[mlir][linalg] Specialize LinalgOp canonicalization patterns (NFC).

[gysit]

Summary

Specialize the DeduplicateInputs and RemoveIdentityLinalgOps patterns for GenericOp instead of implementing them for the LinalgOp interface.

This revsion is based on https://reviews.llvm.org/D105622 that moves the logic to erase identity CopyOps in a separate pattern.

[gysit]

Ensure the optional doc and library call attributes are copied as well.

[gysit]

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

726–733

I chose to use the generic op builder and inline the region here instead of using the LinalgOp.clone. I though this is more in line with the new pattern working on GenericOps only but I am fine to switch back to the LinalgOp.clone.

[nicolasvasilache]

Some parts seem to be mechanical and NFC and should be sent for review as such.
The new pattern seems to no be NFC and to require a test?

[gysit]

• move CopyOp pattern to a separate patch https://reviews.llvm.org/D105622 to make this revision an nfc revision.

[gysit]

Rebase.

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

let extraClassDeclaration = structuredOpsBaseDecls # [{
     }
   }];
 
   let printer = [{ return ::print(p, *this); }];
   let parser = [{ return ::parseGenericOp(parser, result); }];
 
   let verifier = [{ return ::verify(*this); }];
 
+  let hasCanonicalizer = 1;
   let hasFolder = 1;
 }
 
 
 //===----------------------------------------------------------------------===//
 // Named Linalg ops, implemented as a declarative configurations of generic ops.
 //===----------------------------------------------------------------------===//
 
 // This file is auto-generated from a TC def specification.
 include "mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.tcgen.td"
 include "mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yamlgen.td"
 
 #endif // LINALG_STRUCTURED_OPS

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

 
 template <typename GenericOpType>
 static LogicalResult verifyGenericOp(GenericOpType op) {
   return success();
 }
 
