| /* | |
| * Copyright (c) 2020-2022 Arm Limited. | |
| * | |
| * SPDX-License-Identifier: MIT | |
| * | |
| * Permission is hereby granted, free of charge, to any person obtaining a copy | |
| * of this software and associated documentation files (the "Software"), to | |
| * deal in the Software without restriction, including without limitation the | |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or | |
| * sell copies of the Software, and to permit persons to whom the Software is | |
| * furnished to do so, subject to the following conditions: | |
| * | |
| * The above copyright notice and this permission notice shall be included in all | |
| * copies or substantial portions of the Software. | |
| * | |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| * SOFTWARE. | |
| */ | |
| #include "arm_compute/runtime/NEON/functions/NEQLSTMLayer.h" | |
| #include "arm_compute/core/ITensorPack.h" | |
| #include "arm_compute/core/KernelDescriptors.h" | |
| #include "arm_compute/core/QuantizationInfo.h" | |
| #include "arm_compute/core/Utils.h" | |
| #include "arm_compute/core/Validate.h" | |
| #include "arm_compute/core/utils/misc/InfoHelpers.h" | |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" | |
| #include "arm_compute/runtime/NEON/NEScheduler.h" | |
| #include "src/common/utils/Log.h" | |
| #include "src/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h" | |
| #include "src/core/helpers/WindowHelpers.h" | |
| #include "src/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h" | |
| namespace arm_compute | |
| { | |
| using namespace arm_compute::utils::info_helpers; | |
| namespace | |
| { | |
| Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm_input, const ITensorInfo *mm_weights, const ITensorInfo *bias, | |
| float gemmlowp_scale, const TensorInfo *mm_res_info, const TensorInfo *outstage_tensor_info) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(mm_input, mm_weights, nullptr, mm_res_info)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(mm_res_info, bias, outstage_tensor_info, gemmlowp_info)); | |
| return Status{}; | |
| } | |
| } // namespace | |
| Status NEQLSTMLayer::validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias) | |
| { | |
| // Output quantization scale will be different, but ignored here | |
| // since it will be configured at configure() stage. | |
| const TensorInfo out | |
| { | |
| in | |
| }; | |
| return NEQLSTMLayerNormalizationKernel::validate(&in, &out, &weight, &bias); | |
| } | |
| void NEQLSTMLayer::configure_layer_norm(NEQLSTMLayer::LayerNormGate g, const ITensor *in) | |
| { | |
| ARM_COMPUTE_ERROR_ON(!_has_layer_norm); | |
| Tensor &out = get_layer_norm_output(g); | |
| _memory_group.manage(&out); | |
| out.allocator()->init(*(in->info())); | |
| get_layer_norm(g) = std::make_unique<NEQLSTMLayerNormalizationKernel>(); | |
| get_layer_norm(g)->configure(in, &out, get_layer_norm_weight(g), get_layer_norm_bias(g)); | |
| } | |
| NEQLSTMLayer::TensorCopyKernel::~TensorCopyKernel() = default; | |
| Status NEQLSTMLayer::TensorCopyKernel::validate(const ITensorInfo &src, const ITensorInfo &dst) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON(src.tensor_shape().num_dimensions() > max_dimension_supported); | |
| ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().num_dimensions() > max_dimension_supported); | |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst); | |
| ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().y() != src.tensor_shape().y()); | |
| return Status{}; | |
| } | |
| void NEQLSTMLayer::TensorCopyKernel::configure(ITensor &src, ITensor &dst) | |
| { | |
| ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::TensorCopyKernel::validate(*src.info(), *dst.info())); | |
| ARM_COMPUTE_LOG_PARAMS(src, dst); | |
| _src = &src; | |
| _dst = &dst; | |
| _row_size = std::min(_src->info()->tensor_shape().x(), _dst->info()->tensor_shape().x()); | |
| _window = calculate_max_window(*_src->info(), Steps()); | |
| } | |
| void NEQLSTMLayer::TensorCopyKernel::run() | |
| { | |
| Iterator input_iter{ _src, _window }; | |
| Iterator output_iter{ _dst, _window }; | |
| execute_window_loop(_window, [&](const Coordinates &) | |
| { | |
| memcpy(output_iter.ptr(), input_iter.ptr(), _row_size); | |
| }, | |
| input_iter, output_iter); | |
| } | |
| NEQLSTMLayer::~NEQLSTMLayer() = default; | |
| NEQLSTMLayer::NEQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager) | |
| : _memory_group(), | |
| _dequantize_input_to_forget_weights(), | |
| _quantize_input_to_forget_weights(), | |
| _transpose_input_to_forget_weights(), | |
| _transpose_input_to_cell_weights(), | |
| _transpose_input_to_output_weights(), | |
| _transpose_input_to_input_weights(), | |
| _transpose_recurrent_to_forget_weights(), | |
| _transpose_recurrent_to_cell_weights(), | |
| _transpose_recurrent_to_output_weights(), | |
| _transpose_recurrent_to_input_weights(), | |
| _transpose_projection_weights(), | |
| _input_to_input_reduction(), | |
| _recurrent_to_input_reduction(), | |
| _input_to_forget_reduction(), | |
| _recurrent_to_forget_reduction(), | |
| _input_to_cell_reduction(), | |
| _recurrent_to_cell_reduction(), | |
| _input_to_output_reduction(), | |
| _recurrent_to_output_reduction(), | |
| _projection_reduction(), | |
| _projection_bias_add(), | |
| _mm_input_to_forget(), | |
| _mm_recurrent_to_forget(), | |
| _pixelwise_mul_cell_to_forget(), | |
| _input_to_forget_outstage(), | |
| _recurrent_to_forget_outstage(), | |
| _cell_to_forget_outstage(), | |
| _accumulate_input_recurrent_forget(), | |
| _accumulate_cell_forget(), | |
| _forget_gate_sigmoid(), | |
| _mm_input_to_cell(), | |
| _input_to_cell_outstage(), | |
| _mm_recurrent_to_cell(), | |
| _recurrent_to_cell_outstage(), | |
| _accumulate_input_recurrent_modulation(), | |
| _cell_gate_tanh(), | |
| _input_gate_sub(), | |
| _mm_input_to_input(), | |
| _input_to_input_outstage(), | |
| _mm_recurrent_to_input(), | |
| _recurrent_to_input_outstage(), | |
| _accumulate_input_recurrent_input(), | |
| _pixelwise_mul_cell_to_input(), | |
| _cell_to_input_outstage(), | |
| _accumulate_cell_input(), | |
| _input_gate_sigmoid(), | |
| _pixelwise_mul_forget_cell(), | |
| _pixelwise_mul_input_cell(), | |
| _add_forget_cell(), | |
| _cell_clip(), | |
| _mm_input_to_output(), | |
| _input_to_output_outstage(), | |
| _mm_recurrent_to_output(), | |
| _recurrent_to_output_outstage(), | |
| _accumulate_input_recurrent_output(), | |
| _pixelwise_mul_cell_to_output(), | |
| _cell_to_output_outstage(), | |
| _accumulate_cell_to_output(), | |
| _output_gate_sigmoid(), | |
| _hidden_tanh(), | |
| _pixelwise_mul_hidden(), | |
| _hidden_outstage(), | |
| _mm_projection(), | |
| _projection_outstage(), | |
| _accumulate_projection(), | |
| _projection_clip(), | |
| _projection_bias_copy(), | |
| _projection_output_to_accumulate_copy(), | |
| _projection_accumulate_to_output_copy(), | |
| _hidden_to_output_copy(), | |
| _layer_norms(), | |
| _copy_output(), | |
| _layer_norm_weights(), | |
| _layer_norm_bias(), | |
| _layer_norm_output() | |
| { | |
| _memory_group = MemoryGroup(std::move(memory_manager)); | |
| } | |
| void NEQLSTMLayer::configure_mm(NEGEMMLowpMatrixMultiplyCore &mm, NEGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info, | |
