[Mlir-commits] [mlir] [mlir][spirv] Implement vector type legalization for function signatures (PR #98337)

[Jakub Kuderski]

================
@@ -813,6 +860,250 @@ void mlir::populateBuiltinFuncToSPIRVPatterns(SPIRVTypeConverter &typeConverter,
   patterns.add<FuncOpConversion>(typeConverter, patterns.getContext());
 }
 
+//===----------------------------------------------------------------------===//
+// func::FuncOp Conversion Patterns
+//===----------------------------------------------------------------------===//
+
+namespace {
+/// A pattern for rewriting function signature to convert vector arguments of
+/// functions to be of valid types
+class FuncOpVectorUnroll : public OpRewritePattern<func::FuncOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(func::FuncOp funcOp,
+                                PatternRewriter &rewriter) const override {
+    FunctionType fnType = funcOp.getFunctionType();
+
+    // TODO: Handle declarations.
+    if (funcOp.isDeclaration()) {
+      LLVM_DEBUG(llvm::dbgs()
+                 << fnType << " illegal: declarations are unsupported\n");
+      return failure();
+    }
+
+    // Create a new func op with the original type and copy the function body.
+    auto newFuncOp = rewriter.create<func::FuncOp>(funcOp.getLoc(),
+                                                   funcOp.getName(), fnType);
+    rewriter.inlineRegionBefore(funcOp.getBody(), newFuncOp.getBody(),
+                                newFuncOp.end());
+
+    Location loc = newFuncOp.getBody().getLoc();
+
+    Block &entryBlock = newFuncOp.getBlocks().front();
+    OpBuilder::InsertionGuard guard(rewriter);
+    rewriter.setInsertionPointToStart(&entryBlock);
+
+    OneToNTypeMapping oneToNTypeMapping(fnType.getInputs());
+
+    // For arguments that are of illegal types and require unrolling.
+    // `unrolledInputNums` stores the indices of arguments that result from
+    // unrolling in the new function signature. `newInputNo` is a counter.
+    SmallVector<size_t> unrolledInputNums;
+    size_t newInputNo = 0;
+
+    // For arguments that are of legal types and do not require unrolling.
+    // `tmpOps` stores a mapping from temporary operations that serve as
+    // placeholders for new arguments that will be added later. These operations
+    // will be erased once the entry block's argument list is updated.
+    llvm::SmallDenseMap<Operation *, size_t> tmpOps;
+
+    // This counts the number of new operations created.
+    size_t newOpCount = 0;
+
+    // Enumerate through the arguments.
+    for (auto [origInputNo, origType] : enumerate(fnType.getInputs())) {
+      // Check whether the argument is of vector type.
+      auto origVecType = dyn_cast<VectorType>(origType);
+      if (!origVecType) {
+        // We need a placeholder for the old argument that will be erased later.
+        Value result = rewriter.create<arith::ConstantOp>(
+            loc, origType, rewriter.getZeroAttr(origType));
+        rewriter.replaceAllUsesWith(newFuncOp.getArgument(origInputNo), result);
+        tmpOps.insert({result.getDefiningOp(), newInputNo});
+        oneToNTypeMapping.addInputs(origInputNo, origType);
+        newInputNo++;
+        newOpCount++;
+        continue;
+      }
+      // Check whether the vector needs unrolling.
+      auto targetShape = getTargetShape(origVecType);
+      if (!targetShape) {
+        // We need a placeholder for the old argument that will be erased later.
+        Value result = rewriter.create<arith::ConstantOp>(
+            loc, origType, rewriter.getZeroAttr(origType));
+        rewriter.replaceAllUsesWith(newFuncOp.getArgument(origInputNo), result);
+        tmpOps.insert({result.getDefiningOp(), newInputNo});
+        oneToNTypeMapping.addInputs(origInputNo, origType);
+        newInputNo++;
+        newOpCount++;
+        continue;
+      }
+      VectorType unrolledType =
+          VectorType::get(*targetShape, origVecType.getElementType());
+      SmallVector<int64_t> originalShape =
+          llvm::to_vector(origVecType.getShape());
+
+      // Prepare the result vector.
+      Value result = rewriter.create<arith::ConstantOp>(
+          loc, origVecType, rewriter.getZeroAttr(origVecType));
+      newOpCount++;
+      // Prepare the placeholder for the new arguments that will be added later.
+      Value dummy = rewriter.create<arith::ConstantOp>(
+          loc, unrolledType, rewriter.getZeroAttr(unrolledType));
+      newOpCount++;
+
+      // Create the `vector.insert_strided_slice` ops.
+      SmallVector<int64_t> strides(targetShape->size(), 1);
+      SmallVector<Type> newTypes;
+      for (SmallVector<int64_t> offsets :
+           StaticTileOffsetRange(originalShape, *targetShape)) {
+        result = rewriter.create<vector::InsertStridedSliceOp>(
+            loc, dummy, result, offsets, strides);
+        newTypes.push_back(unrolledType);
+        unrolledInputNums.push_back(newInputNo);
+        newInputNo++;
+        newOpCount++;
----------------
kuhar wrote:

also here

https://github.com/llvm/llvm-project/pull/98337