info);
364 
365  _accumulate_input_recurrent_modulation.configure(compile_context, &_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res,
367  _input_to_cell_outstage_res.allocator()->allocate();
368 
369  CLTensor *cell_activation_input = &_recurrent_to_cell_outstage_res;
370 
371  if(_has_layer_norm)
372  {
373  configure_layer_norm(LayerNormGate::Cell, &_recurrent_to_cell_outstage_res);
374  _recurrent_to_cell_outstage_res.allocator()->allocate();
375  cell_activation_input = &get_layer_norm_output(LayerNormGate::Cell);
376  }
377 
378  const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
379  _memory_group.manage(&_cell_gate);
380  _cell_gate.allocator()->init(cell_gate_info);
381  _cell_gate_tanh.configure(compile_context, cell_activation_input, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
382  cell_activation_input->allocator()->allocate();
383 
384  // Input gate.
385  const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
386  _input_gate.allocator()->init(input_gate_info);
387  _memory_group.manage(&_input_gate);
388  if(_has_cifg)
389  {
390  _ones.allocator()->init(*_forget_gate.info());
391  _input_gate_sub.configure(compile_context, &_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE);
392  _ones.allocator()->allocate();
393  }
394  else
395  {
396  const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
397  const float input_to_input_scale = _input_to_input_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
398  configure_mm(compile_context, _mm_input_to_input, _input_to_input_outstage, gemmlowp_info,
399  input, &_input_to_input_weights_transposed, &_input_to_input_eff_bias,
400  &_mm_input_to_input_res, &_input_to_input_outstage_res, input_to_input_scale,
401  mm_out_info, input_outstage_info);
402 
403  const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
404  configure_mm(compile_context, _mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info,
405  output_state_in, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
406  &_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale,
407  mm_out_info, input_outstage_info);
408  _accumulate_input_recurrent_input.configure(compile_context, &_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res,
410  _input_to_input_outstage_res.allocator()->allocate();
411 
412  if(_has_peephole)
413  {
414  _mul_cell_to_input_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
415  _memory_group.manage(&_mul_cell_to_input_res);
416  _pixelwise_mul_cell_to_input.configure(compile_context, cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
417  const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
418  quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
419  _cell_to_input_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_input_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0)));
420  _memory_group.manage(&_cell_to_input_outstage_res);
421  _cell_to_input_outstage.configure(compile_context, &_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res, gemmlowp_info);
422  _mul_cell_to_input_res.allocator()->allocate();
423  _accumulate_cell_input.configure(&_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
424  _cell_to_input_outstage_res.allocator()->allocate();
425  }
426 
427  CLTensor *input_activation_input = &_recurrent_to_input_outstage_res;
428 
429  if(_has_layer_norm)
430  {
431  configure_layer_norm(LayerNormGate::Input, &_recurrent_to_input_outstage_res);
432  _recurrent_to_input_outstage_res.allocator()->allocate();
433  input_activation_input = &get_layer_norm_output(LayerNormGate::Input);
434  }
435 
436  _input_gate_sigmoid.configure(compile_context, input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
437  input_activation_input->allocator()->allocate();
438  }
439  // Cell.
440  // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplication
441  _pixelwise_mul_forget_cell.configure(compile_context, &_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
442  const float cell_gate_scale = _cell_gate.info()->quantization_info().uniform().scale;
443  const float mul_input_cell_scale = cell_gate_scale * std::pow(2, 15 + cell_shift);
444  const TensorInfo mul_input_cell_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(mul_input_cell_scale, 0));
445  _memory_group.manage(&_mul_input_cell_res);
446  _mul_input_cell_res.allocator()->init(mul_input_cell_info);
447  _pixelwise_mul_input_cell.configure(compile_context, &_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
448  _cell_gate.allocator()->allocate();
449  _add_forget_cell.configure(compile_context, &_forget_gate, &_mul_input_cell_res, cell_state_out, ConvertPolicy::SATURATE);
450  _mul_input_cell_res.allocator()->allocate();
451  _forget_gate.allocator()->allocate();
452  if(_has_cell_clipping)
453  {
454  _cell_clip.configure(compile_context, cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, quantized_cell_clip));
455  }
456  // Output gate.