| const ITensor *mm_input, const ITensor *mm_weights, const ITensor *bias, | |
| Tensor *mm_res, Tensor *outstage_res, float gemmlowp_scale, | |
| const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info) | |
| { | |
| _memory_group.manage(mm_res); | |
| _memory_group.manage(outstage_res); | |
| mm_res->allocator()->init(mm_res_info); | |
| outstage_res->allocator()->init(outstage_tensor_info); | |
| // Configure matrix-multiplication | |
| mm.configure(mm_input, mm_weights, nullptr, mm_res); | |
| // Configure output stage | |
| quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); | |
| outstage.configure(mm_res, bias, outstage_res, gemmlowp_info); | |
| mm_res->allocator()->allocate(); | |
| } | |
| void NEQLSTMLayer::configure(const ITensor *input, | |
| const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights, | |
| const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights, | |
| const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias, | |
| const ITensor *cell_state_in, ITensor *output_state_in, | |
| ITensor *cell_state_out, ITensor *output_state_out, ITensor *output, | |
| const LSTMParams<ITensor> &lstm_params) | |
| { | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, | |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, | |
| forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out); | |
| ARM_COMPUTE_LOG_PARAMS(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, | |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, | |
| forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out); | |
| // Set lstm parameters | |
| LSTMParams<ITensorInfo> lstm_params_info{}; | |
| build_lstm_params_tensor_info(lstm_params, &lstm_params_info); | |
| _input_to_forget_weights_transposed.info()->set_quantization_info(input_to_forget_weights->info()->quantization_info()); | |
| _input_to_cell_weights_transposed.info()->set_quantization_info(input_to_cell_weights->info()->quantization_info()); | |
| _input_to_output_weights_transposed.info()->set_quantization_info(input_to_output_weights->info()->quantization_info()); | |
| _recurrent_to_forget_weights_transposed.info()->set_quantization_info(recurrent_to_forget_weights->info()->quantization_info()); | |
| _recurrent_to_cell_weights_transposed.info()->set_quantization_info(recurrent_to_cell_weights->info()->quantization_info()); | |
| _recurrent_to_output_weights_transposed.info()->set_quantization_info(recurrent_to_output_weights->info()->quantization_info()); | |
| if(input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED) | |
| { | |
| _convert_input_to_forget_weights_to_qsymm8 = true; | |
| // Setup dequantize output tensor to go from QASYMM8_SIGNED -> F32 | |
| _input_to_forget_weights_f32.allocator()->init(TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::F32) | |
| .set_data_layout(input_to_forget_weights->info()->data_layout())); | |
| // Setup the quantize output tensor to go from F32 -> QSYMM8 | |
| _input_to_forget_weights_symm8.allocator()->init((TensorInfo(input_to_forget_weights->info()->tensor_shape(), 1, DataType::QSYMM8) | |
| .set_data_layout(input_to_forget_weights->info()->data_layout()) | |
| .set_quantization_info(input_to_forget_weights->info()->quantization_info()))); | |
| _dequantize_input_to_forget_weights.configure(input_to_forget_weights, &_input_to_forget_weights_f32); | |
| _quantize_input_to_forget_weights.configure(&_input_to_forget_weights_f32, &_input_to_forget_weights_symm8); | |
| ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(input->info(), _input_to_forget_weights_symm8.info(), input_to_cell_weights->info(), input_to_output_weights->info(), | |
| recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->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(), output->info(), | |
| lstm_params_info)); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::validate(input->info(), input_to_forget_weights->info(), input_to_cell_weights->info(), input_to_output_weights->info(), | |
| recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->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(), output->info(), | |
| lstm_params_info)); | |
| } | |
| const int batch_size = input->info()->dimension(1); | |
| const int num_units = input_to_output_weights->info()->dimension(1); | |
| const int output_size = output_state_out->info()->dimension(_out_state_output_size_dimension_idx); | |
| const UniformQuantizationInfo qinput = input->info()->quantization_info().uniform(); | |
| const UniformQuantizationInfo qcell_state_in = cell_state_in->info()->quantization_info().uniform(); | |
| const UniformQuantizationInfo qoutput_state_in = output_state_in->info()->quantization_info().uniform(); | |
| _projection_bias = lstm_params.projection_bias(); | |
| _input_to_forget_weights = (input_to_forget_weights->info()->data_type() == DataType::QASYMM8_SIGNED) ? &_input_to_forget_weights_symm8 : input_to_forget_weights; | |
| _input_to_cell_weights = input_to_cell_weights; | |
| _input_to_output_weights = input_to_output_weights; | |
| _recurrent_to_forget_weights = recurrent_to_forget_weights; | |
| _recurrent_to_cell_weights = recurrent_to_cell_weights; | |
| _recurrent_to_output_weights = recurrent_to_output_weights; | |
| _projection_weights = lstm_params.projection_weights(); | |
| // Layer normalization | |
| _has_layer_norm = lstm_params.use_layer_norm(); | |
| if(_has_layer_norm) | |
| { | |
| set_layer_norm_weight(lstm_params.forget_layer_norm_weights(), LayerNormGate::Forget); | |
| set_layer_norm_weight(lstm_params.cell_layer_norm_weights(), LayerNormGate::Cell); | |
| set_layer_norm_weight(lstm_params.input_layer_norm_weights(), LayerNormGate::Input); | |
| set_layer_norm_weight(lstm_params.output_layer_norm_weights(), LayerNormGate::Output); | |
| set_layer_norm_bias(forget_gate_bias, LayerNormGate::Forget); | |
| set_layer_norm_bias(cell_bias, LayerNormGate::Cell); | |
| set_layer_norm_bias(lstm_params.input_gate_bias(), LayerNormGate::Input); | |
| set_layer_norm_bias(output_gate_bias, LayerNormGate::Output); | |
| } | |
| _has_cifg = lstm_params.has_cifg_opt(); | |
| _has_projection = lstm_params.has_projection(); | |
| _has_peephole = lstm_params.has_peephole_opt(); | |
| // Calculate and decompose effective scales for optimizing matmul calculation | |
| const int32_t cell_shift = log2(qcell_state_in.scale); | |
| // Calculate quantized parameters for clipping. | |
| int16_t quantized_cell_clip = 0; | |
| if(lstm_params.cell_clip() > 0.0f) | |
| { | |
| quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in); | |
| } | |
| _has_cell_clipping = quantized_cell_clip > 0; | |
| // Precompute effective bias for optimizing the matmul computations. | |
| if(!_has_cifg) | |
| { | |
| _input_to_input_weights = lstm_params.input_to_input_weights(); | |
| _recurrent_to_input_weights = lstm_params.recurrent_to_input_weights(); | |
| _input_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _recurrent_to_input_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _input_to_input_reduction->configure(_input_to_input_weights->info(), _input_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)); | |
