diff --git a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
--- a/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
+++ b/mlir/include/mlir/Dialect/MemRef/IR/MemRefOps.td
@@ -28,7 +28,6 @@
 class MemRef_Op<string mnemonic, list<Trait> traits = []>
     : Op<MemRef_Dialect, mnemonic, traits> {
   let printer = [{ return ::print(p, *this); }];
-  let verifier = [{ return ::verify(*this); }];
   let parser = [{ return ::parse$cppClass(parser, result); }];
 }
 
@@ -93,6 +92,7 @@
   }];
 
   let hasCanonicalizer = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -115,6 +115,7 @@
   let results = (outs);
 
   let assemblyFormat = "$memref `,` $alignment attr-dict `:` type($memref)";
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -162,6 +163,7 @@
       memref<8x64xf32, affine_map<(d0, d1)[s0] -> ((d0 + s0), d1)>, 1>
     ```
   }];
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -205,6 +207,7 @@
     an alignment on any convenient boundary compatible with the type will be
     chosen.
   }];
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -253,6 +256,7 @@
 
   let results = (outs Variadic<AnyType>:$results);
   let regions = (region SizedRegion<1>:$bodyRegion);
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -279,11 +283,7 @@
   let arguments = (ins Variadic<AnyType>:$results);
   let builders = [OpBuilder<(ins), [{ /*nothing to do */ }]>];
 
-  let assemblyFormat =
-      [{ attr-dict ($results^ `:` type($results))? }];
-
-  // No custom verification needed.
-  let verifier = ?;
+  let assemblyFormat = "attr-dict ($results^ `:` type($results))?";
 }
 
 //===----------------------------------------------------------------------===//
@@ -355,7 +355,6 @@
   let arguments = (ins AnyRankedOrUnrankedMemRef:$source);
   let results = (outs AnyRankedOrUnrankedMemRef:$dest);
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($dest)";
-  let verifier = "return impl::verifyCastOp(*this, areCastCompatible);";
   let builders = [
     OpBuilder<(ins "Value":$source, "Type":$destType), [{
        impl::buildCastOp($_builder, $_state, source, destType);
@@ -370,6 +369,7 @@
   }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -408,7 +408,6 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
-  let verifier = ?;
 }
 
 //===----------------------------------------------------------------------===//
@@ -434,7 +433,6 @@
   let arguments = (ins Arg<AnyRankedOrUnrankedMemRef, "", [MemFree]>:$memref);
 
   let hasFolder = 1;
-  let verifier = ?;
   let assemblyFormat = "$memref attr-dict `:` type($memref)";
 }
 
@@ -488,6 +486,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -646,6 +645,7 @@
     }
   }];
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -697,6 +697,7 @@
     Value getNumElements() { return numElements(); }
   }];
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -757,6 +758,7 @@
       return memref().getType().cast<MemRefType>();
     }
   }];
+  let hasVerifier = 1;
 }
 
 def AtomicYieldOp : MemRef_Op<"atomic_yield", [
@@ -772,6 +774,7 @@
 
   let arguments = (ins AnyType:$result);
   let assemblyFormat = "$result attr-dict `:` type($result)";
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -797,9 +800,6 @@
   let arguments = (ins FlatSymbolRefAttr:$name);
   let results = (outs AnyStaticShapeMemRef:$result);
   let assemblyFormat = "$name `:` type($result) attr-dict";
-
-  // `GetGlobalOp` is fully verified by its traits.
-  let verifier = ?;
 }
 
 //===----------------------------------------------------------------------===//
@@ -866,6 +866,7 @@
        return !isExternal() && initial_value().getValue().isa<UnitAttr>();
      }
   }];
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -939,6 +940,7 @@
   }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 
   let assemblyFormat = "$memref `[` $indices `]` attr-dict `:` type($memref)";
 }
@@ -982,6 +984,7 @@
   }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1034,6 +1037,7 @@
 
   let parser = ?;
   let printer = ?;
+  let hasVerifier = 1;
 
   let builders = [
     // Build a ReinterpretCastOp with mixed static and dynamic entries.
@@ -1096,7 +1100,6 @@
   let arguments = (ins AnyRankedOrUnrankedMemRef:$memref);
   let results = (outs Index);
 
-  let verifier = ?;
   let hasFolder = 1;
   let assemblyFormat = "$memref attr-dict `:` type($memref)";
 }
@@ -1161,6 +1164,7 @@
   let assemblyFormat = [{
     $source `(` $shape `)` attr-dict `:` functional-type(operands, results)
   }];
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1226,6 +1230,7 @@
 
   let hasFolder = 1;
   let hasCanonicalizer = 1;
+  let hasVerifier = 1;
   let printer = [{ return ::print(p, *this); }];
   let parser = [{ return ::parseReshapeLikeOp(parser, result); }];
 }
@@ -1265,6 +1270,7 @@
     ```
   }];
   let extraClassDeclaration = commonExtraClassDeclaration;
+  let hasVerifier = 1;
 }
 
 def MemRef_CollapseShapeOp : MemRef_ReassociativeReshapeOp<"collapse_shape"> {
@@ -1302,6 +1308,7 @@
     ```
   }];
   let extraClassDeclaration = commonExtraClassDeclaration;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1369,6 +1376,7 @@
   }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 
   let assemblyFormat = [{
     $value `,` $memref `[` $indices `]` attr-dict `:` type($memref)
@@ -1617,6 +1625,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1645,8 +1654,6 @@
 
   let arguments = (ins AnyTensor:$tensor, Arg<AnyRankedOrUnrankedMemRef,
                        "the reference to store to", [MemWrite]>:$memref);
-  // TensorStoreOp is fully verified by traits.
-  let verifier = ?;
 
   let assemblyFormat = "$tensor `,` $memref attr-dict `:` type($memref)";
 }
@@ -1681,6 +1688,7 @@
   }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1749,6 +1757,7 @@
   }];
 
   let hasCanonicalizer = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1796,6 +1805,7 @@
     }
   }];
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 #endif // MEMREF_OPS
diff --git a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
--- a/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
+++ b/mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
@@ -21,7 +21,6 @@
 class SparseTensor_Op<string mnemonic, list<Trait> traits = []>
   : Op<SparseTensor_Dialect, mnemonic, traits> {
   let printer = [{ return ::print(p, *this); }];
-  let verifier = [{ return ::verify(*this); }];
   let parser = [{ return ::parse$cppClass(parser, result); }];
 }
 
