Add dynamic shape support for ONNX Conv to Linalg lowering

[kimm240]

Changes

• Added IndexExprBuilderForLinalg
• Integrated ShapeHelper for dynamic shape computation
• Enhanced tensor::EmptyOp creation

Testing

• Added conv_dynamic test case in test/mlir/conversion/onnx_to_linalg/NN/Conv.mlir
  • Tests dynamic input shapes: tensor<?x3x?x?xf32>
  • Verifies dynamic output shapes: tensor<?x2x?x?xf32>
  • Checks that tensor.dim, affine.apply, and tensor.empty with dynamic sizes are generated correctly

Example

Before (static only):

func.func @conv(%arg0: tensor<1x3x5x5xf32>, %arg1: tensor<2x3x3x3xf32>)
    -> tensor<1x2x3x3xf32> {
  %0 = tensor.empty() : tensor<1x2x3x3xf32>
  // ...
}

After (dynamic support):

func.func @conv(%arg0: tensor<?x3x?x?xf32>, %arg1: tensor<2x3x3x3xf32>)
    -> tensor<?x2x?x?xf32> {
  %dim0 = tensor.dim %arg0, %c0 : tensor<?x3x?x?xf32>
  %dim2 = tensor.dim %arg0, %c2 : tensor<?x3x?x?xf32>
  %out_h = affine.apply #map(%dim2)
  %dim3 = tensor.dim %arg0, %c3 : tensor<?x3x?x?xf32>
  %out_w = affine.apply #map1(%dim2, %dim3)
  %0 = tensor.empty(%dim0, %out_h, %out_w) : tensor<?x2x?x?xf32>
  // ...
}

Related

• Related to Optimize ConvOp on CPU #3354 (ONNX Conv to Linalg lowering PR)

[jenkins-droid]

Can one of the admins verify this patch?