457  const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
458  const float input_to_output_scale = input_to_output_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
459  configure_mm(compile_context, _mm_input_to_output, _input_to_output_outstage, gemmlowp_info,
460  input, &_input_to_output_weights_transposed, &_input_to_output_eff_bias,
461  &_mm_input_to_output_res, &_input_to_output_outstage_res, input_to_output_scale,
462  mm_out_info, output_outstage_info);
463 
464  const float recurrent_to_output_scale = recurrent_to_output_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
465  configure_mm(compile_context, _mm_recurrent_to_output, _recurrent_to_output_outstage, gemmlowp_info,
466  output_state_in, &_recurrent_to_output_weights_transposed, &_recurrent_to_output_eff_bias,
467  &_mm_recurrent_to_output_res, &_recurrent_to_output_outstage_res, recurrent_to_output_scale,
468  mm_out_info, output_outstage_info);
469 
470  _accumulate_input_recurrent_output.configure(compile_context, &_recurrent_to_output_outstage_res, &_input_to_output_outstage_res, &_recurrent_to_output_outstage_res,
472  _input_to_output_outstage_res.allocator()->allocate();
473 
474  if(_has_peephole)
475  {
476  // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplication
477  // Here we are not using the output stage because all operations are done in float
478  _mul_cell_to_output_res.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::S32));
479  _memory_group.manage(&_mul_cell_to_output_res);
480  _pixelwise_mul_cell_to_output.configure(compile_context, cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
481 
482  const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
483  quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
484  _cell_to_output_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_output_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0)));
485  _memory_group.manage(&_cell_to_output_outstage_res);
486  _cell_to_output_outstage.configure(compile_context, &_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res, gemmlowp_info);
487  _mul_cell_to_output_res.allocator()->allocate();
488 
489  _accumulate_cell_to_output.configure(compile_context, &_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res, &_recurrent_to_output_outstage_res,
491  _cell_to_output_outstage_res.allocator()->allocate();
492  }
493 
494  CLTensor *output_activation_input = &_recurrent_to_output_outstage_res;
495 
496  if(_has_layer_norm)
497  {
498  configure_layer_norm(LayerNormGate::Output, &_recurrent_to_output_outstage_res);
499  _recurrent_to_output_outstage_res.allocator()->allocate();
500  output_activation_input = &get_layer_norm_output(LayerNormGate::Output);
501  }
502 
503  const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
504  _memory_group.manage(&_output_gate);
505  _output_gate.allocator()->init(output_gate_info);
506  _output_gate_sigmoid.configure(compile_context, output_activation_input, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
507  output_activation_input->allocator()->allocate();
508 
509  // Hidden.
510  _hidden_tanh.configure(compile_context, cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
511  // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplication
512  _memory_group.manage(&_hidden_mul_res);
513  const TensorInfo hidden_mul_res(_input_gate.info()->tensor_shape(), 1, DataType::S32);
514  _hidden_mul_res.allocator()->init(hidden_mul_res);
515  _pixelwise_mul_hidden.configure(compile_context, &_output_gate, &_input_gate, &_hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
516  _output_gate.allocator()->allocate();
517  _input_gate.allocator()->allocate();
518  const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
519  quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true);
520  gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
521  gemmlowp_info.output_data_type = output_state_in->info()->data_type();
522 
523  _projection_tensor_copy_required = (num_units != output_size);
524  ICLTensor *hidden_gate_result = output_state_out;
525 
526  _memory_group.manage(&_hidden_gate);
527 
528  if(_projection_tensor_copy_required)
529  {
530  _hidden_gate.allocator()->init(*output_state_out->info());
531  _hidden_gate.info()->set_tensor_shape(_hidden_mul_res.info()->tensor_shape());
532  hidden_gate_result = &_hidden_gate;
533  }
534 
535  _hidden_outstage.configure(compile_context, &_hidden_mul_res, nullptr, hidden_gate_result, gemmlowp_info);
536  _hidden_mul_res.allocator()->allocate();
537 
538  // Projection.
539  if(_has_projection)
540  {
541  const TensorInfo projection_outstage_info(*output_state_out->info());
542  const UniformQuantizationInfo qprojection = _projection_weights->info()->quantization_info().uniform();
543  const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
544  gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset;
546  gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
548 
549  TensorInfo projection_mm_out_info{ mm_out_info };
550  projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size));
551 
552  configure_mm(compile_context, _mm_projection, _projection_outstage, gemmlowp_info,
553  hidden_gate_result, &_projection_weights_transposed, &_projection_eff_bias,
554  &_mm_projection_res, &_projection_outstage_res, projection_scale,
555  projection_mm_out_info, projection_outstage_info);
556 
557  ICLTensor *accumulate_destination = output_state_out;
558 
559  if(_projection_tensor_copy_required)
560  {
561  _hidden_gate.allocator()->allocate();
562  _projection_accumulate_res.allocator()->init(*output_state_in->info());
563  _projection_accumulate_res.info()->set_tensor_shape(_projection_outstage_res.info()->tensor_shape());
564  _projection_output_to_accumulate_copy.configure(*output_state_in, _projection_accumulate_res);
565  accumulate_destination = &_projection_accumulate_res;
566  }
567 
568  _accumulate_projection.configure(compile_context, &_projection_outstage_res, accumulate_destination, accumulate_destination, ConvertPolicy::SATURATE);
569  _projection_outstage_res.allocator()->allocate();
570 
571  if(_projection_tensor_copy_required)
572  {
573  _projection_accumulate_to_output_copy.configure(_projection_accumulate_res, *output_state_out);
574  _projection_accumulate_res.allocator()->allocate();
575  }
576 
577  int8_t quantized_projection_clip{ 0 };
578  if(lstm_params.projection_clip() > 0.0f)
579  {