| _recurrent_to_input_reduction->configure(_recurrent_to_input_weights->info(), _recurrent_to_input_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); | |
| } | |
| _input_to_forget_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _recurrent_to_forget_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _input_to_cell_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _recurrent_to_cell_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _input_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _recurrent_to_output_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _input_to_forget_reduction->configure(input_to_forget_weights->info(), _input_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)); | |
| _recurrent_to_forget_reduction->configure(recurrent_to_forget_weights->info(), _recurrent_to_forget_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); | |
| _input_to_cell_reduction->configure(input_to_cell_weights->info(), _input_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)); | |
| _recurrent_to_cell_reduction->configure(recurrent_to_cell_weights->info(), _recurrent_to_cell_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); | |
| _input_to_output_reduction->configure(input_to_output_weights->info(), _input_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)); | |
| _recurrent_to_output_reduction->configure(recurrent_to_output_weights->info(), _recurrent_to_output_eff_bias.info(), GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)); | |
| if(_has_projection) | |
| { | |
| _projection_reduction = std::make_unique<cpu::kernels::CpuGemmLowpMatrixAReductionKernel>(); | |
| _projection_reduction->configure(_projection_weights->info(), _projection_eff_bias.info(), GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true)); | |
| if(_projection_bias != nullptr) | |
| { | |
| _projection_bias_add.configure(_projection_bias, &_projection_eff_bias, &_projection_eff_bias, ConvertPolicy::SATURATE); | |
| } | |
| } | |
| // Pre-transpose weights to be used in GEMM. | |
| _transpose_input_to_forget_weights.configure(input_to_forget_weights, &_input_to_forget_weights_transposed); | |
| _transpose_input_to_cell_weights.configure(input_to_cell_weights, &_input_to_cell_weights_transposed); | |
| _transpose_input_to_output_weights.configure(input_to_output_weights, &_input_to_output_weights_transposed); | |
| _transpose_recurrent_to_forget_weights.configure(recurrent_to_forget_weights, &_recurrent_to_forget_weights_transposed); | |
| _transpose_recurrent_to_cell_weights.configure(recurrent_to_cell_weights, &_recurrent_to_cell_weights_transposed); | |
| _transpose_recurrent_to_output_weights.configure(recurrent_to_output_weights, &_recurrent_to_output_weights_transposed); | |
| if(!_has_cifg) | |
| { | |
| _transpose_input_to_input_weights.configure(lstm_params.input_to_input_weights(), &_input_to_input_weights_transposed); | |
| _transpose_recurrent_to_input_weights.configure(lstm_params.recurrent_to_input_weights(), &_recurrent_to_input_weights_transposed); | |
| } | |
| if(_has_projection) | |
| { | |
| _transpose_projection_weights.configure(_projection_weights, &_projection_weights_transposed); | |
| } | |
| GEMMLowpOutputStageInfo gemmlowp_info; | |
| gemmlowp_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; | |
| gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int16_t>::lowest(); | |
| gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max(); | |
| gemmlowp_info.output_data_type = DataType::QSYMM16; | |
| const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32); | |
| // Forget gate. | |
| const TensorInfo forget_gate_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)); | |
| const float input_to_forget_scale = input_to_forget_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale(); | |
| configure_mm(_mm_input_to_forget, _input_to_forget_outstage, gemmlowp_info, | |
| input, &_input_to_forget_weights_transposed, &_input_to_forget_eff_bias, | |
| &_mm_input_to_forget_res, &_input_to_forget_outstage_res, input_to_forget_scale, | |
| mm_out_info, forget_gate_outstage_info); | |
| const float recurrent_to_forget_scale = recurrent_to_forget_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale(); | |
| configure_mm(_mm_recurrent_to_forget, _recurrent_to_forget_outstage, gemmlowp_info, | |
| output_state_in, &_recurrent_to_forget_weights_transposed, &_recurrent_to_forget_eff_bias, | |
| &_mm_recurrent_to_forget_res, &_recurrent_to_forget_outstage_res, recurrent_to_forget_scale, | |
| mm_out_info, forget_gate_outstage_info); | |
| _accumulate_input_recurrent_forget.configure(&_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE); | |
| _input_to_forget_outstage_res.allocator()->allocate(); | |
| if(_has_peephole) | |
| { | |
| _mul_cell_to_forget_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32)); | |
| _memory_group.manage(&_mul_cell_to_forget_res); | |
| _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| _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))); | |
| _memory_group.manage(&_cell_to_forget_outstage_res); | |
| 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(); | |
| quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); | |
| _cell_to_forget_outstage.configure(&_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res, gemmlowp_info); | |
| _mul_cell_to_forget_res.allocator()->allocate(); | |
| _accumulate_cell_forget.configure(&_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE); | |
| _cell_to_forget_outstage_res.allocator()->allocate(); | |
| } | |
| Tensor *forget_activation_input = &_recurrent_to_forget_outstage_res; | |
| if(_has_layer_norm) | |
| { | |
| configure_layer_norm(LayerNormGate::Forget, forget_activation_input); | |
| forget_activation_input->allocator()->allocate(); | |
| forget_activation_input = &get_layer_norm_output(LayerNormGate::Forget); | |
| } | |
| // Output quantization info of Sigmoid and Tanh activations | |
| const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0); | |
| const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| _memory_group.manage(&_forget_gate); | |
| _forget_gate.allocator()->init(forget_gate_info); | |
| _forget_gate_sigmoid.configure(forget_activation_input, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); | |
| forget_activation_input->allocator()->allocate(); | |
| // Modulation gate. | |
| const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0)); | |
| const float input_to_cell_scale = input_to_cell_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale(); | |
| configure_mm(_mm_input_to_cell, _input_to_cell_outstage, gemmlowp_info, | |
| input, &_input_to_cell_weights_transposed, &_input_to_cell_eff_bias, | |
| &_mm_input_to_cell_res, &_input_to_cell_outstage_res, input_to_cell_scale, | |
| mm_out_info, cell_outstage_info); | |
| const float recurrent_to_cell_scale = recurrent_to_cell_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale(); | |
| configure_mm(_mm_recurrent_to_cell, _recurrent_to_cell_outstage, gemmlowp_info, | |
| output_state_in, &_recurrent_to_cell_weights_transposed, &_recurrent_to_cell_eff_bias, | |
| &_mm_recurrent_to_cell_res, &_recurrent_to_cell_outstage_res, recurrent_to_cell_scale, | |
| mm_out_info, cell_outstage_info); | |
