/* * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "src/gpu/cl/operators/ClFullyConnected.h" #include "arm_compute/core/Size2D.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/runtime/CL/CLScheduler.h" #include "src/common/utils/Log.h" #include "src/core/CL/kernels/CLFillBorderKernel.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/gpu/cl/operators/ClConvertFullyConnectedWeights.h" #include "src/gpu/cl/operators/ClFlatten.h" #include "src/gpu/cl/operators/ClGemm.h" #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" #include "src/gpu/cl/operators/ClMatMul.h" #include "src/gpu/cl/operators/ClTranspose.h" #include "src/gpu/cl/utils/ClAuxTensorHandler.h" #include "src/runtime/heuristics/matmul_native/ClMatMulNativeKernelConfig.h" #include "src/runtime/heuristics/matmul_native/IClMatMulNativeKernelConfig.h" #include "support/Cast.h" #include namespace arm_compute { namespace opencl { using namespace arm_compute::experimental; using namespace arm_compute::misc::shape_calculator; namespace { // Function to calculate batched tensor shape in format [M, 1, B0, B1 ..] which is the format matmul expects inline TensorShape get_reshaped_matmul_tensor(const TensorShape &src) { return TensorShape(src.x(), 1, src.y(), src.collapsed_from(2).z()); // Return value optimisation } Status construct_gemmlowp_output_stage(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo &dst, GEMMLowpOutputStageInfo &gemmlowp_output_stage, ActivationLayerInfo activation_info) { 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; const auto data_type = src.data_type(); // Configure output stage for quantized case if (is_data_type_quantized_asymmetric(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(); const auto output_quant_info = (dst.total_size() == 0) ? iq_unif : oq_unif; const float multiplier = (iq_unif.scale * wq_unif.scale) / output_quant_info.scale; int output_multiplier = 0; int output_shift = 0; 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) = get_min_max(data_type); if (activation_info.enabled()) { std::tie(type_min, type_max) = get_quantized_activation_min_max(activation_info, data_type, output_quant_info); } // 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_multipliers.push_back(output_multiplier); gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift); type_min.get(gemmlowp_output_stage.gemmlowp_min_bound); type_max.get(gemmlowp_output_stage.gemmlowp_max_bound); } return Status{}; } Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info, bool use_matmul) { // Note : If input is dynamic and data is not batched, use matmul, else use gemm const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; const bool use_dynamic_gemm = !use_matmul && !weights.are_values_constant() && transpose_weights; // use dynamic gemm as fallback for matmul const bool is_quantized = is_data_type_quantized_asymmetric(src.data_type()); if (use_matmul) { const MatMulInfo m_info = MatMulInfo().adj_rhs(transpose_weights); // Note: LHS is reshaped here to match ClMatMul expectations of batch index - From [M, B0, B1] to [M, 1, B0, B1] TensorInfo lhs_to_use = src.clone()->set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape())); const GPUTarget gpu_target = CLScheduler::get().target(); std::unique_ptr t = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target); const MatMulKernelInfo kernel_info = t->configure(&lhs_to_use, &weights, m_info); return is_quantized ? kernels::ClMatMulLowpNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info) : kernels::ClMatMulNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info); } else { GEMMLowpOutputStageInfo gemmlowp_output_stage; ARM_COMPUTE_RETURN_ON_ERROR( construct_gemmlowp_output_stage(src, weights, dst, gemmlowp_output_stage, fc_info.activation_info)); const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped false, // is_b_reshaped !use_dynamic_gemm, // reshape_b_only_on_first_run 0, // depth_output_gemm3d false, // reinterpret_input_as_3d fc_info.retain_internal_weights, // retain_internal_weights gemmlowp_output_stage, // gemmlowp_output_stage fc_info.fp_mixed_precision, // fp_mixed_precision false, // fast_math true, // broadcast_bias ActivationLayerInfo()); // activation_info if (is_quantized) { const UniformQuantizationInfo iq_info = src.quantization_info().uniform(); const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate src and weights offset const QuantizationInfo src_quantization_info(iq_info.scale, -iq_info.offset); const QuantizationInfo weights_quantization_info(wq_info.scale, -wq_info.offset); // Validate gemmlowp function ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyCore::validate( &src.clone()->set_quantization_info(src_quantization_info), &weights.clone()->set_quantization_info(weights_quantization_info), bias, &dst, gemm_info)); } else { ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&src, &weights, bias, &dst, 1.f, 