580  quantized_projection_clip = utility::clamp<int8_t>(lstm_params.projection_clip() / qprojection.scale, -128, 127);
581  }
582 
583  if(quantized_projection_clip > 0)
584  {
585  _projection_clip.configure(compile_context, output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip,
586  quantized_projection_clip));
587  _has_projection_clipping = true;
588  }
589  }
590  else
591  {
592  if(_projection_tensor_copy_required)
593  {
594  _hidden_to_output_copy.configure(_hidden_gate, *output_state_out);
595  _hidden_gate.allocator()->allocate();
596  }
597  }
598 
599  // Copy output_state_out to output
600  _copy_output.configure(compile_context, output_state_out, output);
601 }
602 
606  const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
607  const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
608  const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out, const ITensorInfo *output,
609  const LSTMParams<ITensorInfo> &lstm_params)
610 {
611  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
612  recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in,
613  cell_state_out, output_state_out, output);
614 
616  ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != 2, "Input must have exactly 2 dimensions");
617 
618  const unsigned int input_size = input->dimension(0);
619  const unsigned int batch_size = input->dimension(1);
620  const unsigned int num_units = input_to_output_weights->dimension(1);
621  const unsigned int output_size = output_state_out->dimension(_out_state_output_size_dimension_idx);
622 
623  ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2);
624  ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size);
625  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_output_weights, input_to_forget_weights, input_to_cell_weights);
626  ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() != 2);
627  ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->dimension(1) != num_units);
628  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights);
630  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
631  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
632 
633  ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() != 1);
634  ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->dimension(0) != num_units);
635  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, cell_bias, output_gate_bias);
637  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, cell_bias, output_gate_bias);
638 
639  ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() != 2);
640  ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(0) != num_units);
641  ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(1) != batch_size);
643 
644  ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() != 2);
645  ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(0) != output_size);
646  ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(1) != batch_size);
648 
649  // Check whether peephole weights are all there or none
650  if(lstm_params.has_peephole_opt())
651  {
654  ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() != 1);
655  ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->dimension(0) != num_units);
658 
659  if(!lstm_params.has_cifg_opt())
660  {
664  }
665  }
666 
667  const UniformQuantizationInfo qinput = input->quantization_info().uniform();
668  const UniformQuantizationInfo qcell_state_in = cell_state_in->quantization_info().uniform();
669  const UniformQuantizationInfo qoutput_state_in = output_state_in->quantization_info().uniform();
670 
671  // Calculate and decompose effective scales for optimizing matmul calculation
672  const int32_t cell_shift = log2(qcell_state_in.scale);
673  ARM_COMPUTE_RETURN_ERROR_ON(cell_shift > -9);
674 
675  // Calculate quantized parameters for clipping.
676  int16_t quantized_cell_clip = 0;
677  if(lstm_params.cell_clip() > 0.0f)
678  {
679  quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
680  }
681 
682  // Precompute effective bias for optimizing the matmul computations.
683  const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32);
684  const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32);
685  if(!lstm_params.has_cifg_opt())
686  {
687  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
689  true)));
690  }
691  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
692  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
693  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
694  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
695  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
696  ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
697  if(lstm_params.has_projection())
698  {
700  lstm_params.hidden_state_zero(),
701  true)));
702  if(lstm_params.projection_bias() != nullptr)
703  {
705  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(lstm_params.projection_bias(), &projection_eff_bias_info,
706  &projection_eff_bias_info, ConvertPolicy::SATURATE));
707  }
708  }
709 
710  const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_forget_weights->quantization_info());
711  const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
712 
713  // Validate weights transpose
714  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_forget_weights, &input_weights_transposed));
715  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_cell_weights, &input_weights_transposed));
716  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_output_weights, &input_weights_transposed));
717  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_forget_weights, &recurrent_weights_transposed));
718  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_cell_weights, &recurrent_weights_transposed));
719  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_output_weights, &recurrent_weights_transposed));
720  if(!lstm_params.has_cifg_opt())
721  {
722  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.input_to_input_weights(), &input_weights_transposed));
723  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_weights_transposed));
724  }
725  if(lstm_params.has_projection())
726  {
727  const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info());
728  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed));
729  }
730 
731  GEMMLowpOutputStageInfo gemmlowp_info;
734  gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max();
735  gemmlowp_info.output_data_type = DataType::QSYMM16;
736 
737  const bool has_layer_norm = lstm_params.use_layer_norm();
738 
739  // Forget gate.