| _accumulate_input_recurrent_modulation.configure(&_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, ConvertPolicy::SATURATE); | |
| _input_to_cell_outstage_res.allocator()->allocate(); | |
| Tensor *cell_activation_input = &_recurrent_to_cell_outstage_res; | |
| if(_has_layer_norm) | |
| { | |
| configure_layer_norm(LayerNormGate::Cell, cell_activation_input); | |
| cell_activation_input->allocator()->allocate(); | |
| cell_activation_input = &get_layer_norm_output(LayerNormGate::Cell); | |
| } | |
| const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| _memory_group.manage(&_cell_gate); | |
| _cell_gate.allocator()->init(cell_gate_info); | |
| _cell_gate_tanh.configure(cell_activation_input, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); | |
| cell_activation_input->allocator()->allocate(); | |
| // Input gate. | |
| const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| _input_gate.allocator()->init(input_gate_info); | |
| _memory_group.manage(&_input_gate); | |
| if(_has_cifg) | |
| { | |
| _ones.allocator()->init(*_forget_gate.info()); | |
| _input_gate_sub.configure(&_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE); | |
| _ones.allocator()->allocate(); | |
| } | |
| else | |
| { | |
| 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 = _input_to_input_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale(); | |
| configure_mm(_mm_input_to_input, _input_to_input_outstage, gemmlowp_info, | |
| input, &_input_to_input_weights_transposed, &_input_to_input_eff_bias, | |
| &_mm_input_to_input_res, &_input_to_input_outstage_res, input_to_input_scale, | |
| mm_out_info, input_outstage_info); | |
| const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale(); | |
| configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info, | |
| output_state_in, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias, | |
| &_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale, | |
| mm_out_info, input_outstage_info); | |
| _accumulate_input_recurrent_input.configure(&_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE); | |
| _input_to_input_outstage_res.allocator()->allocate(); | |
| if(_has_peephole) | |
| { | |
| _mul_cell_to_input_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32)); | |
| _memory_group.manage(&_mul_cell_to_input_res); | |
| _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| 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(); | |
| quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); | |
| _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))); | |
| _memory_group.manage(&_cell_to_input_outstage_res); | |
| _cell_to_input_outstage.configure(&_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res, gemmlowp_info); | |
| _mul_cell_to_input_res.allocator()->allocate(); | |
| _accumulate_cell_input.configure(&_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE); | |
| _cell_to_input_outstage_res.allocator()->allocate(); | |
| } | |
| Tensor *input_activation_input = &_recurrent_to_input_outstage_res; | |
| if(_has_layer_norm) | |
| { | |
| configure_layer_norm(LayerNormGate::Input, input_activation_input); | |
| input_activation_input->allocator()->allocate(); | |
| input_activation_input = &get_layer_norm_output(LayerNormGate::Input); | |
| } | |
| _input_gate_sigmoid.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); | |
| input_activation_input->allocator()->allocate(); | |
| } | |
| // Cell. | |
| // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication | |
| _pixelwise_mul_forget_cell.configure(&_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| const float cell_gate_scale = _cell_gate.info()->quantization_info().uniform().scale; | |
| const float mul_input_cell_scale = cell_gate_scale * std::pow(2, 15 + cell_shift); | |
| const TensorInfo mul_input_cell_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(mul_input_cell_scale, 0)); | |
| _memory_group.manage(&_mul_input_cell_res); | |
| _mul_input_cell_res.allocator()->init(mul_input_cell_info); | |
| _pixelwise_mul_input_cell.configure(&_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| _cell_gate.allocator()->allocate(); | |
| _add_forget_cell.configure(&_forget_gate, &_mul_input_cell_res, cell_state_out, ConvertPolicy::SATURATE); | |
| _mul_input_cell_res.allocator()->allocate(); | |
| _forget_gate.allocator()->allocate(); | |
| if(_has_cell_clipping) | |
| { | |
| _cell_clip.configure(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, quantized_cell_clip)); | |
| } | |
| // 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->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale(); | |
| configure_mm(_mm_input_to_output, _input_to_output_outstage, gemmlowp_info, | |
| input, &_input_to_output_weights_transposed, &_input_to_output_eff_bias, | |
| &_mm_input_to_output_res, &_input_to_output_outstage_res, input_to_output_scale, | |
| mm_out_info, output_outstage_info); | |
| const float recurrent_to_output_scale = recurrent_to_output_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale(); | |
| configure_mm(_mm_recurrent_to_output, _recurrent_to_output_outstage, gemmlowp_info, | |
| output_state_in, &_recurrent_to_output_weights_transposed, &_recurrent_to_output_eff_bias, | |
| &_mm_recurrent_to_output_res, &_recurrent_to_output_outstage_res, recurrent_to_output_scale, | |
| mm_out_info, output_outstage_info); | |
| _accumulate_input_recurrent_output.configure(&_recurrent_to_output_outstage_res, &_input_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE); | |
| _input_to_output_outstage_res.allocator()->allocate(); | |
| if(_has_peephole) | |
| { | |
| // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication | |
| // Here we are not using the output stage because all operations are done in float | |
| _mul_cell_to_output_res.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::S32)); | |
| _memory_group.manage(&_mul_cell_to_output_res); | |
| _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| 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(); | |
| quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift); | |
| _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))); | |
| _memory_group.manage(&_cell_to_output_outstage_res); | |
| _cell_to_output_outstage.configure(&_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res, gemmlowp_info); | |
| _mul_cell_to_output_res.allocator()->allocate(); | |
| _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE); | |
| _cell_to_output_outstage_res.allocator()->allocate(); | |
| } | |
| Tensor *output_activation_input = &_recurrent_to_output_outstage_res; | |
| if(_has_layer_norm) | |
| { | |
| configure_layer_norm(LayerNormGate::Output, output_activation_input); | |
| output_activation_input->allocator()->allocate(); | |
| output_activation_input = &get_layer_norm_output(LayerNormGate::Output); | |
| } | |
| const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| _memory_group.manage(&_output_gate); | |
| _output_gate.allocator()->init(output_gate_info); | |
| _output_gate_sigmoid.configure(output_activation_input, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); | |
| output_activation_input->allocator()->allocate(); | |
