diff options
Diffstat (limited to 'src/cpu/operators/CpuMatMul.cpp')
| -rw-r--r-- | src/cpu/operators/CpuMatMul.cpp | 52 |
1 files changed, 50 insertions, 2 deletions
diff --git a/src/cpu/operators/CpuMatMul.cpp b/src/cpu/operators/CpuMatMul.cpp index b5359e51af..369466b669 100644 --- a/src/cpu/operators/CpuMatMul.cpp +++ b/src/cpu/operators/CpuMatMul.cpp @@ -23,7 +23,9 @@ */ #include "src/cpu/operators/CpuMatMul.h" +#include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/core/experimental/Types.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" @@ -40,6 +42,40 @@ namespace arm_compute { 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) +{ + const auto data_type = src->data_type(); + const QuantizationInfo oq_info = dst->quantization_info(); + const UniformQuantizationInfo iq_unif = src->quantization_info().uniform(); + const UniformQuantizationInfo wq_unif = weights->quantization_info().uniform(); + const UniformQuantizationInfo oq_unif = oq_info.uniform(); + + float multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale; + int32_t output_multiplier; + int32_t output_shift; + + ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + + PixelValue type_min{}; + PixelValue type_max{}; + 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; + gemmlowp_output_stage_info.gemmlowp_shift = output_shift; + gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset; + gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + gemmlowp_output_stage_info.gemmlowp_min_bound = type_min.get<int32_t>(); + gemmlowp_output_stage_info.gemmlowp_max_bound = type_max.get<int32_t>(); + + return Status{}; +} + +} + CpuMatMul::CpuMatMul() : _transpose_kernel_lhs(), _transpose_kernel_rhs(), _asm_glue(), _lhs_transposed(), _rhs_transposed(), _original_lhs_shape(), _original_rhs_shape(), _original_dst_shape() { @@ -47,8 +83,8 @@ CpuMatMul::CpuMatMul() Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16); + 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_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); @@ -96,6 +132,12 @@ Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const 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())) + { + 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); return Status{}; @@ -157,6 +199,12 @@ void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, lhs_to_use = (_adj_lhs) ? _lhs_transposed : lhs_to_use; rhs_to_use = (_adj_rhs) ? _rhs_transposed : rhs_to_use; + // Quantized-specific configuration + 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); + } + // 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 |