arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/src/runtime/CL/functions/CLConvolution.cpp b/src/runtime/CL/functions/CLConvolution.cpp
index a9b0867..0131801 100644
--- a/src/runtime/CL/functions/CLConvolution.cpp
+++ b/src/runtime/CL/functions/CLConvolution.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016, 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -53,7 +53,8 @@
}
template <unsigned int matrix_size>
-void CLConvolutionSquare<matrix_size>::configure(ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
+void CLConvolutionSquare<matrix_size>::configure(ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode,
+ uint8_t constant_border_value)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON(conv == nullptr);
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 0ed3351..b3af11e 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -43,9 +43,6 @@
void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
@@ -82,13 +79,14 @@
{
_weights_reshape_kernel.configure(weights, biases_to_use, output);
}
+
+ output->info()->set_quantization_info(weights->info()->quantization_info());
}
void CLConvolutionLayerReshapeWeights::run()
{
_memory_group.acquire();
- cl::CommandQueue q = CLScheduler::get().queue();
CLScheduler::get().enqueue(_weights_reshape_kernel);
if(_transpose1xW)
{
@@ -99,33 +97,49 @@
}
CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(),
- _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
- _are_weights_reshaped(false), _is_quantized(false)
+ : _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 CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed)
+void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped)
{
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();
+ 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));
+ 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*/));
+ _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);
+ // 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_transposed);
+ 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*/));
+ }
}
}
@@ -134,6 +148,7 @@
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()));
@@ -157,14 +172,16 @@
const DataType dt = input->info()->data_type();
- // Set the GPU target for matrix multiply
+ // 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());
- _append_bias = (biases != nullptr) && (!_is_quantized);
- _are_weights_reshaped = weights_info.are_reshaped();
+ 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;
+ 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;
@@ -181,8 +198,8 @@
conv_info);
// Check if its a "fully connected" convolution
- _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- const bool run_interleaved = (!_is_fully_connected_convolution && !_is_quantized);
+ 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;
@@ -190,7 +207,7 @@
// Reshape weights if needed
if(_are_weights_reshaped)
{
- if(_is_fully_connected_convolution || _is_quantized)
+ if(is_fully_connected_convolution || _is_quantized)
{
mat_weights_cols = weights->info()->dimension(0);
mat_weights_rows = weights->info()->dimension(1);
@@ -204,22 +221,10 @@
}
else
{
- if(_is_fully_connected_convolution || _is_quantized)
- {
- // Create tensor to store the reshaped weights
- TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
- }
- _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info());
+ // _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;
}
@@ -230,50 +235,43 @@
shape_im2col.set(0, mat_input_cols);
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
+ //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());
- _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(run_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 interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
- interleaved_info.set_quantization_info(input->info()->quantization_info());
- _input_interleaved_reshaped.allocator()->init(interleaved_info);
- _memory_group.manage(&_input_interleaved_reshaped);
- }
+ _im2col_output.allocator()->init(im2col_reshaped_info);
+ _memory_group.manage(&_im2col_output);
// Create GEMM output tensor
- TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
+ 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.