| // Hidden. | |
| _hidden_tanh.configure(cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)); | |
| // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication | |
| _memory_group.manage(&_hidden_mul_res); | |
| const TensorInfo hidden_mul_res(_input_gate.info()->tensor_shape(), 1, DataType::S32); | |
| _hidden_mul_res.allocator()->init(hidden_mul_res); | |
| _pixelwise_mul_hidden.configure(&_output_gate, &_input_gate, &_hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); | |
| _output_gate.allocator()->allocate(); | |
| _input_gate.allocator()->allocate(); | |
| const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15); | |
| quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true); | |
| gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero(); | |
| gemmlowp_info.output_data_type = output_state_in->info()->data_type(); | |
| _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()); | |
| _hidden_gate.info()->set_tensor_shape(_hidden_mul_res.info()->tensor_shape()); | |
| hidden_gate_result = &_hidden_gate; | |
| } | |
| _hidden_outstage.configure(&_hidden_mul_res, nullptr, hidden_gate_result, gemmlowp_info); | |
| _hidden_mul_res.allocator()->allocate(); | |
| // Projection. | |
| if(_has_projection) | |
| { | |
| 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; | |
| gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest(); | |
| gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); | |
| gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; | |
| 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, &_projection_eff_bias, | |
| &_mm_projection_res, &_projection_outstage_res, projection_scale, | |
| projection_mm_out_info, projection_outstage_info); | |
| ITensor *accumulate_destination = output_state_out; | |
| if(_projection_tensor_copy_required) | |
| { | |
| _hidden_gate.allocator()->allocate(); | |
| _projection_accumulate_res.allocator()->init(*output_state_in->info()); | |
| _projection_accumulate_res.info()->set_tensor_shape(_projection_outstage_res.info()->tensor_shape()); | |
| _projection_output_to_accumulate_copy.configure(*output_state_in, _projection_accumulate_res); | |
| accumulate_destination = &_projection_accumulate_res; | |
| } | |
| _accumulate_projection.configure(&_projection_outstage_res, accumulate_destination, accumulate_destination, ConvertPolicy::SATURATE); | |
| _projection_outstage_res.allocator()->allocate(); | |
| if(_projection_tensor_copy_required) | |
| { | |
| _projection_accumulate_to_output_copy.configure(_projection_accumulate_res, *output_state_out); | |
| _projection_accumulate_res.allocator()->allocate(); | |
| } | |
| int8_t quantized_projection_clip{ 0 }; | |
| if(lstm_params.projection_clip() > 0.0f) | |
| { | |
| quantized_projection_clip = utility::clamp<int8_t>(lstm_params.projection_clip() / qprojection.scale, -128, 127); | |
| } | |
| if(quantized_projection_clip > 0) | |
| { | |
| _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip, quantized_projection_clip)); | |
| _has_projection_clipping = true; | |
| } | |
| } | |
| else | |
| { | |
| if(_projection_tensor_copy_required) | |
| { | |
| _hidden_to_output_copy.configure(_hidden_gate, *output_state_out); | |
| _hidden_gate.allocator()->allocate(); | |
| } | |
| } | |
| // Copy output_state_out to output | |
| _copy_output.configure(output_state_out, output); | |
| } | |
| Status NEQLSTMLayer::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) | |
| { | |
| 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, | |
| recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, | |
| cell_state_out, output_state_out, output); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != 2, "Input must have exactly 2 dimensions"); | |
| const unsigned int input_size = input->dimension(0); | |
| const unsigned int batch_size = input->dimension(1); | |
| const unsigned int num_units = input_to_output_weights->dimension(1); | |
| const unsigned int output_size = output_state_out->dimension(_out_state_output_size_dimension_idx); | |
| ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2); | |
| ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_output_weights, input_to_forget_weights, input_to_cell_weights); | |
| ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() != 2); | |
| ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->dimension(1) != num_units); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_to_forget_weights, 1, DataType::QASYMM8_SIGNED, DataType::QSYMM8); | |
| // If the input_to_forget_weights data type is DataType::QSYMM8 then it can never match the other weights as they are all DataType::QASYMM8_SIGNED | |
| if (input_to_forget_weights->data_type() == DataType::QSYMM8) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_cell_weights, input_to_output_weights, | |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, input_to_cell_weights, input_to_output_weights, | |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() != 1); | |
| ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->dimension(0) != num_units); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, cell_bias, output_gate_bias); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(forget_gate_bias, 1, DataType::S32); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, cell_bias, output_gate_bias); | |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() != 2); | |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(0) != num_units); | |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(1) != batch_size); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(cell_state_in, 1, DataType::QSYMM16); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() != 2); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(0) != output_size); | |
| ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(1) != batch_size); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_in); | |
| // Check whether peephole weights are all there or none | |
| if(lstm_params.has_peephole_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() != 1); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->dimension(0) != num_units); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights()); | |
| if(!lstm_params.has_cifg_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights()); | |
| } | |
| } | |
| const UniformQuantizationInfo qinput = input->quantization_info().uniform(); | |
| const UniformQuantizationInfo qcell_state_in = cell_state_in->quantization_info().uniform(); | |
| const UniformQuantizationInfo qoutput_state_in = output_state_in->quantization_info().uniform(); | |
| // Calculate and decompose effective scales for optimizing matmul calculation | |
| const int32_t cell_shift = log2(qcell_state_in.scale); | |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_shift > -9); | |
| // Calculate quantized parameters for clipping. | |
| int16_t quantized_cell_clip = 0; | |
| if(lstm_params.cell_clip() > 0.0f) | |
| { | |
| quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in); | |
| } | |
| // Precompute effective bias for optimizing the matmul computations. | |
| const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32); | |