@@ -50,6 +49,7 @@
     ```
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($result)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_InitOp : SparseTensor_Op<"init", [NoSideEffect]>,
@@ -72,6 +72,7 @@
     ```
   }];
   let assemblyFormat = "`[` $sizes `]` attr-dict `:` type($result)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ConvertOp : SparseTensor_Op<"convert",
@@ -113,6 +114,7 @@
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($dest)";
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ToPointersOp : SparseTensor_Op<"pointers", [NoSideEffect]>,
@@ -137,6 +139,7 @@
   }];
   let assemblyFormat = "$tensor `,` $dim attr-dict `:` type($tensor)"
       " `to` type($result)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ToIndicesOp : SparseTensor_Op<"indices", [NoSideEffect]>,
@@ -161,6 +164,7 @@
   }];
   let assemblyFormat = "$tensor `,` $dim attr-dict `:` type($tensor)"
       " `to` type($result)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ToValuesOp : SparseTensor_Op<"values", [NoSideEffect]>,
@@ -183,6 +187,7 @@
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($result)";
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -217,6 +222,7 @@
   }];
   let assemblyFormat = "$tensor `,` $indices `,` $value attr-dict `:`"
                        " type($tensor) `,` type($indices) `,` type($value)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ExpandOp : SparseTensor_Op<"expand", []>,
@@ -258,6 +264,7 @@
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($values)"
                        " `,` type($filled) `,` type($added) `,` type($count)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_CompressOp : SparseTensor_Op<"compress", []>,
@@ -292,6 +299,7 @@
                         " $added `,` $count attr-dict `:` type($tensor) `,`"
 			" type($indices) `,` type($values) `,` type($filled) `,`"
 			" type($added) `,` type($count)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_LoadOp : SparseTensor_Op<"load", [SameOperandsAndResultType]>,
@@ -324,6 +332,7 @@
     ```
   }];
   let assemblyFormat = "$tensor (`hasInserts` $hasInserts^)? attr-dict `:` type($tensor)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_ReleaseOp : SparseTensor_Op<"release", []>,
@@ -349,6 +358,7 @@
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor)";
+  let hasVerifier = 1;
 }
 
 def SparseTensor_OutOp : SparseTensor_Op<"out", []>,
@@ -369,6 +379,7 @@
     ```
   }];
   let assemblyFormat = "$tensor `,` $dest attr-dict `:` type($tensor) `,` type($dest)";
+  let hasVerifier = 1;
 }
 
 #endif // SPARSETENSOR_OPS
diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
--- a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -20,7 +20,6 @@
 class Tensor_Op<string mnemonic, list<Trait> traits = []>
     : Op<Tensor_Dialect, mnemonic, traits> {
   let printer = [{ return ::print(p, *this); }];
-  let verifier = [{ return ::verify(*this); }];
   let parser = [{ return ::parse$cppClass(parser, result); }];
 }
 
@@ -59,7 +58,6 @@
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($dest)";
 
   let hasCanonicalizer = 1;
-  let verifier = ?;
 }
 
 //===----------------------------------------------------------------------===//
@@ -111,6 +109,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -151,6 +150,7 @@
     }]>];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 
@@ -303,6 +303,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -339,9 +340,6 @@
 
   let assemblyFormat = "$elements attr-dict `:` type($result)";
 
-  // This op is fully verified by its traits.
-  let verifier = ?;
-
   let skipDefaultBuilders = 1;
   let builders = [
     OpBuilder<(ins "Type":$resultType, "ValueRange":$elements)>,
@@ -394,6 +392,7 @@
   ];
 
   let hasCanonicalizer = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -445,6 +444,7 @@
     }]>];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -564,6 +564,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -586,7 +587,6 @@
   let arguments = (ins AnyTensor:$tensor);
   let results = (outs Index);
 
-  let verifier = ?;
   let hasFolder = 1;
   let assemblyFormat = "$tensor attr-dict `:` type($tensor)";
 }
@@ -650,6 +650,7 @@
   let assemblyFormat = [{
     $source `(` $shape `)` attr-dict `:` functional-type(operands, results)
   }];
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -718,6 +719,7 @@
 
   let hasFolder = 1;
   let hasCanonicalizer = 1;
+  let hasVerifier = 1;
   let printer = [{ return ::print(p, *this); }];
   let parser = [{ return ::parseReshapeLikeOp(parser, result); }];
 }
@@ -748,6 +750,7 @@
     ```
   }];
   let extraClassDeclaration = commonExtraClassDeclaration;
+  let hasVerifier = 1;
 }
 
 def Tensor_CollapseShapeOp : Tensor_ReassociativeReshapeOp<"collapse_shape"> {
@@ -776,6 +779,7 @@
     ```
   }];
   let extraClassDeclaration = commonExtraClassDeclaration;
+  let hasVerifier = 1;
 }
 
 //===----------------------------------------------------------------------===//
@@ -961,6 +965,7 @@
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
+  let hasVerifier = 1;
 }
 
 
@@ -984,7 +989,6 @@
   // Dummy builder to appease code in templated ensureTerminator that
   // GenerateOp's auto-generated parser calls.
   let builders = [OpBuilder<(ins), [{ /* nothing to do */ }]>];
-  let verifier = ?;
 }
 
 #endif // TENSOR_OPS
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -67,6 +67,10 @@
   return NoneType::get(type.getContext());
 }
 
+LogicalResult memref::CastOp::verify() {
+  return impl::verifyCastOp(*this, areCastCompatible);
+}
+
 //===----------------------------------------------------------------------===//
 // AllocOp / AllocaOp
 //===----------------------------------------------------------------------===//
@@ -95,15 +99,15 @@
   return success();
 }
 
-static LogicalResult verify(AllocOp op) { return verifyAllocLikeOp(op); }
+LogicalResult AllocOp::verify() { return verifyAllocLikeOp(*this); }
 
-static LogicalResult verify(AllocaOp op) {
+LogicalResult AllocaOp::verify() {
   // An alloca op needs to have an ancestor with an allocation scope trait.
-  if (!op->getParentWithTrait<OpTrait::AutomaticAllocationScope>())
-    return op.emitOpError(
+  if (!(*this)->getParentWithTrait<OpTrait::AutomaticAllocationScope>())
+    return emitOpError(
         "requires an ancestor op with AutomaticAllocationScope trait");
 
-  return verifyAllocLikeOp(op);
+  return verifyAllocLikeOp(*this);
 }
 
 namespace {
@@ -246,11 +250,8 @@
   return success();
 }
 
-static LogicalResult verify(AllocaScopeOp op) {
-  if (failed(RegionBranchOpInterface::verifyTypes(op)))
-    return failure();
-
-  return success();
+LogicalResult AllocaScopeOp::verify() {
+  return RegionBranchOpInterface::verifyTypes(*this);
 }
 
 void AllocaScopeOp::getSuccessorRegions(
@@ -268,10 +269,9 @@
 // AssumeAlignmentOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(AssumeAlignmentOp op) {
-  unsigned alignment = op.alignment();
-  if (!llvm::isPowerOf2_32(alignment))
-    return op.emitOpError("alignment must be power of 2");
+LogicalResult AssumeAlignmentOp::verify() {
+  if (!llvm::isPowerOf2_32(alignment()))
+    return emitOpError("alignment must be power of 2");
   return success();
 }
 
