/* * Copyright (c) 2023-2024 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/cpu/operators/CpuMatMul.h" #include "arm_compute/core/experimental/Types.h" #include "arm_compute/core/Types.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/function_info/MatMulInfo.h" #include "arm_compute/runtime/NEON/functions/NEMatMul.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include "src/common/utils/Log.h" #include "src/core/CPP/Validate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/core/utils/quantization/AsymmHelpers.h" #include "src/cpu/utils/CpuAuxTensorHandler.h" using namespace arm_compute::experimental; namespace arm_compute { namespace cpu { namespace { Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const ActivationLayerInfo &act, GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) { const auto data_type = src->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(); float multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale; int32_t output_multiplier; int32_t output_shift; ARM_COMPUTE_RETURN_ON_ERROR( quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); int32_t type_min = 0; int32_t type_max = 0; std::tie(type_min, type_max) = quantization::get_quantized_asymmetric_output_min_max(oq_info, act, data_type); gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; gemmlowp_output_stage_info.gemmlowp_shift = output_shift; gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset; gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; gemmlowp_output_stage_info.gemmlowp_min_bound = type_min; gemmlowp_output_stage_info.gemmlowp_max_bound = type_max; return Status{}; } } // namespace CpuMatMul::CpuMatMul() : _transpose_kernel_lhs(), _transpose_kernel_rhs(), _asm_glue(), _lhs_transposed(), _rhs_transposed(), _original_lhs_shape(), _original_rhs_shape(), _original_dst_shape() { } Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings, const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16, DataType::BFLOAT16, DataType::QASYMM8, DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs->are_values_constant(), "LHS Tensor must be dynamic."); ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs->are_values_constant(), "RHS Tensor must be dynamic."); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs); ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(lhs); const auto adj_lhs = info.adj_lhs(); const auto adj_rhs = info.adj_rhs(); const ITensorInfo *lhs_to_use = lhs; const ITensorInfo *rhs_to_use = rhs; TensorInfo lhs_transposed{}; TensorInfo rhs_transposed{}; auto gemm_info = AsmGemmInfo(); gemm_info.activation_info = act_info; gemm_info.fast_mode = settings.fast_math(); gemm_info.fixed_format = settings.fixed_format(); // Validate and then permute a/b if (adj_lhs) { auto_init_if_empty(lhs_transposed, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*lhs))); ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(lhs_to_use, &lhs_transposed)); // Assign lhs_to_use pointer to use transposed TensorInfo lhs_to_use = &lhs_transposed; } if (adj_rhs) { auto_init_if_empty(rhs_transposed, rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*rhs))); ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(rhs_to_use, &rhs_transposed)); // Assign rhs_to_use pointer to use transposed TensorInfo rhs_to_use = &rhs_transposed; } ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(0) != rhs_to_use->dimension(1), "The product AB is defined only if the number of columns in A is equal to the " "number of rows in B (after transpose)"); // Iterate over dimensions to be collapsed in operator - check dimensions are equivalent between tensors for (unsigned int i = 2; i < Coordinates::num_max_dimensions; i++) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(i) != rhs_to_use->dimension(i), "Broadcasting in Batch dimension is unsupported by this operator."); } // Quantized-specific configuration if (is_data_type_quantized(lhs->data_type())) { ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(lhs_to_use, rhs_to_use, dst, gemm_info.activation_info, gemm_info.output_stage)); } if (gemm_info.fixed_format) { gemm_info.weight_format = WeightFormat::ANY; arm_compute::WeightFormat expected_weight_format = WeightFormat::ANY; ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuGemmAssemblyDispatch::has_opt_impl(expected_weight_format, lhs_to_use, rhs_to_use, nullptr, dst, gemm_info)); } cpu::CpuGemmAssemblyDispatch::validate(lhs_to_use, rhs_to_use, nullptr, dst, gemm_info); return Status{}; } void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings, const ActivationLayerInfo &act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst); ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, info, settings); ARM_COMPUTE_ERROR_THROW_ON(CpuMatMul::validate(lhs, rhs, dst, info, settings)); _adj_lhs = info.adj_lhs(); _adj_rhs = info.adj_rhs(); _fast_math = settings.fast_math(); // 1. Create and reshape tensors // ------------------------------------------------------ // a. Clone TensorInfo to prevent changing original tensor values during setup // b. Change shape of lhs/dst to [x, y, 1, collapsed(z)] to match assembly kernel configuration // c. For rhs collapse all