Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.cpp b/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.cpp
index 5d552b8..2f1f3b8 100644
--- a/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.cpp
+++ b/src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.cpp
@@ -29,14 +29,13 @@
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/StringUtils.h"
+#include "arm_compute/core/Validate.h"
#include "src/core/AccessWindowStatic.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
-
#include "support/Cast.h"
#include "support/StringSupport.h"
@@ -54,45 +53,57 @@
{
using ElementsProcessed = Steps;
-Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
- const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
- const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status validate_arguments(const ITensorInfo *src0,
+ const ITensorInfo *src1,
+ const ITensorInfo *dst,
+ const GEMMKernelInfo &gemm_info,
+ const ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row,
+ const ITensorInfo *bias,
+ const ITensorInfo *output_multipliers,
+ const ITensorInfo *output_shifts)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
- if(src0->data_type() == DataType::QASYMM8)
+ if (src0->data_type() == DataType::QASYMM8)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
}
else
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::QASYMM8, DataType::QSYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::QASYMM8, DataType::QSYMM8,
+ DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL);
}
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4,
+ "The number of dimensions for the LHS matrix must be <= 4");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3,
+ "The number of dimensions for the RHS matrix must be <= 3");
const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)), "Only 2,3,4,8,16 are supported for k0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)),
+ "Only 2,3,4,8,16 are supported for k0");
ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16), "Only 2,3,4,8,16 are supported for n0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16),
+ "Only 2,3,4,8,16 are supported for n0");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM");
const int m = gemm_info.m;
const int n = gemm_info.n;
const int k = gemm_info.k;
- TensorShape tensor_shape1{ src1->tensor_shape() };
+ TensorShape tensor_shape1{src1->tensor_shape()};
tensor_shape1.set(0, n);
tensor_shape1.set(1, k);
- const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
- const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
+ const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
+ const TensorInfo tensor_info_reshaped1 =
+ src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != static_cast<unsigned int>(k));
- if(gemm_info.reinterpret_input_as_3d)
+ if (gemm_info.reinterpret_input_as_3d)
{
ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast<unsigned int>(m));
}
@@ -103,11 +114,11 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
- if(dst->total_size() != 0)
+ if (dst->total_size() != 0)
{
const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_dst_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
- if(output_stage.type == GEMMLowpOutputStageType::NONE)
+ if (output_stage.type == GEMMLowpOutputStageType::NONE)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32);
}
@@ -117,39 +128,42 @@
}
}
- if(bias != nullptr)
+ if (bias != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != bias->dimension(0));
}
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT),
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) ||
+ (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT),
"Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported");
// Checks performed if the dst stage needs to be fused
- if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
{
// If a_offset == 0, vector_sum_col can be a nullptr
- if(gemm_info.a_offset != 0)
+ if (gemm_info.a_offset != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_dst_shape[0]);
}
// If b_offset == 0, vector_sum_row can be a nullptr
- if(gemm_info.b_offset != 0)
+ if (gemm_info.b_offset != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
// Check if mm result is a 3D reinterpretation
- const bool reinterpret_as_3d = expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x();
+ const bool reinterpret_as_3d =
+ expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x();
// Validate input
- ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_dst_shape[1] * expected_dst_shape[2]));
+ ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) !=
+ (expected_dst_shape[1] * expected_dst_shape[2]));
ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_dst_shape[1]);
- if(expected_dst_shape.num_dimensions() > 1)
+ if (expected_dst_shape.num_dimensions() > 1)
{
const unsigned int dst_batch_idx = reinterpret_as_3d ? 3 : 2;
@@ -161,30 +175,32 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_dst_shape[dst_batch_idx],
"vector_sum_row must have the same number of batches of dst tensor");
- if(gemm_info.a_offset != 0)
+ if (gemm_info.a_offset != 0)
{
TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
vector_sum_col_shape.collapse_from(1);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
- "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 &&
+ vector_sum_col_shape[1] != vector_sum_row_shape[1],
+ "vector_sum_col tensor must have the same number of batches of "
+ "vector_sum_row_shape or the number of batches must be set to 1");
}
}
}
- if(dst->total_size() != 0)
+ if (dst->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != dst->data_type());
}
ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
- if(output_multipliers != nullptr && output_shifts != nullptr)
+ if (output_multipliers != nullptr && output_shifts != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
- if(output_stage.is_quantized_per_channel)
+ if (output_stage.is_quantized_per_channel)
{
ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_shifts->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_multipliers->dimension(0));