 static LogicalResult verify(GenericOp op) { return verifyGenericOp(op); }
 
+namespace {
+// Deduplicate redundant args of a linalg generic op.
+// An arg is redundant if it has the same Value and indexing map as another.
+struct DeduplicateGenericOpInputs : public OpRewritePattern<GenericOp> {
+  using OpRewritePattern<GenericOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(GenericOp genericOp,
+                                PatternRewriter &rewriter) const override {
+    // Associate each input to an equivalent "canonical" input that has the same
+    // Value and indexing map.
+    //
+    // In the non-duplicate case, input `i` will have canonical input `i`. But
+    // in the case of duplicated inputs, the canonical input could be some other
+    // input `< i`. That is, a later input will have some earlier input as its
+    // canonical input.
+    llvm::SmallDenseMap<std::pair<Value, AffineMap>, unsigned> canonicalInput;
+    // For later remapping tasks like deduplicating payload block arguments,
+    // having a simple "inputIndex -> canonicalInputIndex" integer mapping is
+    // convenient.
+    SmallVector<unsigned> canonicalInputIndices;
+    for (OpOperand *opOperand : genericOp.getInputOperands()) {
+      AffineMap indexingMap = genericOp.getTiedIndexingMap(opOperand);
+      // STL-like maps have a convenient behavior for our use case here. In the
+      // case of duplicate keys, the insertion is rejected, and the returned
+      // iterator gives access to the value already in the map.
+      auto pair = canonicalInput.insert(
+          {{opOperand->get(), indexingMap}, opOperand->getOperandNumber()});
+      canonicalInputIndices.push_back(pair.first->second);
+    }
+
+    // If there are no duplicate args, then bail out.
+    if (canonicalInput.size() == genericOp.getNumInputs())
+      return failure();
+
+    // The operands for the newly canonicalized op.
+    SmallVector<Value> newInputOperands;
+    for (OpOperand *opOperand : genericOp.getInputOperands())
+      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
+          opOperand->getOperandNumber())
+        newInputOperands.push_back(opOperand->get());
+
+    // Repair the indexing maps by filtering out the ones that have been
+    // eliminated.
+    SmallVector<AffineMap> newIndexingMaps;
+    for (OpOperand *opOperand : genericOp.getInputOperands())
+      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
+          opOperand->getOperandNumber())
+        newIndexingMaps.push_back(genericOp.getTiedIndexingMap(opOperand));
+    for (OpOperand *opOperand : genericOp.getOutputOperands())
+      newIndexingMaps.push_back(genericOp.getTiedIndexingMap(opOperand));
+
+    // Clone the old op with new operands.
+    SmallVector<Value> outputOperands = genericOp.getOutputOperands();
+    auto newOp = rewriter.create<GenericOp>(
+        genericOp.getLoc(), genericOp->getResultTypes(), newInputOperands,
+        outputOperands, rewriter.getAffineMapArrayAttr(newIndexingMaps),
+        genericOp.iterator_types(), genericOp.docAttr(),
+        genericOp.library_callAttr());
+    rewriter.inlineRegionBefore(genericOp.region(), newOp.region(),
+                                newOp.region().begin());
+
+    // Repair the payload entry block by RAUW'ing redundant arguments and
+    // erasing them.
+    Block &payload = newOp.region().front();
+    SmallVector<OpOperand *> inputOperands = genericOp.getInputOperands();
+    for (OpOperand *opOperand : llvm::reverse(inputOperands)) {
+      // Iterate in reverse, so that we erase later args first, preventing the
+      // argument list from shifting unexpectedly and invalidating all our
+      // indices.
+      unsigned operandNumber = opOperand->getOperandNumber();
+      if (canonicalInputIndices[operandNumber] == operandNumber)
+        continue;
+      payload.getArgument(operandNumber)
+          .replaceAllUsesWith(
+              payload.getArgument(canonicalInputIndices[operandNumber]));
+      payload.eraseArgument(operandNumber);
+    }
+
+    rewriter.replaceOp(genericOp, newOp->getResults());
+    return success();
+  }
+};
+
+/// Remove generic operations (on tensors) that are just copying
+/// the values from inputs to the results. Requirements are
+/// 1) All iterator types are parallel
+/// 2) The body contains just a yield operation with the yielded values being
+///    the arguments corresponding to the operands.
+struct EraseIdentityGenericOp : public OpRewritePattern<GenericOp> {
+  using OpRewritePattern<GenericOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(GenericOp genericOp,
+                                PatternRewriter &rewriter) const override {
+    if (!genericOp.hasTensorSemantics())
+      return failure();
+    // Check all indexing maps are identity.
+    if (llvm::any_of(genericOp.getIndexingMaps(),
+                     [](AffineMap map) { return !map.isIdentity(); }))
+      return failure();
+
+    // Check that the body of the linalg operation is just a linalg.yield
+    // operation.
+    Block &body = genericOp.region().front();
+    if (!llvm::hasSingleElement(body))
+      return failure();
+    auto yieldOp = dyn_cast<linalg::YieldOp>(body.getTerminator());
+    if (!yieldOp)
+      return failure();
+
+    // Get the argument number of the returned values. That is the operand
+    // number to use for replacing uses of this operation.
+    SmallVector<Value> returnedArgs;
+    for (Value yieldVal : yieldOp.values()) {
+      auto yieldArg = yieldVal.dyn_cast<BlockArgument>();
+      if (!yieldArg || yieldArg.getOwner() != &body)
+        return failure();
+      unsigned argumentNumber = yieldArg.getArgNumber();
+      returnedArgs.push_back(genericOp->getOperand(argumentNumber));
+    }
+    if (returnedArgs.size() != genericOp->getNumResults())
+      return failure();
+    rewriter.replaceOp(genericOp, returnedArgs);
+    return success();
+  }
+};
+} // namespace
+
+void GenericOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                            MLIRContext *context) {
+  results.add<DeduplicateGenericOpInputs, EraseIdentityGenericOp>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // InitTensorOp
 //===----------------------------------------------------------------------===//
 void InitTensorOp::build(OpBuilder &b, OperationState &result,
                          ArrayRef<OpFoldResult> sizes, Type elementType,
                          ArrayRef<NamedAttribute> attrs) {
   unsigned rank = sizes.size();
   SmallVector<Value, 4> dynamicSizes;
LogicalResult matchAndRewrite(LinalgOp op,
     }
     rewriter.replaceOp(op, replacements);
 