741  const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
742  const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
743  const float input_to_forget_scale = input_to_forget_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
744  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_forget_scale, &mm_out_info, &forget_outstage_info));
745 
746  const float recurrent_to_forget_scale = recurrent_to_forget_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
747  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_forget_scale, &mm_out_info, &forget_outstage_info));
748 
749  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
750 
751  if(lstm_params.has_peephole_opt())
752  {
756  const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
758  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&mm_out_info, nullptr, &forget_outstage_info, gemmlowp_info));
759  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
760  }
761 
762  if(has_layer_norm)
763  {
764  const ITensorInfo *w_info = lstm_params.forget_layer_norm_weights();
765  const ITensorInfo *b_info = forget_gate_bias;
766  ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(forget_outstage_info, *w_info, *b_info));
767  }
768 
769  // Output quantization info of Sigmoid and Tanh activations
770  const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
771 
772  const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
774 
775  // Modulation gate.
777  const TensorInfo cell_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
778  const float input_to_cell_scale = input_to_cell_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
779  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_cell_scale, &mm_out_info, &cell_outstage_info));
780 
781  const float recurrent_to_cell_scale = recurrent_to_cell_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
782  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &input_weights_transposed, &eff_bias_info, recurrent_to_cell_scale, &mm_out_info, &cell_outstage_info));
783 
784  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE));
785 
786  if(has_layer_norm)
787  {
788  const ITensorInfo *w_info = lstm_params.cell_layer_norm_weights();
789  const ITensorInfo *b_info = cell_bias;
790  ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info));
791  }
792 
793  const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
795 
796  // Input gate.
797  const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
798  if(lstm_params.has_cifg_opt())
799  {
800  ARM_COMPUTE_RETURN_ERROR_ON_MSG(lstm_params.input_gate_bias() != nullptr, "Input gate bias must not be present when CIFG is used");
801  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtraction::validate(&input_gate_info, &forget_gate_info, &forget_gate_info, ConvertPolicy::SATURATE));
802  }
803  else
804  {
807  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_forget_weights, lstm_params.input_to_input_weights());
808  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_forget_weights, lstm_params.recurrent_to_input_weights());
810  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias());
811 
813  const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
814  const float input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
815  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info));
816 
817  const float recurrent_to_input_scale = lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
818  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info));
819 
820  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
821 
822  if(lstm_params.has_peephole_opt())
823  {
826  const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
828  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
829  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
830  }
831 
832  if(has_layer_norm)
833  {
834  const ITensorInfo *w_info = lstm_params.input_layer_norm_weights();
835  const ITensorInfo *b_info = lstm_params.input_gate_bias();
836  ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info));
837  }
838 
840  }
841  // Cell.
842  ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
844  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&forget_gate_info, &cell_gate_info, cell_state_out, ConvertPolicy::SATURATE));
845  if(quantized_cell_clip > 0)
846  {
848  quantized_cell_clip)));
849  }
850  // Output gate.
852  const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
853  const float input_to_output_scale = input_to_output_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
854  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &output_outstage_info));
855 
856  const float recurrent_to_output_scale = recurrent_to_output_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
857  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_output_scale, &mm_out_info, &output_outstage_info));
858 
859  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
860  if(lstm_params.has_peephole_opt())
861  {
863  // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplicationKernel
864  // Here we are not using the output stage because all operations are done in float
865  // const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
866  // ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
869  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
870  }
871 
872  if(has_layer_norm)
873  {
874  const ITensorInfo *w_info = lstm_params.output_layer_norm_weights();
875  const ITensorInfo *b_info = output_gate_bias;
876  ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(output_outstage_info, *w_info, *b_info));
877  }
878 
879  const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
881 
882  // Hidden.
884  const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32);
885  const TensorInfo hidden_out_info(TensorShape(num_units, batch_size), 1, DataType::QASYMM8_SIGNED);
886 
888  ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
889  const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
890  ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true));
891  gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
892  gemmlowp_info.output_data_type = hidden_out_info.data_type();
893  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info));
894 
895  const bool projection_tensor_copy_required = num_units != output_size;
896 
897  // Projection.
