COMPMID-754: Add validation to kernels.
Adds validation method to:
- CLConvolutionLayer
Change-Id: I95516e20cfb71c1e603c60fc6491ac695883a856
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117355
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index a0bee52..1a486ce 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -26,6 +26,8 @@
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/Utils.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 <cmath>
@@ -33,6 +35,7 @@
#include <tuple>
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_manager(std::move(memory_manager)), _function()
@@ -70,7 +73,7 @@
Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
//Configure if the parameters match the direct convolution or the gemm-based
const GPUTarget gpu_target = CLScheduler::get().target();
@@ -86,8 +89,7 @@
case ConvolutionMethod::GEMM:
{
// Validate gemm-based convolution layer
- /* TODO COMPMID-754: Add validation methods for CLGEMMConvolutionLayer
- CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info); */
+ CLGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
break;
}
default:
diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index e7b5468..e3bbe0f 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -80,7 +80,7 @@
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));
- // TODO (COMPMID-754): Add validation of CLConvolutionLayer when added.
+ ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, info, WeightsInfo()));
return Status{};
}
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index f02eb16..da00d2d 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -60,6 +60,31 @@
return flag;
}
+
+Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const ICLTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo())
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+ 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");
+
+ if(c != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info());
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(c->info()->dimension(0) != output->dimension(0), "The C matrix must have the same number of rows as the output matrix");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(c->info()->dimension(1) != output->dimension(1), "The C matrix must have the same number of columns as the output matrix");
+ }
+
+ 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_UNUSED(alpha);
+ ARM_COMPUTE_UNUSED(beta);
+ return Status{};
+}
} // namespace
CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
@@ -70,21 +95,10 @@
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");
+ ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
- 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 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");
- }
-
- 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");
+ // Perform validation step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info));
// 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();
@@ -152,6 +166,12 @@
}
}
+Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ICLTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info));
+ return Status{};
+}
+
void CLGEMM::run()
{
_memory_group.acquire();
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 60e1bde..23c3050 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -27,6 +27,7 @@
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.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"
@@ -35,53 +36,52 @@
#include <tuple>
using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+ : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped()
{
}
-void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output)
{
+ // Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+ ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(),
+ (biases != nullptr) ? biases->info() : nullptr,
+ output->info()));
+
+ const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+ const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+ _weights_reshape_kernel.configure(weights, biases_to_use, output);
+
+ output->info()->set_quantization_info(weights->info()->quantization_info());
+}
+
+Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
+ 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(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);
+ 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(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->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)
+ if((output != nullptr) && (output->total_size() != 0))
{
- // 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);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
- _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);
+ CLWeightsReshapeKernel::validate(weights, biases, output);
}
- output->info()->set_quantization_info(weights->info()->quantization_info());
+ return Status{};
}
void CLConvolutionLayerReshapeWeights::run()
@@ -89,99 +89,92 @@
_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)
+ : _memory_group(memory_manager), _reshape_weights(), _im2col_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(), _is_quantized(false)
{
}
-void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped)
+void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));
+
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();
+ // 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
{
- 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*/));
- }
+ // 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*/));
}
}
+Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
+{
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+
+ const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
+ 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->quantization_info();
+ const QuantizationInfo weights_quantization_info = weights->quantization_info();
+
+ std::unique_ptr<ITensorInfo> input_qa = input->clone();
+ std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
+ input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+ weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+ // Perform validation step on GEMMLowp
+ CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
+ }
+ else
+ {
+ // Perform validation step on Matrix multiply function
+ CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
+ }
+ return Status{};
+}
+
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()));
+
+ ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ conv_info,
+ weights_info));
_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());
+ // Set the GPU target for im2col and col2im
_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;
@@ -195,41 +188,19 @@
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);
+ const unsigned int kernel_width = weights->info()->dimension(0);
+ const unsigned int kernel_height = 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_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);
- weights = &_weights_reshaped;
- }
+ weights = &_weights_reshaped;
// Create tensor to store im2col reshaped inputs
const unsigned int mat_input_cols = mat_weights_rows;
@@ -259,21 +230,9 @@
// 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(&_im2col_output, weights, &_gemm_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
@@ -299,53 +258,117 @@
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();
+
+ ARM_COMPUTE_UNUSED(weights_info);
+}
+
+Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
+ 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->dimension(2) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool append_bias = (biases != nullptr) && (!is_quantized);
+ const unsigned bias_element = (append_bias) ? 1 : 0;
+ const DataType dt = input->data_type();
+
+ // Get convolved dimensions
+ unsigned int conv_w = 0;
+ unsigned int conv_h = 0;
+
+ const unsigned int kernel_width = weights->dimension(0);
+ const unsigned int kernel_height = weights->dimension(1);
+
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info);
+
+ unsigned int mat_weights_cols = weights->dimension(3);
+ unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;
+
+ CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr);
+
+ // Create tensor info for 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->tensor_shape();
+ shape_im2col.set(0, mat_input_cols);
+ shape_im2col.set(1, mat_input_rows);
+ shape_im2col.set(2, 1);
+ TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
+ im2col_reshaped_info.set_quantization_info(input->quantization_info());
+ CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias);
+
+ // Create GEMM output tensor
+ TensorShape shape_gemm = 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->fixed_point_position());
+ info_gemm.set_quantization_info(output->quantization_info());
+
+ validate_mm(&im2col_reshaped_info, weights, &info_gemm);
+
+ TensorInfo tmp_info(input->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position());
+ if(is_quantized)
{
- _weights_reshaped.allocator()->allocate();
+ float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ // Validate output stage for quantized case
+ CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset);
}
+
+ // Validate Col2Im
+ CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h));
+
+ 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(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+
+ return Status{};
}
void CLGEMMConvolutionLayer::run()
{
// Run weights reshaping (Runs once for every configure)
- if(!_are_weights_reshaped)
- {
- _are_weights_reshaped = true;
- _reshape_weights.run();
- }
+ _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)
+ // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
+ if(_is_quantized)
{
- // Run interleave4x4 kernel
- CLScheduler::get().enqueue(_interleave_kernel);
+ // Run gemmlowp
+ _mm_gemmlowp.run();
- // Run matrix multiply kernel
- CLScheduler::get().enqueue(_mm_kernel);
+ // Run output stage
+ _gemmlowp_output_stage.run();
}
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();
- }
+ // Run gemm
+ _mm_gemm.run();
}
// Reshape output matrix