[nnc] Adapt ONNX importer to modified ScaleOp interface (#2860)

Take into account modified interface of ScaleOp in ONNX importer.

Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>

diff --git a/contrib/nnc/passes/onnx_frontend/ONNXOpCreator.cpp b/contrib/nnc/passes/onnx_frontend/ONNXOpCreator.cpp
index a76ea1e..04031af 100644 (file)
--- a/contrib/nnc/passes/onnx_frontend/ONNXOpCreator.cpp
+++ b/contrib/nnc/passes/onnx_frontend/ONNXOpCreator.cpp
@@ -332,7 +332,8 @@ ONNXOpCreator::convertBatchNorm(const std::vector<mir::IODescriptor>& inputs,
   Tensor<float> var_accessor(var_tensor);
   for (auto& idx: ShapeRange(scale_tensor.getShape()))
     multiplier.at(idx) /= std::sqrt(var_accessor.at(idx) + epsilon);
-  result = createOp<ops::ScaleOp>(result->getOutput(0), scale_tensor);
+  auto scale = createOp<ops::ConstantOp>(scale_tensor)->getOutput(0);
+  result = createOp<ops::ScaleOp>(result->getOutput(0), scale);
 
   // overall_res = res2 + bias
   auto bias = createOp<ops::ConstantOp>(bias_tensor)->getOutput(0);
@@ -358,9 +359,11 @@ ONNXOpCreator::convertScale(const std::vector<mir::IODescriptor>& inputs,
   bool found;
   float value;
   std::tie(found, value) = getFloatAttribute(onnx_node, "scale");
-  float scale = found ? value : 1.0;
+  float scale_val = found ? value : 1.0;
   const auto& shape = inputs[0].op->getOutputShape(inputs[0].index);
-  auto result = createOp<ops::ScaleOp>(inputs[0], createTensor(scale, shape));
+  auto scale_tensor = createTensor(scale_val, shape);
+  auto scale = createOp<ops::ConstantOp>(scale_tensor)->getOutput(0);
+  auto result = createOp<ops::ScaleOp>(inputs[0], scale);
   return {result->getOutput(0)};
 }
 
@@ -385,9 +388,9 @@ ONNXOpCreator::convertGemm(const std::vector<mir::IODescriptor>& inputs,
   std::tie (found, ivalue) = getIntAttribute(onnx_node, "broadcast");
   bool broadcast = found ? ivalue : 0;
   std::tie (found, fvalue) = getFloatAttribute(onnx_node, "alpha");
-  float alpha = found ? fvalue : 1.0;
+  float alpha_val = found ? fvalue : 1.0;
   std::tie (found, fvalue) = getFloatAttribute(onnx_node, "beta");
-  float beta = found ? fvalue : 1.0;
+  float beta_val = found ? fvalue : 1.0;
 
   // 1. Prepare input matrix A
   // Flatten the shape by dim(0)
@@ -396,9 +399,11 @@ ONNXOpCreator::convertGemm(const std::vector<mir::IODescriptor>& inputs,
   auto input_a = createOp<ops::ReshapeOp>(inputs[0], shape0);
   if (trans_a)
     input_a = createOp<ops::TransposeOp>(input_a->getOutput(0), std::vector<std::size_t>{1, 0});
-  if (alpha != 1.0)
-    input_a = createOp<ops::ScaleOp>(input_a->getOutput(0),
-                                     createTensor(alpha, input_a->getOutputShape(0)));
+  if (alpha_val != 1.0) {
+    auto alpha_tensor = createTensor(alpha_val, input_a->getOutputShape(0));
+    auto alpha = createOp<ops::ConstantOp>(alpha_tensor)->getOutput(0);
+    input_a = createOp<ops::ScaleOp>(input_a->getOutput(0), alpha);
+  }
 
   // 2. Prepare input matrix B
   //
@@ -414,13 +419,13 @@ ONNXOpCreator::convertGemm(const std::vector<mir::IODescriptor>& inputs,
   // 3. Prepare input matrix C
   //
   auto input_c = inputs[2];
-  auto beta_tensor = createTensor(beta, input_c.op->getOutputShape(0));
+  auto beta_tensor = createTensor(beta_val, input_c.op->getOutputShape(0));
   // TODO: check 'broadcast' attribute here
   if ((mult_a_b.rank() == 2) && (input_c.op->getOutputShape(0).rank() == 1)) {
     beta_tensor = TensorVariant(beta_tensor, mult_a_b);
   }
-  auto constant = createOp<ops::ConstantOp>(beta_tensor)->getOutput(0);
-  std::vector<IODescriptor> descriptors = {constant, input_c};
+  auto beta = createOp<ops::ConstantOp>(beta_tensor)->getOutput(0);
+  std::vector<IODescriptor> descriptors = {beta, input_c};
   auto c_mult = createOp<ops::ElementwiseOp>(descriptors, ops::ElementwiseOp::OpType::mul);
   assert(c_mult->getOutputShape(0) == mult_a_b);
   auto result = createOp<ops::GemmOp>(input_a->getOutput(0), input_b, c_mult->getOutput(0));