diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEQLSTMLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEQLSTMLayer.cpp | 59 |
1 files changed, 21 insertions, 38 deletions
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp index 466c41307b..beb180fda5 100644 --- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp +++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp @@ -437,6 +437,8 @@ void NEQLSTMLayer::configure(const ITensor *input, _projection_tensor_copy_required = (num_units != output_size); ITensor *hidden_gate_result = output_state_out; + _memory_group.manage(&_hidden_gate); + if(_projection_tensor_copy_required) { _hidden_gate.allocator()->init(*output_state_out->info()); @@ -450,7 +452,7 @@ void NEQLSTMLayer::configure(const ITensor *input, // Projection. if(_has_projection) { - const TensorInfo projection_outstage_info(*hidden_gate_result->info()); + const TensorInfo projection_outstage_info(*output_state_out->info()); const UniformQuantizationInfo qprojection = _projection_weights->info()->quantization_info().uniform(); const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale; gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset; @@ -458,23 +460,13 @@ void NEQLSTMLayer::configure(const ITensor *input, gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; - _memory_group.manage(&_projection_eff_bias_adjusted); - ITensor *bias_to_use = &_projection_eff_bias; - - if(_projection_tensor_copy_required) - { - _projection_eff_bias_adjusted.allocator()->init(*_projection_eff_bias.info()); - _projection_eff_bias_adjusted.info()->set_tensor_shape(TensorShape(num_units)); - _projection_bias_copy.configure(_projection_eff_bias, _projection_eff_bias_adjusted); - bias_to_use = &_projection_eff_bias_adjusted; - } + TensorInfo projection_mm_out_info{ mm_out_info }; + projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size)); configure_mm(_mm_projection, _projection_outstage, gemmlowp_info, - hidden_gate_result, &_projection_weights_transposed, bias_to_use, + hidden_gate_result, &_projection_weights_transposed, &_projection_eff_bias, &_mm_projection_res, &_projection_outstage_res, projection_scale, - mm_out_info, projection_outstage_info); - - _projection_eff_bias_adjusted.allocator()->allocate(); + projection_mm_out_info, projection_outstage_info); ITensor *accumulate_destination = output_state_out; @@ -655,10 +647,10 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)); const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32); const float input_to_forget_scale = input_to_forget_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale(); - validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_forget_scale, &mm_out_info, &forget_outstage_info); + 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)); const float recurrent_to_forget_scale = recurrent_to_forget_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale(); - validate_mm(gemmlowp_info, input, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_forget_scale, &mm_out_info, &forget_outstage_info); + 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)); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE)); @@ -689,10 +681,10 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, // Modulation gate. const TensorInfo cell_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0)); const float input_to_cell_scale = input_to_cell_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale(); - validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_cell_scale, &mm_out_info, &cell_outstage_info); + 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)); const float recurrent_to_cell_scale = recurrent_to_cell_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale(); - validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, recurrent_to_cell_scale, &mm_out_info, &cell_outstage_info); + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_cell_scale, &mm_out_info, &cell_outstage_info)); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE)); @@ -724,10 +716,10 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0)); const float input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale(); - validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info); + 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)); 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(); - validate_mm(gemmlowp_info, input, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info); + 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)); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); @@ -762,10 +754,10 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, // Output gate. const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0)); const float input_to_output_scale = input_to_output_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale(); - validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &output_outstage_info); + 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)); const float recurrent_to_output_scale = recurrent_to_output_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale(); - validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_output_scale, &mm_out_info, &output_outstage_info); + 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)); ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE)); if(lstm_params.has_peephole_opt()) @@ -816,17 +808,14 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input, gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; - TensorInfo projection_outstage_info(hidden_out_info); - - if(projection_tensor_copy_required) - { - TensorInfo projection_eff_bias_adjusted_info{ projection_eff_bias_info }; - projection_eff_bias_adjusted_info.set_tensor_shape(TensorShape(num_units)); + const TensorInfo projection_outstage_info(*output_state_out); + const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info()); - ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_eff_bias_info, projection_eff_bias_adjusted_info)); - } + TensorInfo projection_mm_out_info{ mm_out_info }; + projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size)); - validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &projection_eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info); + 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, + &projection_outstage_info)); if(projection_tensor_copy_required) { @@ -985,11 +974,6 @@ void NEQLSTMLayer::run() // Projection. if(_has_projection) { - if(_projection_tensor_copy_required) - { - _projection_bias_copy.run(); - } - _mm_projection.run(); _projection_outstage.run(); @@ -1088,7 +1072,6 @@ void NEQLSTMLayer::prepare() if(!_projection_tensor_copy_required) { _hidden_gate.mark_as_unused(); - _projection_eff_bias_adjusted.mark_as_unused(); _projection_accumulate_res.mark_as_unused(); } } |