/* * 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/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwMatMul.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/Validate.h" #include "src/core/helpers/WindowHelpers.h" #include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/utils/CkwHelper.h" #include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/utils/type_converter/Common.h" #include "src/dynamic_fusion/sketch/gpu/ckw_driver/GpuCkwComponentArgument.h" #include "src/dynamic_fusion/sketch/gpu/ckw_driver/GpuCkwVariableTable.h" #include "support/StringSupport.h" #include "compute_kernel_writer/include/ckw/KernelWriter.h" #include namespace arm_compute { namespace experimental { namespace dynamic_fusion { GpuCkwMatMul::GpuCkwMatMul(ComponentId id, const ArgumentPack &tensors, const Attributes &attributes, const Settings &settings) : IGpuCkwComponentDriver{id, tensors}, _lhs{}, _rhs{}, _dst{}, _attributes{attributes}, _settings{settings} { _lhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); _rhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_1); _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); ARM_COMPUTE_ERROR_ON_NULLPTR(_lhs, _rhs, _dst); } void GpuCkwMatMul::write_component_code(const ComponentGroup &comp_group, GpuCkwVariableTable &vtable, GpuCkwScopedKernelWriter writer) const { /******************************************************************************** * 1 - Define tensors ********************************************************************************/ GpuCkwComponentArgument *lhs = vtable.declare_variable(comp_group, writer, _lhs, "lhs"); GpuCkwComponentArgument *rhs = vtable.declare_variable(comp_group, writer, _rhs, "rhs"); GpuCkwComponentArgument *dst = vtable.declare_variable(comp_group, writer, _dst, "dst"); /******************************************************************************** * 2 - Define CKW constants ********************************************************************************/ const auto k = _attributes.adj_lhs() ? static_cast(_lhs->dimension(1)) : static_cast(_lhs->dimension(0)); const auto k0 = static_cast(adjust_vec_size(_settings.k0(), k)); const auto dst_dt = to_ckw(_dst->data_type()); // CKW constants auto const_k_i32 = writer->declare_constant_tile(ckw::ConstantData({{k}}, ckw::DataType::Int32)); auto const_k0_i32 = writer->declare_constant_tile(ckw::ConstantData({{k0}}, ckw::DataType::Int32)); auto const_0_i32 = writer->declare_constant_tile(ckw::ConstantData({{0}}, ckw::DataType::Int32)); auto const_pos_1_i32 = writer->declare_constant_tile(ckw::ConstantData({{1}}, ckw::DataType::Int32)); auto const_0_fp = writer->declare_constant_tile(ckw::ConstantData({{0.0f}}, dst_dt)); auto const_k_minus_k0_i32 = writer->declare_constant_tile(ckw::ConstantData({{k - k0}}, ckw::DataType::Int32)); /******************************************************************************** * 3 - Define the compute block parameters and destination tile (if not root component) * Bind the tile to the tensor to share it among different components and * initialize the compute block parameters ********************************************************************************/ // The n0 and m0 parameters from root_window only refers to the output const auto root_window = comp_group.get_root_component()->ckw_component_driver()->get_window(); // Destination compute block size const int32_t dst_n0 = root_window.x().step(); const int32_t dst_m0 = root_window.y().step(); // Destination compute block size left-over const int32_t dst_n0_partial = _dst->dimension(0) % dst_n0; const int32_t dst_m0_partial = _dst->dimension(1) % dst_m0; // Shift-back for the overlapping-min strategy const int32_t dst_shift_back = (dst_n0 - dst_n0_partial) % dst_n0; ckw::TensorSampler sampler_dst; sampler_dst.format(ckw::TensorSamplerFormat::Dim0_Dim1_Dim2); if (dst_n0_partial == 0) { sampler_dst.address_mode_x(ckw::TensorSamplerAddressModeX::None); } else { sampler_dst.address_mode_x(ckw::TensorSamplerAddressModeX::OverlappingMin); } if (dst_m0_partial == 0) { sampler_dst.address_mode_y(ckw::TensorSamplerAddressModeY::None); } else { sampler_dst.address_mode_y(ckw::TensorSamplerAddressModeY::ClampToBorderMaxOnly); } sampler_dst.address_mode_z(ckw::TensorSamplerAddressModeZ::None); sampler_dst.storage(ckw::TensorStorageType::BufferUint8Ptr); // Declare destination tile auto tile_dst = writer->declare_tile("dst", ckw::TileInfo(dst_dt, dst_m0, dst_n0)); // Initialize destination tile writer->op_assign(tile_dst, const_0_fp); // Bind tile to the tensor dst->init_virtual_tensor(tile_dst, sampler_dst); /******************************************************************************** * 4 - Define the compute block parameters CKW constants ********************************************************************************/ // Only now we can declare the N0 and M0 as constant auto const_dst_n0_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_n0}}, ckw::DataType::Int32)); auto const_dst_m0_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_m0}}, ckw::DataType::Int32)); auto const_shift_back_dst_n0_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_shift_back}}, ckw::DataType::Int32)); /******************************************************************************** * 5 - Define the samplers for the input tensors ********************************************************************************/ // LHS SAMPLER // The assumption here is that M is multiple of M0. This limitation will be removed once // we have the support for OverlappingMin as address mode for the Y direction ckw::TensorSampler sampler_lhs; sampler_lhs.format(ckw::TensorSamplerFormat::Dim0_Dim1_Dim2); sampler_lhs.address_mode_x(ckw::TensorSamplerAddressModeX::None); sampler_lhs.address_mode_y(ckw::TensorSamplerAddressModeY::None); sampler_lhs.address_mode_z(ckw::TensorSamplerAddressModeZ::None); sampler_lhs.storage(ckw::TensorStorageType::BufferUint8Ptr); // RHS SAMPLER ckw::TensorSampler sampler_rhs; sampler_rhs.format(ckw::TensorSamplerFormat::Dim0_Dim1_Dim2); sampler_rhs.address_mode_x(ckw::TensorSamplerAddressModeX::None); sampler_rhs.address_mode_y(ckw::TensorSamplerAddressModeY::None); sampler_rhs.address_mode_z(ckw::TensorSamplerAddressModeZ::None); sampler_rhs.storage(ckw::TensorStorageType::BufferUint8Ptr); /******************************************************************************** * 6 - Extra operations required before writing the main code (optional) ********************************************************************************/ // Not required /******************************************************************************** * 7 - Get the coordinates of the destination tile ********************************************************************************/ auto tile_gid_0 = writer->declare_tile("gid_0", ckw::TileInfo(ckw::DataType::Int32)); auto tile_gid_1 = writer->declare_tile("gid_1", ckw::TileInfo(ckw::DataType::Int32)); auto tile_gid_2 = writer->declare_tile("gid_2", ckw::TileInfo(ckw::DataType::Int32)); writer->op_get_global_id(tile_gid_0, 0); writer->op_get_global_id(tile_gid_1, 1); writer->op_get_global_id(tile_gid_2, 2); auto tile_idx_n = writer->declare_tile("idx_n", ckw::TileInfo(ckw::DataType::Int32)); // N index auto tile_idx_m = writer->declare_tile("idx_m", ckw::TileInfo(ckw::DataType::Int32)); // M index auto tile_idx_b = writer->declare_tile("idx_b", ckw::TileInfo(ckw::DataType::Int32)); // BATCH index // Calculate