     return success();
   }
 };
 } // namespace
 
-namespace {
-// Deduplicate redundant args of a linalg op.
-// An arg is redundant if it has the same Value and indexing map as another.
-struct DeduplicateInputs : public OpInterfaceRewritePattern<LinalgOp> {
-  using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;
-
-  LogicalResult matchAndRewrite(LinalgOp op,
-                                PatternRewriter &rewriter) const override {
-    // This pattern reduces the number of arguments of an op, which breaks
-    // the invariants of semantically charged named ops.
-    if (!isa<GenericOp>(op))
-      return failure();
-
-    // Associate each input to an equivalent "canonical" input that has the same
-    // Value and indexing map.
-    //
-    // In the non-duplicate case, input `i` will have canonical input `i`. But
-    // in the case of duplicated inputs, the canonical input could be some other
-    // input `< i`. That is, a later input will have some earlier input as its
-    // canonical input.
-    llvm::SmallDenseMap<std::pair<Value, AffineMap>, unsigned> canonicalInput;
-    // For later remapping tasks like deduplicating payload block arguments,
-    // having a simple "inputIndex -> canonicalInputIndex" integer mapping is
-    // convenient.
-    SmallVector<unsigned> canonicalInputIndices;
-    for (OpOperand *opOperand : op.getInputOperands()) {
-      AffineMap indexingMap = op.getTiedIndexingMap(opOperand);
-      // STL-like maps have a convenient behavior for our use case here. In the
-      // case of duplicate keys, the insertion is rejected, and the returned
-      // iterator gives access to the value already in the map.
-      auto pair = canonicalInput.insert(
-          {{opOperand->get(), indexingMap}, opOperand->getOperandNumber()});
-      canonicalInputIndices.push_back(pair.first->second);
-    }
-
-    // If there are no duplicate args, then bail out.
-    if (canonicalInput.size() == op.getNumInputs())
-      return failure();
-
-    // The operands for the newly canonicalized op.
-    SmallVector<Value> newOperands;
-    for (OpOperand *opOperand : op.getInputOperands())
-      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
-          opOperand->getOperandNumber())
-        newOperands.push_back(opOperand->get());
-    SmallVector<Value> outputOperands = op.getOutputOperands();
-    llvm::append_range(newOperands, outputOperands);
-
-    // Repair the indexing maps by filtering out the ones that have been
-    // eliminated.
-    SmallVector<AffineMap> newIndexingMaps;
-    for (OpOperand *opOperand : op.getInputOperands())
-      if (canonicalInputIndices[opOperand->getOperandNumber()] ==
-          opOperand->getOperandNumber())
-        newIndexingMaps.push_back(op.getTiedIndexingMap(opOperand));
-    for (OpOperand *opOperand : op.getOutputOperands())
-      newIndexingMaps.push_back(op.getTiedIndexingMap(opOperand));
-
-    // Clone the old op with new operands.
-    Operation *newOp =
-        op.clone(rewriter, op->getLoc(), op->getResultTypes(), newOperands);
-    auto newLinalgOp = cast<LinalgOp>(newOp);
-    newOp->setAttr("indexing_maps",
-                   rewriter.getAffineMapArrayAttr(newIndexingMaps));
-
-    // Set the number of inputs to the new value. The `clone` call above kept
-    // the value from the original op.
-    newLinalgOp.setNumInputs(canonicalInput.size());
-
-    // Repair the payload entry block by RAUW'ing redundant arguments and
-    // erasing them.
-    Block &payload = newOp->getRegion(0).front();
-    SmallVector<OpOperand *> inputOperands = op.getInputOperands();
