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/cpu/operators/CpuMatMul.cpp b/src/cpu/operators/CpuMatMul.cpp
index 8811a7e..8908712 100644
--- a/src/cpu/operators/CpuMatMul.cpp
+++ b/src/cpu/operators/CpuMatMul.cpp
@@ -23,14 +23,16 @@
*/
#include "src/cpu/operators/CpuMatMul.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Validate.h"
+
#include "arm_compute/core/experimental/Types.h"
+#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/core/Validate.h"
#include "arm_compute/function_info/MatMulInfo.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/NEON/functions/NEMatMul.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
#include "src/common/utils/Log.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
@@ -46,8 +48,11 @@
{
namespace
{
-Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const ActivationLayerInfo &act,
- GEMMLowpOutputStageInfo &gemmlowp_output_stage_info)
+Status get_gemmlowp_output_stage_info(const ITensorInfo *src,
+ const ITensorInfo *weights,
+ const ITensorInfo *dst,
+ const ActivationLayerInfo &act,
+ GEMMLowpOutputStageInfo &gemmlowp_output_stage_info)
{
const auto data_type = src->data_type();
const QuantizationInfo oq_info = dst->quantization_info();
@@ -59,10 +64,11 @@
int32_t output_multiplier;
int32_t output_shift;
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
- int32_t type_min = 0;
- int32_t type_max = 0;
+ int32_t type_min = 0;
+ int32_t type_max = 0;
std::tie(type_min, type_max) = quantization::get_quantized_asymmetric_output_min_max(oq_info, act, data_type);
gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
@@ -77,14 +83,27 @@
} // namespace
CpuMatMul::CpuMatMul()
- : _transpose_kernel_lhs(), _transpose_kernel_rhs(), _asm_glue(), _lhs_transposed(), _rhs_transposed(), _original_lhs_shape(), _original_rhs_shape(), _original_dst_shape()
+ : _transpose_kernel_lhs(),
+ _transpose_kernel_rhs(),
+ _asm_glue(),
+ _lhs_transposed(),
+ _rhs_transposed(),
+ _original_lhs_shape(),
+ _original_rhs_shape(),
+ _original_dst_shape()
{
}
-Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings, const ActivationLayerInfo &act_info)
+Status CpuMatMul::validate(const ITensorInfo *lhs,
+ const ITensorInfo *rhs,
+ const ITensorInfo *dst,
+ const MatMulInfo &info,
+ const CpuMatMulSettings &settings,
+ const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16, DataType::QASYMM8,
+ DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs->are_values_constant(), "LHS Tensor must be dynamic.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs->are_values_constant(), "RHS Tensor must be dynamic.");
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs);
@@ -103,34 +122,39 @@
gemm_info.fast_mode = settings.fast_math();
// Validate and then permute a/b
- if(adj_lhs)
+ if (adj_lhs)
{
- auto_init_if_empty(lhs_transposed, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*lhs)));
+ auto_init_if_empty(lhs_transposed,
+ lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*lhs)));
ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(lhs_to_use, &lhs_transposed));
// Assign lhs_to_use pointer to use transposed TensorInfo
lhs_to_use = &lhs_transposed;
}
- if(adj_rhs)
+ if (adj_rhs)
{
- auto_init_if_empty(rhs_transposed, rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*rhs)));
+ auto_init_if_empty(rhs_transposed,
+ rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*rhs)));
ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(rhs_to_use, &rhs_transposed));
// Assign rhs_to_use pointer to use transposed TensorInfo
rhs_to_use = &rhs_transposed;
}
ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(0) != rhs_to_use->dimension(1),
- "The product AB is defined only if the number of columns in A is equal to the number of rows in B (after transpose)");
+ "The product AB is defined only if the number of columns in A is equal to the "
+ "number of rows in B (after transpose)");
// Iterate over dimensions to be collapsed in operator - check dimensions are equivalent between tensors
- for(unsigned int i = 2; i < Coordinates::num_max_dimensions; i++)
+ for (unsigned int i = 2; i < Coordinates::num_max_dimensions; i++)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(i) != rhs_to_use->dimension(i), "Broadcasting in Batch dimension is unsupported by this operator.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(i) != rhs_to_use->dimension(i),
+ "Broadcasting in Batch dimension is unsupported by this operator.");
}
// Quantized-specific configuration
- if(is_data_type_quantized(lhs->data_type()))
+ if (is_data_type_quantized(lhs->data_type()))
{
- ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(lhs_to_use, rhs_to_use, dst, gemm_info.activation_info, gemm_info.output_stage));
+ ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(lhs_to_use, rhs_to_use, dst,
+ gemm_info.activation_info, gemm_info.output_stage));
}
cpu::CpuGemmAssemblyDispatch::validate(lhs_to_use, rhs_to_use, nullptr, dst, gemm_info);
@@ -138,7 +162,12 @@
return Status{};
}
-void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings, const ActivationLayerInfo &act_info)
+void CpuMatMul::configure(ITensorInfo *lhs,
+ ITensorInfo *rhs,
+ ITensorInfo *dst,
+ const MatMulInfo &info,
+ const CpuMatMulSettings &settings,
+ const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, info, settings);
@@ -163,21 +192,23 @@
_original_rhs_shape = rhs_to_use.tensor_shape();
// Reshape lhs for use with assembly kernels.