+ //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 kernels
- _input_im2col_kernel.set_target(CLScheduler::get().target());
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+ // Configure im2col
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);
// Configure matrix multiply
- if(run_interleaved)
+ if(_is_interleaved_transposed)
{
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
- _input_interleaved_reshaped.allocator()->allocate();
+ // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+ _interleave_kernel.configure(&_im2col_output, &_interleave_output);
+ _memory_group.manage(&_interleave_output);
+
+ // Configure GEMM
+ configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped);
+ _interleave_output.allocator()->allocate();
}
else
{
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
+ configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped);
}
- _input_im2col_reshaped.allocator()->allocate();
+ _im2col_output.allocator()->allocate();
// Configure output stage for quantized case
if(_is_quantized)
@@ -286,8 +284,7 @@
}
// Configure Col2Im
- _output_col2im_kernel.set_target(CLScheduler::get().target());
- _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
+ _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
if(_is_quantized)
{
_tmp_output.allocator()->allocate();
@@ -318,32 +315,39 @@
_memory_group.acquire();
// Run im2col
- CLScheduler::get().enqueue(_input_im2col_kernel);
+ CLScheduler::get().enqueue(_im2col_kernel);
- if(!_is_fully_connected_convolution && !_is_quantized)
+ // 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
- CLScheduler::get().enqueue(_input_interleave_kernel);
- }
+ // Run interleave4x4 kernel
+ CLScheduler::get().enqueue(_interleave_kernel);
- // Runs matrix multiply on reshaped matrices
- if(_is_quantized)
- {
- _mm_gemmlowp.run();
+ // Run matrix multiply kernel
+ CLScheduler::get().enqueue(_mm_kernel);
}
else
{
- CLScheduler::get().enqueue(_mm_kernel);
- }
+ // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
+ if(_is_quantized)
+ {
+ // Run gemmlowp
+ _mm_gemmlowp.run();
- // Run output stage for quantized case
- if(_is_quantized)
- {
- _gemmlowp_output_stage.run();
+ // Run output stage
+ _gemmlowp_output_stage.run();
+ }
+ else
+ {
+ // Run gemm
+ _mm_gemm.run();
+ }
}
// Reshape output matrix
- CLScheduler::get().enqueue(_output_col2im_kernel, false);
+ CLScheduler::get().enqueue(_col2im_kernel, false);
_memory_group.release();
}
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
new file mode 100644
index 0000000..1c55722
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -0,0 +1,132 @@
+/*
+ * 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/CLDeconvolutionLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
+ : _memory_group(std::move(memory_manager)),
+ _scale_f(),
+ _conv_f(),
+ _scaled_output()
+{
+}
+
+Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+ unsigned int inner_border_right, unsigned int inner_border_top)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
+
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
+
+ auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->dimension(1),
+ info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
+
+ const TensorShape output_shape = deconvolution_output_shape(out_dims, input->tensor_shape(), weights->tensor_shape());
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias);
+
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, bias);
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
+
+ TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
+ info)));
+ const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info));
+
+ return Status{};
+}
+
+void CLDeconvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+ unsigned int inner_border_right, unsigned int inner_border_top)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+ const unsigned int stride_x = info.stride().first;
+ const unsigned int stride_y = info.stride().second;
+
+ auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
+ info.pad().first, info.pad().second, inner_border_top, inner_border_right, stride_x, stride_y);
+
+ const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
+
+ // Output auto initialization if not yet initialized
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
+
+ _memory_group.manage(&_scaled_output);
+
+ // configure scale function
+ // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
+ TensorShape scale_out_shape(input->info()->tensor_shape());
+ const unsigned int out_x = input->info()->dimension(0) + (input->info()->dimension(0) - 1) * (stride_x - 1) + inner_border_right + 2 * info.pad().first;