1.f, gemm_info)); } } return Status{}; } } // namespace ClFullyConnected::ClFullyConnected() : _convert_weights(nullptr), _flatten(nullptr), _reshape_weights(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _matmul_native_kernel(nullptr), _matmul_lowp_native_kernel(nullptr), _aux_mem(Count) { } ClFullyConnected::~ClFullyConnected() = default; void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { // If weights are dynamic and matmul is supported use matmul, else use gemm if (_use_matmul) { // Specify whether transpose weights is necessary in matmul info const MatMulInfo mat_info = MatMulInfo().adj_rhs(_transpose_weights); // Note: MatMul does not need offset negation unlike gemm // 1. Change shape when calling matmul to fit batch expectations. _lhs_to_use = src->clone()->set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape())); // 2. Use heuristics to get kernel info object const GPUTarget gpu_target = CLScheduler::get().target(); std::unique_ptr kernel_config = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target); MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info); // 3. Configure relevant matmul kernel if (_is_quantized) { _matmul_lowp_native_kernel = std::make_unique(); _matmul_lowp_native_kernel->set_target(gpu_target); _matmul_lowp_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info); } else { _matmul_native_kernel = std::make_unique(); _matmul_native_kernel->set_target(gpu_target); _matmul_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info); } } else { // Configure GEMM GEMMLowpOutputStageInfo gemmlowp_output_stage; construct_gemmlowp_output_stage(*src, *weights, *dst, gemmlowp_output_stage, fc_info.activation_info); const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped false, // is_b_reshaped !_dynamic_gemm, // reshape_b_only_on_first_run 0, // depth_output_gemm3d false, // reinterpret_input_as_3d fc_info.retain_internal_weights, // retain_internal_weights gemmlowp_output_stage, // gemmlowp_output_stage fc_info.fp_mixed_precision, // fp_mixed_precision false, // fast_math true, // broadcast_bias fc_info.activation_info); // activation_info if (_is_quantized) { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset const QuantizationInfo src_quantization_info = src->quantization_info(); const QuantizationInfo weights_quantization_info = weights->quantization_info(); TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); src_info.set_quantization_info( QuantizationInfo(src_quantization_info.uniform().scale, -src_quantization_info.uniform().offset)); weights_info.set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); // Configure gemmlowp function _mm_gemmlowp = std::make_unique(); _mm_gemmlowp->configure(compile_context, &src_info, &weights_info, bias, dst, gemm_info); } else { // Configure matrix multiply kernel _mm_gemm = std::make_unique(); _mm_gemm->configure(compile_context, src, weights, bias, dst, 1.f, 1.f, gemm_info); } } } void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate. ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // If the fully connected layer is called after a convolution layer, the input tensor must be linearized // Initialize output tensor for flatten _flattened_src = src->clone() ->set_is_resizable(true) .reset_padding() .set_tensor_shape(compute_flatten_shape(src)) .set_data_layout(DataLayout::NCHW); // Configure flatten kernel _flatten = std::make_unique(); _flatten->configure(compile_context, src, &_flattened_src); // Note: if flatten has > 1 dimensions after, these dimensions are batch // Configure matrix multiply kernel configure_mm(compile_context, &_flattened_src, weights, bias, dst, fc_info); } void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate. ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1)); // Configure matrix multiply kernel configure_mm(compile_context, src, weights, bias, dst, fc_info); } void ClFullyConnected::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, FullyConnectedLayerInfo fc_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); const GPUTarget gpu_target = get_arch_from_target(CLScheduler::get().target()); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info); _transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; _is_fc_after_conv = true; _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); _is_prepared = fc_info.retain_internal_weights; _weights_to_use = TensorInfo(*weights); _weights_to_use_idx = ACL_SRC_1; // When using dynamic weights - use matmul kernels. // Note: MatMul is not used in the following cases (Gemm is used as fallback) : // 1. When the weights tensor is not dynamic // 2. MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. // 3. When FC is after convolution and src tensor data layout does not match weights trained data layout (weights conversion kernel is required) const bool is_batched_fc_layer = dst->dimension(1) > 1; _use_matmul = gpu_target != GPUTarget::MIDGARD && !