@@ -556,17 +556,17 @@
   return {};
 }
 
-static LogicalResult verify(DimOp op) {
+LogicalResult DimOp::verify() {
   // Assume unknown index to be in range.
-  Optional<int64_t> index = op.getConstantIndex();
+  Optional<int64_t> index = getConstantIndex();
   if (!index.hasValue())
     return success();
 
   // Check that constant index is not knowingly out of range.
-  auto type = op.source().getType();
+  auto type = source().getType();
   if (auto memrefType = type.dyn_cast<MemRefType>()) {
     if (index.getValue() >= memrefType.getRank())
-      return op.emitOpError("index is out of range");
+      return emitOpError("index is out of range");
   } else if (type.isa<UnrankedMemRefType>()) {
     // Assume index to be in range.
   } else {
@@ -866,67 +866,66 @@
   return success();
 }
 
-static LogicalResult verify(DmaStartOp op) {
-  unsigned numOperands = op.getNumOperands();
+LogicalResult DmaStartOp::verify() {
+  unsigned numOperands = getNumOperands();
 
   // Mandatory non-variadic operands are: src memref, dst memref, tag memref and
   // the number of elements.
   if (numOperands < 4)
-    return op.emitOpError("expected at least 4 operands");
+    return emitOpError("expected at least 4 operands");
 
   // Check types of operands. The order of these calls is important: the later
   // calls rely on some type properties to compute the operand position.
   // 1. Source memref.
-  if (!op.getSrcMemRef().getType().isa<MemRefType>())
-    return op.emitOpError("expected source to be of memref type");
-  if (numOperands < op.getSrcMemRefRank() + 4)
-    return op.emitOpError()
-           << "expected at least " << op.getSrcMemRefRank() + 4 << " operands";
-  if (!op.getSrcIndices().empty() &&
-      !llvm::all_of(op.getSrcIndices().getTypes(),
+  if (!getSrcMemRef().getType().isa<MemRefType>())
+    return emitOpError("expected source to be of memref type");
+  if (numOperands < getSrcMemRefRank() + 4)
+    return emitOpError() << "expected at least " << getSrcMemRefRank() + 4
+                         << " operands";
+  if (!getSrcIndices().empty() &&
+      !llvm::all_of(getSrcIndices().getTypes(),
                     [](Type t) { return t.isIndex(); }))
-    return op.emitOpError("expected source indices to be of index type");
+    return emitOpError("expected source indices to be of index type");
 
   // 2. Destination memref.
-  if (!op.getDstMemRef().getType().isa<MemRefType>())
-    return op.emitOpError("expected destination to be of memref type");
-  unsigned numExpectedOperands =
-      op.getSrcMemRefRank() + op.getDstMemRefRank() + 4;
+  if (!getDstMemRef().getType().isa<MemRefType>())
+    return emitOpError("expected destination to be of memref type");
+  unsigned numExpectedOperands = getSrcMemRefRank() + getDstMemRefRank() + 4;
   if (numOperands < numExpectedOperands)
-    return op.emitOpError()
-           << "expected at least " << numExpectedOperands << " operands";
-  if (!op.getDstIndices().empty() &&
-      !llvm::all_of(op.getDstIndices().getTypes(),
+    return emitOpError() << "expected at least " << numExpectedOperands
+                         << " operands";
+  if (!getDstIndices().empty() &&
+      !llvm::all_of(getDstIndices().getTypes(),
                     [](Type t) { return t.isIndex(); }))
-    return op.emitOpError("expected destination indices to be of index type");
+    return emitOpError("expected destination indices to be of index type");
 
   // 3. Number of elements.
-  if (!op.getNumElements().getType().isIndex())
-    return op.emitOpError("expected num elements to be of index type");
+  if (!getNumElements().getType().isIndex())
+    return emitOpError("expected num elements to be of index type");
 
   // 4. Tag memref.
-  if (!op.getTagMemRef().getType().isa<MemRefType>())
-    return op.emitOpError("expected tag to be of memref type");
-  numExpectedOperands += op.getTagMemRefRank();
+  if (!getTagMemRef().getType().isa<MemRefType>())
+    return emitOpError("expected tag to be of memref type");
+  numExpectedOperands += getTagMemRefRank();
   if (numOperands < numExpectedOperands)
-    return op.emitOpError()
-           << "expected at least " << numExpectedOperands << " operands";
-  if (!op.getTagIndices().empty() &&
-      !llvm::all_of(op.getTagIndices().getTypes(),
+    return emitOpError() << "expected at least " << numExpectedOperands
+                         << " operands";
+  if (!getTagIndices().empty() &&
+      !llvm::all_of(getTagIndices().getTypes(),
                     [](Type t) { return t.isIndex(); }))
-    return op.emitOpError("expected tag indices to be of index type");
+    return emitOpError("expected tag indices to be of index type");
 
   // Optional stride-related operands must be either both present or both
   // absent.
   if (numOperands != numExpectedOperands &&
       numOperands != numExpectedOperands + 2)
-    return op.emitOpError("incorrect number of operands");
+    return emitOpError("incorrect number of operands");
 
   // 5. Strides.
-  if (op.isStrided()) {
-    if (!op.getStride().getType().isIndex() ||
-        !op.getNumElementsPerStride().getType().isIndex())
-      return op.emitOpError(
+  if (isStrided()) {
+    if (!getStride().getType().isIndex() ||
+        !getNumElementsPerStride().getType().isIndex())
+      return emitOpError(
           "expected stride and num elements per stride to be of type index");
   }
 
@@ -949,14 +948,14 @@
   return foldMemRefCast(*this);
 }
 
-static LogicalResult verify(DmaWaitOp op) {
+LogicalResult DmaWaitOp::verify() {
   // Check that the number of tag indices matches the tagMemRef rank.
-  unsigned numTagIndices = op.tagIndices().size();
-  unsigned tagMemRefRank = op.getTagMemRefRank();
+  unsigned numTagIndices = tagIndices().size();
+  unsigned tagMemRefRank = getTagMemRefRank();
   if (numTagIndices != tagMemRefRank)
-    return op.emitOpError() << "expected tagIndices to have the same number of "
-                               "elements as the tagMemRef rank, expected "
-                            << tagMemRefRank << ", but got " << numTagIndices;
+    return emitOpError() << "expected tagIndices to have the same number of "
+                            "elements as the tagMemRef rank, expected "
+                         << tagMemRefRank << ", but got " << numTagIndices;
   return success();
 }
 
@@ -979,14 +978,13 @@
   }
 }
 
-static LogicalResult verify(GenericAtomicRMWOp op) {
-  auto &body = op.getRegion();
+LogicalResult GenericAtomicRMWOp::verify() {
+  auto &body = getRegion();
   if (body.getNumArguments() != 1)
-    return op.emitOpError("expected single number of entry block arguments");
+    return emitOpError("expected single number of entry block arguments");
 
