Compute Library
 21.11
CLLSTMLayerQuantized.cpp
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1 /*
2  * Copyright (c) 2019-2021 Arm Limited.
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24 
26 
27 #include "arm_compute/core/Utils.h"
32 
33 #include "src/common/utils/Log.h"
34 
35 #include <memory>
36 
37 namespace arm_compute
38 {
39 namespace
40 {
41 // Quantization info structures used in the LSTMQuantize layer
42 const QuantizationInfo qasymm(1.f / 128.f, 128);
43 const QuantizationInfo qsymm_3(8.f / 32768.f, 0); // qsymm16 with 3 integer bit
44 const QuantizationInfo qsymm_4(16.f / 32768.f, 0); // qsymm16 with 4 integer bit
45 const QuantizationInfo qsymm_0(1.f / 32768.f, 0); // qsymm16 with 0 integer bit
46 } // namespace
47 
48 CLLSTMLayerQuantized::CLLSTMLayerQuantized(std::shared_ptr<IMemoryManager> memory_manager)
49  : _memory_group(std::move(memory_manager)), _gemmlowp(), _output_stage(), _transpose_weights(), _concat_input_weights(), _concat_recurrent_weights(), _concat_weights(), _concat_inputs(),
50  _concat_bias(), _sigmoid_forget_gate(), _sigmoid_input_gate(), _sigmoid_output_gate(), _tanh_modulation_gate(), _tanh_output_state(), _add_cell_state_tmps(), _add2(), _mul_forget_gate_cell_state(),
51  _mul_input_gate_input_mod_gate(), _mul_output_state_tmp_output_gate(), _slice_input_tensor(), _slice_forget_tensor(), _slice_cell_tensor(), _slice_output_tensor(), _dequantize(), _quantize(),
52  _input_to_input_weights(nullptr), _input_to_forget_weights(nullptr), _input_to_cell_weights(nullptr), _input_to_output_weights(nullptr), _recurrent_to_input_weights(nullptr),
53  _recurrent_to_forget_weights(nullptr), _recurrent_to_cell_weights(nullptr), _recurrent_to_output_weights(nullptr), _input_gate_bias(nullptr), _forget_gate_bias(nullptr), _cell_bias(nullptr),
54  _output_gate_bias(nullptr), _recurrent_weights(), _input_weights(), _weights(), _input(), _weights_transposed(), _output_highp(), _output_lowp(), _bias(), _forget_gate_input(), _input_gate_input(),
55  _output_gate_input(), _input_modulation_gate_input(), _forget_gate_output(), _input_gate_output(), _output_gate_output(), _input_modulation_gate_output(), _cell_state_tmp1(), _cell_state_tmp2(),
56  _output_state_tmp(), _output_state_out_symm(), _output_state_out_f32(), _is_prepared(false)
57 {
58 }
59 
64  ICLTensor *cell_state_in, const ICLTensor *output_state_in,
65  ICLTensor *cell_state_out, ICLTensor *output_state_out)
66 {
67  configure(CLKernelLibrary::get().get_compile_context(), input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
68  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out,
69  output_state_out);
70 }
71 
76  ICLTensor *cell_state_in, const ICLTensor *output_state_in,
77  ICLTensor *cell_state_out, ICLTensor *output_state_out)
78 {
79  ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
80  recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
81  input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
82 
83  ARM_COMPUTE_LOG_PARAMS(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
84  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out,
85  output_state_out);
86 
87  ARM_COMPUTE_ERROR_THROW_ON(CLLSTMLayerQuantized::validate(input->info(), input_to_input_weights->info(), input_to_forget_weights->info(), input_to_cell_weights->info(),
88  input_to_output_weights->info(),
89  recurrent_to_input_weights->info(), recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
90  input_gate_bias->info(), forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(), cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info()));
91 
92  const int input_size = input->info()->dimension(0);
93  const int batch_size = input->info()->dimension(1);
94  const int output_size = input_to_input_weights->info()->dimension(1);
95 
96  const QuantizationInfo qweights = input_to_input_weights->info()->quantization_info(); // Weights quantization
97 
98  auto_init_if_empty(*cell_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QSYMM16, qsymm_4));
99  auto_init_if_empty(*output_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QASYMM8, qasymm));
100 
101  _input_to_input_weights = input_to_input_weights;
102  _input_to_forget_weights = input_to_forget_weights;
103  _input_to_cell_weights = input_to_cell_weights;
104  _input_to_output_weights = input_to_output_weights;
105  _recurrent_to_input_weights = recurrent_to_input_weights;
106  _recurrent_to_forget_weights = recurrent_to_forget_weights;
107  _recurrent_to_cell_weights = recurrent_to_cell_weights;
