diff options
Diffstat (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp | 236 |
1 files changed, 137 insertions, 99 deletions
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index 61180fd5d3..67f55d56e2 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -91,19 +91,21 @@ void CLConvolutionLayerReshapeWeights::run() } CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(), - _add_bias_kernel(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false), - _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false) + : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), + _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), + _is_activationlayer_enabled(false), _is_prepared(false) { } -void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, int gemm_3d_depth) +void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, + int gemm_3d_depth) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); - ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col)); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), gemmlowp_output_stage, gemm_3d_depth, _skip_im2col)); const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, - gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); + gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */, + false, gemmlowp_output_stage); if(_is_quantized) { @@ -115,7 +117,7 @@ void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTenso input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); - _mm_gemmlowp.configure(input, weights, output, gemm_info); + _mm_gemmlowp.configure(input, weights, biases, output, gemm_info); // Revert back QuantizatioInfo as input and weights could be used in other convolution layers input->info()->set_quantization_info(input_quantization_info); @@ -128,12 +130,14 @@ void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTenso } } -Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col) +Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col) { const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, - gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); + gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */, + false, gemmlowp_output_stage); if(is_quantized) { @@ -148,7 +152,7 @@ Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); // Perform validation step on GEMMLowp - return CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info); + return CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, gemm_info); } else { @@ -176,27 +180,26 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * const DataLayout data_layout = input->info()->data_layout(); const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); const unsigned int kernel_width = weights->info()->dimension(idx_width); const unsigned int kernel_height = weights->info()->dimension(idx_height); - _is_prepared = weights_info.retain_internal_weights(); - _original_weights = weights; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _data_layout = data_layout; - _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); - _skip_col2im = data_layout == DataLayout::NHWC; - _append_bias = (biases != nullptr) && (!_is_quantized); + _is_prepared = weights_info.retain_internal_weights(); + _original_weights = weights; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _data_layout = data_layout; + _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); + _skip_col2im = data_layout == DataLayout::NHWC; + _append_bias = (biases != nullptr) && (!_is_quantized); + _is_activationlayer_enabled = act_info.enabled(); // Set the GPU target for im2col and col2im _im2col_kernel.set_target(CLScheduler::get().target()); _col2im_kernel.set_target(CLScheduler::get().target()); - const ICLTensor *gemm_input_to_use = input; - ICLTensor *gemm_output_to_use = output; - ICLTensor *gemm_output_staged_to_use = output; + const ICLTensor *gemm_input_to_use = input; + ICLTensor *gemm_output_to_use = output; const ICLTensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr; @@ -243,26 +246,17 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * } // Create GEMM output tensor - if(!_skip_col2im || _is_quantized) + if(!_skip_col2im) { TensorShape shape_gemm; - if(_skip_col2im) - { - shape_gemm = input->info()->tensor_shape(); - shape_gemm.set(idx_width, conv_w); - shape_gemm.set(idx_height, conv_h); - shape_gemm.set(idx_channel, mat_weights_cols); - } - else - { - shape_gemm = _im2col_output.info()->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - } - // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type; + + // If we cannot skip col2im it means we run im2col as well + shape_gemm = _im2col_output.info()->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); + // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo info_gemm(shape_gemm, 1, gemm_data_type); + TensorInfo info_gemm(shape_gemm, 1, data_type); info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); @@ -271,42 +265,64 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * gemm_output_to_use = &_gemm_output; } - // Configure and tune GEMM - configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1); - - if(!_skip_im2col) - { - _im2col_output.allocator()->allocate(); - } + GEMMLowpOutputStageInfo gemmlowp_output_stage; + gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + gemmlowp_output_stage.gemmlowp_offset = 0; + gemmlowp_output_stage.gemmlowp_multiplier = 0; + gemmlowp_output_stage.gemmlowp_shift = 0; // Configure output stage for quantized case if(_is_quantized) { const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info(); - if(!_skip_col2im) + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + int min_activation = 0; + int max_activation = 0; + + const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, + ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU + }; + + if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0) { - _memory_group.manage(&_tmp_output); - gemm_output_staged_to_use = &_tmp_output; + const int a_const_int = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); + const int b_const_int = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + + min_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? input->info()->quantization_info().offset : b_const_int; + max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; + + // If the activation layer is RELU, BOUNDED_RELU or LU_BOUNDED_RELU, we can use the GEMMLowp output stage to perform this operation + _is_activationlayer_enabled = false; } - float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale; - _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, multiplier, output_quant_info.offset); + // Set the GEMMLowp output stage info + gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; + gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier; + gemmlowp_output_stage.gemmlowp_shift = output_shift; + gemmlowp_output_stage.gemmlowp_min_bound = min_activation; + gemmlowp_output_stage.gemmlowp_max_bound = max_activation; } - if(!