| /* |
| * Copyright (c) 2017-2023 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/ClGemm.h" |
| |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/GPUTarget.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/KernelDescriptors.h" |
| #include "arm_compute/core/Log.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "arm_compute/runtime/ITensorAllocator.h" |
| |
| #include "arm_compute/core/experimental/IPostOp.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/core/utils/helpers/float_ops.h" |
| #include "src/gpu/cl/IClKernel.h" |
| #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
| #include "src/runtime/CL/gemm/CLGEMMKernelSelection.h" |
| #include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" |
| |
| #include "src/common/utils/Log.h" |
| #include "support/Cast.h" |
| #include "utils/TypePrinter.h" |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| using namespace arm_compute::cl_gemm; |
| using namespace arm_compute::experimental; |
| using namespace arm_compute::utils::cast; |
| using namespace arm_compute::opencl::kernels; |
| |
| namespace |
| { |
| inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type) |
| { |
| return kernel_type == CLGEMMKernelType::NATIVE ? false : true; |
| } |
| //Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type |
| inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run, bool constant_weights) |
| { |
| if(!constant_weights) |
| { |
| return CLGEMMKernelType::NATIVE; |
| } |
| |
| auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run); |
| if(bool(gemm_kernel)) |
| { |
| if(validate_gemm_kernel(gemm_kernel.gemm_type)) |
| { |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); |
| return gemm_kernel.gemm_type; |
| } |
| } |
| gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run); |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); |
| return gemm_kernel.gemm_type; |
| } |
| // Validate lhs_info and rhs_info for reshaped only rhs kernel |
| inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, |
| const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info) |
| { |
| // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel |
| TensorInfo tmp_b_info{}; |
| // Validate reshape RHS kernel |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) |
| { |
| return false; |
| } |
| // Validate mm kernel |
| gemm_kernel_info.lhs_info = lhs_info; |
| gemm_kernel_info.rhs_info = rhs_info; |
| gemm_kernel_info.has_pad_y = false; |
| if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) |
| { |
| return false; |
| } |
| gemm_kernel_info.has_pad_y = true; |
| if(!bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) |
| { |
| return false; |
| } |
| return true; |
| } |
| |
| //Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs |
| inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, |
| const ITensorInfo *b, |
| const ITensorInfo *c, const ITensorInfo *output) |
| { |
| auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query); |
| if(config) |
| { |
| if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info)) |
| { |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| return { config.lhs_info, config.rhs_info }; |
| } |
| } |
| config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| return { config.lhs_info, config.rhs_info }; |
| } |
| |
| // Validate lhs_info and rhs_info for reshaped kernel |
| inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, |
| const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d) |
| { |
| // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel |
| TensorInfo tmp_a_info{}; |
| TensorInfo tmp_b_info{}; |
| |
| // Validate reshape LHS kernel |
| auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d))); |
| if(!bool(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d))) |
| { |
| return false; |
| } |
| |
| // Validate reshape RHS kernel |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) |
| { |
| return false; |
| } |
| // Validate mm kernel |
| gemm_kernel_info.lhs_info = lhs_info; |
| gemm_kernel_info.rhs_info = rhs_info; |
| if(!bool(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) |
| { |
| return false; |
| } |
| return true; |
| } |
| |
| //Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs |
| inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b, |
| const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d) |
| { |
| auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query); |
| if(config) |
| { |
| if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d)) |
| { |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| return { config.lhs_info, config.rhs_info }; |
| } |
| } |
| config = auto_heuristics::select_default_gemm_config_reshaped(query); |
| ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| return { config.lhs_info, config.rhs_info }; |
