arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
new file mode 100644
index 0000000..a85078c
--- /dev/null
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -0,0 +1,652 @@
+/*
+ * Copyright (c) 2017-2018 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/NEGEMMConvolutionLayer.h"
+
+#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
+} // namespace arm_compute
+
+#include <cmath>
+#include <tuple>
+
+namespace
+{
+arm_compute::TensorShape get_reshaped_weights_shape(const arm_compute::ITensorInfo *weights, bool append_bias)
+{
+ const unsigned int mat_weights_cols = weights->dimension(3);
+ const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+ return arm_compute::TensorShape(mat_weights_cols, mat_weights_rows);
+}
+} // namespace
+
+namespace arm_compute
+{
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
+{
+ // Perform validation step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
+ (biases != nullptr) ? biases->info() : nullptr,
+ output->info(),
+ transpose1xW));
+
+ // Check if bias are present, if yes they will be embedded to the weights matrix
+ const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+ //const unsigned bias_element = (append_biases) ? 1 : 0;
+ const ITensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+ _transpose1xW = transpose1xW;
+
+ if(transpose1xW)
+ {
+ // Create tensor to store the reshaped weights
+ TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), append_biases));
+
+ _weights_reshaped.allocator()->init(info_wr);
+ _memory_group.manage(&_weights_reshaped);
+
+ _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+ _weights_transposed_kernel.configure(&_weights_reshaped, output);
+
+ _weights_reshaped.allocator()->allocate();
+ }
+ else
+ {
+ _weights_reshape_kernel.configure(weights, biases_to_use, output);
+ }
+
+ output->info()->set_quantization_info(weights->info()->quantization_info());
+}
+
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+ if(!is_data_type_quantized_asymmetric(weights->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+ }
+ // Check if bias are present, if yes they will be embedded to the weights matrix
+ const bool append_bias = (biases != nullptr);
+
+ if(append_bias)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+
+ // Checks performed when biases are present
+ if(append_bias)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+
+ if(transpose1xW)
+ {
+ TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
+ }
+
+ return Status{};
+}
+
+void NEConvolutionLayerReshapeWeights::run()
+{
+ _memory_group.acquire();
+
+ NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
+
+ if(_transpose1xW)
+ {
+ NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
+ }
+
+ _memory_group.release();
+}
+
+namespace
+{
+TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution)
+{
+ unsigned int mat_weights_cols = weights->dimension(3);
+ unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+ if(is_fully_connected_convolution)
+ {
+ // Create tensor to store the reshaped weights
+ return TensorShape(mat_weights_cols, mat_weights_rows);
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / weights->element_size();
+ return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+ }
+}
+
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
+ bool &append_bias,
+ bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
+ bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized,
+ unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+ unsigned int &conv_w, unsigned int &conv_h)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+
+ dt = input->data_type();
+ is_quantized = is_data_type_quantized_asymmetric(dt);
+
+ if(biases != nullptr)
+ {
+ if(is_quantized)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ }
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+
+ append_bias = (biases != nullptr) && (!is_quantized);
+ are_weights_reshaped = weights_info.are_reshaped();
+ kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
+ kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+ mat_weights_cols = weights->dimension(3);
+ mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+ conv_info);
+
+ // Check if its a "fully connected" convolution
+ is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+ is_interleaved = (!is_fully_connected_convolution && !is_quantized);
+
+ return Status{};
+}
+} // namespace
+
+NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
+ : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
+ _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
+ _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false)
+{
+}
+
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+{
+ if(_is_quantized)
+ {
+ // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
+ // Extract and negate input and weights offset
+ const QuantizationInfo input_quantization_info = input->info()->quantization_info();
+ const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
+