_skip_col2im) + // Configure and tune GEMM + configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, gemmlowp_output_stage, (data_layout == DataLayout::NHWC) ? conv_h : 1); + + if(!_skip_im2col) { - // Configure and tune Col2Im - _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); - CLScheduler::get().tune_kernel_static(_col2im_kernel); + _im2col_output.allocator()->allocate(); } if(!_skip_col2im) { - _tmp_output.allocator()->allocate(); + // Configure and tune Col2Im + _col2im_kernel.configure(gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); + CLScheduler::get().tune_kernel_static(_col2im_kernel); } - if(!_skip_col2im || _is_quantized) + if(!_skip_col2im) { _gemm_output.allocator()->allocate(); } @@ -314,9 +330,6 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), "Output shape does not match the expected one"); - //Configure Activation Layer - _is_activationlayer_enabled = act_info.enabled(); - if(_is_activationlayer_enabled) { _activationlayer_function.configure(output, nullptr, act_info); @@ -347,16 +360,16 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI const unsigned int kernel_width = weights->dimension(idx_width); const unsigned int kernel_height = weights->dimension(idx_height); - TensorInfo im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info; - const ITensorInfo *gemm_input_to_use = input; - const ITensorInfo *gemm_output_to_use = output; - const ITensorInfo *gemm_output_staged_to_use = output; - const ITensorInfo *weights_to_use = weights; + TensorInfo im2col_reshaped_info, info_gemm, weights_reshaped_info; + const ITensorInfo *gemm_input_to_use = input; + const ITensorInfo *gemm_output_to_use = output; + const ITensorInfo *weights_to_use = weights; - const bool is_quantized = is_data_type_quantized_asymmetric(data_type); - const bool append_bias = (biases != nullptr) && (!is_quantized); - const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); - const bool skip_col2im = data_layout == DataLayout::NHWC; + const bool is_quantized = is_data_type_quantized_asymmetric(data_type); + const bool append_bias = (biases != nullptr) && (!is_quantized); + const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); + const bool skip_col2im = data_layout == DataLayout::NHWC; + bool is_activationlayer_enabled = act_info.enabled(); ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel)); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); @@ -418,52 +431,80 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI } // Create GEMM output tensor - if(!skip_col2im || is_quantized) + if(!skip_col2im) { - const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type; - TensorShape shape_gemm; - if(skip_col2im) - { - shape_gemm = input->tensor_shape(); - shape_gemm.set(idx_width, conv_w); - shape_gemm.set(idx_height, conv_h); - shape_gemm.set(idx_channel, mat_weights_cols); - } - else - { - shape_gemm = gemm_input_to_use->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - } - // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type); + TensorShape shape_gemm; + + shape_gemm = gemm_input_to_use->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); + + info_gemm = TensorInfo(shape_gemm, 1, data_type); info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout()); gemm_output_to_use = &info_gemm; } - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 1, skip_im2col)); + GEMMLowpOutputStageInfo gemmlowp_output_stage; + gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + gemmlowp_output_stage.gemmlowp_offset = 0; + gemmlowp_output_stage.gemmlowp_multiplier = 0; + gemmlowp_output_stage.gemmlowp_shift = 0; if(is_quantized) { - if(!skip_col2im) + const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input->quantization_info() : output->quantization_info(); + + float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output_quant_info.scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + int min_activation = 0; + int max_activation = 0; + + const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, + ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU + }; + + if(is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0) { - tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); - tmp_info.set_quantization_info(output->quantization_info()); - gemm_output_staged_to_use = &tmp_info; + const int a_const_int = input->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); + const int b_const_int = input->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + + min_activation = b_const_int; + max_activation = a_const_int; + + if(act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) + { + min_activation = input->quantization_info().offset; + } + if(act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU) + { + max_activation = 255; + } + + // If the activation layer is RELU, BOUNDED_RELU or LU_BOUNDED_RELU, we can use the GEMMLowp output stage to perform this operation + is_activationlayer_enabled = false; + + // Set the GEMMLowp output stage info + gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; + gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier; + gemmlowp_output_stage.gemmlowp_shift = output_shift; + gemmlowp_output_stage.gemmlowp_min_bound = min_activation; + gemmlowp_output_stage.gemmlowp_max_bound = max_activation; } - // Validate output stage for quantized case - CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use); } + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, gemmlowp_output_stage, skip_col2im ? conv_h : 1, skip_im2col)); + // Validate Col2Im if(!skip_col2im) { - ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, - Size2D(conv_w, conv_h), num_groups)); + ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups)); } //Validate Activation Layer - if(act_info.enabled()) + if(is_activationlayer_enabled) { ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); } @@ -488,9 +529,6 @@ void CLGEMMConvolutionLayer::run() { // Run gemmlowp _mm_gemmlowp.run(); - - // Run output stage - _gemmlowp_output_stage.run(); } else { |