[mlir][tosa] Work around GCC bug in tosa-to-tensor

GCC 12 and 13 generate incorrect code for a pattern in the
tosa-to-tensor pass responsible for lowering tosa.reshape.
This results in the tosa.reshape lowering producing IR which fails to
verify. I've narrowed down the set of cmake flags needed to reproduce
the issue to this:

    cmake -G Ninja ../llvm \
      -DLLVM_ENABLE_PROJECTS="mlir" \
      -DLLVM_TARGETS_TO_BUILD=host \
      -DLLVM_ENABLE_PROJECTS=mlir \
      -DCMAKE_BUILD_TYPE="Release" \
      -DCMAKE_CXX_FLAGS_RELEASE="-O2" \
      -DCMAKE_CXX_FLAGS="-O2" \
      -DCMAKE_CXX_COMPILER=g++ \
      -DCMAKE_C_COMPILER=gcc

This is the failing test case:

    func.func @fails_in_gcc_12(%arg0: tensor<?xf32>) -> tensor<1x1x1x?xf32> {
      %0 = tosa.reshape %arg0 {new_shape = array<i64: 1, 1, 1, -1>} : (tensor<?xf32>) -> tensor<1x1x1x?xf32>
      return %0 : tensor<1x1x1x?xf32>
    }

This should correctly lower to a single tensor.expand_shape operation
like so:

    func.func @foo(%arg0: tensor<?xf32>) -> tensor<1x1x1x?xf32> {
      %c0 = arith.constant 0 : index
      %dim = tensor.dim %arg0, %c0 : tensor<?xf32>
      %c1 = arith.constant 1 : index
      %expanded = tensor.expand_shape %arg0 [[0, 1, 2, 3]] output_shape [1, 1, 1, %dim] : tensor<?xf32> into tensor<1x1x1x?xf32>
      return %expanded : tensor<1x1x1x?xf32>
    }

Under GCC 12/13 with the above cmake configuration, the tensor.expand_shape
looks like this

    %2 = "tensor.expand_shape"(%arg0) <{reassociation = [[0, 1, 2, 3]], static_output_shape = array<i64>}> : (tensor<?xf32>) -> tensor<?x1x1x?xf32>

This expand_shape fails to verify with this error message:

    error: 'tensor.expand_shape' op expected number of static shape dims to be equal to the output rank (4) but found 0 inputs instead

The problematic code is calculating the intermediate shape of the
generated tensor.expand_shape operation in the
expand_shape/collapse_shape sequence that implements tosa.reshape.

    // Compute result shape
    bool resultIsStatic = true;
    auto resultShape = llvm::map_to_vector(newShape, [&](int64_t size) {
      // Omitted

      // If we do not know the total size of the tensor, keep this dimension
      // dynamic in the result shape.
      if (!inputIsStatic) {
        resultIsStatic = false;
        return ShapedType::kDynamic;
      }
    });

    if (resultIsStatic) {
      // do something
      return;
    }

    // do something else
    return;

The failure point seems to be the update of the resultIsStatic variable
in the lambda body. The assignment of false is not propagated to the use
in the if-statement, resulting in the branch being taken when it should
not.

I've found several modification to the code that gets around the bug.
The version I settled on is one which makes the logic a little more
obvious.

mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp

@@ -55,20 +55,17 @@ TensorType inferReshapeExpandedType(TensorType inputType,
   // Check if the input is static, and if so, get its total size
   bool inputIsStatic = inputType.hasStaticShape();
   int64_t totalSize = inputIsStatic ? inputType.getNumElements() : -1;
- 
+
   // Compute result shape
-  bool resultIsStatic = true;
   auto resultShape = llvm::map_to_vector(newShape, [&](int64_t size) -> int64_t {
     // If this is not a placeholder, do not change it
     if (size >= 0)
       return size;
 
     // If we do not know the total size of the tensor, keep this dimension
     // dynamic in the result shape.
-    if (!inputIsStatic) {
-      resultIsStatic = false;
+    if (!inputIsStatic)
       return ShapedType::kDynamic;
-    }
 
     // Calculate the product of all elements in 'newShape' except for the -1
     // placeholder, which we discard by negating the result.
@@ -84,12 +81,14 @@ TensorType inferReshapeExpandedType(TensorType inputType,
     return totalSize / totalSizeNoPlaceholder;
   });
 
+  bool resultIsStatic = !ShapedType::isDynamicShape(resultShape);
+
   // A syntactic restriction in 'tensor.expand_shape' forbids a dynamically
   // shaped input from being reshaped into a statically shaped result. We may
   // simply turn the first result dimension dynamic to address this.
   if (!inputIsStatic && resultIsStatic)
     resultShape[0] = ShapedType::kDynamic;
-  
+
   // The 'tensor.expand_shape' op also forbids a statically shaped input from
   // being reshaped into a dynamically shaped result, but the placeholder
   // inference algorithm above guarantees that this will never be the case.