-  if (op.getResult().getType() != body.getArgument(0).getType())
-    return op.emitOpError(
-        "expected block argument of the same type result type");
+  if (getResult().getType() != body.getArgument(0).getType())
+    return emitOpError("expected block argument of the same type result type");
 
   bool hasSideEffects =
       body.walk([&](Operation *nestedOp) {
@@ -1034,12 +1032,12 @@
 // AtomicYieldOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(AtomicYieldOp op) {
-  Type parentType = op->getParentOp()->getResultTypes().front();
-  Type resultType = op.result().getType();
+LogicalResult AtomicYieldOp::verify() {
+  Type parentType = (*this)->getParentOp()->getResultTypes().front();
+  Type resultType = result().getType();
   if (parentType != resultType)
-    return op.emitOpError() << "types mismatch between yield op: " << resultType
-                            << " and its parent: " << parentType;
+    return emitOpError() << "types mismatch between yield op: " << resultType
+                         << " and its parent: " << parentType;
   return success();
 }
 
@@ -1090,19 +1088,19 @@
   return success();
 }
 
-static LogicalResult verify(GlobalOp op) {
-  auto memrefType = op.type().dyn_cast<MemRefType>();
+LogicalResult GlobalOp::verify() {
+  auto memrefType = type().dyn_cast<MemRefType>();
   if (!memrefType || !memrefType.hasStaticShape())
-    return op.emitOpError("type should be static shaped memref, but got ")
-           << op.type();
+    return emitOpError("type should be static shaped memref, but got ")
+           << type();
 
   // Verify that the initial value, if present, is either a unit attribute or
   // an elements attribute.
-  if (op.initial_value().hasValue()) {
-    Attribute initValue = op.initial_value().getValue();
+  if (initial_value().hasValue()) {
+    Attribute initValue = initial_value().getValue();
     if (!initValue.isa<UnitAttr>() && !initValue.isa<ElementsAttr>())
-      return op.emitOpError("initial value should be a unit or elements "
-                            "attribute, but got ")
+      return emitOpError("initial value should be a unit or elements "
+                         "attribute, but got ")
              << initValue;
 
     // Check that the type of the initial value is compatible with the type of
@@ -1111,17 +1109,17 @@
       Type initType = initValue.getType();
       Type tensorType = getTensorTypeFromMemRefType(memrefType);
       if (initType != tensorType)
-        return op.emitOpError("initial value expected to be of type ")
+        return emitOpError("initial value expected to be of type ")
                << tensorType << ", but was of type " << initType;
     }
   }
 
-  if (Optional<uint64_t> alignAttr = op.alignment()) {
+  if (Optional<uint64_t> alignAttr = alignment()) {
     uint64_t alignment = alignAttr.getValue();
 
     if (!llvm::isPowerOf2_64(alignment))
-      return op->emitError() << "alignment attribute value " << alignment
-                             << " is not a power of 2";
+      return emitError() << "alignment attribute value " << alignment
+                         << " is not a power of 2";
   }
 
   // TODO: verify visibility for declarations.
@@ -1154,9 +1152,9 @@
 // LoadOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(LoadOp op) {
-  if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
-    return op.emitOpError("incorrect number of indices for load");
+LogicalResult LoadOp::verify() {
+  if (getNumOperands() != 1 + getMemRefType().getRank())
+    return emitOpError("incorrect number of indices for load");
   return success();
 }
 
@@ -1224,9 +1222,9 @@
   return success();
 }
 
-static LogicalResult verify(PrefetchOp op) {
-  if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
-    return op.emitOpError("too few indices");
+LogicalResult PrefetchOp::verify() {
+  if (getNumOperands() != 1 + getMemRefType().getRank())
+    return emitOpError("too few indices");
 
   return success();
 }
@@ -1306,26 +1304,25 @@
 
 // TODO: ponder whether we want to allow missing trailing sizes/strides that are
 // completed automatically, like we have for subview and extract_slice.
-static LogicalResult verify(ReinterpretCastOp op) {
+LogicalResult ReinterpretCastOp::verify() {
   // The source and result memrefs should be in the same memory space.
-  auto srcType = op.source().getType().cast<BaseMemRefType>();
-  auto resultType = op.getType().cast<MemRefType>();
+  auto srcType = source().getType().cast<BaseMemRefType>();
+  auto resultType = getType().cast<MemRefType>();
   if (srcType.getMemorySpace() != resultType.getMemorySpace())
-    return op.emitError("different memory spaces specified for source type ")
+    return emitError("different memory spaces specified for source type ")
            << srcType << " and result memref type " << resultType;
   if (srcType.getElementType() != resultType.getElementType())
-    return op.emitError("different element types specified for source type ")
+    return emitError("different element types specified for source type ")
            << srcType << " and result memref type " << resultType;
 
   // Match sizes in result memref type and in static_sizes attribute.
-  for (auto &en :
-       llvm::enumerate(llvm::zip(resultType.getShape(),
-                                 extractFromI64ArrayAttr(op.static_sizes())))) {
+  for (auto &en : llvm::enumerate(llvm::zip(
+           resultType.getShape(), extractFromI64ArrayAttr(static_sizes())))) {
     int64_t resultSize = std::get<0>(en.value());
     int64_t expectedSize = std::get<1>(en.value());
     if (!ShapedType::isDynamic(resultSize) &&
         !ShapedType::isDynamic(expectedSize) && resultSize != expectedSize)
-      return op.emitError("expected result type with size = ")
+      return emitError("expected result type with size = ")
              << expectedSize << " instead of " << resultSize
              << " in dim = " << en.index();
   }
@@ -1336,27 +1333,26 @@
   int64_t resultOffset;
   SmallVector<int64_t, 4> resultStrides;
   if (failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
-    return op.emitError(
-               "expected result type to have strided layout but found ")
+    return emitError("expected result type to have strided layout but found ")
            << resultType;
 
   // Match offset in result memref type and in static_offsets attribute.
-  int64_t expectedOffset = extractFromI64ArrayAttr(op.static_offsets()).front();
+  int64_t expectedOffset = extractFromI64ArrayAttr(static_offsets()).front();
   if (!ShapedType::isDynamicStrideOrOffset(resultOffset) &&
       !ShapedType::isDynamicStrideOrOffset(expectedOffset) &&
       resultOffset != expectedOffset)
-    return op.emitError("expected result type with offset = ")
+    return emitError("expected result type with offset = ")
            << resultOffset << " instead of " << expectedOffset;
 
   // Match strides in result memref type and in static_strides attribute.
   for (auto &en : llvm::enumerate(llvm::zip(
-           resultStrides, extractFromI64ArrayAttr(op.static_strides())))) {
+           resultStrides, extractFromI64ArrayAttr(static_strides())))) {
     int64_t resultStride = std::get<0>(en.value());
     int64_t expectedStride = std::get<1>(en.value());
     if (!ShapedType::isDynamicStrideOrOffset(resultStride) &&
         !ShapedType::isDynamicStrideOrOffset(expectedStride) &&
         resultStride != expectedStride)
-      return op.emitError("expected result type with stride = ")
+      return emitError("expected result type with stride = ")
              << expectedStride << " instead of " << resultStride
              << " in dim = " << en.index();
   }
@@ -1532,8 +1528,8 @@
   return success();
 }
 
