COMPMID-845: Create a ConvolutionLayer for CL

Change-Id: Ifcc406d2d0a99c911d6b6c875657b0e0028255d5
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/119148
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 353 insertions, 0 deletions

diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
new file mode 100644
index 0000000000..c4cfe1e24c
--- /dev/null
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -0,0 +1,353 @@
+/*
+ * 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/CL/functions/CLGEMMConvolutionLayer.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/CL/CLScheduler.h"
+
+#include <cmath>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+
+CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type()));
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    const bool       append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+    const unsigned   bias_element  = (append_biases) ? 1 : 0;
+    const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+    _transpose1xW = transpose1xW;
+
+    if(transpose1xW)
+    {
+        // Create tensor to store the reshaped weights
+        const unsigned int mat_weights_cols = weights->info()->dimension(3);
+        const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
+        TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
+        const DataType     dt                   = weights->info()->data_type();
+        const int          fixed_point_position = weights->info()->fixed_point_position();
+        TensorInfo         info_wr(shape_wr, 1, dt, fixed_point_position);
+
+        _weights_reshaped.allocator()->init(info_wr);
+        _memory_group.manage(&_weights_reshaped);
+        _weights_reshape_kernel.configure(weights, biases_to_use, &_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());
+}
+
+void CLConvolutionLayerReshapeWeights::run()
+{
+    _memory_group.acquire();
+
+    CLScheduler::get().enqueue(_weights_reshape_kernel);
+    if(_transpose1xW)
+    {
+        CLScheduler::get().enqueue(_weights_transposed_kernel);
+    }
+
+    _memory_group.release();
+}
+
+CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _interleave_kernel(), _mm_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
+      _col2im_kernel(), _im2col_output(), _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false),
+      _is_interleaved_transposed(false)
+{
+}
+
+void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+    if(_is_quantized)
+    {
+        if(are_weights_reshaped)
+        {
+            ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp");
+        }
+        else
+        {
+            // 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
+    {
+        if(are_weights_reshaped)
+        {
+            // Configure matrix multiply kernel
+            _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+        }
+        else
+        {
+            // Configure matrix multiply function
+            _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+        }
+    }
+}
+
+void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && CLScheduler::get().target() == GPUTarget::BIFROST);
+    ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+    ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type()));
+
+    _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+    if(biases != nullptr)
+    {
+        if(_is_quantized)
+        {
+            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        }
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    const DataType dt = input->info()->data_type();
+
+    // Set the GPU target for matrix multiply and im2col and col2im
+    _mm_kernel.set_target(CLScheduler::get().target());
+    _im2col_kernel.set_target(CLScheduler::get().target());
+    _col2im_kernel.set_target(CLScheduler::get().target());
+
+    const bool append_bias = (biases != nullptr) && (!_is_quantized);
+    _are_weights_reshaped  = weights_info.are_reshaped();
+
+    const unsigned   bias_element  = (append_bias) ? 1 : 0;
+    const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;
+
+    // Get parameters from conv_info
+    unsigned int stride_x = 0;
+    unsigned int stride_y = 0;
+    std::tie(stride_x, stride_y) = conv_info.stride();
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+
+    const unsigned int kernel_width  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
+    const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    // Check if its a "fully connected" convolution
+    const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    _is_interleaved_transposed                = (!is_fully_connected_convolution) && (!_is_quantized) && (_are_weights_reshaped);
+
+    unsigned int mat_weights_cols = weights->info()->dimension(3);
+    unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
+
+    // Reshape weights if needed
+    if(_are_weights_reshaped)
+    {
+        if(is_fully_connected_convolution || _is_quantized)
+        {
+            mat_weights_cols = weights->info()->dimension(0);
+            mat_weights_rows = weights->info()->dimension(1);
+        }
+        else
+        {
+            mat_weights_cols                         = weights_info.num_kernels();
+            const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
+            mat_weights_rows                         = quarter_reshaped_cols + bias_element;
+        }
+    }
+    else
+    {
+        // _weights_reshaped will be auto configured in the kernel.
+        // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
+        _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false);
+
+        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);
+    // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+    TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
+    im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
+    _im2col_output.allocator()->init(im2col_reshaped_info);
+    _memory_group.manage(&_im2col_output);
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm = _im2col_output.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.
+    // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+    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 im2col
+    _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);
+
+    // Configure matrix multiply
+    if(_is_interleaved_transposed)
+    {
+        // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+        _memory_group.manage(&_interleave_output);
+        _interleave_kernel.configure(&_im2col_output, &_interleave_output);
+
+        // Configure GEMM
+        configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped);
+        _interleave_output.allocator()->allocate();
+    }
+    else
+    {
+        configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped);
+    }
+    _im2col_output.allocator()->allocate();
+
+    // Configure output stage for quantized case
+    if(_is_quantized)
+    {
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_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->info()->quantization_info().offset);
+    }
+
+    // Configure Col2Im
+    _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(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();
+    }
+}
+
+void CLGEMMConvolutionLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights.run();
+    }
+
+    _memory_group.acquire();
+
+    // Run im2col
+    CLScheduler::get().enqueue(_im2col_kernel);
+
+    // Note: _is_interleaved_transposed is true only if the weights passed to the function have been passed already reshaped
+    //       and if we do not have QASYMM8 data type. If this flag is true, we need to run the
+    //       gemm kernel instead of gemm function
+    if(_is_interleaved_transposed)
+    {
+        // Run interleave4x4 kernel
+        CLScheduler::get().enqueue(_interleave_kernel);
+
+        // Run matrix multiply kernel
+        CLScheduler::get().enqueue(_mm_kernel);
+    }
+    else
+    {
+        // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
+        if(_is_quantized)
+        {
+            // Run gemmlowp
+            _mm_gemmlowp.run();
+
+            // Run output stage
+            _gemmlowp_output_stage.run();
+        }
+        else
+        {
+            // Run gemm
+            _mm_gemm.run();
+        }
+    }
+
+    // Reshape output matrix
+    CLScheduler::get().enqueue(_col2im_kernel, false);
+
+    _memory_group.release();
+}