+ const unsigned int out_y = input->info()->dimension(1) + (input->info()->dimension(1) - 1) * (stride_y - 1) + inner_border_top + 2 * info.pad().second;
+ scale_out_shape.set(0, out_x);
+ scale_out_shape.set(1, out_y);
+ TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ _scaled_output.allocator()->init(scale_out_info);
+
+ _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), info);
+
+ // setup the function to convolve the upscaled output
+ const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+ _conv_f.configure(&_scaled_output, weights, bias, output, conv_info);
+ _scaled_output.allocator()->allocate();
+}
+
+void CLDeconvolutionLayer::run()
+{
+ _memory_group.acquire();
+ _scale_f.run();
+ _conv_f.run();
+ _memory_group.release();
+}
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
new file mode 100644
index 0000000..13a24f8
--- /dev/null
+++ b/src/runtime/CL/functions/CLDeconvolutionLayerUpsample.cpp
@@ -0,0 +1,64 @@
+/*
+ * 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/CLDeconvolutionLayerUpsample.h"
+
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <cmath>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute;
+
+CLDeconvolutionLayerUpsample::CLDeconvolutionLayerUpsample() // NOLINT
+ : _upsample(),
+ _output(nullptr)
+{
+}
+
+Status CLDeconvolutionLayerUpsample::validate(const ITensorInfo *input, const ITensorInfo *output, const BorderSize &inner_border,
+ const PadStrideInfo &info)
+{
+ return CLDeconvolutionLayerUpsampleKernel::validate(input, output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::configure(ICLTensor *input, ICLTensor *output, const BorderSize &inner_border,
+ const PadStrideInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+ _output = output;
+ _upsample.configure(input, _output, inner_border, info);
+}
+
+void CLDeconvolutionLayerUpsample::run()
+{
+ _output->map(CLScheduler::get().queue(), true);
+ memset(_output->buffer(), 0, _output->info()->total_size());
+ _output->unmap(CLScheduler::get().queue());
+
+ CLScheduler::get().enqueue(_upsample, false);
+}
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 7fd81cd..68c6576 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -25,6 +25,7 @@
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "support/ToolchainSupport.h"
@@ -32,6 +33,34 @@
#include <algorithm>
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output, bool is_interleaved_transposed)
+{
+ const GPUTarget gpu_target = CLScheduler::get().target();
+
+ if(is_data_type_quantized_asymmetric(input.data_type()))
+ {
+ // 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.quantization_info().scale, -input.quantization_info().offset);
+ const QuantizationInfo weights_quantization_info(weights.quantization_info().scale, -weights.quantization_info().offset);
+
+ // Validate gemmlowp function
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info),
+ &weights.clone()->set_quantization_info(weights_quantization_info),
+ &output));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(&input, &weights, &output, 1.f, is_interleaved_transposed, gpu_target));
+ }
+
+ return Status{};
+}
+} // namespace
void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output)
{
@@ -40,6 +69,11 @@
_kernel = std::move(k);
}
+Status CLFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+ return CLTransposeKernel::validate(input, output);
+}
+
CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _im2col_output(),
_gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false)
@@ -80,8 +114,7 @@
// If the fully connected layer is called after a convolution layer, the input tensor must be linearized
// Initialize output tensor for im2col
- TensorShape shape_im2col = input->info()->tensor_shape();
- shape_im2col.collapse(3);
+ TensorShape shape_im2col = compute_im2col_shape(*input->info());
_im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
// Configure im2col kernel
@@ -105,9 +138,15 @@
void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights, bool are_weights_reshaped)
{
- 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, output);
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayer::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ transpose_weights,
+ are_weights_reshaped));
_are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
_is_fc_after_conv = true;
@@ -192,6 +231,86 @@
}
}
+Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ 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, output);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
+
+ bool weights_reshaped = transpose_weights ? are_weights_reshaped : true;