| const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32); | |
| if(!lstm_params.has_cifg_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, | |
| -qinput.offset, true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.recurrent_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, | |
| -qoutput_state_in.offset, | |
| true))); | |
| } | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, | |
| -qoutput_state_in.offset, true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, | |
| true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true))); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, | |
| -qoutput_state_in.offset, true))); | |
| if(lstm_params.has_projection()) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false, | |
| lstm_params.hidden_state_zero(), | |
| true))); | |
| if(lstm_params.projection_bias() != nullptr) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.projection_bias(), 1, DataType::S32); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(lstm_params.projection_bias(), &projection_eff_bias_info, &projection_eff_bias_info, ConvertPolicy::SATURATE)); | |
| } | |
| } | |
| const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_cell_weights->data_type(), input_to_cell_weights->quantization_info()); | |
| const TensorInfo input_to_output_weights_transposed(TensorShape(num_units, input_size), 1, input_to_output_weights->data_type(), input_to_output_weights->quantization_info()); | |
| const TensorInfo recurrent_to_forget_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info()); | |
| const TensorInfo recurrent_to_cell_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_cell_weights->data_type(), recurrent_to_cell_weights->quantization_info()); | |
| const TensorInfo recurrent_to_output_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_output_weights->data_type(), recurrent_to_output_weights->quantization_info()); | |
| const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info()); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_cell_weights, &input_weights_transposed)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(input_to_output_weights, &input_to_output_weights_transposed)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_forget_weights, &recurrent_to_forget_weights_transposed)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_cell_weights, &recurrent_to_cell_weights_transposed)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(recurrent_to_output_weights, &recurrent_to_output_weights_transposed)); | |
| if(!lstm_params.has_cifg_opt()) | |
| { | |
| const TensorInfo recurrent_to_input_weights_transposed(TensorShape(num_units, output_size), 1, | |
| recurrent_to_forget_weights->data_type(), lstm_params.recurrent_to_input_weights()->quantization_info()); | |
| const TensorInfo input_to_input_weights_transposed(TensorShape(num_units, input_size), 1, | |
| lstm_params.input_to_input_weights()->data_type(), lstm_params.input_to_input_weights()->quantization_info()); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.input_to_input_weights(), &input_to_input_weights_transposed)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_to_input_weights_transposed)); | |
| } | |
| if(lstm_params.has_projection()) | |
| { | |
| 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(NETranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed)); | |
| } | |
| GEMMLowpOutputStageInfo gemmlowp_info; | |
| gemmlowp_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; | |
| gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int16_t>::lowest(); | |
| gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max(); | |
| gemmlowp_info.output_data_type = DataType::QSYMM16; | |
| const bool has_layer_norm = lstm_params.use_layer_norm(); | |
| // Forget gate. | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_intermediate_scale() == 0); | |
| 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(); | |
| 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(); | |
| 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(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE)); | |
| if(lstm_params.has_peephole_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE, | |
| RoundingPolicy::TO_ZERO)); | |
| 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(); | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, nullptr, &forget_outstage_info, gemmlowp_info)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE)); | |
| } | |
| if(has_layer_norm) | |
| { | |
| const ITensorInfo *w_info = lstm_params.forget_layer_norm_weights(); | |
| const ITensorInfo *b_info = forget_gate_bias; | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(forget_outstage_info, *w_info, *b_info)); | |
| } | |
| // Output quantization info of Sigmoid and Tanh activations | |
| const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0); | |
| const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&forget_outstage_info, &forget_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); | |
| // Modulation gate. | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_intermediate_scale() == 0); | |
| 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(); | |
| 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(); | |
| 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(NEArithmeticAddition::validate(&cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE)); | |
| if(has_layer_norm) | |
| { | |
| const ITensorInfo *w_info = lstm_params.cell_layer_norm_weights(); | |
| const ITensorInfo *b_info = cell_bias; | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info)); | |
| } | |
| const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&cell_outstage_info, &cell_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); | |
| // Input gate. | |
| const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| if(lstm_params.has_cifg_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(lstm_params.input_gate_bias() != nullptr, "Input gate bias must not be present when CIFG is used"); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtraction::validate(&input_gate_info, &forget_gate_info, &forget_gate_info, ConvertPolicy::SATURATE)); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.input_gate_bias()); | |
| // If the input_to_forget_weights data type is DataType::QSYMM8 then it can never match the other weights as they are all DataType::QASYMM8_SIGNED | |
| if (input_to_forget_weights->data_type() == DataType::QSYMM8) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights()); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights()); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_forget_weights, lstm_params.input_to_input_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_forget_weights, lstm_params.recurrent_to_input_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.input_gate_bias()); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias()); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_intermediate_scale() == 0); | |
| 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(); | |
| 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(); | |
| 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(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); | |