| } |
| } // namespace |
| |
| ClGemm::ClGemm() |
| : _reshape_lhs_kernel(std::make_unique<ClGemmReshapeLhsMatrixKernel>()), |
| _reshape_rhs_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()), |
| _mm_native_kernel(std::make_unique<ClGemmMatrixMultiplyNativeKernel>()), |
| _mm_reshaped_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedKernel>()), |
| _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()), |
| _mm_reshaped_only_rhs_mmul_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel>()), |
| _tmp_a(), |
| _tmp_b(), |
| _reshape_b_only_on_first_run(false), |
| _gemm_kernel_type(CLGEMMKernelType::NATIVE), |
| _is_prepared(false), |
| _aux_mem(AuxTensorIdx::Count) |
| { |
| } |
| |
| void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| // Set the target for the kernels |
| _mm_native_kernel->set_target(gpu_target); |
| |
| auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| |
| // Configure and tune matrix multiply kernel |
| _mm_native_kernel->configure(compile_context, a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info); |
| } |
| |
| void ClGemm::configure_reshaped(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = false; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| // Set the target for the kernels |
| _reshape_lhs_kernel->set_target(gpu_target); |
| _mm_reshaped_kernel->set_target(gpu_target); |
| |
| GEMMLHSMatrixInfo lhs_info{}; |
| GEMMRHSMatrixInfo rhs_info{}; |
| |
| // Pick up the GEMM configuration |
| std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b, |
| c, output, gemm_info.reinterpret_input_as_3d()); |
| |
| _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d()); |
| _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); |
| |
| // Configure and tune matrix multiply kernel |
| _mm_reshaped_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| // Request memory for LHS and RHS reshape matrix |
| _aux_mem[LhsReshape] = MemoryInfo(offset_int_vec(LhsReshape), MemoryLifetime::Temporary, _tmp_a.total_size()); |
| _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); |
| } |
| |
| void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| // Set the target for the kernels |
| _mm_reshaped_only_rhs_kernel->set_target(gpu_target); |
| |
| GEMMLHSMatrixInfo lhs_info{}; |
| GEMMRHSMatrixInfo rhs_info{}; |
| |
| // Pick up the GEMM configuration |
| std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a, b, c, output); |
| |
| // Transpose matrix |
| _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); |
| |
| // Configure two variants of CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (has_pad_y = false/true) |
| // During the prepare stage we check the padding requirement for the lhs and dst tensors. If they do not have |
| // pad y, we dispatch CLGEMMMatrixMultiplyReshapedOnlyRHSKernel with has_pad_y = false |
| |
| // Configure matrix multiply kernel with no y padding support |
| kernel_info.has_pad_y = false; |
| _mm_reshaped_only_rhs_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| // Request memory for RHS reshape matrix |
| _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); |
| } |
| |
| void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| // Set the target for the kernels |
| _mm_reshaped_only_rhs_mmul_kernel->set_target(gpu_target); |
| |
| GEMMLHSMatrixInfo lhs_info{}; |
| GEMMRHSMatrixInfo rhs_info{}; |
| |
| // Pick up the GEMM configuration |
| auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| lhs_info = gemm_config.lhs_info; |
| rhs_info = gemm_config.rhs_info; |
| // Force H0 to 4 in order to use the MMUL extension |
| rhs_info.h0 = 4; |
| |
| // Reshape Rhs matrix |
| _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); |
| |
| // Configure matrix multiply kernel with no y padding support |
| kernel_info.has_pad_y = false; |
| _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| // Request memory for RHS reshape matrix |
| _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); |
| } |
| |
| Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| ARM_COMPUTE_UNUSED(output); |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| |
| // Validate matrix multiply |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyNativeKernel::validate(a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info)); |
| |
| return Status{}; |
| } |
| |
| Status ClGemm::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| ARM_COMPUTE_UNUSED(output); |
| |
| TensorInfo tmp_a_info{}; |
| TensorInfo tmp_b_info{}; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = false; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| GEMMLHSMatrixInfo lhs_info; |
| GEMMRHSMatrixInfo rhs_info; |
| |
| // Pick up the GEMM configuration |
| // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails |
