| /* |
| * Copyright (c) 2020 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/NEGEMMConv2d.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include <set> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| GEMMLowpOutputStageInfo calculate_output_stage_metadata(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act) |
| { |
| // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| // Extract and negate input and weights offset |
| const QuantizationInfo iqinfo = input->quantization_info(); |
| const QuantizationInfo wqinfo = weights->quantization_info(); |
| const QuantizationInfo oqinfo = (output->total_size() == 0) ? iqinfo : output->quantization_info(); |
| const UniformQuantizationInfo uoqinfo = oqinfo.uniform(); |
| const DataType data_type = input->data_type(); |
| // Merge activation with output stage |
| const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, |
| ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU |
| }; |
| PixelValue type_min{}; |
| PixelValue type_max{}; |
| std::tie(type_min, type_max) = get_min_max(data_type); |
| int32_t min_activation = type_min.get<int32_t>(); |
| int32_t max_activation = type_max.get<int32_t>(); |
| if(supported_acts.count(act.activation()) != 0) |
| { |
| std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act, data_type, uoqinfo); |
| } |
| GEMMLowpOutputStageInfo os_info; |
| os_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| os_info.gemmlowp_offset = uoqinfo.offset; |
| os_info.gemmlowp_min_bound = min_activation; |
| os_info.gemmlowp_max_bound = max_activation; |
| os_info.is_quantized_per_channel = (weights->data_type() == DataType::QSYMM8_PER_CHANNEL); |
| quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, os_info); |
| return os_info; |
| } |
| AsmGemmInfo init_assembly_metadata(const Conv2dInfo &info, bool is_indirect) |
| { |
| AsmGemmInfo asm_info; |
| asm_info.method = is_indirect ? AsmConvMethod::Indirect : AsmConvMethod::Conv; |
| asm_info.ps_info = info.conv_info; |
| asm_info.activation_info = info.act_info; |
| asm_info.depth_output_gemm3d = true; |
| asm_info.reinterpret_input_as_3d = true; |
| asm_info.padding_top = info.conv_info.pad_top(); |
| asm_info.padding_left = info.conv_info.pad_left(); |
| asm_info.padding_value = 0.f; |
| asm_info.negated_offsets = false; |
| return asm_info; |
| } |
| } // namespace |
| |
| NEGEMMConv2d::NEGEMMConv2d(const std::shared_ptr<IMemoryManager> &memory_manager) |
| : _gemm_asm_func(memory_manager), _activation_func(), _weights_permute_func(), _original_weights(nullptr), _permuted_weights(), _is_prepared(false), _run_activation(false) |
| { |
| } |
| void NEGEMMConv2d::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const Conv2dInfo &info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(NEGEMMConv2d::validate(input->info(), |
| weights->info(), |
| biases != nullptr ? biases->info() : nullptr, |
| output->info(), |
| info)); |
| _original_weights = weights; |
| _weights_permute_func.configure(weights, &_permuted_weights, PermutationVector{ 3, 0, 1, 2 }); |
| |
| // Configure assembly dispatch |
| AsmGemmInfo asm_info = init_assembly_metadata(info, false); |
| if(is_data_type_quantized(input->info()->data_type())) |
| { |
| asm_info.output_stage = calculate_output_stage_metadata(input->info(), weights->info(), output->info(), info.act_info); |
| } |
| _gemm_asm_func.configure(input, &_permuted_weights, biases, output, asm_info); |
| |
| // Configure activation |
| if(info.act_info.enabled() && !_gemm_asm_func.is_activation_supported(info.act_info)) |
| { |
| _activation_func.configure(output, nullptr, info.act_info); |
| _run_activation = true; |
| } |
| } |
| Status NEGEMMConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const Conv2dInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.num_groups > 1, "Grouping (num_groups != 1) is not supported on NEON"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() != DataLayout::NHWC, "Data layout supported is NHWC"); |
| const DataType data_type = input->data_type(); |
| const TensorShape i_shape = input->tensor_shape(); |
| const TensorShape w_shape = weights->tensor_shape(); |
| ARM_COMPUTE_RETURN_ERROR_ON(w_shape[0] != i_shape[0]); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| // Validate biases |
| if(biases != nullptr) |
| { |
| if(is_data_type_quantized_asymmetric(data_type)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else if(data_type == DataType::BFLOAT16) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| AsmGemmInfo asm_info = init_assembly_metadata(info, false); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMAssemblyDispatch::validate(input, weights, biases, output, asm_info)); |
| return Status{}; |
| } |
| void NEGEMMConv2d::run() |
| { |
| prepare(); |
| |
| _gemm_asm_func.run(); |
| if(_run_activation) |
| { |
| _activation_func.run(); |
| } |
| } |
| void NEGEMMConv2d::prepare() |
| { |
| if(!_is_prepared) |
| { |
| _permuted_weights.allocator()->allocate(); |
| _weights_permute_func.run(); |
| _original_weights->mark_as_unused(); |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |