mirror of
https://github.com/blakeblackshear/frigate.git
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236 lines
9.6 KiB
C++
236 lines
9.6 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*/
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#include <fstream>
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#include <migraphx/filesystem.hpp>
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#include <migraphx/gpu/compiler.hpp>
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#include <migraphx/make_op.hpp>
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#include <migraphx/gpu/context.hpp>
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#include <migraphx/gpu/ck.hpp>
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#include <migraphx/env.hpp>
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#include <migraphx/file_buffer.hpp>
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#include <migraphx/gpu/compile_gen.hpp>
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#include <migraphx/gpu/compile_hip.hpp>
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#include <migraphx/gpu/compile_hip_code_object.hpp>
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#include <migraphx/module.hpp>
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#include <migraphx/ranges.hpp>
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#include <migraphx/reduce_dims.hpp>
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#include <migraphx/stringutils.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace gpu {
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using namespace migraphx::gpu::gen; // NOLINT
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// NOLINTNEXTLINE
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static const char* const ck_gemm_kernel = R"__migraphx__(
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#include <args.hpp>
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#include <migraphx/kernels/ck_gemm.hpp>
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#include <migraphx/kernels/pointwise.hpp>
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#include <migraphx/kernels/ops.hpp>
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#include <${include}>
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namespace migraphx {
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${preamble}
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extern "C" {
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MIGRAPHX_GLOBAL void ${kernel}(${params})
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{
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transform_args(make_tensors(), rotate_last())(${args})([](auto... xs) {
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ck_gemm<${solution}, ${blocks_per_batch}>(xs...);
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});
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}
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}
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} // namespace migraphx
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)__migraphx__";
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struct ck_gemm_compiler : compiler<ck_gemm_compiler>
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{
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std::vector<std::string> names() const { return {"ck_gemm", "gpu::ck_gemm"}; }
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ck::host::device_gemm_multiple_d::Problem create_problem(const std::vector<shape>& inputs,
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const value& v) const
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{
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const auto& a_shape = inputs[0];
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const auto& b_shape = inputs[1];
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const auto& c_shape = inputs.back();
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// cppcheck-suppress unreadVariable
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auto rank = a_shape.ndim();
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auto batch_count = get_batch_count(c_shape);
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auto m = c_shape.lens()[rank - 2];
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m = can_fold_batch(inputs) ? m * batch_count : m;
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auto n = c_shape.lens().back();
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auto k = a_shape.lens().back();
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const bool trans_a = transposed_matrix(a_shape);
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const bool trans_b = transposed_matrix(b_shape);
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const bool trans_e = transposed_matrix(c_shape);
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const auto a_type = get_type(a_shape);
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const auto b_type = get_type(b_shape);
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const auto e_type = get_type(c_shape);
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std::vector<bool> ds_layout;
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std::transform(inputs.begin() + 2,
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inputs.end() - 1,
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std::back_inserter(ds_layout),
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[](const auto& i) { return transposed_matrix(i); });
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std::vector<ck::host::DataType> ds_type;
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std::transform(inputs.begin() + 2,
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inputs.end() - 1,
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std::back_inserter(ds_type),
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[](const auto& i) { return get_type(i); });
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std::string ck_passthrough = "ck_passthrough";
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std::string cde_op = ck_passthrough;
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assert(inputs.size() < 4 or v.contains("post"));
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if(v.contains("post"))
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{
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cde_op = v.at("post").to<std::string>();
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}
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return ck::host::device_gemm_multiple_d::Problem{m,
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n,
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k,
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trans_a,
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trans_b,
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trans_e,
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ds_layout,
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a_type,
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b_type,
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e_type,
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ds_type,
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ck_passthrough,
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ck_passthrough,
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cde_op};
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}
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operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
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{
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const auto& c_shape = inputs.back();
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auto tuning_value = v.get("tuning_value", 34);
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auto batch_count = get_batch_count(c_shape);
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auto problem = create_problem(inputs, v);
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const auto include_header = problem.GetIncludeHeader();
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const auto solutions = problem.GetSolutions(ctx.get_current_device().get_gfx_name());
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const auto& solution = solutions.at(tuning_value);
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const auto template_str = solution.template_str;
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const auto blocks_per_batch = solution.grid_size;
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const auto block_size = solution.block_size;
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hip_compile_options options;
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options.additional_src_files = ck_headers();
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auto grid_size = can_fold_batch(inputs) ? blocks_per_batch : batch_count * blocks_per_batch;
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options.set_launch_params(v, grid_size * block_size, block_size);
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options.inputs = inputs;
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options.output = c_shape;
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options.kernel_name = v.get("kernel", "ck_gemm_kernel");
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options.virtual_inputs = inputs;
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if(can_fold_batch(inputs))
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{
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auto vinputs = inputs;
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fold_batch_dims(vinputs[0]);
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remove_batch_dims(vinputs[1]);
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std::for_each(vinputs.begin() + 2, vinputs.end(), fold_batch_dims);
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options.virtual_inputs = vinputs;
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}
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if(v.get("check", false) or enabled(MIGRAPHX_CK_DEBUG{}))
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options.emplace_param("-DMIGRAPHX_CK_CHECK=1");
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auto src = interpolate_string(ck_gemm_kernel,
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{{"solution", template_str},
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{"include", include_header},
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{"params", enum_params(inputs.size(), "void * private_p")},
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{"args", enum_params(inputs.size(), "private_p")},
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{"blocks_per_batch", to_string(blocks_per_batch)},
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{"preamble", v.get("preamble", std::string{})},
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{"kernel", options.kernel_name}});
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return compile_hip_code_object(ctx, src, options);
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}
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value create_settings(instruction_ref ins, const operation& op) const
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{
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auto v = op.to_value();
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v["kernel"] = "ck_gemm_kernel";
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if(not ins->module_inputs().empty())
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{
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auto* pm = ins->module_inputs().front();
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v["preamble"] = generate_pointwise(*pm, "post_ck_gemm_function") +
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"\nMIGRAPHX_LIFT_CLASS(post_ck_gemm, post_ck_gemm_function);";
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v["post"] = "ck_function_adaptor<post_ck_gemm>";
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v["kernel"] = to_c_id("ck_gemm_" + generate_name_from_ops(*pm) + "_kernel");
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}
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return v;
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}
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compiler_replace
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compile(context& ctx, instruction_ref ins, const operation& op, const value& solution) const
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{
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auto shapes = to_shapes(ins->inputs());
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auto v = create_settings(ins, op);
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if(not solution.is_null())
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v["tuning_value"] = solution;
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return {compile_op(ctx, shapes, v),
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[=](module& m, instruction_ref ins2, const operation& code_object) {
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if(enabled(MIGRAPHX_LOG_CK_GEMM{}))
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{
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std::vector<shape> gemm_shapes{
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shapes[0], shapes[1], shapes.back().with_type(shapes[0].type())};
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std::cout << "gpu::ck_gemm: " << to_json_string(to_value(gemm_shapes))
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<< std::endl;
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}
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m.replace_instruction(ins2, code_object, ins2->inputs());
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}};
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}
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optional<tuning_config>
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get_tuning_config(context& ctx, instruction_ref ins, const operation& op, bool exhaustive) const
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{
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if(not exhaustive and not enabled(MIGRAPHX_TUNE_CK{}))
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return nullopt;
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tuning_config tc;
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auto shapes = to_shapes(ins->inputs());
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auto problem = create_problem(shapes, create_settings(ins, op));
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auto solutions = problem.GetSolutions(ctx.get_current_device().get_gfx_name());
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tc.solutions.resize(solutions.size());
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std::iota(tc.solutions.begin(), tc.solutions.end(), 0);
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std::vector<shape> gemm_shapes{shapes[0], shapes[1], shapes.back()};
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tc.problem = to_value(gemm_shapes);
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return tc;
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}
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};
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} // namespace gpu
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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