| const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| lhs_info = gemm_config.lhs_info; |
| rhs_info = gemm_config.rhs_info; |
| |
| auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d()))); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d())); |
| |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); |
| |
| // Validate matrix multiply |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); |
| |
| return Status{}; |
| } |
| |
| Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| ARM_COMPUTE_UNUSED(output); |
| |
| TensorInfo tmp_b_info{}; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| const DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| GEMMLHSMatrixInfo lhs_info; |
| GEMMRHSMatrixInfo rhs_info; |
| |
| // Pick up the GEMM configuration |
| // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails |
| const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| lhs_info = gemm_config.lhs_info; |
| rhs_info = gemm_config.rhs_info; |
| |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); |
| |
| // Validate matrix multiply |
| kernel_info.has_pad_y = false; |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); |
| |
| kernel_info.has_pad_y = true; |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); |
| |
| return Status{}; |
| } |
| |
| Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| ARM_COMPUTE_UNUSED(output); |
| TensorInfo tmp_b_info{}; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| const DataType data_type = a->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const bool broadcast_bias = gemm_info.broadcast_bias(); |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = m; |
| kernel_info.n = n; |
| kernel_info.k = k; |
| kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = gemm_info.activation_info(); |
| kernel_info.post_ops = gemm_info.post_ops(); |
| |
| GEMMLHSMatrixInfo lhs_info; |
| GEMMRHSMatrixInfo rhs_info; |
| |
| // Pick up the GEMM configuration |
| // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails |
| const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); |
| lhs_info = gemm_config.lhs_info; |
| rhs_info = gemm_config.rhs_info; |
| // Force H0 to 4 in order to use the MMUL extension |
| rhs_info.h0 = 4; |
| |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); |
| |
| // Validate matrix multiply |
| kernel_info.has_pad_y = false; |
| ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); |
| |
| return Status{}; |
| } |
| |
| void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate(a, b, c, output, alpha, beta, gemm_info)); |
| ARM_COMPUTE_LOG_PARAMS(a, b, c, output, alpha, beta, gemm_info); |
| |
| // Check if we need to reshape the matrix B only on the first run |
| _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| _is_prepared = gemm_info.retain_internal_weights(); |
| |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| |
| // Select GEMMType |
| _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run, |
| b->are_values_constant()); |
| |
| const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); |
| |
| ITensorInfo *c_to_use = fuse_add_c ? c : nullptr; |
| |
| switch(_gemm_kernel_type) |
| { |
| case CLGEMMKernelType::NATIVE: |
| { |
| configure_native(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED: |
| { |
| configure_reshaped(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
| { |
| configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: |
| { |
| configure_reshaped_only_rhs_mmul(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("GEMMType not supported"); |
| } |
| } |
| } |
| |
| Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| // Get the GPU target |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const unsigned int n = b->dimension(0); |
| const unsigned int k = a->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| |
| // Check data type early because the auto_select_gemm_kernel has assertions on supported data types |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16); |
| |
| // Select GEMMType |
| CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery |
| { |
| CLScheduler::get().target(), a->data_type(), m, n, k, batch_size, |
| }, |
| gemm_info.reshape_b_only_on_first_run(), b->are_values_constant()); |
| |
| const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); |
| |
| const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr; |
| |
| switch(gemm_kernel_type) |
| { |
| case CLGEMMKernelType::NATIVE: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_native(a, b, c_to_use, output, alpha, beta, gemm_info)); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped(a, b, c_to_use, output, alpha, beta, gemm_info)); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info)); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs_mmul(a, b, c_to_use, output, alpha, beta, gemm_info)); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported"); |