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); _dynamic_gemm = !weights->are_values_constant() && _transpose_weights && !_use_matmul; // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches // 2) Fully Connected layer -> Fully Connected layer without batches // 3) Convolution layer -> Fully Connected layer with batches // 4) Fully Connected layer -> Fully Connected layer with batches // Check if we have a fully connected layer with batches if (is_batched_fc_layer) { _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, src->tensor_shape().cend(), dst->tensor_shape().cbegin() + 1)); } else { _is_fc_after_conv = src->num_dimensions() > 1; } ITensorInfo *weights_used = weights; // Reshape weights if needed - Not needed when matmul is in use as matmul fuses transpose op. if (_transpose_weights && !_use_matmul) { // Reshape the weights _reshape_weights = std::make_unique(); _reshape_weights->configure(compile_context, weights, &_reshaped_weights); weights_used = &_reshaped_weights; _weights_to_use_idx = offset_int_vec(TransposedWeights); } // Convert weights if needed if (_is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) { // Convert weights _convert_weights = std::make_unique(); _convert_weights->configure(compile_context, weights_used, &_converted_weights, src->tensor_shape(), fc_info.weights_trained_layout); weights_used = &_converted_weights; _weights_to_use_idx = offset_int_vec(ConvertedWeights); _run_convert_weights = true; } if (_is_fc_after_conv) { // Fully Connected layer after a Convolution Layer without batches configure_conv_fc(compile_context, src, weights_used, biases, dst, fc_info); } else { // Fully Connected layer after a Fully Connected Layer without batches configure_fc_fc(compile_context, src, weights_used, biases, dst, fc_info); } // Update TensorInfo of final weights used (Need to be done in the end due to padding expansion) _weights_to_use = *weights_used; if (_use_matmul) { // Note : MatMul does not use transpose and does not need auxillary memory, so only converted weights are added to aux_mem _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Temporary, _converted_weights.total_size()); } else { // Set auxiliary memory requirements for gemm operators auto gemm_mem_req = (_is_quantized) ? _mm_gemmlowp->workspace() : _mm_gemm->workspace(); for (unsigned int i = 0; i < gemm_mem_req.size(); ++i) { _aux_mem[i] = gemm_mem_req[i]; } if (_aux_mem[1].size > 0 || _aux_mem[2].size > 0) // Persistent weights memory on GEMMs { // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch // Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time _aux_mem[TransposedWeights] = MemoryInfo( offset_int_vec(TransposedWeights), _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, _converted_weights.total_size()); } else { // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch const auto transposed_wei_lft = (_weights_to_use_idx == offset_int_vec(TransposedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; const auto converted_wei_lft = (_weights_to_use_idx == offset_int_vec(ConvertedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), _dynamic_gemm ? MemoryLifetime::Temporary : transposed_wei_lft, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), _dynamic_gemm ? MemoryLifetime::Temporary : converted_wei_lft, _converted_weights.total_size()); } } _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.total_size()); } Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, FullyConnectedLayerInfo fc_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights, dst); ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); ARM_COMPUTE_RETURN_ERROR_ON( fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); const GPUTarget gpu_target = get_arch_from_target(CLScheduler::get().target()); const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; bool is_fc_after_conv = true; // When using dynamic weights - use matmul kernels. // Note: MatMul does not support broadcasting so fallback with batched cases. const bool is_batched_fc_layer = dst->dimension(1) > 1; const bool use_matmul = gpu_target != GPUTarget::MIDGARD && !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); const ITensorInfo &flatten_src = TensorInfo(src->clone() ->set_is_resizable(true) .reset_padding() .set_tensor_shape(compute_flatten_shape(src)) .set_data_layout(DataLayout::NCHW)); const ITensorInfo &reshaped_weights = TensorInfo( weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); const ITensorInfo &converted_weights = (transpose_weights && !use_matmul) ? TensorInfo(*reshaped_weights.clone()) : TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()); // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches // 2) Fully Connected layer -> Fully Connected layer without batches // 3) Convolution layer -> Fully Connected layer with batches // 4) Fully Connected layer -> Fully Connected