| /* |
| * Copyright (c) 2017-2020 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 "arm_compute/runtime/CL/functions/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/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 "src/core/CL/ICLGEMMKernelConfiguration.h" |
| #include "src/core/CL/gemm/reshaped/CLGEMMReshapedKernelConfiguration.h" |
| #include "src/core/CL/gemm/reshaped_only_rhs/CLGEMMReshapedOnlyRHSKernelConfiguration.h" |
| #include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" |
| #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h" |
| #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h" |
| #include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" |
| #include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/utils/helpers/float_ops.h" |
| #include "src/runtime/CL/gemm/CLGEMMKernelSelection.h" |
| #include "support/Cast.h" |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| using namespace arm_compute::cl_gemm; |
| using namespace arm_compute::utils::cast; |
| |
| namespace weights_transformations |
| { |
| CLGEMMReshapeRHSMatrixKernelManaged::CLGEMMReshapeRHSMatrixKernelManaged() |
| : _kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()) |
| { |
| } |
| |
| CLGEMMReshapeRHSMatrixKernelManaged::~CLGEMMReshapeRHSMatrixKernelManaged() = default; |
| |
| void CLGEMMReshapeRHSMatrixKernelManaged::run() |
| { |
| _output.allocator()->allocate(); |
| CLScheduler::get().enqueue(*_kernel, false); |
| _reshape_run = true; |
| } |
| |
| void CLGEMMReshapeRHSMatrixKernelManaged::release() |
| { |
| _output.allocator()->free(); |
| } |
| |
| ICLTensor *CLGEMMReshapeRHSMatrixKernelManaged::get_weights() |
| { |
| return &_output; |
| } |
| |
| uint32_t CLGEMMReshapeRHSMatrixKernelManaged::uid() |
| { |
| return _uid; |
| } |
| |
| void CLGEMMReshapeRHSMatrixKernelManaged::configure(const ICLTensor *input, GEMMRHSMatrixInfo info) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, info); |
| } |
| |
| void CLGEMMReshapeRHSMatrixKernelManaged::configure(const CLCompileContext &compile_context, const ICLTensor *input, GEMMRHSMatrixInfo info) |
| { |
| _kernel->configure(compile_context, input, &_output, info); |
| } |
| } // namespace weights_transformations |
| |
| CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager) |
| : _memory_group(std::move(memory_manager)), |
| _weights_manager(weights_manager), |
| _mm_kernel(std::make_unique<CLGEMMMatrixMultiplyKernel>()), |
| _reshape_lhs_kernel(std::make_unique<CLGEMMReshapeLHSMatrixKernel>()), |
| _reshape_rhs_kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()), |
| _reshape_rhs_kernel_managed(std::make_unique<weights_transformations::CLGEMMReshapeRHSMatrixKernelManaged>()), |
| _mm_reshaped_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedKernel>()), |
| _mm_reshaped_only_rhs_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()), |
| _mm_reshaped_only_rhs_fallback_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()), |
| _tmp_a(), |
| _tmp_b(), |
| _original_b(nullptr), |
| _lhs(nullptr), |
| _dst(nullptr), |
| _reshape_b_only_on_first_run(false), |
| _is_prepared(false), |
| _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1) |
| { |
| } |
| |
| CLGEMM::~CLGEMM() = default; |
| |
| CLGEMMKernelType CLGEMM::select_gemm_kernel(unsigned int m, unsigned int n, unsigned int k, unsigned int b, DataType data_type, bool reshape_b_only_on_first_run) |
| { |
| std::unique_ptr<ICLGEMMKernelSelection> gemm_kernel = CLGEMMKernelSelectionFactory::create(CLScheduler::get().target()); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_kernel.get()); |
| |
| CLGEMMKernelSelectionParams params; |
| params.m = m; |
| params.n = n; |
| params.k = k; |
| params.b = b; |
| params.is_rhs_constant = reshape_b_only_on_first_run; |
| params.data_type = data_type; |
| |
| return gemm_kernel->select_kernel(params); |
| } |
| |
| void CLGEMM::configure_native_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| const unsigned int m = gemm_info.reinterpret_input_as_3d() ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| |
| // Set the target for the kernels |
| _mm_kernel->set_target(gpu_target); |
| |
| GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias()); |
| |
| // Configure and tune matrix multiply kernel |
| _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info()); |
| |
| // Tune kernel statically |
| CLScheduler::get().tune_kernel_static(*_mm_kernel); |
| } |
| |
| void CLGEMM::configure_reshaped_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| |
| // Set the target for the kernels |
| _reshape_lhs_kernel->set_target(gpu_target); |
| _mm_kernel->set_target(gpu_target); |
| |
| if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST) |
| { |
| mult_transpose1xW_width = 4; |
| mult_interleave4x4_height = 2; |
| } |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = 16 / b->info()->element_size(); |
| rhs_info.k0 = 1; |
| rhs_info.h0 = mult_transpose1xW_width; |
| rhs_info.interleave = false; |
| rhs_info.transpose = false; |
| |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = 4; |
| lhs_info.k0 = 4; |
| lhs_info.v0 = mult_interleave4x4_height; |
| lhs_info.interleave = true; |
| lhs_info.transpose = true; |
| |
| GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias()); |
| |
| const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp_a); |
