Mousius commented on a change in pull request #8951: URL: https://github.com/apache/tvm/pull/8951#discussion_r710930215
########## File path: src/relay/backend/contrib/cmsisnn/relay_to_tir.cc ########## @@ -0,0 +1,140 @@ + +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/tir/builtin.h> +#include <tvm/tir/expr.h> +#include <tvm/tir/function.h> +#include <tvm/tir/op.h> +#include <tvm/tir/stmt_functor.h> + +#include "../../../qnn/utils.h" + +namespace tvm { +namespace relay { +namespace contrib { +namespace cmsisnn { + +class RelayToTIR : public MixedModeVisitor { + public: + explicit RelayToTIR(String func_name) : func_name_(func_name) {} + + private: + void emit_softmax_tir(const Expr& expr) { + auto* quantize_call = expr.as<CallNode>(); + auto* softmax_call = quantize_call->args[0].as<CallNode>(); + auto* dequant_call = softmax_call->args[0].as<CallNode>(); + auto* scale_const = dequant_call->args[1].as<ConstantNode>(); + const float quant_scale = static_cast<const float*>(scale_const->data->data)[0]; + + // assuming layout as NHWC + auto shape = quantize_call->type_as<TensorTypeNode>()->shape; + int trailing_dim = shape.size() - 1; + int row_size = shape[trailing_dim].as<tir::IntImmNode>()->value; + int num_rows = 1; + for (int i = 0; i < trailing_dim; ++i) { + num_rows *= shape[i].as<tir::IntImmNode>()->value; + } + + // calculate multiplier and shift for CMSIS-NN softmax API + // Note: TensorFlow Lite Micro assumptions + // Output zero point and scale are fixed to -128 and 1 / 256 + // https://github.com/tensorflow/tflite-micro/blob/d97cd0908d8cf5021e9d86f05a49888bee28c2a4/tensorflow/lite/micro/kernels/softmax_common.cc#L47 + double beta = 1.0; + int32_t input_bits = 5; + double beta_multiplier = (beta * quant_scale * (1 << (31 - input_bits))); + beta_multiplier = std::min<double>(beta_multiplier, (1ll << 31) - 1.0); + auto mult_shift_pair = tvm::relay::qnn::GetFixedPointMultiplierShift(beta_multiplier); + int32_t mult = std::get<0>(mult_shift_pair); + int32_t shift = std::get<1>(mult_shift_pair); + int32_t diff_min = (1 << 5) - 1; + diff_min <<= (31 - 5); + diff_min >>= shift; + diff_min *= -1; + + auto in_var = tir::Var("input", DataType::Handle(8)); + auto out_var = tir::Var("output", DataType::Handle(8)); + + Array<tir::Var> func_signature{in_var, out_var}; + + tvm::Array<PrimExpr> args = { + tir::StringImm("arm_softmax_s8"), in_var, + IntImm(DataType::Int(32), num_rows), IntImm(DataType::Int(32), row_size), + IntImm(DataType::Int(32), mult), IntImm(DataType::Int(32), shift), + IntImm(DataType::Int(32), diff_min), out_var}; + tir::Stmt body = + tir::Evaluate(tvm::tir::Call(DataType::Int(8), tir::builtin::call_extern(), args)); + + Map<String, ObjectRef> dict_attrs; + dict_attrs.Set("global_symbol", func_name_); + dict_attrs.Set("tir.noalias", Bool(true)); + + primfunc_ = tir::PrimFunc(func_signature, body, VoidType(), Map<tir::Var, tir::Buffer>(), + DictAttrs(dict_attrs)); + } + + void VisitExpr_(const CallNode* call) final { + auto* func = call->op.as<FunctionNode>(); + if (func == nullptr) { + return; + } + + auto comp_name = func->GetAttr<String>(attr::kComposite); + if (comp_name.defined() && comp_name == "cmsisnn.quantized_softmax") { + emit_softmax_tir(func->body); + } + } + + public: + String func_name_; + tir::PrimFunc primfunc_; +}; + +IRModule GenerateTIR(IRModule mod) { + String func_name; + Function func; + + // Obtain external Relay Function that needs to be translated into TIR + ICHECK(mod->functions.size() == 1) << "Supports modules with single external Relay function."; + for (auto kv : mod->functions) { + func = Downcast<Function>(kv.second); + func_name = func->GetAttr<String>(tvm::attr::kGlobalSymbol).value(); + } + + // Prepare PrimFunc from Relay Function + auto relay_to_tir = RelayToTIR(func_name); + relay_to_tir.VisitExpr(func->body); + + // Build the TIR IRModule from the generated PrimFunc + Map<GlobalVar, BaseFunc> var_func_map; + var_func_map.Set(GlobalVar(func_name), relay_to_tir.primfunc_); + return IRModule(var_func_map); +} + +transform::Pass RelayToTIR() { Review comment: Unfortunately not, as CMSIS NN currently requires `TIRToRuntime` as well to generate the structures. I don't think we should intertwine the two pieces of work if possible and unpick it later. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