108  _recurrent_to_output_weights = recurrent_to_output_weights;
109  _input_gate_bias = input_gate_bias;
110  _forget_gate_bias = forget_gate_bias;
111  _cell_bias = cell_bias;
112  _output_gate_bias = output_gate_bias;
113 
114  // Weights concatenation
115  std::vector<const ICLTensor *> inputs_weights_vector;
116  inputs_weights_vector.emplace_back(input_to_input_weights);
117  inputs_weights_vector.emplace_back(input_to_forget_weights);
118  inputs_weights_vector.emplace_back(input_to_cell_weights);
119  inputs_weights_vector.emplace_back(input_to_output_weights);
120 
121  std::vector<const ICLTensor *> recurrent_weights_vector;
122  recurrent_weights_vector.emplace_back(recurrent_to_input_weights);
123  recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);
124  recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);
125  recurrent_weights_vector.emplace_back(recurrent_to_output_weights);
126 
127  _input_weights.allocator()->init(TensorInfo(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights));
128  _concat_input_weights.configure(compile_context, inputs_weights_vector, &_input_weights, Window::DimY);
129 
130  _recurrent_weights.allocator()->init(TensorInfo(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights));
131  _concat_recurrent_weights.configure(compile_context, recurrent_weights_vector, &_recurrent_weights, Window::DimY);
132 
133  std::vector<const ICLTensor *> weights_vector;
134  weights_vector.emplace_back(&_recurrent_weights);
135  weights_vector.emplace_back(&_input_weights);
136 
137  _weights.allocator()->init(TensorInfo(TensorShape(output_size + input_size, 4 * output_size), 1, DataType::QASYMM8, qweights));
138  _concat_weights.configure(compile_context, weights_vector, &_weights, Window::DimX);
139  _transpose_weights.configure(compile_context, &_weights, &_weights_transposed);
140 
141  // Input concatenation
142  std::vector<const ICLTensor *> input_vector;
143  input_vector.emplace_back(input);
144  input_vector.emplace_back(output_state_in);
145 
146  _memory_group.manage(&_input);
147  _input.allocator()->init(TensorInfo(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm));
148  _concat_inputs.configure(compile_context, input_vector, &_input, Window::DimX);
149 
150  // Bias concatenation
151  std::vector<const ICLTensor *> bias_vector;
152  bias_vector.emplace_back(input_gate_bias);
153  bias_vector.emplace_back(forget_gate_bias);
154  bias_vector.emplace_back(cell_bias);
155  bias_vector.emplace_back(output_gate_bias);
156 
157  _bias.allocator()->init(TensorInfo(TensorShape(4 * output_size), 1, DataType::S32));
158  _concat_bias.configure(compile_context, bias_vector, &_bias, Window::DimX);
159 
160  // Invert the offset for gemmlowp
162  _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));
163 
164  // Run gemmlowp
165  _memory_group.manage(&_output_highp);
166  _output_highp.allocator()->init(TensorInfo(TensorShape(4 * output_size, batch_size), 1, DataType::S32));
167  _gemmlowp.configure(compile_context, &_input, &_weights_transposed, nullptr, &_output_highp);
168  _input.allocator()->allocate();
169 
170  // Set the offset back
172  _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));
173 
174  // multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12))
175  _output_lowp.allocator()->init(TensorInfo(_output_highp.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_3));
176 
177  const float multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale;
178  int output_multiplier = 0;
179  int output_shift = 0;
180  quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
181 
182  _memory_group.manage(&_output_lowp);
183 
186  info.gemmlowp_multiplier = output_multiplier;
187  info.gemmlowp_shift = output_shift;
188  info.output_data_type = DataType::QSYMM16;
189  _output_stage.configure(compile_context, &_output_highp, &_bias, &_output_lowp, info);
190  _output_highp.allocator()->allocate();
191  _bias.allocator()->allocate();
192 
193  // Get the gate tensors
194  if(batch_size > 1)
195  {
196  _memory_group.manage(&_input_gate_input);
197  _slice_input_tensor.configure(compile_context, &_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size });
198  _memory_group.manage(&_forget_gate_input);
199  _slice_forget_tensor.configure(compile_context, &_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size });
200  _memory_group.manage(&_input_modulation_gate_input);
201  _slice_cell_tensor.configure(compile_context, &_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size });