- lhs_to_use.set_tensor_shape(TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z()));
- dst_to_use.set_tensor_shape(TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z()));
+ lhs_to_use.set_tensor_shape(
+ TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z()));
+ dst_to_use.set_tensor_shape(
+ TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z()));
rhs_to_use.set_tensor_shape(_original_rhs_shape.collapsed_from(2));
// 2. Configuration for transpose of lhs/rhs
// ------------------------------------------------------
// Initialise transposed TensorInfo class for aux tensors (intermediary tensors)
- if(_adj_lhs)
+ if (_adj_lhs)
{
// Setup transpose LHS
_transpose_kernel_lhs = std::make_unique<cpu::kernels::CpuTransposeKernel>();
_transpose_kernel_lhs->configure(&lhs_to_use, &_lhs_transposed);
}
- if(_adj_rhs)
+ if (_adj_rhs)
{
// Setup transpose RHS
_transpose_kernel_rhs = std::make_unique<cpu::kernels::CpuTransposeKernel>();
@@ -196,20 +227,22 @@
rhs_to_use = (_adj_rhs) ? _rhs_transposed : rhs_to_use;
// Quantized-specific configuration
- if(is_data_type_quantized(lhs->data_type()))
+ if (is_data_type_quantized(lhs->data_type()))
{
- get_gemmlowp_output_stage_info(&lhs_to_use, &rhs_to_use, &dst_to_use, _gemm_info.activation_info, _gemm_info.output_stage);
+ get_gemmlowp_output_stage_info(&lhs_to_use, &rhs_to_use, &dst_to_use, _gemm_info.activation_info,
+ _gemm_info.output_stage);
}
// Configure Asm Kernel
_asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>();
- _asm_glue->configure(&lhs_to_use, &rhs_to_use, nullptr, &dst_to_use, _gemm_info); // c is nullptr as bias not supported in MatMul
+ _asm_glue->configure(&lhs_to_use, &rhs_to_use, nullptr, &dst_to_use,
+ _gemm_info); // c is nullptr as bias not supported in MatMul
// Specify memory requirements for intermediate tensors
auto asm_mem_req = _asm_glue->workspace();
// Specify memory required by gemm kernel
int idx = 0;
- for(const auto &aux : asm_mem_req)
+ for (const auto &aux : asm_mem_req)
{
_aux_mem[idx] = aux;
idx++;
@@ -228,8 +261,12 @@
// Reshape LHS and DST to ensure compatibility with GEMM asm kernel (Batch dimensions is 4th for lhs and dst within asm)
// Collapse RHS (necessary to support dimensions larger than 3 in gemm assembly)
- lhs->info()->set_tensor_shape(TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
- dst->info()->set_tensor_shape(TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
+ lhs->info()->set_tensor_shape(
+ TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1,
+ _original_lhs_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
+ dst->info()->set_tensor_shape(
+ TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1,
+ _original_dst_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
rhs->info()->set_tensor_shape(_original_rhs_shape.collapsed_from(2));
// Initialise object to handle stored transposed tensors in auxillary memory
@@ -240,17 +277,19 @@
ITensorPack asm_tensors(tensors);
// Run transpose lhs if necessary
- if(_adj_lhs)
+ if (_adj_lhs)
{
- ITensorPack lhs_transpose_pack = { { TensorType::ACL_SRC, lhs }, { TensorType::ACL_DST, lhs_transposed.get() } };
- NEScheduler::get().schedule_op(_transpose_kernel_lhs.get(), Window::DimY, _transpose_kernel_lhs->window(), lhs_transpose_pack);
+ ITensorPack lhs_transpose_pack = {{TensorType::ACL_SRC, lhs}, {TensorType::ACL_DST, lhs_transposed.get()}};
+ NEScheduler::get().schedule_op(_transpose_kernel_lhs.get(), Window::DimY, _transpose_kernel_lhs->window(),
+ lhs_transpose_pack);
asm_tensors.add_const_tensor(TensorType::ACL_SRC_0, lhs_transposed.get());
}
// Run transpose rhs if necessary
- if(_adj_rhs)
+ if (_adj_rhs)
{
- ITensorPack rhs_transpose_pack = { { TensorType::ACL_SRC, rhs }, { TensorType::ACL_DST, rhs_transposed.get() } };
- NEScheduler::get().schedule_op(_transpose_kernel_rhs.get(), Window::DimY, _transpose_kernel_rhs->window(), rhs_transpose_pack);
+ ITensorPack rhs_transpose_pack = {{TensorType::ACL_SRC, rhs}, {TensorType::ACL_DST, rhs_transposed.get()}};
+ NEScheduler::get().schedule_op(_transpose_kernel_rhs.get(), Window::DimY, _transpose_kernel_rhs->window(),
+ rhs_transpose_pack);
asm_tensors.add_const_tensor(TensorType::ACL_SRC_1, rhs_transposed.get());
}
// Run asm kernel