| } |
| } |
| |
| return Status{}; |
| } |
| |
| void ClGemm::run(ITensorPack &tensors) |
| { |
| const ITensor *lhs = tensors.get_const_tensor(ACL_SRC_0); |
| const ITensor *rhs = tensors.get_const_tensor(ACL_SRC_1); |
| ITensor *dst = tensors.get_tensor(ACL_DST); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, dst); |
| |
| CLAuxTensorHandler lhs_reshaped(offset_int_vec(LhsReshape), _tmp_a, tensors, true); |
| CLAuxTensorHandler rhs_reshaped(offset_int_vec(RhsReshape), _tmp_b, tensors, true); |
| |
| // Prepare the consts if needed |
| prepare(tensors); |
| |
| // Run matrix multiply kernel |
| switch(_gemm_kernel_type) |
| { |
| case CLGEMMKernelType::NATIVE: |
| { |
| CLScheduler::get().enqueue_op(*_mm_native_kernel, tensors, true); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED: |
| { |
| // Run interleave kernel |
| ITensorPack reshape_lhs_pack{ { ACL_SRC, lhs }, { ACL_DST, lhs_reshaped.get() } }; |
| CLScheduler::get().enqueue_op(*_reshape_lhs_kernel, reshape_lhs_pack, false); |
| |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; |
| CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); |
| } |
| // Copy original tensor pack and overwrite lhs and rhs with reshaped counterparts |
| ITensorPack gemm_reshaped_pack(tensors); |
| gemm_reshaped_pack.add_const_tensor(ACL_SRC_0, lhs_reshaped.get()); |
| gemm_reshaped_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); |
| |
| if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED) |
| { |
| CLScheduler::get().enqueue_op(*_mm_reshaped_kernel, gemm_reshaped_pack, true); |
| } |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
| { |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; |
| CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); |
| } |
| // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement |
| // Check if the lhs or dst tensors have padding |
| const unsigned int cross_plane_pad_lhs = lhs->info()->padding().top + lhs->info()->padding().bottom; |
| const unsigned int cross_plane_pad_dst = dst->info()->padding().top + dst->info()->padding().bottom; |
| bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0); |
| |
| // Copy original tensor pack and overwrite rhs with reshaped counterpart |
| ITensorPack gemm_reshaped_onlyrhs_pack(tensors); |
| gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); |
| |
| if(has_pad_y) |
| { |
| ARM_COMPUTE_ERROR_ON(has_pad_y); |
| } |
| else |
| { |
| CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_onlyrhs_pack, true); |
| } |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: |
| { |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; |
| CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); |
| } |
| // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement |
| // Check if the lhs or dst tensors have padding |
| const unsigned int cross_plane_pad_lhs = lhs->info()->padding().top + lhs->info()->padding().bottom; |
| const unsigned int cross_plane_pad_dst = dst->info()->padding().top + dst->info()->padding().bottom; |
| bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0); |
| |
| // Copy original tensor pack and overwrite rhs with reshaped counterpart |
| ITensorPack gemm_reshaped_onlyrhs_pack(tensors); |
| gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); |
| |
| if(has_pad_y) |
| { |
| ARM_COMPUTE_ERROR_ON(has_pad_y); |
| } |
| else |
| { |
| CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_mmul_kernel, gemm_reshaped_onlyrhs_pack, true); |
| } |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("GEMMType not supported"); |
| } |
| } |
| } |
| |
| void ClGemm::prepare(ITensorPack &constants) |
| { |
| if(!_is_prepared) |
| { |
| const ITensor *src1 = constants.get_const_tensor(ACL_SRC_1); |
| ICLTensor *rhs_aux = utils::cast::polymorphic_downcast<ICLTensor *>(constants.get_tensor(offset_int_vec(RhsReshape))); |
| |
| // If memory for RHS is persistent and src1 is provided re-transform else assume that RHS is transformed |
| if((_aux_mem[AuxTensorIdx::RhsReshape].lifetime == MemoryLifetime::Persistent) && (src1 != nullptr && rhs_aux != nullptr) && rhs_aux) |
| { |
| ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Transforming RHS Matrix!"); |
| |
| CLAuxTensorHandler rhs_reshaped(_tmp_b, *rhs_aux); |
| ARM_COMPUTE_ERROR_ON(rhs_reshaped.get()->cl_buffer().get() == nullptr); |
| |
| ITensorPack reshape_rhs_pack{ { ACL_SRC, src1 }, { ACL_DST, rhs_reshaped.get() } }; |
| CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, true); |
| } |
| _is_prepared = true; |
| } |
| } |
| |
| experimental::MemoryRequirements ClGemm::workspace() const |
| { |
| return _aux_mem; |
| } |
| } // namespace opencl |
| } // namespace arm_compute |