-static LogicalResult verify(ExpandShapeOp op) {
-  return verifyReshapeOp(op, op.getResultType(), op.getSrcType());
+LogicalResult ExpandShapeOp::verify() {
+  return verifyReshapeOp(*this, getResultType(), getSrcType());
 }
 
 void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
@@ -1542,8 +1538,8 @@
               CollapseMixedReshapeOps<ExpandShapeOp, CollapseShapeOp>>(context);
 }
 
-static LogicalResult verify(CollapseShapeOp op) {
-  return verifyReshapeOp(op, op.getSrcType(), op.getResultType());
+LogicalResult CollapseShapeOp::verify() {
+  return verifyReshapeOp(*this, getSrcType(), getResultType());
 }
 
 struct CollapseShapeOpMemRefCastFolder
@@ -1593,32 +1589,30 @@
 // ReshapeOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(ReshapeOp op) {
-  Type operandType = op.source().getType();
-  Type resultType = op.result().getType();
+LogicalResult ReshapeOp::verify() {
+  Type operandType = source().getType();
+  Type resultType = result().getType();
 
   Type operandElementType = operandType.cast<ShapedType>().getElementType();
   Type resultElementType = resultType.cast<ShapedType>().getElementType();
   if (operandElementType != resultElementType)
-    return op.emitOpError("element types of source and destination memref "
-                          "types should be the same");
+    return emitOpError("element types of source and destination memref "
+                       "types should be the same");
 
   if (auto operandMemRefType = operandType.dyn_cast<MemRefType>())
     if (!operandMemRefType.getLayout().isIdentity())
-      return op.emitOpError(
-          "source memref type should have identity affine map");
+      return emitOpError("source memref type should have identity affine map");
 
-  int64_t shapeSize = op.shape().getType().cast<MemRefType>().getDimSize(0);
+  int64_t shapeSize = shape().getType().cast<MemRefType>().getDimSize(0);
   auto resultMemRefType = resultType.dyn_cast<MemRefType>();
   if (resultMemRefType) {
     if (!resultMemRefType.getLayout().isIdentity())
-      return op.emitOpError(
-          "result memref type should have identity affine map");
+      return emitOpError("result memref type should have identity affine map");
     if (shapeSize == ShapedType::kDynamicSize)
-      return op.emitOpError("cannot use shape operand with dynamic length to "
-                            "reshape to statically-ranked memref type");
+      return emitOpError("cannot use shape operand with dynamic length to "
+                         "reshape to statically-ranked memref type");
     if (shapeSize != resultMemRefType.getRank())
-      return op.emitOpError(
+      return emitOpError(
           "length of shape operand differs from the result's memref rank");
   }
   return success();
@@ -1628,9 +1622,9 @@
 // StoreOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(StoreOp op) {
-  if (op.getNumOperands() != 2 + op.getMemRefType().getRank())
-    return op.emitOpError("store index operand count not equal to memref rank");
+LogicalResult StoreOp::verify() {
+  if (getNumOperands() != 2 + getMemRefType().getRank())
+    return emitOpError("store index operand count not equal to memref rank");
 
   return success();
 }
@@ -1951,29 +1945,29 @@
 }
 
 /// Verifier for SubViewOp.
-static LogicalResult verify(SubViewOp op) {
-  MemRefType baseType = op.getSourceType();
-  MemRefType subViewType = op.getType();
+LogicalResult SubViewOp::verify() {
+  MemRefType baseType = getSourceType();
+  MemRefType subViewType = getType();
 
   // The base memref and the view memref should be in the same memory space.
   if (baseType.getMemorySpace() != subViewType.getMemorySpace())
-    return op.emitError("different memory spaces specified for base memref "
-                        "type ")
+    return emitError("different memory spaces specified for base memref "
+                     "type ")
            << baseType << " and subview memref type " << subViewType;
 
   // Verify that the base memref type has a strided layout map.
   if (!isStrided(baseType))
-    return op.emitError("base type ") << baseType << " is not strided";
+    return emitError("base type ") << baseType << " is not strided";
 
   // Verify result type against inferred type.
   auto expectedType = SubViewOp::inferResultType(
-      baseType, extractFromI64ArrayAttr(op.static_offsets()),
-      extractFromI64ArrayAttr(op.static_sizes()),
-      extractFromI64ArrayAttr(op.static_strides()));
+      baseType, extractFromI64ArrayAttr(static_offsets()),
+      extractFromI64ArrayAttr(static_sizes()),
+      extractFromI64ArrayAttr(static_strides()));
 
   auto result = isRankReducedMemRefType(expectedType.cast<MemRefType>(),
-                                        subViewType, op.getMixedSizes());
-  return produceSubViewErrorMsg(result, op, expectedType);
+                                        subViewType, getMixedSizes());
+  return produceSubViewErrorMsg(result, *this, expectedType);
 }
 
 raw_ostream &mlir::operator<<(raw_ostream &os, const Range &range) {
@@ -2278,18 +2272,17 @@
   return success();
 }
 
-static LogicalResult verify(TransposeOp op) {
-  if (!op.permutation().isPermutation())
-    return op.emitOpError("expected a permutation map");
-  if (op.permutation().getNumDims() != op.getShapedType().getRank())
-    return op.emitOpError(
-        "expected a permutation map of same rank as the input");
+LogicalResult TransposeOp::verify() {
+  if (!permutation().isPermutation())
+    return emitOpError("expected a permutation map");
+  if (permutation().getNumDims() != getShapedType().getRank())
+    return emitOpError("expected a permutation map of same rank as the input");
 
-  auto srcType = op.in().getType().cast<MemRefType>();
-  auto dstType = op.getType().cast<MemRefType>();
-  auto transposedType = inferTransposeResultType(srcType, op.permutation());
+  auto srcType = in().getType().cast<MemRefType>();
+  auto dstType = getType().cast<MemRefType>();
+  auto transposedType = inferTransposeResultType(srcType, permutation());
   if (dstType != transposedType)
-    return op.emitOpError("output type ")
+    return emitOpError("output type ")
            << dstType << " does not match transposed input type " << srcType
            << ", " << transposedType;
   return success();
@@ -2338,29 +2331,28 @@
   p << " : " << op.getOperand(0).getType() << " to " << op.getType();
 }
 
-static LogicalResult verify(ViewOp op) {
-  auto baseType = op.getOperand(0).getType().cast<MemRefType>();
-  auto viewType = op.getType();
+LogicalResult ViewOp::verify() {
+  auto baseType = getOperand(0).getType().cast<MemRefType>();
+  auto viewType = getType();
 