+ bool is_fc_after_conv = true;
+ bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const GPUTarget gpu_target = CLScheduler::get().target();
+
+ const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(*input)));
+ const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
+ const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+
+ // Configure accumulate biases kernel for non quantized asymmetric types
+ if(biases != nullptr && !is_quantized)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAccumulateBiasesKernel::validate(output, biases, gpu_target));
+ }
+
+ // With the Fully Connected layer we can have 4 different cases:
+ // 1) Convolution layer -> Fully Connected layer without batches
+ // 2) Fully Connected layer -> Fully Connected layer without batches
+ // 3) Convolution layer -> Fully Connected layer with batches
+ // 4) Fully Connected layer -> Fully Connected layer with batches
+
+ const ITensorInfo *input_to_use = input;
+ const ITensorInfo *weights_to_use = weights;
+ const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output;
+
+ if(!weights_reshaped)
+ {
+ // Validate reshape weights kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
+ weights_to_use = &reshaped_weights;
+ }
+
+ // Check if we have a fully connected layer with batches
+ const bool is_batched_fc_layer = output->dimension(1) > 1;
+
+ if(is_batched_fc_layer)
+ {
+ is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3,
+ input->tensor_shape().cend(),
+ output->tensor_shape().cbegin() + 1));
+ }
+ else
+ {
+ is_fc_after_conv = input->num_dimensions() > 1;
+ }
+
+ if(is_fc_after_conv)
+ {
+ // Fully Connected layer after a Convolution Layer without batches
+ ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));
+
+ // Validate im2col kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false));
+ input_to_use = &im2col_input;
+ }
+ else
+ {
+ // Fully Connected layer after a Fully Connected Layer without batches
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
+ }
+ // Validate matrix multiply kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, *tmp_output, false));
+
+ // Validate output stage for asymmetric quantized types
+ if(is_quantized)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&gemmlowp_output, biases, output));
+ }
+
+ return Status{};
+}
+
void CLFullyConnectedLayer::run()
{
// Reshape of the weights (happens only once)
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index ca0228f..c676a10 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -39,20 +39,23 @@
using namespace arm_compute;
CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false)
+ : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false),
+ _is_first_run(true), _reshape_b_only_on_first_run(false)
{
}
-void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta)
+void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+ ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
if(c != nullptr)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c);
ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
- ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C");
+ ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix");
ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
}
@@ -60,7 +63,11 @@
ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
// If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
- _is_interleaved_transposed = a->info()->dimension(1) > 16;
+ // For Bifrost architectures we do not reshape the input matrices
+ _is_interleaved_transposed = (a->info()->dimension(1) > 16 && CLScheduler::get().target() != GPUTarget::BIFROST);
+
+ // Check if we need to reshape the matrix B only on the first run
+ _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
const ICLTensor *matrix_a = a;
const ICLTensor *matrix_b = b;
@@ -73,31 +80,17 @@
matrix_a = &_tmp_a;
matrix_b = &_tmp_b;
- TensorShape shape_tmp_a = a->info()->tensor_shape();
- TensorShape shape_tmp_b = b->info()->tensor_shape();
-
- shape_tmp_a.set(0, a->info()->dimension(0) * 4);
- shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
-
- const unsigned int transpose_w = max_cl_vector_width / data_size_from_type(b->info()->data_type());
- shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
-
- TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
- _tmp_a.allocator()->init(info_a);
-
- TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position());
- _tmp_b.allocator()->init(info_b);
-
- // Manage intermediate buffers
- _memory_group.manage(&_tmp_a);
- _memory_group.manage(&_tmp_b);
+ // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel
