/* * The MIT License (MIT) * * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved. * * 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 #include #include #include #include #include #include namespace migraphx { inline namespace MIGRAPHX_INLINE_NS { namespace gpu { using namespace migraphx::gpu::gen; // NOLINT static const char* const layernorm_kernel = R"__migraphx__( #include #include #include #include #include namespace migraphx { ${preamble} extern "C" { MIGRAPHX_GLOBAL void ${kernel}(${params}) { transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto... xs) { ${layernorm}<${axis}>(${post}, ${eps}, xs...); }); } } } // namespace migraphx )__migraphx__"; struct layernorm_compiler : compiler { std::vector names() const { return {"layernorm", "gpu::prelayernorm", "gpu::preadd_layernorm"}; } operation compile_op(context& ctx, const std::vector& inputs, const value& v) const { // TODO: Use reduce_dims auto axis = inputs.front().lens().size() - 1; auto faxis = find_fast_axis({inputs.front()}); vectorize vec{}; // Vectorize if the axis is a reduction axis if(axis == faxis) { vec = vectorize::elements(ctx, faxis, inputs); } auto relements = inputs[0].lens()[axis] / vec.size; auto nelements = (inputs.back().elements() / inputs[0].lens()[axis]); auto block_size = compute_block_size(ctx, relements, 256); hip_compile_options options; options.set_launch_params( v, compute_global_for(ctx, nelements * block_size, 256), block_size); options.output = inputs.back(); options.inputs = inputs; options.kernel_name = v.get("kernel", "layernorm_kernel"); auto eps = v.get("epsilon", 1e-12f); auto src = interpolate_string(layernorm_kernel, {{"kernel", options.kernel_name}, {"params", enum_params(inputs.size(), "void * private_p")}, {"args", enum_params(inputs.size(), "private_p")}, {"transformers", make_transformer_args(vec)}, {"post", v.get("post", std::string{"op::id{}"})}, {"preamble", v.get("preamble", std::string{})}, {"layernorm", v.get("layernorm", std::string{"layernorm"})}, {"axis", to_string(axis)}, {"eps", to_string(eps)}}); return compile_hip_code_object(src, options); } compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const { auto v = op.to_value(); v["layernorm"] = "layernorm"; v["kernel"] = "layernorm_kernel"; if(op.name() == "gpu::preadd_layernorm") { v["layernorm"] = "add_layernorm"; v["kernel"] = "add_layernorm_kernel"; } if(not ins->module_inputs().empty()) { auto* pm = ins->module_inputs().front(); v["preamble"] = generate_pointwise(*pm, "post_layernorm"); v["post"] = "MIGRAPHX_LIFT(post_layernorm)"; v["kernel"] = v["layernorm"].to() + "_" + generate_name_from_ops(*pm) + "_kernel"; } return compile_op(ctx, to_shapes(ins->inputs()), v); } }; } // namespace gpu } // namespace MIGRAPHX_INLINE_NS } // namespace migraphx