| if(lstm_params.has_peephole_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_in, lstm_params.cell_to_input_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE, | |
| RoundingPolicy::TO_ZERO)); | |
| 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(); | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE)); | |
| } | |
| if(has_layer_norm) | |
| { | |
| const ITensorInfo *w_info = lstm_params.input_layer_norm_weights(); | |
| const ITensorInfo *b_info = lstm_params.input_gate_bias(); | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(input_outstage_info, *w_info, *b_info)); | |
| } | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); | |
| } | |
| // Cell. | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&input_gate_info, cell_state_in, &cell_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate_info, &cell_gate_info, cell_state_out, ConvertPolicy::SATURATE)); | |
| if(quantized_cell_clip > 0) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, | |
| quantized_cell_clip))); | |
| } | |
| // Output gate. | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_intermediate_scale() == 0); | |
| 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(); | |
| 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(); | |
| 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(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE)); | |
| if(lstm_params.has_peephole_opt()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_output_weights(), 1, DataType::QSYMM16); | |
| // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplication | |
| // Here we are not using the output stage because all operations are done in float | |
| // 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(); | |
| // ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(cell_state_out, lstm_params.cell_to_output_weights(), &output_outstage_info, 1.f, ConvertPolicy::SATURATE, | |
| RoundingPolicy::TO_ZERO)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE)); | |
| } | |
| if(has_layer_norm) | |
| { | |
| const ITensorInfo *w_info = lstm_params.output_layer_norm_weights(); | |
| const ITensorInfo *b_info = output_gate_bias; | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(output_outstage_info, *w_info, *b_info)); | |
| } | |
| const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&output_outstage_info, &output_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); | |
| // Hidden. | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))); | |
| const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32); | |
| const TensorInfo hidden_out_info(TensorShape(num_units, batch_size), 1, DataType::QASYMM8_SIGNED); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.hidden_state_scale() == 0); | |
| const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15); | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true)); | |
| gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero(); | |
| gemmlowp_info.output_data_type = hidden_out_info.data_type(); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info)); | |
| const bool projection_tensor_copy_required = num_units != output_size; | |
| // Projection. | |
| if(lstm_params.has_projection()) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_forget_weights, lstm_params.projection_weights()); | |
| ARM_COMPUTE_RETURN_ERROR_ON(qoutput_state_in.scale == 0); | |
| const UniformQuantizationInfo qprojection = lstm_params.projection_weights()->quantization_info().uniform(); | |
| const float projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale; | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(projection_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift)); | |
| gemmlowp_info.gemmlowp_offset = qoutput_state_in.offset; | |
| gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest(); | |
| gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max(); | |
| gemmlowp_info.output_data_type = DataType::QASYMM8_SIGNED; | |
| 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()); | |
| TensorInfo projection_mm_out_info{ mm_out_info }; | |
| projection_mm_out_info.set_tensor_shape(TensorShape(output_size, batch_size)); | |
| 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) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(*output_state_in, projection_outstage_info)); | |
| } | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE)); | |
| if(projection_tensor_copy_required) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out)); | |
| } | |
| int8_t quantized_projection_clip{ 0 }; | |
| if(lstm_params.projection_clip() > 0.0f) | |
| { | |
| quantized_projection_clip = quantize_qasymm8_signed(lstm_params.projection_clip(), qprojection); | |
| } | |
| if(quantized_projection_clip > 0) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip, | |
| quantized_projection_clip))); | |
| } | |
| } | |
| else | |
| { | |
| if(projection_tensor_copy_required) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(hidden_out_info, *output_state_out)); | |
| } | |
| } | |
| if(cell_state_out->total_size() > 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(cell_state_in, cell_state_out); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(cell_state_in, cell_state_out); | |
| } | |
| if(output_state_out->total_size() > 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_out); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output_state_in, output_state_out); | |
| } | |
| ARM_COMPUTE_RETURN_ON_ERROR(NECopy::validate(output_state_out, output)); | |
| return Status{}; | |
| } | |
| void NEQLSTMLayer::run() | |
| { | |
| prepare(); | |
| // Acquire all the temporaries | |
| MemoryGroupResourceScope scope_mg(_memory_group); | |
| // Forget gate. | |
| _mm_input_to_forget.run(); | |
| _input_to_forget_outstage.run(); | |
| _mm_recurrent_to_forget.run(); | |
| _recurrent_to_forget_outstage.run(); | |
| _accumulate_input_recurrent_forget.run(); | |
| if(_has_peephole) | |
| { | |
| _pixelwise_mul_cell_to_forget.run(); | |
| _cell_to_forget_outstage.run(); | |
| _accumulate_cell_forget.run(); | |
| } | |
| if(_has_layer_norm) | |
| { | |
| NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Forget).get(), Window::DimY); | |
| } | |
| _forget_gate_sigmoid.run(); | |
| // Modulation gate. | |
| _mm_input_to_cell.run(); | |
| _input_to_cell_outstage.run(); | |
| _mm_recurrent_to_cell.run(); | |
| _recurrent_to_cell_outstage.run(); | |
| _accumulate_input_recurrent_modulation.run(); | |
| if(_has_layer_norm) | |
| { | |
| NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Cell).get(), Window::DimY); | |
| } | |
| _cell_gate_tanh.run(); | |
| // Input gate | |
| if(_has_cifg) | |
| { | |
| _input_gate_sub.run(); | |
| } | |
| else | |
| { | |
| _mm_input_to_input.run(); | |
| _input_to_input_outstage.run(); | |
| _mm_recurrent_to_input.run(); | |
| _recurrent_to_input_outstage.run(); | |
| _accumulate_input_recurrent_input.run(); | |
| if(_has_peephole) | |
| { | |
| _pixelwise_mul_cell_to_input.run(); | |
| _cell_to_input_outstage.run(); | |
| _accumulate_cell_input.run(); | |
| } | |
| if(_has_layer_norm) | |
| { | |
| NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Input).get(), Window::DimY); | |