coordinates get_coordinate_from_gws_overlapping_min(writer, tile_idx_n, tile_gid_0, const_dst_n0_i32, const_shift_back_dst_n0_i32, const_0_i32); get_coordinate_from_gws(writer, tile_idx_m, tile_gid_1, const_dst_m0_i32); get_coordinate_from_gws(writer, tile_idx_b, tile_gid_2, const_pos_1_i32); /******************************************************************************** * 8 - Write the rest of the code ********************************************************************************/ auto tile_idx_k = writer->declare_tile("idx_k", ckw::TileInfo(ckw::DataType::Int32)); // K index writer->op_assign(tile_idx_k, const_0_i32); // clang-format off writer->op_for_loop(tile_idx_k, ckw::BinaryOp::LessEqual, const_k_minus_k0_i32, tile_idx_k, ckw::AssignmentOp::Increment, const_k0_i32, [&]() { auto tile_lhs = writer->declare_tile("lhs", ckw::TileInfo(to_ckw(_lhs->data_type()), dst_m0, k0)); auto tile_rhs = writer->declare_tile("rhs", ckw::TileInfo(to_ckw(_rhs->data_type()), dst_n0, k0)); writer->op_assign(tile_lhs, const_0_fp); writer->op_assign(tile_rhs, const_0_fp); writer->op_load(tile_lhs, lhs->tensor(), sampler_lhs, tile_idx_k, tile_idx_m, tile_idx_b, const_0_i32); writer->op_load(tile_rhs, rhs->tensor(), sampler_rhs, tile_idx_k, tile_idx_n, tile_idx_b, const_0_i32); writer->op_binary(tile_dst, ckw::BinaryOp::MatMul_Nt_T, tile_lhs, tile_rhs); }); // Left-over accumulations for when K is not a multiple of k0 if(((k % k0) != 0)) { writer->op_for_loop(tile_idx_k, ckw::BinaryOp::Less, const_k_i32, tile_idx_k, ckw::AssignmentOp::Increment, const_pos_1_i32, [&]() { auto tile_lhs = writer->declare_tile("lhs", ckw::TileInfo(to_ckw(_lhs->data_type()), dst_m0, 1)); auto tile_rhs = writer->declare_tile("rhs", ckw::TileInfo(to_ckw(_rhs->data_type()), dst_n0, 1)); writer->op_assign(tile_lhs, const_0_fp); writer->op_assign(tile_rhs, const_0_fp); writer->op_load(tile_lhs, lhs->tensor(), sampler_lhs, tile_idx_k, tile_idx_m, tile_idx_b, const_0_i32); writer->op_load(tile_rhs, rhs->tensor(), sampler_rhs, tile_idx_k, tile_idx_n, tile_idx_b, const_0_i32); writer->op_binary(tile_dst, ckw::BinaryOp::MatMul_Nt_T, tile_lhs, tile_rhs); }); } // clang-format on } Window GpuCkwMatMul::get_window() const { ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); const int32_t m = _dst->dimension(1); const int32_t n = _dst->dimension(0); const bool adj_lhs = _attributes.adj_lhs(); const int32_t m0 = adj_lhs ? adjust_vec_size(_settings.m0(), m) : std::min(_settings.m0(), m); const int32_t n0 = adjust_vec_size(_settings.n0(), n); // Configure kernel window Window win = calculate_max_window(_dst->tensor_shape(), Steps(n0, m0)); win = win.collapse(win, Window::DimZ); return win; } std::string GpuCkwMatMul::get_name(const ComponentGroup &comp_group) const { ARM_COMPUTE_UNUSED(comp_group); std::string kernel_name("mat_mul_native"); const int32_t m = _dst->dimension(1); const int32_t n = _dst->dimension(0); const int32_t k = _attributes.adj_lhs() ? _lhs->tensor_shape().y() : _lhs->tensor_shape().x(); kernel_name += _attributes.adj_lhs() ? "_t" : "_nt"; kernel_name += _attributes.adj_rhs() ? "_t" : "_nt"; kernel_name += "_"; kernel_name += support::cpp11::to_string(m); kernel_name += "_"; kernel_name += support::cpp11::to_string(n); kernel_name += "_"; kernel_name += support::cpp11::to_string(k); kernel_name += "_"; kernel_name += support::cpp11::to_string(_dst->dimension(2)); kernel_name += "_"; kernel_name += support::cpp11::to_string(_settings.m0()); kernel_name += "_"; kernel_name += support::cpp11::to_string(_settings.n0()); kernel_name += "_"; kernel_name += support::cpp11::to_string(_settings.k0()); return kernel_name; } } // namespace dynamic_fusion } // namespace experimental } // namespace arm_compute