   // The base memref should have identity layout map (or none).
   if (!baseType.getLayout().isIdentity())
-    return op.emitError("unsupported map for base memref type ") << baseType;
+    return emitError("unsupported map for base memref type ") << baseType;
 
   // The result memref should have identity layout map (or none).
   if (!viewType.getLayout().isIdentity())
-    return op.emitError("unsupported map for result memref type ") << viewType;
+    return emitError("unsupported map for result memref type ") << viewType;
 
   // The base memref and the view memref should be in the same memory space.
   if (baseType.getMemorySpace() != viewType.getMemorySpace())
-    return op.emitError("different memory spaces specified for base memref "
-                        "type ")
+    return emitError("different memory spaces specified for base memref "
+                     "type ")
            << baseType << " and view memref type " << viewType;
 
   // Verify that we have the correct number of sizes for the result type.
   unsigned numDynamicDims = viewType.getNumDynamicDims();
-  if (op.sizes().size() != numDynamicDims)
-    return op.emitError("incorrect number of size operands for type ")
-           << viewType;
+  if (sizes().size() != numDynamicDims)
+    return emitError("incorrect number of size operands for type ") << viewType;
 
   return success();
 }
@@ -2467,19 +2459,19 @@
 // AtomicRMWOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(AtomicRMWOp op) {
-  if (op.getMemRefType().getRank() != op.getNumOperands() - 2)
-    return op.emitOpError(
+LogicalResult AtomicRMWOp::verify() {
+  if (getMemRefType().getRank() != getNumOperands() - 2)
+    return emitOpError(
         "expects the number of subscripts to be equal to memref rank");
-  switch (op.kind()) {
+  switch (kind()) {
   case arith::AtomicRMWKind::addf:
   case arith::AtomicRMWKind::maxf:
   case arith::AtomicRMWKind::minf:
   case arith::AtomicRMWKind::mulf:
-    if (!op.value().getType().isa<FloatType>())
-      return op.emitOpError()
-             << "with kind '" << arith::stringifyAtomicRMWKind(op.kind())
-             << "' expects a floating-point type";
+    if (!value().getType().isa<FloatType>())
+      return emitOpError() << "with kind '"
+                           << arith::stringifyAtomicRMWKind(kind())
+                           << "' expects a floating-point type";
     break;
   case arith::AtomicRMWKind::addi:
   case arith::AtomicRMWKind::maxs:
@@ -2489,10 +2481,10 @@
   case arith::AtomicRMWKind::muli:
   case arith::AtomicRMWKind::ori:
   case arith::AtomicRMWKind::andi:
-    if (!op.value().getType().isa<IntegerType>())
-      return op.emitOpError()
-             << "with kind '" << arith::stringifyAtomicRMWKind(op.kind())
-             << "' expects an integer type";
+    if (!value().getType().isa<IntegerType>())
+      return emitOpError() << "with kind '"
+                           << arith::stringifyAtomicRMWKind(kind())
+                           << "' expects an integer type";
     break;
   default:
     break;
diff --git a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
--- a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
+++ b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
@@ -209,53 +209,51 @@
   return failure();
 }
 
-static LogicalResult verify(NewOp op) {
-  if (!getSparseTensorEncoding(op.result().getType()))
-    return op.emitError("expected a sparse tensor result");
+LogicalResult NewOp::verify() {
+  if (!getSparseTensorEncoding(result().getType()))
+    return emitError("expected a sparse tensor result");
   return success();
 }
 
-static LogicalResult verify(InitOp op) {
-  if (!getSparseTensorEncoding(op.result().getType()))
-    return op.emitError("expected a sparse tensor result");
-  RankedTensorType ttp = op.getType().cast<RankedTensorType>();
+LogicalResult InitOp::verify() {
+  if (!getSparseTensorEncoding(result().getType()))
+    return emitError("expected a sparse tensor result");
+  RankedTensorType ttp = getType().cast<RankedTensorType>();
   unsigned rank = ttp.getRank();
-  if (rank != op.sizes().size())
-    return op.emitError("unexpected mismatch between tensor rank and sizes: ")
-           << rank << " vs. " << op.sizes().size();
+  if (rank != sizes().size())
+    return emitError("unexpected mismatch between tensor rank and sizes: ")
+           << rank << " vs. " << sizes().size();
   auto shape = ttp.getShape();
   for (unsigned i = 0; i < rank; i++) {
     if (shape[i] == ShapedType::kDynamicSize)
       continue;
     IntegerAttr constantAttr;
-    if (!matchPattern(op.sizes()[i], m_Constant(&constantAttr)) ||
+    if (!matchPattern(sizes()[i], m_Constant(&constantAttr)) ||
         constantAttr.getInt() != shape[i]) {
-      return op.emitError("unexpected mismatch with static dimension size ")
+      return emitError("unexpected mismatch with static dimension size ")
              << shape[i];
     }
   }
   return success();
 }
 
-static LogicalResult verify(ConvertOp op) {
-  if (auto tp1 = op.source().getType().dyn_cast<RankedTensorType>()) {
-    if (auto tp2 = op.dest().getType().dyn_cast<RankedTensorType>()) {
+LogicalResult ConvertOp::verify() {
+  if (auto tp1 = source().getType().dyn_cast<RankedTensorType>()) {
+    if (auto tp2 = dest().getType().dyn_cast<RankedTensorType>()) {
       if (tp1.getRank() != tp2.getRank())
-        return op.emitError("unexpected conversion mismatch in rank");
+        return emitError("unexpected conversion mismatch in rank");
       auto shape1 = tp1.getShape();
       auto shape2 = tp2.getShape();
       // Accept size matches between the source and the destination type
       // (e.g. 10 vs. 10, 10 vs. ?, or ? vs. ?), but reject direct mismatches or
       // matches that would need a runtime assert (e.g. 10 vs. 20 or ? vs. 10).
-      for (unsigned d = 0, rank = tp1.getRank(); d < rank; d++) {
+      for (unsigned d = 0, rank = tp1.getRank(); d < rank; d++)
         if (shape1[d] != shape2[d] && shape2[d] != ShapedType::kDynamicSize)
-          return op.emitError("unexpected conversion mismatch in dimension ")
-                 << d;
-      }
+          return emitError("unexpected conversion mismatch in dimension ") << d;
       return success();
     }
   }
-  return op.emitError("unexpected type in convert");
+  return emitError("unexpected type in convert");
 }
 
 OpFoldResult ConvertOp::fold(ArrayRef<Attribute> operands) {
@@ -264,35 +262,35 @@
   return {};
 }
 
