| /* | |
| * Copyright (c) 2021-2023 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 "src/cpu/operators/CpuGemmDirectConv2d.h" | |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" | |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" | |
| #include "arm_compute/runtime/FunctionDescriptors.h" | |
| #include "src/common/utils/Log.h" | |
| #include "src/core/helpers/MemoryHelpers.h" | |
| #include "src/cpu/utils/CpuAuxTensorHandler.h" | |
| #include "support/Cast.h" | |
| #include <set> | |
| namespace arm_compute | |
| { | |
| namespace cpu | |
| { | |
| using namespace arm_compute::experimental; | |
| using namespace arm_compute::utils::cast; | |
| namespace | |
| { | |
| GEMMLowpOutputStageInfo calculate_output_stage_metadata(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, 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 = src->quantization_info(); | |
| const QuantizationInfo wqinfo = weights->quantization_info(); | |
| const QuantizationInfo oqinfo = (dst->total_size() == 0) ? iqinfo : dst->quantization_info(); | |
| const UniformQuantizationInfo uoqinfo = oqinfo.uniform(); | |
| const DataType data_type = src->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; | |
| } | |
| cpu::AsmGemmInfo init_assembly_metadata(const Conv2dInfo &info, bool is_indirect) | |
| { | |
| cpu::AsmGemmInfo asm_info; | |
| asm_info.method = is_indirect ? cpu::AsmConvMethod::Indirect : cpu::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; | |
| asm_info.fast_mode = info.enable_fast_math; | |
| asm_info.fixed_format = info.weights_info.weight_format() != WeightFormat::UNSPECIFIED; | |
| asm_info.weight_format = info.weights_info.weight_format(); | |
| return asm_info; | |
| } | |
| } // namespace | |
| CpuGemmDirectConv2d::CpuGemmDirectConv2d() | |
| : _gemm_asm_func(std::make_unique<CpuGemmAssemblyDispatch>()), | |
| _activation_func(std::make_unique<CpuActivation>()), | |
| _weights_permute_func(std::make_unique<CpuPermute>()), | |
| _aux_mem(AuxTensorIdx::Count), | |
| _perm_weights(), | |
| _run_activation(false), | |
| _is_prepared(false) | |
| { | |
| } | |
| CpuGemmDirectConv2d::~CpuGemmDirectConv2d() = default; | |
| void CpuGemmDirectConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const Conv2dInfo &info) | |
| { | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); | |
| ARM_COMPUTE_ERROR_THROW_ON(CpuGemmDirectConv2d::validate(src, | |
| weights, | |
| biases != nullptr ? biases : nullptr, | |
| dst, | |
| info)); | |
| ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, info); | |
| _run_activation = info.act_info.enabled() && !_gemm_asm_func->is_activation_supported(info.act_info); | |
| _is_prepared = false; | |
| _weights_permute_func->configure(weights, &_perm_weights, PermutationVector{ 3, 0, 1, 2 }); | |
| // Configure assembly dispatch | |
| cpu::AsmGemmInfo asm_info = init_assembly_metadata(info, false); | |
| if(is_data_type_quantized(src->data_type())) | |
| { | |
| asm_info.output_stage = calculate_output_stage_metadata(src, weights, dst, info.act_info); | |
| } | |
| _gemm_asm_func->configure(src, &_perm_weights, biases, dst, asm_info); | |
| // Configure activation | |
| if(_run_activation) | |
| { | |
| _activation_func->configure(dst, nullptr, info.act_info); | |
| } | |
| // Add auxiliary memory requirements of the assembly dispatch | |
| auto asm_mem_req = _gemm_asm_func->workspace(); | |
| _aux_mem[AsmGemmWorkspace] = asm_mem_req[AsmGemmWorkspace]; | |
| _aux_mem[Pretranspose] = asm_mem_req[Pretranspose]; | |
| if(_aux_mem[Pretranspose].size > 0) | |
| { | |
| // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch | |
| _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, weights->total_size()); | |
| } | |
| else | |
| { | |
| // We must permute weights if they are WeightFormat::UNSPECIFIED | |
| if(info.weights_info.weight_format() == WeightFormat::UNSPECIFIED) | |
| _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Persistent, weights->total_size()); | |
| } | |
| } | |
| Status CpuGemmDirectConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &info) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 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); | |
| if(!is_fixed_format(info.weights_info.weight_format())) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, 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(src->data_layout() != DataLayout::NHWC, "Data layout supported is NHWC"); | |
| const DataType data_type = src->data_type(); | |
| const TensorShape i_shape = src->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(info.dilation != Size2D(1U, 1U)); | |
| 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(src, biases); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); | |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); | |
| } | |
| cpu::AsmGemmInfo asm_info = init_assembly_metadata(info, false); | |
| ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuGemmAssemblyDispatch::validate(src, weights, biases, dst, asm_info)); | |
| return Status{}; | |
| } | |
| void CpuGemmDirectConv2d::run(ITensorPack &tensors) | |
| { | |
| prepare(tensors); | |
| _gemm_asm_func->run(tensors); | |
| if(_run_activation) | |
| { | |
| ITensor *io = tensors.get_tensor(ACL_DST); | |
| ITensorPack pack{ { ACL_SRC, io }, { ACL_DST, io } }; | |
| _activation_func->run(pack); | |
| } | |
| } | |
| void CpuGemmDirectConv2d::prepare(ITensorPack &tensors) | |
| { | |
| if(!_is_prepared) | |
| { | |
| // If we are using fixed-format kernel the weights are already reshaped | |
| if(_gemm_asm_func && _gemm_asm_func->isVarWeightsKernel()) | |
| { | |
| _gemm_asm_func->prepare(tensors); | |
| _is_prepared = true; | |
| return; | |
| } | |
| const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1); | |
| ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights))); | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux); | |
| CpuAuxTensorHandler permuted_weights(_perm_weights, *weights_aux); | |
| ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } }; | |
| _weights_permute_func->run(permute_tensors); | |
| tensors.add_const_tensor(ACL_SRC_1, permuted_weights.get()); | |
| // Call prepare of assembly dispatch | |
| _gemm_asm_func->prepare(tensors); | |
| _is_prepared = true; | |
| } | |
| } | |
| experimental::MemoryRequirements CpuGemmDirectConv2d::workspace() const | |
| { | |
| return _aux_mem; | |
| } | |
| } // namespace cpu | |
| } // namespace arm_compute |