202  _memory_group.manage(&_output_gate_input);
203  _slice_output_tensor.configure(compile_context, &_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size });
204  _output_lowp.allocator()->allocate();
205  }
206  else
207  {
208  _memory_group.manage(&_input_gate_input);
209  _slice_input_tensor.configure(compile_context, &_output_lowp, &_input_gate_input, { 0 }, { output_size });
210  _memory_group.manage(&_forget_gate_input);
211  _slice_forget_tensor.configure(compile_context, &_output_lowp, &_forget_gate_input, { output_size }, { 2 * output_size });
212  _memory_group.manage(&_input_modulation_gate_input);
213  _slice_cell_tensor.configure(compile_context, &_output_lowp, &_input_modulation_gate_input, { 2 * output_size }, { 3 * output_size });
214  _memory_group.manage(&_output_gate_input);
215  _slice_output_tensor.configure(compile_context, &_output_lowp, &_output_gate_input, { 3 * output_size }, { 4 * output_size });
216  _output_lowp.allocator()->allocate();
217  }
218 
219  // Forget gate
220  _memory_group.manage(&_forget_gate_output);
221  _forget_gate_output.allocator()->init(TensorInfo(_forget_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
222  _sigmoid_forget_gate.configure(compile_context, &_forget_gate_input, &_forget_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
223  _forget_gate_input.allocator()->allocate();
224 
225  // Input gate
226  _memory_group.manage(&_input_gate_output);
227  _input_gate_output.allocator()->init(TensorInfo(_input_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
228  _sigmoid_input_gate.configure(compile_context, &_input_gate_input, &_input_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
229  _input_gate_input.allocator()->allocate();
230 
231  // Input modulation gate equation
232  _memory_group.manage(&_input_modulation_gate_output);
233  _input_modulation_gate_output.allocator()->init(TensorInfo(_input_modulation_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
234  _tanh_modulation_gate.configure(compile_context, &_input_modulation_gate_input, &_input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f));
235  _input_modulation_gate_input.allocator()->allocate();
236 
237  // Output gate
238  _memory_group.manage(&_output_gate_output);
239  _output_gate_output.allocator()->init(TensorInfo(_output_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
240  _sigmoid_output_gate.configure(compile_context, &_output_gate_input, &_output_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
241  _output_gate_input.allocator()->allocate();
242 
243  // Long term memory
244  _memory_group.manage(&_cell_state_tmp1);
245  _cell_state_tmp1.allocator()->init(TensorInfo(_forget_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4));
246  _mul_forget_gate_cell_state.configure(compile_context, &_forget_gate_output, cell_state_in, &_cell_state_tmp1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
247  _forget_gate_output.allocator()->allocate();
248 
249  _memory_group.manage(&_cell_state_tmp2);
250  _cell_state_tmp2.allocator()->init(TensorInfo(_input_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4));
251  _mul_input_gate_input_mod_gate.configure(compile_context, &_input_gate_output, &_input_modulation_gate_output, &_cell_state_tmp2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
252  _input_modulation_gate_output.allocator()->allocate();
253  _input_gate_output.allocator()->allocate();
254 
255  _add_cell_state_tmps.configure(compile_context, &_cell_state_tmp1, &_cell_state_tmp2, cell_state_out, ConvertPolicy::SATURATE);
256  _cell_state_tmp1.allocator()->allocate();
257  _cell_state_tmp2.allocator()->allocate();
258 
259  // Short term memory
260  _memory_group.manage(&_output_state_tmp);
261  _output_state_tmp.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
262  _tanh_output_state.configure(compile_context, cell_state_out, &_output_state_tmp, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f));
263 
264  _memory_group.manage(&_output_state_out_symm);
265  _output_state_out_symm.allocator()->init(TensorInfo(_output_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));
266  _mul_output_state_tmp_output_gate.configure(compile_context, &_output_state_tmp, &_output_gate_output, &_output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
267  _output_gate_output.allocator()->allocate();
268  _output_state_tmp.allocator()->allocate();
269 
270  // Requantize the output state from QSYMM16 to QASYMM8
