[Mlir-commits] [mlir] [mlir][tosa] Enhance the conv2d verifier (PR #128693)

[Georgios Pinitas]

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@@ -2012,6 +2015,98 @@ LogicalResult Conv2DOp::inferReturnTypeComponents(
 LogicalResult Conv2DOp::verify() {
   if (verifyConvOp(*this).failed() || verifyConvOpModes(*this).failed())
     return failure();
+
+  llvm::ArrayRef<int64_t> padding = getPad();
+  if (llvm::any_of(padding, [](int64_t p) { return p < 0; }))
+    return emitOpError("expect all padding values to be >= 0, got ") << padding;
+
+  llvm::ArrayRef<int64_t> strides = getStride();
+  if (llvm::any_of(strides, [](int64_t s) { return s < 1; }))
+    return emitOpError("expect all stride values to be >= 1, got ") << strides;
+
+  llvm::ArrayRef<int64_t> dilations = getDilation();
+  if (llvm::any_of(dilations, [](int64_t d) { return d < 1; }))
+    return emitOpError("expect all dilation values to be >= 1, got ")
+           << dilations;
+
+  const RankedTensorType outputType =
+      llvm::dyn_cast<RankedTensorType>(getOutput().getType());
+  if (!outputType)
+    // Skip following checks if output is not ranked
+    return success();
+
+  const RankedTensorType inputType =
+      llvm::dyn_cast<RankedTensorType>(getInput().getType());
+  const RankedTensorType weightType =
+      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
+
+  if (inputType && weightType) {
+    const int64_t ih = inputType.getDimSize(1);
----------------
GeorgeARM wrote:

This logic is replicated for W,H. Should we refactor to a common function?
I presume in conv3d verifier this shall be used across 3 dimensions as well?

https://github.com/llvm/llvm-project/pull/128693