dimensions larger than 3 to z dimension TensorInfo lhs_to_use = *lhs->clone(); TensorInfo dst_to_use = *dst->clone(); TensorInfo rhs_to_use = *rhs->clone(); // Save starting shape of tensors _original_lhs_shape = lhs_to_use.tensor_shape(); _original_dst_shape = dst_to_use.tensor_shape(); _original_rhs_shape = rhs_to_use.tensor_shape(); // Reshape lhs for use with assembly kernels. lhs_to_use.set_tensor_shape( TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z())); dst_to_use.set_tensor_shape( TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z())); rhs_to_use.set_tensor_shape(_original_rhs_shape.collapsed_from(2)); // 2. Configuration for transpose of lhs/rhs // ------------------------------------------------------ // Initialise transposed TensorInfo class for aux tensors (intermediary tensors) if (_adj_lhs) { // Setup transpose LHS _transpose_kernel_lhs = std::make_unique(); _transpose_kernel_lhs->configure(&lhs_to_use, &_lhs_transposed); } if (_adj_rhs) { // Setup transpose RHS _transpose_kernel_rhs = std::make_unique(); _transpose_kernel_rhs->configure(&rhs_to_use, &_rhs_transposed); } // 3. Configure assembly kernel using transposed tensors. // ----------------------------------------------------- // Use transposed tensors if the corresponding transpose flags are set // Fill AsmGemmInfo class object before configuration _gemm_info.activation_info = act_info; _gemm_info.fast_mode = settings.fast_math(); _gemm_info.fixed_format = settings.fixed_format(); _gemm_info.negated_offsets = false; lhs_to_use = (_adj_lhs) ? _lhs_transposed : lhs_to_use; rhs_to_use = (_adj_rhs) ? _rhs_transposed : rhs_to_use; // Quantized-specific configuration if (is_data_type_quantized(lhs->data_type())) { get_gemmlowp_output_stage_info(&lhs_to_use, &rhs_to_use, &dst_to_use, _gemm_info.activation_info, _gemm_info.output_stage); } if (_gemm_info.fixed_format) { _gemm_info.weight_format = WeightFormat::ANY; arm_compute::WeightFormat expected_weight_format = WeightFormat::ANY; ARM_COMPUTE_ERROR_THROW_ON(cpu::CpuGemmAssemblyDispatch::has_opt_impl(expected_weight_format, &lhs_to_use, &rhs_to_use, nullptr, dst, _gemm_info)); // Set gemm weights info to the one returned by has_opt_impl _gemm_info.weight_format = expected_weight_format; // has_opt_impl may return a non fast math kernel, even if we requested one _gemm_info.fast_mode = arm_compute::is_fixed_format_fast_math(expected_weight_format); } // Configure Asm Kernel _asm_glue = std::make_unique(); _asm_glue->configure(&lhs_to_use, &rhs_to_use, nullptr, &dst_to_use, _gemm_info); // c is nullptr as bias not supported in MatMul // Specify memory requirements for intermediate tensors auto asm_mem_req = _asm_glue->workspace(); // Specify memory required by gemm kernel int idx = 0; for (const auto &aux : asm_mem_req) { _aux_mem[idx] = aux; idx++; } // Memory requirements for transposed tensors _aux_mem[TransposeLHS] = MemoryInfo(offset_int_vec(TransposeLHS), MemoryLifetime::Temporary, lhs->total_size()); _aux_mem[TransposeRHS] = MemoryInfo(offset_int_vec(TransposeRHS), MemoryLifetime::Temporary, rhs->total_size()); } void CpuMatMul::run(ITensorPack &tensors) { // Retrieve tensors from tensor pack auto lhs = tensors.get_tensor(ACL_SRC_0); auto rhs = tensors.get_const_tensor(ACL_SRC_1); auto dst = tensors.get_tensor(ACL_DST); // Reshape LHS and DST to ensure compatibility with GEMM asm kernel (Batch dimensions is 4th for lhs and dst within asm) // Collapse RHS (necessary to support dimensions larger than 3 in gemm assembly) lhs->info()->set_tensor_shape( TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z dst->info()->set_tensor_shape( TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z rhs->info()->set_tensor_shape(_original_rhs_shape.collapsed_from(2)); // Initialise object to handle stored transposed tensors in auxillary memory CpuAuxTensorHandler lhs_transposed(offset_int_vec(TransposeLHS), _lhs_transposed, tensors, true); CpuAuxTensorHandler rhs_transposed(offset_int_vec(TransposeRHS), _rhs_transposed, tensors, true); // Create tensor pack for asm kernel ITensorPack asm_tensors(tensors); // Run transpose lhs if necessary if (_adj_lhs) { ITensorPack lhs_transpose_pack = {{TensorType::ACL_SRC, lhs}, {TensorType::ACL_DST, lhs_transposed.get()}}; NEScheduler::get().schedule_op(_transpose_kernel_lhs.get(), Window::DimY, _transpose_kernel_lhs->window(), lhs_transpose_pack); asm_tensors.add_const_tensor(TensorType::ACL_SRC_0, lhs_transposed.get()); } // Run transpose rhs if necessary if (_adj_rhs) { ITensorPack rhs_transpose_pack = {{TensorType::ACL_SRC, rhs}, {TensorType::ACL_DST, rhs_transposed.get()}}; NEScheduler::get().schedule_op(_transpose_kernel_rhs.get(), Window::DimY, _transpose_kernel_rhs->window(), rhs_transpose_pack); asm_tensors.add_const_tensor(TensorType::ACL_SRC_1, rhs_transposed.get()); } // Run asm kernel _asm_glue->run(asm_tensors); // Undo reshape of tensors dst->info()->set_tensor_shape(_original_dst_shape); lhs->info()->set_tensor_shape(_original_lhs_shape); rhs->info()->set_tensor_shape(_original_rhs_shape); } experimental::MemoryRequirements CpuMatMul::workspace() const { return _aux_mem; } } // namespace cpu } // namespace arm_compute