271  _memory_group.manage(&_output_state_out_f32);
272  _output_state_out_f32.allocator()->init(TensorInfo(_output_state_out_symm.info()->tensor_shape(), 1, DataType::F32));
273  _dequantize.configure(compile_context, &_output_state_out_symm, &_output_state_out_f32);
274  _output_state_out_symm.allocator()->allocate();
275 
276  _quantize.configure(compile_context, &_output_state_out_f32, output_state_out);
277  _output_state_out_f32.allocator()->allocate();
278 }
279 
284  const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
285  const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out)
286 {
287  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
288  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in,
289  output_state_in, cell_state_out, output_state_out);
291 
292  const int input_size = input->dimension(0);
293  const int batch_size = input->dimension(1);
294  const int output_size = input_to_input_weights->dimension(1);
295 
296  // Dimensionality checks
298  ARM_COMPUTE_RETURN_ERROR_ON(input_to_input_weights->num_dimensions() > 2);
299  ARM_COMPUTE_RETURN_ERROR_ON(input_gate_bias->num_dimensions() > 1);
300  ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2);
301 
302  TensorInfo input_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(input_size, output_size)).set_data_type(DataType::QASYMM8));
303  TensorInfo recurrent_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(output_size, output_size)).set_data_type(DataType::QASYMM8));
304  TensorInfo bias_info(input_gate_bias->clone()->set_tensor_shape(TensorShape(output_size)).set_data_type(DataType::S32));
305  TensorInfo output_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QASYMM8).set_quantization_info(qasymm));
306  TensorInfo cell_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QSYMM16).set_quantization_info(qsymm_4));
307 
308  // Shape checks
309  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);
310  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
311  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);
312  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_in);
313  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_in);
314 
315  // Data type checks
316  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);
317  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
318  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);
319  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_in);
320  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_in);
321 
322  // Quantization checks
323  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);
324  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
325  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_in);
326  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_in);
327 
328  // Validate internal functions
329  // _concat_input_weights
330  std::vector<const ITensorInfo *> inputs_weights_vector;
331  inputs_weights_vector.emplace_back(input_to_input_weights);
332  inputs_weights_vector.emplace_back(input_to_forget_weights);
333  inputs_weights_vector.emplace_back(input_to_cell_weights);
334  inputs_weights_vector.emplace_back(input_to_output_weights);
335  const QuantizationInfo qweights = input_to_input_weights->quantization_info(); // Weights quantization
336  const TensorInfo input_weights(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights);
337  ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(inputs_weights_vector, &input_weights, Window::DimY));
338 
339  // _concat_recurrent_weights
340  std::vector<const ITensorInfo *> recurrent_weights_vector;
341  recurrent_weights_vector.emplace_back(recurrent_to_input_weights);
342  recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);
343  recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);
344  recurrent_weights_vector.emplace_back(recurrent_to_output_weights);
345  const TensorInfo recurrent_weights(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights);
346  ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(recurrent_weights_vector, &recurrent_weights, Window::DimY));
347 
348  // _concat_weights
349  std::vector<const ITensorInfo *> weights_vector;
350  weights_vector.emplace_back(&recurrent_weights);
351  weights_vector.emplace_back(&input_weights);
352  const TensorInfo weights(TensorShape(input_size + output_size, 4 * output_size), 1, DataType::QASYMM8, qweights);
354  // _transpose_weights
355  const TensorShape weights_transposed_shape(weights.tensor_shape()[1], weights.tensor_shape()[0]);