-static LogicalResult verify(ToPointersOp op) {
-  if (auto e = getSparseTensorEncoding(op.tensor().getType())) {
-    if (failed(isInBounds(op.dim(), op.tensor())))
-      return op.emitError("requested pointers dimension out of bounds");
-    if (failed(isMatchingWidth(op.result(), e.getPointerBitWidth())))
-      return op.emitError("unexpected type for pointers");
+LogicalResult ToPointersOp::verify() {
+  if (auto e = getSparseTensorEncoding(tensor().getType())) {
+    if (failed(isInBounds(dim(), tensor())))
+      return emitError("requested pointers dimension out of bounds");
+    if (failed(isMatchingWidth(result(), e.getPointerBitWidth())))
+      return emitError("unexpected type for pointers");
     return success();
   }
-  return op.emitError("expected a sparse tensor to get pointers");
+  return emitError("expected a sparse tensor to get pointers");
 }
 
-static LogicalResult verify(ToIndicesOp op) {
-  if (auto e = getSparseTensorEncoding(op.tensor().getType())) {
-    if (failed(isInBounds(op.dim(), op.tensor())))
-      return op.emitError("requested indices dimension out of bounds");
-    if (failed(isMatchingWidth(op.result(), e.getIndexBitWidth())))
-      return op.emitError("unexpected type for indices");
+LogicalResult ToIndicesOp::verify() {
+  if (auto e = getSparseTensorEncoding(tensor().getType())) {
+    if (failed(isInBounds(dim(), tensor())))
+      return emitError("requested indices dimension out of bounds");
+    if (failed(isMatchingWidth(result(), e.getIndexBitWidth())))
+      return emitError("unexpected type for indices");
     return success();
   }
-  return op.emitError("expected a sparse tensor to get indices");
+  return emitError("expected a sparse tensor to get indices");
 }
 
-static LogicalResult verify(ToValuesOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor to get values");
-  RankedTensorType ttp = op.tensor().getType().cast<RankedTensorType>();
-  MemRefType mtp = op.result().getType().cast<MemRefType>();
+LogicalResult ToValuesOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor to get values");
+  RankedTensorType ttp = tensor().getType().cast<RankedTensorType>();
+  MemRefType mtp = result().getType().cast<MemRefType>();
   if (ttp.getElementType() != mtp.getElementType())
-    return op.emitError("unexpected mismatch in element types");
+    return emitError("unexpected mismatch in element types");
   return success();
 }
 
@@ -300,39 +298,39 @@
 // TensorDialect Management Operations.
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(LexInsertOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor for insertion");
+LogicalResult LexInsertOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor for insertion");
   return success();
 }
 
-static LogicalResult verify(ExpandOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor for expansion");
+LogicalResult ExpandOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor for expansion");
   return success();
 }
 
-static LogicalResult verify(CompressOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor for compression");
+LogicalResult CompressOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor for compression");
   return success();
 }
 
-static LogicalResult verify(LoadOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor to materialize");
+LogicalResult LoadOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor to materialize");
   return success();
 }
 
-static LogicalResult verify(ReleaseOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor to release");
+LogicalResult ReleaseOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor to release");
   return success();
 }
 
-static LogicalResult verify(OutOp op) {
-  if (!getSparseTensorEncoding(op.tensor().getType()))
-    return op.emitError("expected a sparse tensor for output");
+LogicalResult OutOp::verify() {
+  if (!getSparseTensorEncoding(tensor().getType()))
+    return emitError("expected a sparse tensor for output");
   return success();
 }
 
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -228,17 +228,17 @@
   return {};
 }
 
-static LogicalResult verify(DimOp op) {
+LogicalResult DimOp::verify() {
   // Assume unknown index to be in range.
-  Optional<int64_t> index = op.getConstantIndex();
+  Optional<int64_t> index = getConstantIndex();
   if (!index.hasValue())
     return success();
 
   // Check that constant index is not knowingly out of range.
-  auto type = op.source().getType();
+  auto type = source().getType();
   if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
     if (index.getValue() >= tensorType.getRank())
-      return op.emitOpError("index is out of range");
+      return emitOpError("index is out of range");
   } else if (type.isa<UnrankedTensorType>()) {
     // Assume index to be in range.
   } else {
@@ -328,11 +328,11 @@
 // ExtractOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(ExtractOp op) {
+LogicalResult ExtractOp::verify() {
   // Verify the # indices match if we have a ranked type.
-  if (auto tensorType = op.tensor().getType().dyn_cast<RankedTensorType>())
-    if (tensorType.getRank() != static_cast<int64_t>(op.indices().size()))
-      return op.emitOpError("incorrect number of indices for extract_element");
+  if (auto tensorType = tensor().getType().dyn_cast<RankedTensorType>())
+    if (tensorType.getRank() != static_cast<int64_t>(indices().size()))
+      return emitOpError("incorrect number of indices for extract_element");
 
   return success();
 }
@@ -480,11 +480,11 @@
 // InsertOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(InsertOp op) {
+LogicalResult InsertOp::verify() {
   // Verify the # indices match if we have a ranked type.
-  if (auto destType = op.dest().getType().dyn_cast<RankedTensorType>())
-    if (destType.getRank() != static_cast<int64_t>(op.indices().size()))
-      return op.emitOpError("incorrect number of indices");
+  if (auto destType = dest().getType().dyn_cast<RankedTensorType>())
+    if (destType.getRank() != static_cast<int64_t>(indices().size()))
+      return emitOpError("incorrect number of indices");
   return success();
 }
 
@@ -502,27 +502,26 @@
 // GenerateOp
 //===----------------------------------------------------------------------===//
 
-static LogicalResult verify(GenerateOp op) {
+LogicalResult GenerateOp::verify() {
   // Ensure that the tensor type has as many dynamic dimensions as are specified
   // by the operands.
-  RankedTensorType resultTy = op.getType().cast<RankedTensorType>();
-  if (op.getNumOperands() != resultTy.getNumDynamicDims())
-    return op.emitError("must have as many index operands as dynamic extents "
-                        "in the result type");
+  RankedTensorType resultTy = getType().cast<RankedTensorType>();
+  if (getNumOperands() != resultTy.getNumDynamicDims())
+    return emitError("must have as many index operands as dynamic extents "
+                     "in the result type");
 
   // Ensure that region arguments span the index space.
-  if (!llvm::all_of(op.body().getArgumentTypes(),
+  if (!llvm::all_of(body().getArgumentTypes(),
                     [](Type ty) { return ty.isIndex(); }))
-    return op.emitError("all body arguments must be index");
-  if (op.body().getNumArguments() != resultTy.getRank())
-    return op.emitError("must have one body argument per input dimension");
+    return emitError("all body arguments must be index");
+  if (body().getNumArguments() != resultTy.getRank())
+    return emitError("must have one body argument per input dimension");
 
   // Ensure that the region yields an element of the right type.
-  auto yieldOp =
-      llvm::cast<YieldOp>(op.body().getBlocks().front().getTerminator());
+  auto yieldOp = cast<YieldOp>(body().getBlocks().front().getTerminator());
 
   if (yieldOp.value().getType() != resultTy.getElementType())
-    return op.emitOpError(
+    return emitOpError(
         "body must be terminated with a `yield` operation of the tensor "
         "element type");
 