898  if(lstm_params.has_projection())
899  {
900  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_forget_weights, lstm_params.projection_weights());
901  ARM_COMPUTE_RETURN_ERROR_ON(qoutput_state_in.scale == 0);
902 
903  const UniformQuantizationInfo qprojection = lstm_params.projection_weights()->quantization_info().uniform();
904  const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
906  gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset;
908  gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
910 
911  const TensorInfo projection_outstage_info(*output_state_out);
912  const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info());
913 
914  TensorInfo projection_mm_out_info{ mm_out_info };
915  projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size));
916 
917  ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, &hidden_out_info, &projection_weights_transposed, &projection_eff_bias_info, projection_scale, &projection_mm_out_info,
918  &projection_outstage_info));
919 
920  if(projection_tensor_copy_required)
921  {
922  ARM_COMPUTE_RETURN_ON_ERROR(CLQLSTMLayer::TensorCopyKernel::validate(*output_state_in, projection_outstage_info));
923  }
924 
925  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE));
926 
927  if(projection_tensor_copy_required)
928  {
929  ARM_COMPUTE_RETURN_ON_ERROR(CLQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out));
930  }
931 
932  int8_t quantized_projection_clip{ 0 };
933  if(lstm_params.projection_clip() > 0.0f)
934  {
935  quantized_projection_clip = quantize_qasymm8_signed(lstm_params.projection_clip(), qprojection);
936  }
937 
938  if(quantized_projection_clip > 0)
939  {
941  quantized_projection_clip)));
942  }
943  }
944  else
945  {
946  if(projection_tensor_copy_required)
947  {
948  ARM_COMPUTE_RETURN_ON_ERROR(CLQLSTMLayer::TensorCopyKernel::validate(hidden_out_info, *output_state_out));
949  }
950  }
951 
952  if(cell_state_out->total_size() > 0)
953  {
954  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(cell_state_in, cell_state_out);
955  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(cell_state_in, cell_state_out);
956  }
957 
958  if(output_state_out->total_size() > 0)
959  {
961  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output_state_in, output_state_out);
962  }
963 
964  ARM_COMPUTE_RETURN_ON_ERROR(CLCopy::validate(output_state_out, output));
965  return Status{};
966 }
967 
969 {
970  prepare();
971 
972  // Acquire all the temporaries
973  MemoryGroupResourceScope scope_mg(_memory_group);
974 
975  // Forget gate.
976  _mm_input_to_forget.run();
977  _input_to_forget_outstage.run();
978 
979  _mm_recurrent_to_forget.run();
980  _recurrent_to_forget_outstage.run();
981  _accumulate_input_recurrent_forget.run();
982 
983  if(_has_peephole)
984  {
985  _pixelwise_mul_cell_to_forget.run();
986  _cell_to_forget_outstage.run();
987  _accumulate_cell_forget.run();
988  }
989 
990  if(_has_layer_norm)
991  {
992  CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Forget));
993  }
994 
995  _forget_gate_sigmoid.run();
996 
997  // Modulation gate.
998  _mm_input_to_cell.run();
999  _input_to_cell_outstage.run();
1000 
1001  _mm_recurrent_to_cell.run();
1002  _recurrent_to_cell_outstage.run();
1003  _accumulate_input_recurrent_modulation.run();
1004 
1005  if(_has_layer_norm)
1006  {
1007  CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Cell));
1008  }
1009 
1010  _cell_gate_tanh.run();
1011 
1012  // Input gate
1013  if(_has_cifg)
1014  {
1015  _input_gate_sub.run();
1016  }
1017  else
1018  {
1019  _mm_input_to_input.run();
1020  _input_to_input_outstage.run();
1021  _mm_recurrent_to_input.run();
1022  _recurrent_to_input_outstage.run();
1023  _accumulate_input_recurrent_input.run();
1024 
1025  if(_has_peephole)
1026  {
1027  _pixelwise_mul_cell_to_input.run();
1028  _cell_to_input_outstage.run();
1029  _accumulate_cell_input.run();
1030  }
1031 
1032  if(_has_layer_norm)
1033  {
1034  CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Input));
1035  }
1036 
1037  _input_gate_sigmoid.run();
1038  }
1039 
1040  // Cell.
1041  _pixelwise_mul_forget_cell.run();
1042  _pixelwise_mul_input_cell.run();
1043  _add_forget_cell.run();
1044  if(_has_cell_clipping)
1045  {
1046  _cell_clip.run();
1047  }
1048 
1049  // Output gate.
1050  _mm_input_to_output.run();
1051  _input_to_output_outstage.run();
1052  _mm_recurrent_to_output.run();
1053  _recurrent_to_output_outstage.run();
1054  _accumulate_input_recurrent_output.run();
1055  if(_has_peephole)
1056  {
1057  _pixelwise_mul_cell_to_output.run();
1058  _cell_to_output_outstage.run();
1059  _accumulate_cell_to_output.run();
1060  }
1061 
1062  if(_has_layer_norm)
1063  {
1064  CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Output));
1065  }
1066 
1067  _output_gate_sigmoid.run();
1068 
1069  // Hidden.
1070  _hidden_tanh.run();
1071  _pixelwise_mul_hidden.run();
1072  _hidden_outstage.run();
1073 
1074  // Projection.