// Configure interleave kernel
_interleave_kernel.configure(a, &_tmp_a);
// Configure transpose kernel
_transpose_kernel.configure(b, &_tmp_b);
+
+ // Manage intermediate buffers
+ _memory_group.manage(&_tmp_a);
+ _memory_group.manage(&_tmp_b);
}
_mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
@@ -126,8 +119,18 @@
// Run interleave kernel
CLScheduler::get().enqueue(_interleave_kernel, false);
- // Run transpose kernel
- CLScheduler::get().enqueue(_transpose_kernel, false);
+ if(_is_first_run)
+ {
+ // Run transpose kernel
+ CLScheduler::get().enqueue(_transpose_kernel, false);
+
+ _is_first_run = false;
+ }
+ else if(!_reshape_b_only_on_first_run)
+ {
+ // Run transpose kernel
+ CLScheduler::get().enqueue(_transpose_kernel, false);
+ }
}
// Run matrix multiply kernel
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 5c6f5b4..ddcab6a 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -29,9 +29,11 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(),
@@ -41,14 +43,9 @@
void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
- ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(0) != (b)->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
- ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(1) != (output)->info()->dimension(1), "The output matrix must have the same number of rows as the matrix A");
- ARM_COMPUTE_ERROR_ON_MSG((b)->info()->dimension(0) != (output)->info()->dimension(0), "The output matrix must have the same number of columns as the matrix B");
- ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
- ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
+ ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+ ARM_COMPUTE_UNUSED(gemm_info);
+ ARM_COMPUTE_ERROR_THROW_ON(CLGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info));
_reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
_a_offset = a->info()->quantization_info().offset;
@@ -65,18 +62,8 @@
matrix_a = &_tmp_a;
matrix_b = &_tmp_b;
- // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
- TensorShape shape_tmp_a = a->info()->tensor_shape();
- shape_tmp_a.set(0, a->info()->dimension(0) * 4);
- shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.f));
-
- // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
- TensorShape shape_tmp_b = b->info()->tensor_shape();
- shape_tmp_b.set(0, b->info()->dimension(1) * 16);
- shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 16.f));
-
- TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type());
- TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type());
+ TensorInfo info_a(compute_interleaved_shape(*a->info()), 1, a->info()->data_type());
+ TensorInfo info_b(compute_transpose1xW_shape(*b->info()), 1, b->info()->data_type());
_tmp_a.allocator()->init(info_a);
_tmp_b.allocator()->init(info_b);
_memory_group.manage(&_tmp_a);
@@ -95,13 +82,7 @@
// Initialize matrix B reduction kernel only if _a_offset is not equal to 0
if(_a_offset != 0)
{
- TensorShape shape_vector_sum_col = b->info()->tensor_shape();
-
- if(shape_vector_sum_col.num_dimensions() > 1)
- {
- shape_vector_sum_col.remove_dimension(1);
- }
- TensorInfo info_vector_sum_col(shape_vector_sum_col, 1, DataType::S32);
+ TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32);
_vector_sum_col.allocator()->init(info_vector_sum_col);
_memory_group.manage(&_vector_sum_col);
@@ -112,13 +93,7 @@
// Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
if(_b_offset != 0)
{
- TensorShape shape_vector_sum_row = a->info()->tensor_shape();
- shape_vector_sum_row.set(Window::DimX, a->info()->dimension(1));
- if(a->info()->num_dimensions() > 1)
- {
- shape_vector_sum_row.remove_dimension(1);
- }
- TensorInfo info_vector_sum_row(shape_vector_sum_row, 1, DataType::S32);
+ TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 1, DataType::S32);
_vector_sum_row.allocator()->init(info_vector_sum_row);
_memory_group.manage(&_vector_sum_row);
@@ -147,6 +122,67 @@
}
}
+Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
+ "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(1) != (output)->dimension(1),
+ "The output matrix must have the same number of rows as the matrix A");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0),
+ "The output matrix must have the same number of columns as the matrix B");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
+
+ int32_t a_offset = a->quantization_info().offset;
+ int32_t b_offset = b->quantization_info().offset;
+ bool is_interleaved_transposed = a->dimension(1) > 16;
+
+ if(is_interleaved_transposed)
+ {
+ TensorInfo info_a(compute_interleaved_shape(*a), 1, a->data_type());
