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Diffstat (limited to 'src/gpu/cl/operators/ClFullyConnected.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClFullyConnected.cpp | 698 |
1 files changed, 698 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp new file mode 100644 index 0000000000..6969ac8ab3 --- /dev/null +++ b/src/gpu/cl/operators/ClFullyConnected.cpp @@ -0,0 +1,698 @@ +/* + * 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 |