| |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| // Configure interleave kernel |
| _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d); |
| |
| // Configure transpose kernel |
| ICLTensor *reshaped_rhs = &_tmp_b; |
| if(_weights_manager && _weights_manager->are_weights_managed(b)) |
| { |
| _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); |
| reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); |
| } |
| else |
| { |
| _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); |
| } |
| |
| // Configure and tune matrix multiply kernel |
| _mm_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info()); |
| |
| CLScheduler::get().tune_kernel_static(*_mm_kernel); |
| |
| // Allocate intermediate tensors |
| _tmp_a.allocator()->allocate(); |
| |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| void CLGEMM::configure_reshaped_v2(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->info()->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->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(); |
| |
| // Set the target for the kernels |
| _reshape_lhs_kernel->set_target(gpu_target); |
| _mm_kernel->set_target(gpu_target); |
| |
| const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp_a); |
| |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel |
| |
| GEMMLHSMatrixInfo lhs_info{}; |
| GEMMRHSMatrixInfo rhs_info{}; |
| |
| // Pick up the GEMM configuration |
| std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedKernelConfigurationFactory::create(gpu_target); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_config.get()); |
| |
| // Configure lhs_info and rhs_info |
| std::tie(lhs_info, rhs_info) = gemm_config->configure(m, n, k, batch_size, data_type); |
| |
| _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d()); |
| |
| ICLTensor *reshaped_rhs = &_tmp_b; |
| if(_weights_manager && _weights_manager->are_weights_managed(b)) |
| { |
| _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); |
| reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); |
| } |
| else |
| { |
| _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, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| // Allocate intermediate tensors |
| _tmp_a.allocator()->allocate(); |
| |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| void CLGEMM::configure_reshaped_only_rhs(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, |
| const GEMMInfo &gemm_info) |
| { |
| DataType data_type = a->info()->data_type(); |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->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(); |
| |
| // Set the target for the kernels |
| _mm_kernel->set_target(gpu_target); |
| |
| const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); |
| |
| // Manage intermediate buffers |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| GEMMLHSMatrixInfo lhs_info{}; |
| GEMMRHSMatrixInfo rhs_info{}; |
| |
| // Pick up the GEMM configuration |
| std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(gpu_target); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(gemm_config.get()); |
| |
| // Configure lhs_info and rhs_info |
| std::tie(lhs_info, rhs_info) = gemm_config->configure(m, n, k, batch_size, data_type); |
| |
| ICLTensor *reshaped_rhs = &_tmp_b; |
| if(_weights_manager && _weights_manager->are_weights_managed(b)) |
| { |
| _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); |
| reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); |
| } |
| else |
| { |
| _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, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| // Configure matrix multiply kernel with y padding support |
| kernel_info.has_pad_y = true; |
| _mm_reshaped_only_rhs_fallback_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); |
| |
| if(!_reshape_b_only_on_first_run && use_mm_b) |
| { |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| Status CLGEMM::validate_native_v1(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(); |
| 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 int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias()); |
| |
| // Validate matrix multiply |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta, |
| false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info())); |
| |
| return Status{}; |
| } |
| |
| Status CLGEMM::validate_reshaped_v1(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(); |
| const unsigned int m = gemm_info.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); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| |
| if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST) |
| { |
| mult_transpose1xW_width = 4; |
| mult_interleave4x4_height = 2; |
| } |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = 16 / b->element_size(); |
| rhs_info.k0 = 1; |
| rhs_info.h0 = mult_transpose1xW_width; |
| rhs_info.interleave = false; |
| rhs_info.transpose = false; |
| |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = 4; |
| lhs_info.k0 = 4; |
| lhs_info.v0 = mult_interleave4x4_height; |
| lhs_info.interleave = true; |
| lhs_info.transpose = true; |
| |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias()); |
| |
| // Validate interleave kernel |
| 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())); |
| |
| // Validate transpose kernel |
| 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(CLGEMMMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, |