-    for (OpOperand *opOperand : llvm::reverse(inputOperands)) {
-      // Iterate in reverse, so that we erase later args first, preventing the
-      // argument list from shifting unexpectedly and invalidating all our
-      // indices.
-      unsigned operandNumber = opOperand->getOperandNumber();
-      if (canonicalInputIndices[operandNumber] == operandNumber)
-        continue;
-      payload.getArgument(operandNumber)
-          .replaceAllUsesWith(
-              payload.getArgument(canonicalInputIndices[operandNumber]));
-      payload.eraseArgument(operandNumber);
-    }
-
-    rewriter.replaceOp(op, newOp->getResults());
-    return success();
-  }
-};
-
-/// Remove generic operations (on tensors) that are just copying
-/// the values from inputs to the results. Requirements are
-/// 1) All iterator types are parallel
-/// 2) The body contains just a yield operation with the yielded values being
-///    the arguments corresponding to the operands.
-struct RemoveIdentityLinalgOps : public OpInterfaceRewritePattern<LinalgOp> {
-  using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;
-
-  LogicalResult matchAndRewrite(LinalgOp op,
-                                PatternRewriter &rewriter) const override {
-    if (!isa<GenericOp>(op))
-      return failure();
-    if (!op.hasTensorSemantics())
-      return failure();
-    // Check all indexing maps are identity.
-    if (llvm::any_of(op.getIndexingMaps(),
-                     [](AffineMap map) { return !map.isIdentity(); }))
-      return failure();
-
-    // Check that the body of the linalg operation is just a linalg.yield
-    // operation.
-    Block &body = op->getRegion(0).front();
-    if (!llvm::hasSingleElement(body))
-      return failure();
-    auto yieldOp = dyn_cast<linalg::YieldOp>(body.getTerminator());
-    if (!yieldOp)
-      return failure();
-
-    // Get the argument number of the returned values. That is the operand
-    // number to use for replacing uses of this operation.
-    SmallVector<Value, 4> returnedArgs;
-    for (Value yieldVal : yieldOp.values()) {
-      auto yieldArg = yieldVal.dyn_cast<BlockArgument>();
-      if (!yieldArg || yieldArg.getOwner() != &body)
-        return failure();
-      unsigned argumentNumber = yieldArg.getArgNumber();
-      returnedArgs.push_back(op->getOperand(argumentNumber));
-    }
-    if (returnedArgs.size() != op.getOperation()->getNumResults())
-      return failure();
-    rewriter.replaceOp(op, returnedArgs);
-    return success();
-  }
-};
-} // namespace
-
 #define LINALGOP_FOLDERS(XXX)                                                  \
   LogicalResult XXX::fold(ArrayRef<Attribute>,                                 \
                           SmallVectorImpl<OpFoldResult> &) {                   \
     return foldMemRefCast(*this);                                              \
   }
 
 LINALGOP_FOLDERS(ConvOp)
 LINALGOP_FOLDERS(PoolingMaxOp)
 LINALGOP_FOLDERS(PoolingMinOp)
 LINALGOP_FOLDERS(PoolingSumOp)
 LINALGOP_FOLDERS(CopyOp)
 LINALGOP_FOLDERS(FillOp)
 LINALGOP_FOLDERS(GenericOp)
 
 // All named ops canonicalizers and folders are auto-generated in the
 // .cpp.inc.
 
 //===----------------------------------------------------------------------===//
 // LinalgDialect
 //===----------------------------------------------------------------------===//
 
 void LinalgDialect::getCanonicalizationPatterns(
     RewritePatternSet &results) const {
-  results.add<DeduplicateInputs, EraseDeadLinalgOp, FoldTensorCastOp,
-              RemoveIdentityLinalgOps>(getContext());
+  results.add<EraseDeadLinalgOp, FoldTensorCastOp>(getContext());
 }