356  TensorInfo weights_transposed = weights.clone()->set_is_resizable(true).set_tensor_shape(weights_transposed_shape);
357  ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(&weights, &weights_transposed));
358 
359  // _concat_inputs
360  std::vector<const ITensorInfo *> input_vector;
361  input_vector.emplace_back(input);
362  input_vector.emplace_back(output_state_in);
363  TensorInfo input_concatenated(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm);
364  ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(input_vector, &input_concatenated, Window::DimX));
365 
366  // _concat_bias
367  std::vector<const ITensorInfo *> bias_vector;
368  bias_vector.emplace_back(input_gate_bias);
369  bias_vector.emplace_back(forget_gate_bias);
370  bias_vector.emplace_back(cell_bias);
371  bias_vector.emplace_back(output_gate_bias);
372 
373  const TensorInfo bias_concatenated(TensorShape(4 * output_size), 1, DataType::S32);
374  ARM_COMPUTE_RETURN_ON_ERROR(CLConcatenateLayer::validate(bias_vector, &bias_concatenated, Window::DimX));
375 
376  // Invert the offset for gemmlowp
378  weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));
379 
380  // _gemmlowp
381  const TensorInfo output_highp(TensorShape(4 * output_size, batch_size), 1, DataType::S32);
382  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input_concatenated, &weights_transposed, nullptr, &output_highp));
383 
384  // Set the offset back
386  weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));
387 
388  const TensorInfo output_lowp(output_highp.tensor_shape(), 1, DataType::QSYMM16, qsymm_3);
389 
390  const float multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale;
391  int output_multiplier = 0;
392  int output_shift = 0;
393  ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
394 
395  // _output_stage
398  info.gemmlowp_multiplier = output_multiplier;
399  info.gemmlowp_shift = output_shift;
400  info.output_data_type = DataType::QSYMM16;
401  ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&output_highp, &bias_concatenated, &output_lowp, info));
402 
403  TensorInfo input_gate_input;
404  TensorInfo forget_gate_input;
405  TensorInfo input_modulation_gate_input;
406  TensorInfo output_gate_input;
407 
408  if(batch_size > 1)
409  {
410  // _slice_input_tensor
411  input_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);
412  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &input_gate_input, { 0, 0 }, { output_size, batch_size }));
413  // _slice_forget_tensor
414  forget_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);
415  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }));
416  // _slice_cell_tensor
417  input_modulation_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);
418  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }));
419  // _slice_output_tensor
420  output_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);
421  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }));
422  }
423  else
424  {
425  // _slice_input_tensor
426  input_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);
427  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &input_gate_input, { 0 }, { output_size }));
428  // _slice_forget_tensor
429  forget_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);
430  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &forget_gate_input, { output_size }, { 2 * output_size }));
431  // _slice_cell_tensor
432  input_modulation_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);
433  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size }, { 3 * output_size }));
434  // _slice_output_tensor
435  output_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);
436  ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&output_lowp, &output_gate_input, { 3 * output_size }, { 4 * output_size }));
437  }
438 
439  // _sigmoid_forget_gate
440  const TensorInfo forget_gate_output(forget_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
442  // _sigmoid_input_gate
443  const TensorInfo input_gate_output(input_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
445  // _tanh_modulation_gate
446  const TensorInfo input_modulation_gate_output(input_modulation_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
447  ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&input_modulation_gate_input, &input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f)));
448  // _sigmoid_output_gate
