[Mlir-commits] [mlir] [MLIR][XeGPU] Add support for elementwise ops in Wg to Sg distribute pass [1/N] (PR #142797)

[Igor Zamyatin]

================
@@ -314,6 +317,90 @@ struct WgToSgPrefetchNdOp : public OpConversionPattern<xegpu::PrefetchNdOp> {
   }
 };
 
+// This pattern transforms elementwise ops (unary/binary) in math/arith dialect
+template <typename Op>
+struct WgToSgElementwiseOp : public OpConversionPattern<Op> {
+  using OpConversionPattern<Op>::OpConversionPattern;
+  using OneToNOpAdaptor = typename OpConversionPattern<Op>::OneToNOpAdaptor;
+
+  LogicalResult
+  matchAndRewrite(Op op, OneToNOpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    // All operands/results must be 1D or 2D vectors
+    auto resultType = dyn_cast<VectorType>(op.getResult().getType());
+    if (!resultType || (resultType.getRank() != 1 && resultType.getRank() != 2))
+      return rewriter.notifyMatchFailure(
+          op, "Result type is not a 1D or 2D vector");
+
+    ArrayRef<int64_t> shape = resultType.getShape();
+    for (Value operand : op->getOperands()) {
+      auto operandType = dyn_cast<VectorType>(operand.getType());
+      if (!operandType || operandType.getRank() != resultType.getRank() ||
+          operandType.getShape() != shape) {
+        return rewriter.notifyMatchFailure(
+            op, "Operand type is not a 1D or 2D vector with the same shape as "
+                "result type");
+      }
+    }
+
+    auto layout = dyn_cast_or_null<xegpu::LayoutAttr>(op->getAttr("layout"));
+    if (!layout || !layout.getSgLayout())
+      return rewriter.notifyMatchFailure(
+          op, "Operation does not have a valid layout attribute for subgroup "
+              "distribution");
+
+    // Extract sgShape from layout
+    SmallVector<int64_t> sgShape;
----------------
Garra1980 wrote:

I feel like this will be placed in a separate routine here https://github.com/llvm/llvm-project/pull/142618/files#diff-d8b38c5737eab360b97274539d5ec654406dbdf81781871344f745dd55cd5670R35 
If yes, let's make sure this piece of code will be modified to use it

https://github.com/llvm/llvm-project/pull/142797