[mlir][Linalg] Add GenericOp self-copy on buffers folding

[gysit]

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

862

Shouldn't the condition be getNumInputs == 1 && getNumOutputs == 1 && input == output?

[pifon2a]

Thank you!

[nicolasvasilache]

Fix incorrect condition and make tests pass.

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

 /// 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();
 
+    // In the buffer case, we need to check exact buffer equality.
+    if (genericOp.hasBufferSemantics()) {
+      if (genericOp.getNumInputs() != 1 || genericOp.getNumOutputs() != 1 ||
+          genericOp.getInputOperand(0)->get() ==
+              genericOp.getOutputOperand(0)->get()) {
+        rewriter.eraseOp(genericOp);
+        return success();
+      }
+      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 (const auto &yieldVal : llvm::enumerate(yieldOp.values())) {
       auto yieldArg = yieldVal.value().dyn_cast<BlockArgument>();
       if (!yieldArg || yieldArg.getOwner() != &body)
         return failure();
       unsigned argumentNumber = yieldArg.getArgNumber();
       Value returnedArg = genericOp->getOperand(argumentNumber);
       Type resultType = genericOp->getResult(yieldVal.index()).getType();
       // The input can have a different type than the result, e.g. a dynamic
       // input dimension can be turned into a static output dimension.
       if (returnedArg.getType() != resultType)
         returnedArg = rewriter.create<tensor::CastOp>(genericOp.getLoc(),
                                                       resultType, returnedArg);
       returnedArgs.push_back(returnedArg);
     }
+
     if (returnedArgs.size() != genericOp->getNumResults())
       return failure();
     rewriter.replaceOp(genericOp, returnedArgs);
     return success();
   }
 };
 } // namespace
 

mlir/test/Dialect/Linalg/canonicalize.mlir

%r = linalg.tiled_loop (%iv0, %iv1) = (%c0, %c0)
       ins (%in0 = %arg0 : tensor<?x?xf32>, %in1 = %arg1 : tensor<?x?xf32>)
       outs (%out1 = %arg2 : tensor<?x?xf32>) {
     %1 = tensor.insert_slice %arg0 into %arg1 [0, 0] [%s, %s] [1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
     linalg.yield %1 : tensor<?x?xf32>
   }
   %r2 = tensor.dim %r, %c0 : tensor<?x?xf32>
   return %r2 : index
 }
+
+// -----
+
+// CHECK: func @fold_self_copy
+func @fold_self_copy(%0 : memref<4x16xf32>) {
+// CHECK-NEXT: return
+  linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, 
+                                   affine_map<(d0, d1) -> (d0, d1)>], 
+                  iterator_types = ["parallel", "parallel"]} 
+    ins(%0 : memref<4x16xf32>) 
+    outs(%0 : memref<4x16xf32>) {
+      ^bb0(%arg4: f32, %arg5: f32):
+        linalg.yield %arg4 : f32
+    }
+  return 
+}