[GitHub] [tvm] manupa-arm commented on a change in pull request #9163: [CMSIS-NN] Initial operator support for Mul

[GitBox <gi...@apache.org>]

manupa-arm commented on a change in pull request #9163:
URL: https://github.com/apache/tvm/pull/9163#discussion_r719578588



##########
File path: src/relay/backend/contrib/cmsisnn/relay_to_tir.cc
##########
@@ -32,17 +32,37 @@ namespace relay {
 namespace contrib {
 namespace cmsisnn {
 
-class RelayToTIR : public MixedModeVisitor {
+class RelayToTIRVisitor : public MixedModeVisitor {
  public:
-  explicit RelayToTIR(String func_name) : func_name_(func_name) {}
+  explicit RelayToTIRVisitor(String func_name) : func_name_(func_name) {}
+
+  tir::PrimFunc GetReplacementPrimFunc() { return primfunc_; }
 
  private:
-  void emit_softmax_tir(const Expr& expr) {
+  template <typename T>
+  const T ArgumentToConstantValue(const Expr& arg) {

Review comment:
       nit : Why is the function specific to "Argument" ? It seems like it can work for any Expr

##########
File path: src/relay/backend/contrib/cmsisnn/relay_to_tir.cc
##########
@@ -79,15 +99,51 @@ class RelayToTIR : public MixedModeVisitor {
         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));
+    CreatePrimFuncForExtern(func_signature, args);
+  }
 
-    primfunc_ = tir::PrimFunc(func_signature, body, VoidType(), Map<tir::Var, tir::Buffer>(),
-                              DictAttrs(dict_attrs));
+  void EmitMul(const Expr& expr) {
+    auto* mul_call = expr.as<CallNode>();
+
+    const float input_0_scale = ArgumentToConstantValue<float>(mul_call->args[2]);
+    const int32_t input_0_zero_point = ArgumentToConstantValue<int32_t>(mul_call->args[3]);
+    const float input_1_scale = ArgumentToConstantValue<float>(mul_call->args[4]);
+    const int32_t input_1_zero_point = ArgumentToConstantValue<int32_t>(mul_call->args[5]);
+    const float output_scale = ArgumentToConstantValue<float>(mul_call->args[6]);
+    const int32_t output_zero_point = ArgumentToConstantValue<int32_t>(mul_call->args[7]);
+
+    double quantized_multiplier = static_cast<double>(input_0_scale) *
+                                  static_cast<double>(input_1_scale) /
+                                  static_cast<double>(output_scale);
+    auto mult_shift_pair = tvm::relay::qnn::GetFixedPointMultiplierShift(quantized_multiplier);
+    int32_t output_multiplier = std::get<0>(mult_shift_pair);
+    int32_t output_shift = std::get<1>(mult_shift_pair);
+
+    PrimExpr tensor_size = mul_call->type_as<TensorTypeNode>()->Size();
+
+    tir::Var input_0("input_0", DataType::Handle(8));

Review comment:
       Its worth a comment why we create a handle of 8 bits here. Why do we think 8-bits are sufficient for all cases ?




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