1075  if(_has_projection)
1076  {
1077  _mm_projection.run();
1078  _projection_outstage.run();
1079 
1080  if(_projection_tensor_copy_required)
1081  {
1082  _projection_output_to_accumulate_copy.run();
1083  }
1084 
1085  _accumulate_projection.run();
1086 
1087  if(_projection_tensor_copy_required)
1088  {
1089  _projection_accumulate_to_output_copy.run();
1090  }
1091 
1092  if(_has_projection_clipping)
1093  {
1094  _projection_clip.run();
1095  }
1096  }
1097  else
1098  {
1099  if(_projection_tensor_copy_required)
1100  {
1101  _hidden_to_output_copy.run();
1102  }
1103  }
1104 
1105  // Copy output_state_out to output
1106  _copy_output.run();
1107 }
1108 
1110 {
1111  if(!_is_prepared)
1112  {
1113  // Pre-transpose weights to be used in GEMM.
1114  _input_to_forget_weights_transposed.allocator()->allocate();
1115  _input_to_cell_weights_transposed.allocator()->allocate();
1116  _input_to_output_weights_transposed.allocator()->allocate();
1117  _recurrent_to_forget_weights_transposed.allocator()->allocate();
1118  _recurrent_to_cell_weights_transposed.allocator()->allocate();
1119  _recurrent_to_output_weights_transposed.allocator()->allocate();
1120  _transpose_input_to_forget_weights.run();
1121  _transpose_input_to_cell_weights.run();
1122  _transpose_input_to_output_weights.run();
1123  _transpose_recurrent_to_forget_weights.run();
1124  _transpose_recurrent_to_cell_weights.run();
1125  _transpose_recurrent_to_output_weights.run();
1126 
1127  // Precompute effective biases
1128  if(_has_cifg)
1129  {
1130  _ones.map(true);
1131  std::fill_n(reinterpret_cast<int16_t *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 32767);
1132  _ones.unmap();
1133  }
1134  else
1135  {
1136  _input_to_input_eff_bias.allocator()->allocate();
1137  _recurrent_to_input_eff_bias.allocator()->allocate();
1138 
1139  ITensorPack input_to_input_red_pack = { { ACL_SRC, _input_to_input_weights }, { ACL_DST, &_input_to_input_eff_bias } };
1140  CLScheduler::get().enqueue_op(*_input_to_input_reduction, input_to_input_red_pack, false);
1141 
1142  ITensorPack rec_to_input_red_pack = { { ACL_SRC, _recurrent_to_input_weights }, { ACL_DST, &_recurrent_to_input_eff_bias } };
1143  CLScheduler::get().enqueue_op(*_recurrent_to_input_reduction, rec_to_input_red_pack, false);
1144 
1145  _input_to_input_weights_transposed.allocator()->allocate();
1146  _recurrent_to_input_weights_transposed.allocator()->allocate();
1147  _transpose_input_to_input_weights.run();
1148  _transpose_recurrent_to_input_weights.run();
1149  _input_to_input_weights->mark_as_unused();
1150  _recurrent_to_input_weights->mark_as_unused();
1151  }
1152  _input_to_forget_eff_bias.allocator()->allocate();
1153  _recurrent_to_forget_eff_bias.allocator()->allocate();
1154  _input_to_cell_eff_bias.allocator()->allocate();
1155  _recurrent_to_cell_eff_bias.allocator()->allocate();
1156  _input_to_output_eff_bias.allocator()->allocate();
1157  _recurrent_to_output_eff_bias.allocator()->allocate();
1158 
1159  ITensorPack input_to_forget_red_pack = { { ACL_SRC, _input_to_forget_weights }, { ACL_DST, &_input_to_forget_eff_bias } };
1160  CLScheduler::get().enqueue_op(*_input_to_forget_reduction, input_to_forget_red_pack, false);
1161 
1162  ITensorPack rec_to_forget_red_pack = { { ACL_SRC, _recurrent_to_forget_weights }, { ACL_DST, &_recurrent_to_forget_eff_bias } };
1163  CLScheduler::get().enqueue_op(*_recurrent_to_forget_reduction, rec_to_forget_red_pack, false);
1164 
1165  ITensorPack input_to_cell_red_pack = { { ACL_SRC, _input_to_cell_weights }, { ACL_DST, &_input_to_cell_eff_bias } };
1166  CLScheduler::get().enqueue_op(*_input_to_cell_reduction, input_to_cell_red_pack, false);
1167 
1168  ITensorPack rec_to_cell_red_pack = { { ACL_SRC, _recurrent_to_cell_weights }, { ACL_DST, &_recurrent_to_cell_eff_bias } };
1169  CLScheduler::get().enqueue_op(*_recurrent_to_cell_reduction, rec_to_cell_red_pack, false);
1170 
1171  ITensorPack input_to_output_red_pack = { { ACL_SRC, _input_to_output_weights }, { ACL_DST, &_input_to_output_eff_bias } };
1172  CLScheduler::get().enqueue_op(*_input_to_output_reduction, input_to_output_red_pack, false);
1173 
1174  ITensorPack rec_to_output_red_pack = { { ACL_SRC, _recurrent_to_output_weights }, { ACL_DST, &_recurrent_to_output_eff_bias } };
1175  CLScheduler::get().enqueue_op(*_recurrent_to_output_reduction, rec_to_output_red_pack, false);
1176 
1177  if(_has_projection)
1178  {
1179  _projection_eff_bias.allocator()->allocate();
1180  ITensorPack proj_red_pack{ { ACL_SRC, _projection_weights }, { ACL_DST, &_projection_eff_bias } };
1181  CLScheduler::get().enqueue_op(*_projection_reduction, proj_red_pack, false);
1182  if(_projection_bias != nullptr)
1183  {
1184  _projection_bias_add.run();
1185  _projection_bias->mark_as_unused();
