| /* |
| * 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 <algorithm> |
| |
| 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<cl_matmul::IClMatMulNativeKernelConfig> 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<cl_matmul::IClMatMulNativeKernelConfig> 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<kernels::ClMatMulLowpNativeKernel>(); |
| _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<kernels::ClMatMulNativeKernel>(); |
| _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<ClGemmLowpMatrixMultiplyCore>(); |
| _mm_gemmlowp->configure(compile_context, &src_info, &weights_info, bias, dst, gemm_info); |
| } |
| else |
| { |
| // Configure matrix multiply kernel |
| _mm_gemm = std::make_unique<ClGemm>(); |
| _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<ClFlatten>(); |
| _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<ClTranspose>(); |
| _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<ClConvertFullyConnectedWeights>(); |
| _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 |