@@ -686,16 +685,15 @@
   return numElements;
 }
 
-static LogicalResult verify(ReshapeOp op) {
-  TensorType operandType = op.source().getType().cast<TensorType>();
-  TensorType resultType = op.result().getType().cast<TensorType>();
+LogicalResult ReshapeOp::verify() {
+  TensorType operandType = source().getType().cast<TensorType>();
+  TensorType resultType = result().getType().cast<TensorType>();
 
   if (operandType.getElementType() != resultType.getElementType())
-    return op.emitOpError("element types of source and destination tensor "
-                          "types should be the same");
+    return emitOpError("element types of source and destination tensor "
+                       "types should be the same");
 
-  int64_t shapeSize =
-      op.shape().getType().cast<RankedTensorType>().getDimSize(0);
+  int64_t shapeSize = shape().getType().cast<RankedTensorType>().getDimSize(0);
   auto resultRankedType = resultType.dyn_cast<RankedTensorType>();
   auto operandRankedType = operandType.dyn_cast<RankedTensorType>();
 
@@ -703,14 +701,14 @@
     if (operandRankedType && resultRankedType.hasStaticShape() &&
         operandRankedType.hasStaticShape()) {
       if (getNumElements(operandRankedType) != getNumElements(resultRankedType))
-        return op.emitOpError("source and destination tensor should have the "
-                              "same number of elements");
+        return emitOpError("source and destination tensor should have the "
+                           "same number of elements");
     }
     if (ShapedType::isDynamic(shapeSize))
-      return op.emitOpError("cannot use shape operand with dynamic length to "
-                            "reshape to statically-ranked tensor type");
+      return emitOpError("cannot use shape operand with dynamic length to "
+                         "reshape to statically-ranked tensor type");
     if (shapeSize != resultRankedType.getRank())
-      return op.emitOpError(
+      return emitOpError(
           "length of shape operand differs from the result's tensor rank");
   }
   return success();
@@ -814,12 +812,12 @@
   return success();
 }
 
-static LogicalResult verify(ExpandShapeOp op) {
-  return verifyTensorReshapeOp(op, op.getResultType(), op.getSrcType());
+LogicalResult ExpandShapeOp::verify() {
+  return verifyTensorReshapeOp(*this, getResultType(), getSrcType());
 }
 
-static LogicalResult verify(CollapseShapeOp op) {
-  return verifyTensorReshapeOp(op, op.getSrcType(), op.getResultType());
+LogicalResult CollapseShapeOp::verify() {
+  return verifyTensorReshapeOp(*this, getSrcType(), getResultType());
 }
 
 namespace {
@@ -1052,14 +1050,12 @@
 }
 
 /// Verifier for ExtractSliceOp.
-static LogicalResult verify(ExtractSliceOp op) {
+LogicalResult ExtractSliceOp::verify() {
   // Verify result type against inferred type.
-  auto expectedType =
-      ExtractSliceOp::inferResultType(op.getSourceType(), op.getMixedOffsets(),
-                                      op.getMixedSizes(), op.getMixedStrides());
-  auto result =
-      isRankReducedType(expectedType.cast<ShapedType>(), op.getType());
-  return produceSliceErrorMsg(result, op, expectedType);
+  auto expectedType = ExtractSliceOp::inferResultType(
+      getSourceType(), getMixedOffsets(), getMixedSizes(), getMixedStrides());
+  auto result = isRankReducedType(expectedType.cast<ShapedType>(), getType());
+  return produceSliceErrorMsg(result, *this, expectedType);
 }
 
 /// Infer the canonical type of the result of an extract_slice op. Returns a
@@ -1308,16 +1304,16 @@
 }
 
 /// Verifier for InsertSliceOp.
-static LogicalResult verify(InsertSliceOp op) {
+LogicalResult InsertSliceOp::verify() {
   // insert_slice is the inverse of extract_slice, use the same type inference.
   auto expectedType = ExtractSliceOp::inferRankReducedResultType(
-      op.getSourceType().getRank(), op.getType(),
-      extractFromI64ArrayAttr(op.static_offsets()),
-      extractFromI64ArrayAttr(op.static_sizes()),
-      extractFromI64ArrayAttr(op.static_strides()));
+      getSourceType().getRank(), getType(),
+      extractFromI64ArrayAttr(static_offsets()),
+      extractFromI64ArrayAttr(static_sizes()),
+      extractFromI64ArrayAttr(static_strides()));
   auto result =
-      isRankReducedType(expectedType.cast<ShapedType>(), op.getSourceType());
-  return produceSliceErrorMsg(result, op, expectedType);
+      isRankReducedType(expectedType.cast<ShapedType>(), getSourceType());
+  return produceSliceErrorMsg(result, *this, expectedType);
 }
 
 /// If we have two consecutive InsertSliceOp writing to the same slice, we
@@ -1569,40 +1565,40 @@
   return success();
 }
 
-static LogicalResult verify(PadOp op) {
-  auto sourceType = op.source().getType().cast<RankedTensorType>();
-  auto resultType = op.result().getType().cast<RankedTensorType>();
-  auto expectedType = PadOp::inferResultType(
-      sourceType, extractFromI64ArrayAttr(op.static_low()),
-      extractFromI64ArrayAttr(op.static_high()));
+LogicalResult PadOp::verify() {
+  auto sourceType = source().getType().cast<RankedTensorType>();
+  auto resultType = result().getType().cast<RankedTensorType>();
+  auto expectedType =
+      PadOp::inferResultType(sourceType, extractFromI64ArrayAttr(static_low()),
+                             extractFromI64ArrayAttr(static_high()));
   for (int i = 0, e = sourceType.getRank(); i < e; ++i) {
     if (resultType.getDimSize(i) == expectedType.getDimSize(i))
       continue;
     if (expectedType.isDynamicDim(i))
       continue;
-    return op.emitError("specified type ")
+    return emitError("specified type ")
            << resultType << " does not match the inferred type "
            << expectedType;
   }
 
-  auto &region = op.region();
+  auto &region = getRegion();
   unsigned rank = resultType.getRank();
   Block &block = region.front();
   if (block.getNumArguments() != rank)
-    return op.emitError("expected the block to have ") << rank << " arguments";
+    return emitError("expected the block to have ") << rank << " arguments";
 
   // Note: the number and type of yield values are checked in the YieldOp.
   for (const auto &en : llvm::enumerate(block.getArgumentTypes())) {
     if (!en.value().isIndex())
-      return op.emitOpError("expected block argument ")
+      return emitOpError("expected block argument ")
              << (en.index() + 1) << " to be an index";
   }
 
   // Ensure that the region yields an element of the right type.
   auto yieldOp = llvm::cast<YieldOp>(block.getTerminator());
   if (yieldOp.value().getType() !=
-      op.getType().cast<ShapedType>().getElementType())
-    return op.emitOpError("expected yield type to match shape element type");
+      getType().cast<ShapedType>().getElementType())
+    return emitOpError("expected yield type to match shape element type");
 
   return success();
 }