diff options
Diffstat (limited to 'src/cpu/operators/CpuMatMul.cpp')
| -rw-r--r-- | src/cpu/operators/CpuMatMul.cpp | 113 |
1 files changed, 76 insertions, 37 deletions
diff --git a/src/cpu/operators/CpuMatMul.cpp b/src/cpu/operators/CpuMatMul.cpp index 8811a7ea6b..89087129c3 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 cpu { 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 @@ Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo 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 @@ Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo } // 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 @@ Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const 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 @@ Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const 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 @@ void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, _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 @@ void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, 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 @@ void CpuMatMul::run(ITensorPack &tensors) // 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 @@ void CpuMatMul::run(ITensorPack &tensors) 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 |