+ input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+ weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+ _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+ // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
+ input->info()->set_quantization_info(input_quantization_info);
+ weights->info()->set_quantization_info(weights_quantization_info);
+ }
+ else
+ {
+ _mm_kernel.configure(input, weights, output, 1.f, is_interleaved, reshape_info);
+ }
+}
+
+void NEGEMMConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K)
+{
+ ARM_COMPUTE_UNUSED(ci);
+ ARM_COMPUTE_UNUSED(M);
+ ARM_COMPUTE_UNUSED(N);
+ ARM_COMPUTE_UNUSED(K);
+#if defined(__arm__) || defined(__aarch64__)
+#if defined(__arm__)
+ GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
+#elif defined(__aarch64__)
+ GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+ constexpr size_t alignment = 4096;
+ _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
+ _memory_group.manage(&_workspace);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+}
+
+void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+ // Perform validate step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+ DataType dt{};
+ unsigned int kernel_width = 0;
+ unsigned int kernel_height = 0;
+ unsigned int mat_weights_cols = 0;
+ unsigned int mat_weights_rows = 0;
+ unsigned int conv_w = 0;
+ unsigned int conv_h = 0;
+
+ Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
+ kernel_width, kernel_height,
+ _is_fully_connected_convolution, _is_interleaved, _is_quantized,
+ mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+
+ ARM_COMPUTE_ERROR_THROW_ON(status);
+
+ const unsigned int fixed_point_position = input->info()->fixed_point_position();
+ const ITensor *biases_to_use = (_append_bias) ? biases : nullptr;
+
+#if defined(__arm__)
+ if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
+ }
+#elif defined(__aarch64__)
+ if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
+ }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+ // Reshape weights if needed
+ if(_mm_optimised_kernel != nullptr)
+ {
+ if(_are_weights_reshaped)
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->info()->dimension(1);
+ }
+ else
+ {
+ TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+ // Create tensor to store the reshaped weights
+ _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+ weights = &_weights_reshaped;
+ }
+ }
+ else
+ {
+ if(_are_weights_reshaped)
+ {
+ if(_is_fully_connected_convolution || _is_quantized)
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->info()->dimension(1);
+ }
+ else
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(2) + (_append_bias ? 1 : 0);
+ }
+ }
+ else
+ {
+ TensorShape reshaped_weights_shape;
+
+ if(_is_fully_connected_convolution || _is_quantized)
+ {
+ reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / input->info()->element_size();
+ reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+ static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+ }
+
+ // Create tensor to store the reshaped weights
+ _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved /* 1xW transpose */);
+ weights = &_weights_reshaped;
+ }
+ }
+
+ // Create tensor to store im2col reshaped inputs
+ const unsigned int mat_input_cols = mat_weights_rows;
+ const unsigned int mat_input_rows = conv_w * conv_h;
+
+ TensorShape shape_im2col(input->info()->tensor_shape());
+ shape_im2col.set(0, mat_input_cols);
+ shape_im2col.set(1, mat_input_rows);
+ shape_im2col.set(2, 1);
+ _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+ _memory_group.manage(&_input_im2col_reshaped);
+
+ // Create tensor (interleave) to prepare input tensor for GEMM
+ if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
+ {
+ TensorShape shape_interleaved(shape_im2col);
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+ _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+ _memory_group.manage(&_input_interleaved_reshaped);
+ }
+
+ // Create GEMM output tensor
+ TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, mat_input_rows);
+ const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+ // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+ info_gemm.set_quantization_info(output->info()->quantization_info());
+ _gemm_output.allocator()->init(info_gemm);
+ _memory_group.manage(&_gemm_output);
+
+ // Configure kernels
+ // Configure im2col
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+
+ // Configure matrix multiply
+ if(_mm_optimised_kernel != nullptr)
+ {
+ struct CPUInfo ci = NEScheduler::get().cpu_info();
+
+ const int M = _gemm_output.info()->tensor_shape().y();
+ const int N = _gemm_output.info()->tensor_shape().x();
+ const int K = _input_im2col_reshaped.info()->tensor_shape().x();
+
+#if defined(__aarch64__)
+ if((N <= 128) && (K <= 128))
+ {
+ _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64NativeKernel>();
+ }
+ else
+#endif /* defined(__aarch64__) */
+ {
+ configure_asm_mm(ci, M, N, K);
+ }
+
+ // Configure matrix multiplication kernel
+ _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
+