layer with batches const ITensorInfo *src_to_use = src; const ITensorInfo *weights_to_use = weights; if (biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); if (is_data_type_quantized(src->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); } } // Check if FC is after conv (flatten kernel is run in case where FC is after conv.) if (is_batched_fc_layer) { is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, src->tensor_shape().cend(), dst->tensor_shape().cbegin() + 1)); } else { is_fc_after_conv = src->num_dimensions() > 1; } // Transpose kernel does not run when matmul is supported as matmul fuses transpose op. if (transpose_weights && !use_matmul) { // Validate reshape weights kernel ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights)); weights_to_use = &reshaped_weights; } if (is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) { // Validate convert weights kernel ARM_COMPUTE_RETURN_ON_ERROR(ClConvertFullyConnectedWeights::validate( weights_to_use, &converted_weights, src->tensor_shape(), fc_info.weights_trained_layout)); weights_to_use = &converted_weights; } if (is_fc_after_conv) { // Fully Connected layer after a Convolution Layer without batches // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1; ARM_COMPUTE_RETURN_ERROR_ON( (weights_to_use->dimension(weight_idx) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // Validate flatten kernel ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src)); src_to_use = &flatten_src; } else { // Fully Connected layer after a Fully Connected Layer without batches // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1; ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(weight_idx)); } // Validate matrix multiply kernel ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*src_to_use, *weights_to_use, biases, *dst, fc_info, use_matmul)); return Status{}; } void ClFullyConnected::run(ITensorPack &tensors) { prepare(tensors); #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_run_count; ARM_COMPUTE_ERROR_ON(_dynamic_gemm && _asrt_prepare_count != _asrt_run_count); #endif // ARM_COMPUTE_ASSERTS_ENABLED auto src = tensors.get_const_tensor(ACL_SRC_0); CLAuxTensorHandler flattened_src(offset_int_vec(FlattenedSrc), _flattened_src, tensors, false); CLAuxTensorHandler weights(_weights_to_use_idx, _weights_to_use, tensors, false); // Linearize input if it comes from a convolutional layer if (_is_fc_after_conv) { ITensorPack flatten_pack{{ACL_SRC, src}, {ACL_DST, flattened_src.get()}}; _flatten->run(flatten_pack); } ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(ACL_SRC_0, (_is_fc_after_conv) ? flattened_src.get() : src); if (_weights_to_use_idx != ACL_SRC_1) { gemm_pack.add_const_tensor(ACL_SRC_1, weights.get()); } // Run MatMul Op if (_use_matmul) { // Run matmul kernels for matrix multiplication if (_is_quantized) { CLScheduler::get().enqueue_op(*_matmul_lowp_native_kernel, gemm_pack, true); } else { CLScheduler::get().enqueue_op(*_matmul_native_kernel, gemm_pack, true); } } else { // Run matrix multiply if (_is_quantized) { _mm_gemmlowp->run(gemm_pack); } else { _mm_gemm->run(gemm_pack); } } } void ClFullyConnected::prepare(ITensorPack &tensors) { // Note : Running prepare() each run when _use_matmul is true is unnecessary unless weights conversion is needed. if (!_is_prepared || _dynamic_gemm) { #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_prepare_count; ARM_COMPUTE_ERROR_ON(!_dynamic_gemm && !_use_matmul && _asrt_prepare_count > 1); #endif // ARM_COMPUTE_ASSERTS_ENABLED auto weights = tensors.get_const_tensor(ACL_SRC_1); CLAuxTensorHandler reshaped_weights(offset_int_vec(TransposedWeights), _reshaped_weights, tensors, false); CLAuxTensorHandler converted_weights(offset_int_vec(ConvertedWeights), _converted_weights, tensors, false); // Pointer to current weights const ITensor *cur_weights = weights; // Reshape weights if needed. Disabled when matmul kernels are enabled as matmul fuses transpose. if (_transpose_weights && !_use_matmul) { // Run reshape weights kernel and mark weights as unused ITensorPack transpose_pack{{ACL_SRC, weights}, {ACL_DST, reshaped_weights.get()}}; _reshape_weights->run(transpose_pack); cur_weights->mark_as_unused(); cur_weights = reshaped_weights.get(); } // Convert weights if needed if (_run_convert_weights) { ITensorPack convert_pack{{ACL_SRC, cur_weights}, {ACL_DST, converted_weights.get()}}; _convert_weights->run(convert_pack); cur_weights->mark_as_unused(); cur_weights = converted_weights.get(); } ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); // Prepare GEMM prepare and release unused weights if (_dynamic_gemm || !_use_matmul) { if (!_is_quantized) { _mm_gemm->prepare(gemm_pack); } else { _mm_gemmlowp->prepare(gemm_pack); } } _is_prepared = true; } } experimental::MemoryRequirements ClFullyConnected::workspace() const { return _aux_mem; } } // namespace opencl } // namespace arm_compute