449  const TensorInfo output_gate_output(output_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
451 
452  // _mul_forget_gate_cell_state
453  const TensorInfo cell_state_tmp1(forget_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4);
455 
456  // _mul_input_gate_input_mod_gate
457  const TensorInfo cell_state_tmp2(input_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4);
458  ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&input_gate_output, &input_modulation_gate_output, &cell_state_tmp2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
459 
460  // _add_cell_state_tmps
461  ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&cell_state_tmp1, &cell_state_tmp2, cell_state_out, ConvertPolicy::SATURATE));
462 
463  // _tanh_modulation_gate
464  const TensorInfo output_state_tmp(cell_state_out->tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
466 
467  // _mul_output_state_tmp_output_gate
468  const TensorInfo output_state_out_symm(output_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);
469  ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplication::validate(&output_state_tmp, &output_gate_output, &output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
470 
471  // _dequantize
472  const TensorInfo output_state_out_f32(output_state_out_symm.tensor_shape(), 1, DataType::F32);
473  ARM_COMPUTE_RETURN_ON_ERROR(CLDequantizationLayer::validate(&output_state_out_symm, &output_state_out_f32));
474 
475  // _quantize
476  ARM_COMPUTE_RETURN_ON_ERROR(CLQuantizationLayer::validate(&output_state_out_f32, output_state_out));
477 
478  if(cell_state_out->total_size() != 0)
479  {
480  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_out);
481  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_out);
482  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_out);
483  }
484 
485  if(output_state_out->total_size() != 0)
486  {
487  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_out);
488  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_out);
489  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_out);
490  }
491 
492  return Status{};
493 }
494 
496 {
497  prepare();
498 
499  // Acquire all the temporaries
500  MemoryGroupResourceScope scope_mg(_memory_group);
501 
502  // Concat and transpose the input
503  _concat_inputs.run();
504 
505  // Run gemmlowp
506  _gemmlowp.run();
507  _output_stage.run();
508 
509  // Slice the results
510  _slice_input_tensor.run();
511  _slice_forget_tensor.run();
512  _slice_cell_tensor.run();
513  _slice_output_tensor.run();
514 
515  // Gates
516  // Forget gate
517  _sigmoid_forget_gate.run();
518 
519  // Input gate
520  _sigmoid_input_gate.run();
521 
522  // Input modulation gate
523  _tanh_modulation_gate.run();
524 
525  // Output gate
526  _sigmoid_output_gate.run();
527 
528  // Cell state (long term memory)
529  _mul_forget_gate_cell_state.run();
530  _mul_input_gate_input_mod_gate.run();
531  _add_cell_state_tmps.run();
532 
533  // Output state (short term memory)
534  _tanh_output_state.run();
535  _mul_output_state_tmp_output_gate.run();
536 
537  // Requantize output state from QSYMM16 to QASYMM8
538  _dequantize.run();
539  _quantize.run();
540 }
541 
543 {
544  if(!_is_prepared)
545  {
546  _input_weights.allocator()->allocate();
547  _concat_input_weights.run();
548 
549  _input_to_input_weights->mark_as_unused();
550  _input_to_forget_weights->mark_as_unused();
551  _input_to_cell_weights->mark_as_unused();
552  _input_to_output_weights->mark_as_unused();
553 
554  _recurrent_weights.allocator()->allocate();
555  _concat_recurrent_weights.run();
556  _recurrent_to_input_weights->mark_as_unused();
557  _recurrent_to_forget_weights->mark_as_unused();
558  _recurrent_to_cell_weights->mark_as_unused();
559  _recurrent_to_output_weights->mark_as_unused();
560 
561  _weights.allocator()->allocate();
562  _concat_weights.run();
563 
564  _input_weights.mark_as_unused();
565  _input_weights.allocator()->free();
566  _recurrent_weights.mark_as_unused();
567  _recurrent_weights.allocator()->free();
568 
569  _weights_transposed.allocator()->allocate();
570  _transpose_weights.run();
571 
572  _weights.mark_as_unused();
573  _weights.allocator()->free();
574 
575  _bias.allocator()->allocate();
576  _concat_bias.run();
577  _input_gate_bias->mark_as_unused();
578  _forget_gate_bias->mark_as_unused();
579  _cell_bias->mark_as_unused();
580  _output_gate_bias->mark_as_unused();
581 
582  _is_prepared = true;
583  }
584 }
585 
586 } // 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.