+ _workspace.allocator()->allocate();
+ }
+ else
+ {
+ if(_is_interleaved)
+ {
+ // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+ _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+
+ // Configure GEMM
+ configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(1), 0 /* no transpose */,
+ _input_im2col_reshaped.info()->dimension(0)));
+ _input_interleaved_reshaped.allocator()->allocate();
+ }
+ else
+ {
+ configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, _is_interleaved);
+ }
+ }
+
+ _input_im2col_reshaped.allocator()->allocate();
+
+ // Configure output stage for quantized case
+ if(_is_quantized)
+ {
+ const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+ float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ _memory_group.manage(&_tmp_output);
+ _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+ }
+
+ // Configure Col2Im
+ _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+ if(_is_quantized)
+ {
+ _tmp_output.allocator()->allocate();
+ }
+ _gemm_output.allocator()->allocate();
+
+ ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+ // Allocate intermediate tensor
+ if(!_are_weights_reshaped)
+ {
+ _weights_reshaped.allocator()->allocate();
+ }
+}
+
+Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info)
+{
+ ARM_COMPUTE_UNUSED(output);
+
+ DataType dt{};
+ bool append_bias{};
+ bool are_weights_reshaped{};
+ bool is_fully_connected_convolution{};
+ bool is_interleaved{};
+ bool is_quantized{};
+ unsigned int kernel_width = 0;
+ unsigned int kernel_height = 0;
+ unsigned int mat_weights_cols = 0;
+ unsigned int mat_weights_rows = 0;
+ unsigned int conv_w = 0;
+ unsigned int conv_h = 0;
+
+ Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
+ is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows,
+ conv_w, conv_h);
+
+ const Size2D kernel_weights = Size2D(kernel_width, kernel_height);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(status);
+
+ std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
+ bool optimised_kernel = false;
+
+#if defined(__arm__)
+ if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ {
+ optimised_kernel = true;
+ }
+#elif defined(__aarch64__)
+ if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+ {
+ optimised_kernel = true;
+ }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+ // Reshape weights if needed
+ if(optimised_kernel)
+ {
+ if(are_weights_reshaped)
+ {
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->dimension(1);
+ }
+ else
+ {
+ TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+ weights = reshaped_weights.get();
+ }
+ }
+ else
+ {
+ if(are_weights_reshaped)
+ {
+ const unsigned int transpose_width = 16 / input->element_size();
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
+ }
+ else
+ {
+ TensorShape reshaped_weights_shape;
+
+ if(is_fully_connected_convolution || is_quantized)
+ {
+ reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / input->element_size();
+ reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+ static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+ }
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+ weights = reshaped_weights.get();
+ }
+ }
+
+ // Validate im2col
+ const unsigned int mat_input_cols = mat_weights_rows;
+ const unsigned int mat_input_rows = conv_w * conv_h;
+ TensorShape shape_im2col = input->tensor_shape();
+ shape_im2col.set(0, mat_input_cols);
+ shape_im2col.set(1, mat_input_rows);
+ shape_im2col.set(2, 1);
+ TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
+ ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false));
+
+ // Create GEMM output tensor
+ TensorShape shape_gemm(im2_col_info.tensor_shape());
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, mat_input_rows);
+ TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
+
+ // Validate GEMM interleave and multiply
+ if(is_interleaved)
+ {
+ TensorShape shape_interleaved = shape_im2col;
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+ TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ }
+
+ return Status{};
+}
+
+void NEGEMMConvolutionLayer::run()
+{
+ // Run weights reshaping (Runs once for every configure)
+ if(!_are_weights_reshaped)
+ {
+ _are_weights_reshaped = true;
+ _reshape_weights.run();
+ }
+
+ _memory_group.acquire();
+
+ // Run input reshaping
+ NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+
+ // Runs matrix multiply on reshaped matrices
+ if(_mm_optimised_kernel != nullptr)
+ {
+ NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+ }
+ else
+ {
+ if(_is_interleaved)
+ {
+ // Run interleave
+ NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
+ }
+
+ // Runs matrix multiply on reshaped matrices
+ if(_is_quantized)
+ {
+ _mm_gemmlowp.run();
+ }
+ else
+ {
+ NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+ }
+ }
+
+ // Run output stage for quantized case
+ if(_is_quantized)
+ {
+ _gemmlowp_output_stage.run();
+ }
+
+ // Reshape output matrix
+ NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+
+ _memory_group.release();
+}
+} // namespace arm_compute