diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMMConv2d.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGEMMConv2d.cpp | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEGEMMConv2d.cpp b/src/runtime/NEON/functions/NEGEMMConv2d.cpp new file mode 100644 index 0000000000..642b084fb4 --- /dev/null +++ b/src/runtime/NEON/functions/NEGEMMConv2d.cpp @@ -0,0 +1,167 @@ +/* + * 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 |