+ TensorInfo info_b(compute_transpose1xW_shape(*b), 1, b->data_type());
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &info_a));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &info_b));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(a, b, output));
+ }
+
+ TensorInfo info_vector_sum_col, info_vector_sum_row;
+
+ // Validate matrix B reduction kernel only if _a_offset is not equal to 0
+ if(a_offset != 0)
+ {
+ info_vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32);
+
+ // Configure Matrix B reduction kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixBReductionKernel::validate(b, &info_vector_sum_col));
+ }
+
+ // Validate Matrix A reduction kernel only if _b_offset is not equal to 0
+ if(b_offset != 0)
+ {
+ info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32);
+
+ // Configure matrix A reduction kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row));
+ }
+
+ // Validate offset contribution kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOffsetContributionKernel::validate(output,
+ a_offset == 0 ? nullptr : &info_vector_sum_col,
+ b_offset == 0 ? nullptr : &info_vector_sum_row,
+ a_offset, b_offset));
+
+ return Status{};
+}
+
void CLGEMMLowpMatrixMultiplyCore::run()
{
_memory_group.acquire();
diff --git a/src/runtime/CL/functions/CLHarrisCorners.cpp b/src/runtime/CL/functions/CLHarrisCorners.cpp
index 059528f..423faea 100644
--- a/src/runtime/CL/functions/CLHarrisCorners.cpp
+++ b/src/runtime/CL/functions/CLHarrisCorners.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -63,8 +63,9 @@
void CLHarrisCorners::configure(ICLImage *input, float threshold, float min_dist,
float sensitivity, int32_t gradient_size, int32_t block_size, ICLKeyPointArray *corners,
- BorderMode border_mode, uint8_t constant_border_value)
+ BorderMode border_mode, uint8_t constant_border_value, bool use_fp16)
{
+ ARM_COMPUTE_UNUSED(use_fp16);
ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON(!(block_size == 3 || block_size == 5 || block_size == 7));
diff --git a/src/runtime/CL/functions/CLMagnitude.cpp b/src/runtime/CL/functions/CLMagnitude.cpp
index 9d6ac7a..b1284db 100644
--- a/src/runtime/CL/functions/CLMagnitude.cpp
+++ b/src/runtime/CL/functions/CLMagnitude.cpp
@@ -32,7 +32,7 @@
void CLMagnitude::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, MagnitudeType mag_type, bool use_fp16)
{
- ARM_COMPUTE_UNUSED(use_fp16); //TODO(COMPMID-644): Add half float support
+ ARM_COMPUTE_UNUSED(use_fp16);
auto k = arm_compute::support::cpp14::make_unique<CLMagnitudePhaseKernel>();
k->configure(input1, input2, output, nullptr, mag_type);
diff --git a/src/runtime/CL/functions/CLPermute.cpp b/src/runtime/CL/functions/CLPermute.cpp
new file mode 100644
index 0000000..f23e231
--- /dev/null
+++ b/src/runtime/CL/functions/CLPermute.cpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 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/CLPermute.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/kernels/CLPermuteKernel.h"
+#include "support/ToolchainSupport.h"
+
+using namespace arm_compute;
+
+void CLPermute::configure(const ICLTensor *input, ICLTensor *output, const PermutationVector &perm)
+{
+ auto k = arm_compute::support::cpp14::make_unique<CLPermuteKernel>();
+ k->configure(input, output, perm);
+ _kernel = std::move(k);
+}
\ No newline at end of file
diff --git a/src/runtime/CL/functions/CLPoolingLayer.cpp b/src/runtime/CL/functions/CLPoolingLayer.cpp
index 2341633..201bf87 100644
--- a/src/runtime/CL/functions/CLPoolingLayer.cpp
+++ b/src/runtime/CL/functions/CLPoolingLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -40,16 +40,14 @@
k->configure(input, output, pool_info);
_kernel = std::move(k);
- // Configure border depending on operation required
+ // Configure border depending on operation required (quantize border in case of asymmetric data_type)
BorderMode border_mode = (PoolingType::MAX == pool_info.pool_type()) ? BorderMode::REPLICATE : BorderMode::CONSTANT;
- // Quantize border in case data type is quantized asymmetric data type
- uint32_t border_value = 0;
+ PixelValue zero_value(0.f);
if(is_data_type_quantized_asymmetric(input->info()->data_type()) && !pool_info.exclude_padding())
{
- border_value = static_cast<uint32_t>(input->info()->quantization_info().quantize(0.f, RoundingPolicy::TO_NEAREST_UP));
+ zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset));
}
-
- _border_handler.configure(input, _kernel->border_size(), border_mode, PixelValue(border_value));
+ _border_handler.configure(input, _kernel->border_size(), border_mode, zero_value);
}
Status CLPoolingLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)