| } | |
| _input_gate_sigmoid.run(); | |
| } | |
| // Cell. | |
| _pixelwise_mul_forget_cell.run(); | |
| _pixelwise_mul_input_cell.run(); | |
| _add_forget_cell.run(); | |
| if(_has_cell_clipping) | |
| { | |
| _cell_clip.run(); | |
| } | |
| // Output gate. | |
| _mm_input_to_output.run(); | |
| _input_to_output_outstage.run(); | |
| _mm_recurrent_to_output.run(); | |
| _recurrent_to_output_outstage.run(); | |
| _accumulate_input_recurrent_output.run(); | |
| if(_has_peephole) | |
| { | |
| _pixelwise_mul_cell_to_output.run(); | |
| _cell_to_output_outstage.run(); | |
| _accumulate_cell_to_output.run(); | |
| } | |
| if(_has_layer_norm) | |
| { | |
| NEScheduler::get().schedule(get_layer_norm(LayerNormGate::Output).get(), Window::DimY); | |
| } | |
| _output_gate_sigmoid.run(); | |
| // Hidden. | |
| _hidden_tanh.run(); | |
| _pixelwise_mul_hidden.run(); | |
| _hidden_outstage.run(); | |
| // Projection. | |
| if(_has_projection) | |
| { | |
| _mm_projection.run(); | |
| _projection_outstage.run(); | |
| if(_projection_tensor_copy_required) | |
| { | |
| _projection_output_to_accumulate_copy.run(); | |
| } | |
| _accumulate_projection.run(); | |
| if(_projection_tensor_copy_required) | |
| { | |
| _projection_accumulate_to_output_copy.run(); | |
| } | |
| if(_has_projection_clipping) | |
| { | |
| _projection_clip.run(); | |
| } | |
| } | |
| else | |
| { | |
| if(_projection_tensor_copy_required) | |
| { | |
| _hidden_to_output_copy.run(); | |
| } | |
| } | |
| // Copy output_state_out to output | |
| _copy_output.run(); | |
| } | |
| void NEQLSTMLayer::prepare() | |
| { | |
| if(!_is_prepared) | |
| { | |
| if(_convert_input_to_forget_weights_to_qsymm8) | |
| { | |
| _input_to_forget_weights_f32.allocator()->allocate(); | |
| _input_to_forget_weights_symm8.allocator()->allocate(); | |
| _dequantize_input_to_forget_weights.run(); | |
| _quantize_input_to_forget_weights.run(); | |
| } | |
| // Pre-transpose weights to be used in GEMM. | |
| _input_to_forget_weights_transposed.allocator()->allocate(); | |
| _input_to_cell_weights_transposed.allocator()->allocate(); | |
| _input_to_output_weights_transposed.allocator()->allocate(); | |
| _recurrent_to_forget_weights_transposed.allocator()->allocate(); | |
| _recurrent_to_cell_weights_transposed.allocator()->allocate(); | |
| _recurrent_to_output_weights_transposed.allocator()->allocate(); | |
| _transpose_input_to_forget_weights.run(); | |
| _transpose_input_to_cell_weights.run(); | |
| _transpose_input_to_output_weights.run(); | |
| _transpose_recurrent_to_forget_weights.run(); | |
| _transpose_recurrent_to_cell_weights.run(); | |
| _transpose_recurrent_to_output_weights.run(); | |
| // Precompute effective biases | |
| if(_has_cifg) | |
| { | |
| std::fill_n(reinterpret_cast<int16_t *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 32767); | |
| } | |
| else | |
| { | |
| _input_to_input_eff_bias.allocator()->allocate(); | |
| _recurrent_to_input_eff_bias.allocator()->allocate(); | |
| ITensorPack packII = | |
| { | |
| { TensorType::ACL_SRC, _input_to_input_weights }, | |
| { TensorType::ACL_DST, &_input_to_input_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_input_to_input_reduction.get(), Window::DimY, _input_to_input_reduction->window(), packII); | |
| ITensorPack packRI = | |
| { | |
| { TensorType::ACL_SRC, _recurrent_to_input_weights }, | |
| { TensorType::ACL_DST, &_recurrent_to_input_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_recurrent_to_input_reduction.get(), Window::DimY, _recurrent_to_input_reduction->window(), packRI); | |
| _input_to_input_weights_transposed.allocator()->allocate(); | |
| _recurrent_to_input_weights_transposed.allocator()->allocate(); | |
| _transpose_input_to_input_weights.run(); | |
| _transpose_recurrent_to_input_weights.run(); | |
| _input_to_input_weights->mark_as_unused(); | |
| _recurrent_to_input_weights->mark_as_unused(); | |
| } | |
| _input_to_forget_eff_bias.allocator()->allocate(); | |
| _recurrent_to_forget_eff_bias.allocator()->allocate(); | |
| _input_to_cell_eff_bias.allocator()->allocate(); | |
| _recurrent_to_cell_eff_bias.allocator()->allocate(); | |
| _input_to_output_eff_bias.allocator()->allocate(); | |
| _recurrent_to_output_eff_bias.allocator()->allocate(); | |
| ITensorPack packIF = | |
| { | |
| { TensorType::ACL_SRC, _input_to_forget_weights }, | |
| { TensorType::ACL_DST, &_input_to_forget_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_input_to_forget_reduction.get(), Window::DimY, _input_to_forget_reduction->window(), packIF); | |
| ITensorPack packRF = | |
| { | |
| { TensorType::ACL_SRC, _recurrent_to_forget_weights }, | |
| { TensorType::ACL_DST, &_recurrent_to_forget_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_recurrent_to_forget_reduction.get(), Window::DimY, _recurrent_to_forget_reduction->window(), packRF); | |
| ITensorPack packIC = | |
| { | |
| { TensorType::ACL_SRC, _input_to_cell_weights }, | |
| { TensorType::ACL_DST, &_input_to_cell_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_input_to_cell_reduction.get(), Window::DimY, _input_to_cell_reduction->window(), packIC); | |
| ITensorPack packRC = | |
| { | |
| { TensorType::ACL_SRC, _recurrent_to_cell_weights }, | |
| { TensorType::ACL_DST, &_recurrent_to_cell_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_recurrent_to_cell_reduction.get(), Window::DimY, _recurrent_to_cell_reduction->window(), packRC); | |
| ITensorPack packIO = | |
| { | |
| { TensorType::ACL_SRC, _input_to_output_weights }, | |
| { TensorType::ACL_DST, &_input_to_output_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_input_to_output_reduction.get(), Window::DimY, _input_to_output_reduction->window(), packIO); | |
| ITensorPack packRO = | |
| { | |
| { TensorType::ACL_SRC, _recurrent_to_output_weights }, | |
| { TensorType::ACL_DST, &_recurrent_to_output_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_recurrent_to_output_reduction.get(), Window::DimY, _recurrent_to_output_reduction->window(), packRO); | |
| if(_has_projection) | |
| { | |
| _projection_eff_bias.allocator()->allocate(); | |
| ITensorPack pack = | |
| { | |
| { TensorType::ACL_SRC, _projection_weights }, | |
| { TensorType::ACL_DST, &_projection_eff_bias } | |
| }; | |
| NEScheduler::get().schedule_op(_projection_reduction.get(), Window::DimY, _projection_reduction->window(), pack); | |
| if(_projection_bias != nullptr) | |
| { | |
| _projection_bias_add.run(); | |
| _projection_bias->mark_as_unused(); | |
| } | |
| _projection_weights_transposed.allocator()->allocate(); | |
| _transpose_projection_weights.run(); | |
| _projection_weights->mark_as_unused(); | |
| if(!_projection_tensor_copy_required) | |
| { | |
| _hidden_gate.mark_as_unused(); | |
| _projection_accumulate_res.mark_as_unused(); | |
| } | |
| } | |
| // Mark weights as unused | |
| _input_to_forget_weights->mark_as_unused(); | |
| _input_to_cell_weights->mark_as_unused(); | |
| _input_to_output_weights->mark_as_unused(); | |
| _recurrent_to_forget_weights->mark_as_unused(); | |
| _recurrent_to_cell_weights->mark_as_unused(); | |
| _recurrent_to_output_weights->mark_as_unused(); | |
| _is_prepared = true; | |
| } | |
| } | |
| } // namespace arm_compute |