From 0cdbda5e51e6ef9e03017231e56ee85ede69bb9a Mon Sep 17 00:00:00 2001 From: Sheri Zhang Date: Tue, 25 Feb 2020 15:57:21 +0000 Subject: COMPMID-2789: Add support for QASYMM8_SIGNED in CLGEMMDeconvolutionLayer Change-Id: I7e3bcb01025e827f6f62491749c691c205ee7481 Signed-off-by: Sheri Zhang Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2844 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- .../CL/functions/CLGEMMDeconvolutionLayer.cpp | 61 ++++++++++++++-------- .../CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp | 3 +- src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp | 44 ++++++++++++++++ 3 files changed, 84 insertions(+), 24 deletions(-) (limited to 'src/runtime') diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp index 14bda11f5f..3298858215 100644 --- a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp @@ -62,6 +62,33 @@ std::pair compute_start_end_slice_coordinates(const IT return { start, end }; } +Status construct_gemmlowp_output_stage(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, GEMMLowpOutputStageInfo &output_stage_info) +{ + const auto data_type = input->data_type(); + + if(is_data_type_quantized_asymmetric(data_type)) + { + const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); + + float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; + int output_multiplier(0); + int output_shift(0); + ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + + output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + output_stage_info.gemmlowp_multiplier = output_multiplier; + output_stage_info.gemmlowp_shift = output_shift; + output_stage_info.gemmlowp_offset = oq_info.offset; + const auto min_max_bound = get_min_max(data_type); + output_stage_info.gemmlowp_min_bound = (std::get<0>(min_max_bound)).get(); + output_stage_info.gemmlowp_max_bound = (std::get<1>(min_max_bound)).get(); + output_stage_info.output_data_type = data_type; + } + return Status{}; +} + } // namespace CLGEMMDeconvolutionLayer::CLGEMMDeconvolutionLayer(std::shared_ptr memory_manager) // NOLINT @@ -93,7 +120,7 @@ CLGEMMDeconvolutionLayer::CLGEMMDeconvolutionLayer(std::shared_ptrset_tensor_shape(gemm_output_shape).set_is_resizable(true); GEMMInfo gemm_info(false, false, true, input->dimension(idx_h), true); + GEMMLowpOutputStageInfo output_stage_info; + if(is_quantized) { ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, &gemm_output_info.set_data_type(DataType::S32), gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(input, weights, output, output_stage_info)); + } else { @@ -160,9 +191,8 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso { const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw); ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr, - &col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8))); - ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8), output, start_end.first, start_end.second)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&col2im_output_info, nullptr, &col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), output_stage_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(input->data_type()), output, start_end.first, start_end.second)); } else if(padded_input) { @@ -173,16 +203,7 @@ Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITenso else if(is_quantized) { ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info)); - - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - int output_multiplier(0); - int output_shift(0); - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr, output)); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&col2im_output_info, nullptr, output, output_stage_info)); } else { @@ -297,15 +318,9 @@ void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor if(_is_quantized) { - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - int output_multiplier(0); - int output_shift(0); - quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - _gemmlowp_output_stage.configure(&_gemmlowp_final, nullptr, output_stage_output, output_multiplier, output_shift, oq_info.offset); + GEMMLowpOutputStageInfo output_stage_info; + construct_gemmlowp_output_stage(input->info(), weights->info(), output->info(), output_stage_info); + _gemmlowp_output_stage.configure(&_gemmlowp_final, nullptr, output_stage_output, output_stage_info); _gemmlowp_final.allocator()->allocate(); } diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp index 54b63df924..9346e9357c 100644 --- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -142,6 +142,7 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor } // Pick up the GEMM configuration + // Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED doesn't matter, since it only affect the shape configuration std::tie(lhs_info, rhs_info) = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(gpu_target)->configure(m, n, k, batch_size, DataType::QASYMM8); // Configure reshape RHS kernel @@ -570,4 +571,4 @@ void CLGEMMLowpMatrixMultiplyCore::prepare() _is_prepared = true; } } -} // namespace arm_compute \ No newline at end of file +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp index 9551fc7efb..de00fd201b 100644 --- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp @@ -105,4 +105,48 @@ Status CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(const ITens return CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, min, max); } +void CLGEMMLowpOutputStage::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_ON(info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT); + + switch(info.output_data_type) + { + case DataType::QASYMM8: + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound); + _kernel = std::move(k); + break; + } + case DataType::QASYMM8_SIGNED: + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound); + _kernel = std::move(k); + break; + } + default: + ARM_COMPUTE_ERROR("Unsupported output data type."); + } + +} + +Status CLGEMMLowpOutputStage::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); + ARM_COMPUTE_RETURN_ERROR_ON(info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT); + + switch(output->data_type()) + { + case DataType::QASYMM8: + return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound); + case DataType::QASYMM8_SIGNED: + return CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound); + default: + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type."); + } + +} } // namespace arm_compute \ No newline at end of file -- cgit v1.2.1