diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMConv2d.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGEMMConv2d.cpp | 187 |
1 files changed, 68 insertions, 119 deletions
diff --git a/src/runtime/NEON/functions/NEGEMMConv2d.cpp b/src/runtime/NEON/functions/NEGEMMConv2d.cpp index 642b084fb4..6cca02eea9 100644 --- a/src/runtime/NEON/functions/NEGEMMConv2d.cpp +++ b/src/runtime/NEON/functions/NEGEMMConv2d.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020 Arm Limited. + * Copyright (c) 2020-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -22,146 +22,95 @@ * 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> +#include "arm_compute/runtime/Tensor.h" + +#include "src/core/helpers/MemoryHelpers.h" +#include "src/cpu/operators/CpuGemmDirectConv2d.h" + namespace arm_compute { -namespace +using OperatorType = cpu::CpuGemmDirectConv2d; +using namespace arm_compute::experimental; + +struct NEGEMMConv2d::Impl { -GEMMLowpOutputStageInfo calculate_output_stage_metadata(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act) + const ITensor *weights{nullptr}; + std::unique_ptr<OperatorType> op{nullptr}; + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + WorkspaceData<Tensor> workspace{}; + MemoryGroup memory_group{}; + bool is_prepared{false}; + experimental::MemoryRequirements aux_mem_req{}; +}; + +NEGEMMConv2d::NEGEMMConv2d(const std::shared_ptr<IMemoryManager> &memory_manager) : _impl(std::make_unique<Impl>()) { - // 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; + _impl->memory_group = MemoryGroup(memory_manager); } -AsmGemmInfo init_assembly_metadata(const Conv2dInfo &info, bool is_indirect) + +NEGEMMConv2d::~NEGEMMConv2d() = default; + +void NEGEMMConv2d::configure( + ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const Conv2dInfo &info) { - 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; + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + + _impl->weights = weights; + _impl->is_prepared = false; + _impl->op = std::make_unique<OperatorType>(); + + _impl->op->configure(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), + info); + + _impl->aux_mem_req = _impl->op->workspace(); + _impl->run_pack = {{TensorType::ACL_SRC_0, input}, {TensorType::ACL_SRC_2, biases}, {TensorType::ACL_DST, output}}; + _impl->prep_pack = {{TensorType::ACL_SRC_1, weights}, {TensorType::ACL_SRC_2, biases}}; + _impl->workspace = + manage_workspace<Tensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->prep_pack); } -} // 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) +Status NEGEMMConv2d::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const Conv2dInfo &info) { + return OperatorType::validate(input, weights, biases, output, info); } -void NEGEMMConv2d::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const Conv2dInfo &info) + +void NEGEMMConv2d::run() { - 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); + prepare(); - // 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; - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } -Status NEGEMMConv2d::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const Conv2dInfo &info) + +void NEGEMMConv2d::prepare() { - 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 (!_impl->is_prepared) { - 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) + _impl->op->prepare(_impl->prep_pack); + + auto has_reshape = + std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(), + [](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; }); + + if (has_reshape != std::end(_impl->aux_mem_req)) { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32); + _impl->weights->mark_as_unused(); } else { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->weights); } - 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; + // Release temporary tensors that are only used in prepare stage + release_temporaries<Tensor>(_impl->aux_mem_req, _impl->workspace); + _impl->is_prepared = true; } } } // namespace arm_compute |