tensor.concat cast propagation

Adds canonicalization patterns which propagate inferred static shapes to
`tensor.concat` operands and result types. Static is propagated to other
canonicalization patterns through casts.

Author: AaronStGeorge

mlir/lib/Dialect/Tensor/IR/TensorOps.cpp

@@ -330,8 +330,9 @@ bool mlir::tensor::canFoldIntoConsumerOp(CastOp castOp) {
 
 /// Determines whether the tensor::CastOp casts to a more static version of the
 /// source tensor. This is useful to fold into a producing op and implement
-/// canonicaliation patterns with the `tensor.cast` op as the root, but producer
-/// being from different dialects. Returns true when all conditions are met:
+/// canonicalization patterns with the `tensor.cast` op as the root, but
+/// producer being from different dialects. Returns true when all conditions are
+/// met:
 /// 1. source and result and ranked tensors with same element type and rank.
 /// 2. the result type has more static information than the source.
 ///
@@ -773,11 +774,118 @@ struct SingleInputConcatOp : public OpRewritePattern<ConcatOp> {
     return success();
   }
 };
+
+/// Propagate static shapes into the operands of a `tensor.concat`.
+///
+/// `tensor.concat` requires every operand to match on all dimensions except the
+/// concatenation dimension. If one operand is already static in those
+/// dimensions, the other operands may safely be refined to that same static
+/// shape.
+///
+/// Example:
+///
+/// ```mlir
+///   // Second operand dim 1 has dynamic shape constrained by dim 1 of first
+///   // operand.
+///   %2 = tensor.concat dim(0) %0, %1: (tensor<?x12xi32>, tensor<?x?xi32>) ->
+///        tensor<?x12xi32>
+/// ```
+/// ->
+/// ```mlir
+///   %cast = tensor.cast %1 : tensor<?x?xi32> to tensor<?x12xi32>
+///   %2 = tensor.concat dim(0) %0, %cast :
+///        (tensor<?x12xi32>, tensor<?x12xi32>) -> tensor<?x12xi32>
+/// ```
+struct InferConcatOperandTypes : public OpRewritePattern<ConcatOp> {
+  using OpRewritePattern<ConcatOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(ConcatOp concatOp,
+                                PatternRewriter &rewriter) const override {
+    auto operandTensorTypes =
+        llvm::map_range(concatOp->getOperandTypes(), [](Type type) {
+          return llvm::cast<RankedTensorType>(type);
+        });
+
+    int64_t dim = concatOp.getDim();
+    ArrayRef<int64_t> inferredResultShape =
+        concatOp.inferResultType(dim, concatOp->getOperandTypes()).getShape();
+
+    // Find operands for which a more static shape can be inferred.
+    SmallVector<std::tuple<size_t, RankedTensorType>> refinedTypes;
+    for (auto [operandIdx, operandType] : llvm::enumerate(operandTensorTypes)) {
+      // Compute inferred type for operand.
+      SmallVector<int64_t> inferredOperandShape(inferredResultShape);
+      inferredOperandShape[dim] = operandType.getDimSize(dim);
+      auto inferredOperandType = RankedTensorType::get(
+          inferredOperandShape, operandType.getElementType());
+
+      // Check if inferred type is more static.
+      if (!preservesStaticInformation(inferredOperandType, operandType)) {
+        refinedTypes.push_back({operandIdx, inferredOperandType});
+      }
+    }
+
+    if (refinedTypes.empty()) {
+      return failure();
+    }
+
+    // Use refined types for operands, insert casts for original type.
+    SmallVector<Value> newOperands = concatOp.getOperands();
+    for (auto [operandIdx, refinedType] : refinedTypes) {
+      newOperands[operandIdx] = rewriter.create<CastOp>(
+          concatOp->getLoc(), refinedType, concatOp.getOperand(operandIdx));
+    }
+    rewriter.replaceOpWithNewOp<ConcatOp>(concatOp, concatOp.getResultType(),
+                                          dim, newOperands);
+
+    return success();
+  }
+};
+
+// Ensure `tensor.concat`'s result type is at least as static as can be inferred
+// from its operand types.
+///
+/// Example:
+/// ```mlir
+///   %2 = tensor.concat dim(0) %0, %1: (tensor<?x12xi32>, tensor<?x12xi32>) ->
+///   tensor<?x?xi32>
+/// ```
+/// ->
+/// ```mlir
+///   %2 = tensor.concat dim(0) %0, %cast : (tensor<?x12xi32>, tensor<?x12xi32>)
+///   -> tensor<?x12xi32> %cast = tensor.cast %2 : tensor<?x12xi32> to
+///   tensor<?x?xi32>
+/// ```
+struct InferConcatResultType : public OpRewritePattern<ConcatOp> {
+  using OpRewritePattern<ConcatOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(ConcatOp concatOp,
+                                PatternRewriter &rewriter) const override {
+    int64_t dim = concatOp.getDim();
+    RankedTensorType inferredResultType =
+        concatOp.inferResultType(dim, concatOp->getOperandTypes());
+
+    // The result type should be at least as static as inferred result type.
+    if (preservesStaticInformation(inferredResultType,
+                                   concatOp.getResultType())) {
+      return failure();
+    }
+
+    auto newConcatOp = rewriter.create<ConcatOp>(
+        concatOp->getLoc(), inferredResultType, dim, concatOp->getOperands());
+    rewriter.replaceOpWithNewOp<CastOp>(concatOp, concatOp.getResultType(),
+                                        newConcatOp);
+
+    return llvm::success();
+  }
+};
 } // namespace
 