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of CLDequantizationLayer.
Shape of a tensor.
Definition: TensorShape.h:39
Quantize using a fixed point multiplication.
quantized, symmetric fixed-point 16-bit number
std::unique_ptr< ITensorInfo > clone() const override
Provide a clone of the current object of class T.
Definition: TensorInfo.cpp:282
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.
void prepare() override
Prepare the function for executing.
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(...)
Definition: Validate.h:606
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
QuantizationInfo qweights(1.f/16.f, 16)
1 channel, 1 F32 per channel
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info)
Initialise the kernel&#39;s inputs, output.
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.
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
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.
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.
#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 init(const TensorInfo &input, size_t alignment=0)
Initialize a tensor based on the passed TensorInfo.
Copyright (c) 2017-2021 Arm Limited.
void run() override
Run the kernels contained in the function.
void configure(const ICLTensor *input, const ICLTensor *input_to_input_weights, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_input_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *input_gate_bias, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *cell_state_in, const ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out)
Initialize function&#39;s tensors.
QuantizationInfo qsymm_3(8.f/32768.f, 0)
ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info) override
Set the quantization settings (scale and offset) of the tensor.
Definition: TensorInfo.cpp:346
#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
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
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...
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *input_gate_bias, 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)
Static function to check if given info will lead to a valid configuration of CLLSTMLayerQuantized.
void run() override
Run the kernels contained in the function.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
quantized, asymmetric fixed-point 8-bit number unsigned
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...
UniformQuantizationInfo uniform() const
Return per layer quantization info.
bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())
Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...
virtual std::unique_ptr< T > clone() const =0
Provide a clone of the current object of class T.
GEMMLowp output stage info.
Definition: Types.h:1922
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Coordinates &starts, const Coordinates &ends)
Static function to check if given info will lead to a valid configuration of CLSlice.
Definition: CLSlice.cpp:82
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
void configure(const ICLTensor *input, ICLTensor *output)
Set the input and output tensors.
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.
virtual ITensorInfo & set_quantization_info(const QuantizationInfo &quantization_info)=0
Set the quantization settings (scale and offset) of the tensor.
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
CLCompileContext class.
void configure(const ICLTensor *input, ICLTensor *output, const Coordinates &starts, const Coordinates &ends)
Configure kernel.
Definition: CLSlice.cpp:87
void run() override
Run the kernels contained in the function.
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 allocate() override
Allocate size specified by TensorInfo of OpenCL memory.
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
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.
void run() override
Run the kernels contained in the function.
Definition: CLSlice.cpp:100
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:439
void configure(const ICLTensor *input, ICLTensor *output)
Set the input and output tensors.
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:541
void free() override
Free allocated OpenCL memory.
void configure(ICLTensor *input, ICLTensor *output, ActivationLayerInfo act_info)
Set the input and output tensor.
#define ARM_COMPUTE_LOG_PARAMS(...)
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
QuantizationInfo qasymm(1.f/128.f, 128)
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
void configure(const ICLTensor *input, ICLTensor *output)
Initialise the kernel&#39;s inputs and output.
Definition: CLTranspose.cpp:47
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(t,...)
Definition: Validate.h:690
QuantizationInfo qsymm_4(16.f/32768.f, 0)
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.
Truncates the least significant values that are lost in operations.
CLLSTMLayerQuantized(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Default constructor.
static Status validate(const ITensorInfo *input, const ITensorInfo *output)
Static function to check if given info will lead to a valid configuration of CLQuantizationLayer.
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...