@@ -194,9 +210,16 @@
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
- ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
- ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed)
+std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0,
+ const ITensorInfo *src1,
+ ITensorInfo *dst,
+ const GEMMKernelInfo &gemm_info,
+ ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row,
+ ITensorInfo *bias,
+ ITensorInfo *output_multipliers,
+ ITensorInfo *output_shifts,
+ ElementsProcessed &num_elements_processed)
{
const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
@@ -211,16 +234,17 @@
// In case both input and dst have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
- if(reinterpret_input_as_3d == reinterpret_output_as_3d)
+ if (reinterpret_input_as_3d == reinterpret_output_as_3d)
{
reinterpret_output_as_3d = false;
}
// dst tensor auto initialization if not yet initialized
const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
- if(output_stage.type != GEMMLowpOutputStageType::NONE)
+ if (output_stage.type != GEMMLowpOutputStageType::NONE)
{
- auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type));
+ auto_init_if_empty(
+ *dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type));
}
else
{
@@ -229,7 +253,7 @@
TensorInfo tmp_info(*dst);
- if(reinterpret_output_as_3d)
+ if (reinterpret_output_as_3d)
{
// Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
// the window needs to be constructed on the 2D collapsed version of the tensor
@@ -242,12 +266,14 @@
num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0;
num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0;
- win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ win =
+ calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ win_out =
+ calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
{
- if(gemm_info.a_offset != 0)
+ if (gemm_info.a_offset != 0)
{
AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x);
window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access);
@@ -255,17 +281,19 @@
// No access window needed for vector_sum_row
ARM_COMPUTE_UNUSED(vector_sum_row);
- if(bias != nullptr)
+ if (bias != nullptr)
{
AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x);
window_changed = window_changed || update_window_and_padding(win_out, bias_access);
}
- if(output_multipliers != nullptr && output_stage.is_quantized_per_channel)
+ if (output_multipliers != nullptr && output_stage.is_quantized_per_channel)
{
- AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x);
+ AccessWindowHorizontal output_multipliers_access(output_multipliers, 0,
+ num_elems_processed_per_iteration_x);
AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x);
- window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access);
+ window_changed =
+ window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access);
}
}
@@ -275,7 +303,8 @@
const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
collapsed = win.collapse(win, dimension_to_collapse);
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ Status err =
+ (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, collapsed);
}
} // namespace
@@ -285,15 +314,22 @@
_type = CLKernelType::GEMM;
}
-void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst,
- const GEMMKernelInfo &gemm_info,
- ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
- ITensorInfo *output_multipliers, ITensorInfo *output_shifts)
+void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context,
+ const ITensorInfo *src0,
+ const ITensorInfo *src1,
+ ITensorInfo *dst,
+ const GEMMKernelInfo &gemm_info,
+ ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row,
+ ITensorInfo *bias,
+ ITensorInfo *output_multipliers,
+ ITensorInfo *output_shifts)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias,
+ output_multipliers, output_shifts));
- auto padding_info = get_padding_info({ src0, src1, dst, vector_sum_row });
+ auto padding_info = get_padding_info({src0, src1, dst, vector_sum_row});
const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
@@ -307,7 +343,7 @@
// In case both input and dst have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
- if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
+ if (_reinterpret_input_as_3d == _reinterpret_output_as_3d)
{
_reinterpret_input_as_3d = false;
_reinterpret_output_as_3d = false;
@@ -320,7 +356,8 @@
ElementsProcessed num_elements_processed{};
// Configure kernel window
- auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts, num_elements_processed);
+ auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias,
+ output_multipliers, output_shifts, num_elements_processed);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
@@ -341,8 +378,10 @@
CLBuildOptions build_opts;
build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
- build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1)));
- build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2)));
+ build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d,
+ "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1)));
+ build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d,
+ "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2)));
build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
@@ -361,12 +400,12 @@
std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_");
kernel_name += rhs_info.transpose ? "t" : "nt";
- if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
{
kernel_name += "_fused_output_stage_fixedpoint";
_fuse_output_stage = true;
// If a_offset == 0, vector_sum_col can be a nullptr
- if(a_offset != 0 && vector_sum_col != nullptr)
+ if (a_offset != 0 && vector_sum_col != nullptr)
{
build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
@@ -377,9 +416,10 @@
build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset));
// In case of _is_quantized_per_channel, RESULT_MULTIPLIER and RESULT_SHIFT are not utilized, but they are passed as a part of T_QUANTIZE8 macro.