1186  }
1187 
1188  _projection_weights_transposed.allocator()->allocate();
1189  _transpose_projection_weights.run();
1190  _projection_weights->mark_as_unused();
1191 
1192  if(!_projection_tensor_copy_required)
1193  {
1194  _hidden_gate.mark_as_unused();
1195  _projection_accumulate_res.mark_as_unused();
1196  }
1197  }
1198 
1199  // Mark weights as unused
1200  _input_to_forget_weights->mark_as_unused();
1201  _input_to_cell_weights->mark_as_unused();
1202  _input_to_output_weights->mark_as_unused();
1203  _recurrent_to_forget_weights->mark_as_unused();
1204  _recurrent_to_cell_weights->mark_as_unused();
1205  _recurrent_to_output_weights->mark_as_unused();
1206 
1207  CLScheduler::get().queue().finish();
1208  _is_prepared = true;
1209  }
1210 }
1211 
1212 } // namespace arm_compute
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
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.
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
const T * projection_weights() const
Definition: LSTMParams.h:225
int32_t gemmlowp_multiplier
GEMMLowp output stage multiplier used for quantizing to QASYMM8.
Definition: Types.h:1926
const T * input_to_input_weights() const
Definition: LSTMParams.h:195
int16_t quantize_qsymm16(float value, const UniformQuantizationInfo &qinfo, RoundingPolicy rounding_policy=RoundingPolicy::TO_NEAREST_UP)
Quantize a value given a 16-bit symmetric quantization scheme.
Shape of a tensor.
Definition: TensorShape.h:39
Quantize using a fixed point multiplication.
quantized, symmetric fixed-point 16-bit number
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.
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
Basic function to execute GEMMLowpQuantizeDown kernels on CL.
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.
QuantizationInfo quantization_info() const override
Get the quantization settings (scale and offset) of the tensor.
Definition: TensorInfo.h:287
void run() override
Run the kernels contained in the function.
Definition: CLCopy.cpp:76
void run() override
Run the kernels contained in the function.
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info)
Initialise the kernel&#39;s inputs, output.
float output_intermediate_scale() const
Definition: LSTMParams.h:280
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
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
float cell_intermediate_scale() const
Definition: LSTMParams.h:275
float forget_intermediate_scale() const
Definition: LSTMParams.h:270
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
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Quantization info when assuming per layer quantization.
void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info=GEMMInfo())
Initialise the kernel&#39;s inputs, output.
int32_t gemmlowp_offset
GEMMLowp output stage offset used for quantizing to QASYMM8.
Definition: Types.h:1925
T * cell_to_input_weights() const
Definition: LSTMParams.h:205
Status calculate_quantized_multiplier(float multiplier, int32_t *quant_multiplier, int32_t *shift, bool ignore_epsilon=false)
Calculate quantized representation of multiplier.
Status class.
Definition: Error.h:52
void run() override
Run the kernels contained in the function.
int32_t gemmlowp_max_bound
GEMMLowp max value used to saturate down the output result before converting back to QASYMM8...
Definition: Types.h:1929
#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
GEMMLowpOutputStageType type
GEMMLowp output stage type.
Definition: Types.h:1924
CLQLSTMLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
void run() override
Run the kernels contained in the function.
void configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *weight, const ICLTensor *bias)
Initialise the kernel&#39;s input and outputs.
void init(const TensorInfo &input, size_t alignment=0)
Initialize a tensor based on the passed TensorInfo.
SimpleTensor< float > src
Definition: DFT.cpp:155
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, ICLTensor *cell_state_in, ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output, const LSTMParams< ICLTensor > &lstm_params)
Initialize function&#39;s tensors.
Copyright (c) 2017-2021 Arm Limited.
void map(bool blocking=true)
Enqueue a map operation of the allocated buffer.
Definition: CLTensor.cpp:66
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
static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info)
Static function to check if given info will lead to a valid configuration of opencl::kernels::ClGemmL...
void mark_as_unused() const
Marks a tensor as unused.
Definition: ITensor.cpp:168
void prepare() override
Prepare the function for executing.
uint8_t * buffer() const override
Interface to be implemented by the child class to return a pointer to CPU memory. ...