 void ConcatOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                            MLIRContext *context) {
-  results.add<SingleInputConcatOp>(context);
+  results
+      .add<SingleInputConcatOp, InferConcatOperandTypes, InferConcatResultType>(
+          context);
 }
 
 //===----------------------------------------------------------------------===//

mlir/test/Dialect/Tensor/canonicalize.mlir

@@ -136,6 +136,32 @@ func.func @fold_concat(%arg0: tensor<1x2x?xi32>) -> (tensor<1x2x3xi32>, tensor<1
 
 // -----
 
+// CHECK-LABEL: infer_concat_operand_types
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x12xi32>
+// CHECK-SAME: %[[ARG1:.+]]: tensor<?x?xi32>
+func.func @infer_concat_operand_types(%arg0: tensor<?x12xi32>, %arg1: tensor<?x?xi32>) -> (tensor<?x12xi32>) {
+  // CHECK-NEXT: %[[CAST:.+]] = tensor.cast %[[ARG1]] : tensor<?x?xi32> to tensor<?x12xi32>
+  %0 = tensor.concat dim(0) %arg0, %arg1: (tensor<?x12xi32>, tensor<?x?xi32>) -> tensor<?x12xi32>
+  // CHECK-NEXT: %[[CONCAT:.+]] = tensor.concat dim(0) %[[ARG0]], %[[CAST]] : (tensor<?x12xi32>, tensor<?x12xi32>) -> tensor<?x12xi32>
+  return %0 : tensor<?x12xi32>
+  // CHECK-NEXT: return %[[CONCAT]] : tensor<?x12xi32>
+}
+
+// -----
+
+// CHECK-LABEL: infer_concat_return_type
+// CHECK-SAME: %[[ARG0:.+]]: tensor<5x12xi32>
+// CHECK-SAME: %[[ARG1:.+]]: tensor<?x12xi32>
+func.func @infer_concat_return_type(%arg0: tensor<5x12xi32>, %arg1: tensor<?x12xi32>) -> (tensor<?x?xi32>) {
+  %0 = tensor.concat dim(0) %arg0, %arg1: (tensor<5x12xi32>, tensor<?x12xi32>) -> tensor<?x?xi32>
+  // CHECK-NEXT: %[[CONCAT:.+]] = tensor.concat dim(0) %[[ARG0]], %[[ARG1]] : (tensor<5x12xi32>, tensor<?x12xi32>) -> tensor<?x12xi32>
+  // CHECK-NEXT: %[[CAST:.+]] = tensor.cast %[[CONCAT]] : tensor<?x12xi32> to tensor<?x?xi32>
+  return %0 : tensor<?x?xi32>
+  // CHECK-NEXT: return %[[CAST]] : tensor<?x?xi32>
+}
+
+// -----
+
 // CHECK-LABEL: func @fold_extract
 func.func @fold_extract(%arg0 : index) -> (f32, f16, f16, i32, complex<f32>) {
   %const_0 = arith.constant 0 : index