- if(!_is_quantized_per_channel)
+ if (!_is_quantized_per_channel)
{
- build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
+ build_opts.add_option("-DRESULT_MULTIPLIER=" +
+ support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));
}
else
@@ -432,42 +472,56 @@
ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
}
-Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
- const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
- const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0,
+ const ITensorInfo *src1,
+ const ITensorInfo *dst,
+ const GEMMKernelInfo &gemm_info,
+ const ITensorInfo *vector_sum_col,
+ const ITensorInfo *vector_sum_row,
+ const ITensorInfo *bias,
+ const ITensorInfo *output_multipliers,
+ const ITensorInfo *output_shifts)
{
ElementsProcessed num_elements_processed{};
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
- src1->clone().get(),
- dst->clone().get(),
- gemm_info,
- vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
- vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
- bias != nullptr ? bias->clone().get() : nullptr,
- output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
- output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
- num_elements_processed)
- .first);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias,
+ output_multipliers, output_shifts));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ validate_and_configure_window(src0->clone().get(), src1->clone().get(), dst->clone().get(), gemm_info,
+ vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
+ vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
+ bias != nullptr ? bias->clone().get() : nullptr,
+ output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
+ output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
+ num_elements_processed)
+ .first);
return Status{};
}
-void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors,
+ const Window &window,
+ cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- const auto bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS));
- const auto vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));
- const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));
- const auto output_shifts = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SHIFTS));
- const auto output_multipliers = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_MULTIPLIERS));
- auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ const auto src0 =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const auto src1 =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ const auto bias =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS));
+ const auto vector_sum_col =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));
+ const auto vector_sum_row =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));
+ const auto output_shifts =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SHIFTS));
+ const auto output_multipliers =
+ utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_MULTIPLIERS));
+ auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
- if(src1->info()->num_dimensions() < 3)
+ if (src1->info()->num_dimensions() < 3)
{
// The stride_z for matrix B must be zero if we do not slice
ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
@@ -479,7 +533,7 @@
slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
- if(_reinterpret_input_as_3d)
+ if (_reinterpret_input_as_3d)
{
// Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3;
@@ -487,10 +541,10 @@
_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
}
- if(_reinterpret_output_as_3d)
+ if (_reinterpret_output_as_3d)
{
// Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
- const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
+ const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
}
@@ -515,7 +569,7 @@
Window slice_b = slice;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
+ if (!_slide_matrix_b)
{
slice_b = slice_matrix_b;
}
@@ -527,19 +581,19 @@
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
- if(_reinterpret_input_as_3d)
+ if (_reinterpret_input_as_3d)
{
// Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
idx++;
}
- if(_reinterpret_output_as_3d)
+ if (_reinterpret_output_as_3d)
{
// Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
idx++;
}
- if(_fuse_output_stage)
+ if (_fuse_output_stage)
{
add_2D_tensor_argument_if((vector_sum_col != nullptr), idx, vector_sum_col, win_vector_sum_col);
add_2D_tensor_argument_if((vector_sum_row != nullptr), idx, vector_sum_row, win_vector_sum_row);
@@ -548,8 +602,7 @@
add_1D_tensor_argument_if(_is_quantized_per_channel, idx, output_shifts, biases_slice);
}
enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
- }
- while(window.slide_window_slice_3D(slice));
+ } while (window.slide_window_slice_3D(slice));
}
} // namespace kernels
} // namespace opencl