Definition: ICLTensor.cpp:53
1 channel, 1 S32 per channel
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
static Status validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info)
Static function to check if given info will lead to a valid configuration.
int32_t hidden_state_zero() const
Definition: LSTMParams.h:285
const T * projection_bias() const
Definition: LSTMParams.h:230
Interface to enqueue OpenCL kernels and get/set the OpenCL CommandQueue and ICLTuner.
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias)
Static function to check if given info will lead to a valid configuration of CLQLSTMLayerNormalizatio...
Quantization information.
static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info=GEMMInfo())
Static function to check if given info will lead to a valid configuration of CLGEMMLowpMatrixMultiply...
T * output_layer_norm_weights() const
Definition: LSTMParams.h:250
float input_intermediate_scale() const
Definition: LSTMParams.h:265
void run() override
Run the kernels contained in the function.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:539
void configure(ICLTensor *input, ICLTensor *output, Window *dst_window=nullptr)
Initialise the function&#39;s source and destination.
Definition: CLCopy.cpp:54
int8_t quantize_qasymm8_signed(float value, const INFO_TYPE &qinfo, RoundingPolicy rounding_policy=RoundingPolicy::TO_NEAREST_UP)
Quantize a value given a signed 8-bit asymmetric quantization scheme.
float hidden_state_scale() const
Definition: LSTMParams.h:290
void run() override
Run the kernels contained in the function.
Definition: CLTranspose.cpp:66
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...
size_t total_size() const override
Returns the total size of the tensor in bytes.
Definition: TensorInfo.h:250
void enqueue_op(ICLKernel &kernel, ITensorPack &tensors, bool flush=true)
Schedule the execution of the passed kernel if possible.
UniformQuantizationInfo uniform() const
Return per layer quantization info.
GEMMLowp output stage info.
Definition: Types.h:1922
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.
cl::CommandQueue & queue()
Accessor for the associated CL command queue.
Definition: CLScheduler.cpp:39
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...
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
void enqueue(ICLKernel &kernel, bool flush=true)
Schedule the execution of the passed kernel if possible.
quantized, symmetric fixed-point 8-bit number
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:786
CLCompileContext class.
float cell_clip() const
Definition: LSTMParams.h:255
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
bool has_projection() const
Definition: LSTMParams.h:300
float projection_clip() const
Definition: LSTMParams.h:260
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.
int32_t gemmlowp_shift
GEMMLowp output stage shift used for quantizing to uint8.
Definition: Types.h:1927
T * cell_to_output_weights() const
Definition: LSTMParams.h:220
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
Interface for OpenCL tensor.
Definition: ICLTensor.h:42
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of CLTranspose.
Definition: CLTranspose.cpp:61
virtual size_t total_size() const =0
Returns the total size of the tensor in bytes.
T * input_layer_norm_weights() const
Definition: LSTMParams.h:235
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:439
#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.
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
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
OpenCL kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A...
void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)
Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...
Definition: Helpers.inl:77
ITensorInfo & set_tensor_shape(const TensorShape &shape) override
Set the shape of an already initialized tensor.
Definition: TensorInfo.cpp:318
Basic function to execute GEMMLowpMatrixMultiplyCore on OpenCL.
T * cell_layer_norm_weights() const
Definition: LSTMParams.h:245
quantized, asymmetric fixed-point 8-bit number signed
im2col_func configure(src_target.info(), dst_target.info(), spatial_kernel, conv_info, has_bias)
void configure(const ICLTensor *input, ICLTensor *output)
Initialise the kernel&#39;s inputs and output.
Definition: CLTranspose.cpp:47
int32_t gemmlowp_min_bound
GEMMLowp min value used to saturate down the output result before converting back to QASYMM8...
Definition: Types.h:1928
~CLQLSTMLayer()
Default destructor.
const TensorShape & tensor_shape() const override
Size for each dimension of the tensor.
Definition: TensorInfo.h:234
DataType output_data_type
Output tensor data type to use if the output is not initialized.
Definition: Types.h:1934
Truncates the least significant values that are lost in operations.
void unmap()
Enqueue an unmap operation of the allocated and mapped buffer.
Definition: CLTensor.cpp:71
size_t element_size() const override
Element size in bytes calculated as data_size() * num_channels()
Definition: TensorInfo.h:222
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...
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 *cell_state_in, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out, const ITensorInfo *output, const LSTMParams< ITensorInfo > &lstm_params)
Static function to check if given info will lead to a valid configuration of CLQLSTMLayer.
Status validate(const ITensorInfo *scores_in, const ITensorInfo *boxes_in, const ITensorInfo *batch_splits_in, const ITensorInfo *scores_out, const ITensorInfo *boxes_out, const ITensorInfo *classes, const ITensorInfo *batch_splits_out, const ITensorInfo *keeps, const ITensorInfo *keeps_size, const BoxNMSLimitInfo info)
Basic implementation of the OpenCL tensor interface.
Definition: CLTensor.h:41