| true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_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(); |
| |
| GEMMLHSMatrixInfo lhs_info; |
| GEMMRHSMatrixInfo rhs_info; |
| |
| // Pick up the GEMM configuration |
| std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedKernelConfigurationFactory::create(gpu_target); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(gemm_config.get()); |
| |
| // Configure lhs_info and rhs_info |
| std::tie(lhs_info, rhs_info) = gemm_config->configure(m, n, k, batch_size, data_type); |
| |
| 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(); |
| |
| GEMMLHSMatrixInfo lhs_info; |
| GEMMRHSMatrixInfo rhs_info; |
| |
| // Pick up the GEMM configuration |
| std::unique_ptr<ICLGEMMKernelConfiguration> gemm_config = CLGEMMReshapedOnlyRHSKernelConfigurationFactory::create(gpu_target); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(gemm_config.get()); |
| |
| // Configure lhs_info and rhs_info |
| std::tie(lhs_info, rhs_info) = gemm_config->configure(m, n, k, batch_size, data_type); |
| |
| 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{}; |
| } |
| |
| void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, alpha, beta, gemm_info); |
| } |
| |
| void CLGEMM::configure(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *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->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), 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(); |
| _original_b = b; |
| _lhs = a; |
| _dst = output; |
| |
| // Get the GPU target |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const unsigned int n = b->info()->dimension(0); |
| const unsigned int k = a->info()->dimension(0); |
| const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2); |
| |
| // Select GEMMType |
| _gemm_kernel_type = select_gemm_kernel(m, n, k, batch_size, a->info()->data_type(), _reshape_b_only_on_first_run); |
| |
| const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); |
| |
| const ICLTensor *c_to_use = fuse_add_c ? c : nullptr; |
| |
| switch(_gemm_kernel_type) |
| { |
| case CLGEMMKernelType::NATIVE_V1: |
| { |
| configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_V1: |
| { |
| configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED: |
| { |
| configure_reshaped_v2(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; |
| } |
| 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); |
| |
| // Select GEMMType |
| CLGEMMKernelType gemm_kernel_type = select_gemm_kernel(m, n, k, batch_size, a->data_type(), gemm_info.reshape_b_only_on_first_run()); |
| |
| 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_V1: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info)); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_V1: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(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; |
| } |
| default: |
| { |
| ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported"); |
| } |
| } |
| |
| return Status{}; |
| } |
| |
| void CLGEMM::run() |
| { |
| prepare(); |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| // Run matrix multiply kernel |
| switch(_gemm_kernel_type) |
| { |
| case CLGEMMKernelType::NATIVE_V1: |
| { |
| CLScheduler::get().enqueue(*_mm_kernel, true); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_V1: |
| { |
| // Run interleave kernel |
| CLScheduler::get().enqueue(*_reshape_lhs_kernel, false); |
| |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) |
| { |
| _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); |
| } |
| else |
| { |
| CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); |
| } |
| } |
| |
| CLScheduler::get().enqueue(*_mm_kernel, true); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED: |
| { |
| // Run interleave kernel |
| CLScheduler::get().enqueue(*_reshape_lhs_kernel, false); |
| |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) |
| { |
| _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); |
| } |
| else |
| { |
| CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); |
| } |
| } |
| |
| CLScheduler::get().enqueue(*_mm_reshaped_kernel, true); |
| break; |
| } |
| case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
| { |
| if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) |
| { |
| _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); |
| } |
| else |
| { |
| CLScheduler::get().enqueue(*_reshape_rhs_kernel, 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); |
| if(has_pad_y) |
| { |
| CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_fallback_kernel, true); |
| } |
| else |
| { |
| CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_kernel, true); |
| } |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("GEMMType not supported"); |
| } |
| } |
| } |
| |
| void CLGEMM::prepare() |
| { |
| if(!_is_prepared) |
| { |
| if(_gemm_kernel_type != CLGEMMKernelType::NATIVE_V1 && _reshape_b_only_on_first_run) |
| { |
| if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) |
| { |
| _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); |
| } |
| else |
| { |
| // Run transpose kernel and mark original weights tensor as unused |
| _tmp_b.allocator()->allocate(); |
| CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); |
| _original_b->mark_as_unused(); |
| } |
| } |
| CLScheduler::get().queue().finish(); |
| _is_prepared = true; |
| } |
| } |
| } // namespace arm_compute |