diff --git a/mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp b/mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/NamedOpConversions.cpp
@@ -18,6 +18,7 @@
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "llvm/ADT/SmallVector.h"
+#include "llvm/ADT/StringSet.h"
 
 namespace mlir {
 #define GEN_PASS_DEF_LINALGNAMEDOPCONVERSION
@@ -72,28 +73,34 @@
   auto collapsedInit = rewriter.create<tensor::CollapseShapeOp>(
       loc, newInitTy, init, collapsedInitDims);
 
-  Value newConv;
-  if (isa<DepthwiseConv2DNhwcHwcmOp>(operation)) {
-    newConv = rewriter
-                  .create<DepthwiseConv2DNhwcHwcOp>(
-                      loc, newInitTy, ValueRange{input, collapsedKernel},
-                      ValueRange{collapsedInit}, stride, dilation)
-                  .getResult(0);
-  } else if (isa<DepthwiseConv2DNhwcHwcmQOp>(operation)) {
-    newConv =
-        rewriter
-            .create<DepthwiseConv2DNhwcHwcQOp>(
-                loc, newInitTy, ValueRange{input, collapsedKernel, iZp, kZp},
-                ValueRange{collapsedInit}, stride, dilation)
-            .getResult(0);
+  Operation *newConv;
+  llvm::StringSet<> elidedAttrs;
+  if (auto op = dyn_cast<DepthwiseConv2DNhwcHwcmOp>(operation)) {
+    newConv = rewriter.create<DepthwiseConv2DNhwcHwcOp>(
+        loc, newInitTy, ValueRange{input, collapsedKernel},
+        ValueRange{collapsedInit}, stride, dilation);
+    elidedAttrs.insert(op.getAttributeNames().begin(),
+                       op.getAttributeNames().end());
+  } else if (auto op = dyn_cast<DepthwiseConv2DNhwcHwcmQOp>(operation)) {
+    newConv = rewriter.create<DepthwiseConv2DNhwcHwcQOp>(
+        loc, newInitTy, ValueRange{input, collapsedKernel, iZp, kZp},
+        ValueRange{collapsedInit}, stride, dilation);
+    elidedAttrs.insert(op.getAttributeNames().begin(),
+                       op.getAttributeNames().end());
+  } else {
+    return failure();
   }
 
-  if (!newConv)
-    return failure();
+  elidedAttrs.insert(LinalgDialect::kMemoizedIndexingMapsAttrName);
+  for (auto attr : operation->getAttrs()) {
+    if (elidedAttrs.count(attr.getName()))
+      continue;
+    newConv->setAttr(attr.getName(), attr.getValue());
+  }
 
   // Expand dimensions back out to
   rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
-      operation, resultTy, newConv, collapsedInitDims);
+      operation, resultTy, newConv->getResult(0), collapsedInitDims);
   return success();
 }
 
diff --git a/mlir/test/Dialect/Linalg/namedop_conversion.mlir b/mlir/test/Dialect/Linalg/namedop_conversion.mlir
--- a/mlir/test/Dialect/Linalg/namedop_conversion.mlir
+++ b/mlir/test/Dialect/Linalg/namedop_conversion.mlir
@@ -4,9 +4,9 @@
 func.func @depthwise_conv(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x1xf32>, %arg2: tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32> {
   // CHECK-DAG: %[[KERNEL:.+]] = tensor.collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = tensor.collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]] : tensor<?x?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?x?xf32>)
+  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]] : tensor<?x?x?x?xf32>, tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?x?xf32>)
   // CHECK: %[[OUT:.+]] = tensor.expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x1xf32>) outs(%arg2 : tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32>
+  %0 = linalg.depthwise_conv_2d_nhwc_hwcm {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x1xf32>) outs(%arg2 : tensor<?x?x?x?x1xf32>) -> tensor<?x?x?x?x1xf32>
   return %0 : tensor<?x?x?x?x1xf32>
 }
 
@@ -17,8 +17,8 @@
 func.func @depthwise_conv_q(%arg0: tensor<?x?x?x?xi8>, %arg1: tensor<?x?x?x1xi8>, %arg2: tensor<?x?x?x?x1xi32>, %arg3 : i32, %arg4 : i32) -> tensor<?x?x?x?x1xi32> {
   // CHECK-DAG: %[[KERNEL:.+]] = tensor.collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = tensor.collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc_q {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]], %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?xi8>, i32, i32) outs(%[[INIT]] : tensor<?x?x?x?xi32>)
+  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwc_q {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %[[KERNEL]], %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?xi8>, i32, i32) outs(%[[INIT]] : tensor<?x?x?x?xi32>)
   // CHECK: %[[OUT:.+]] = tensor.expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1, %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?x1xi8>, i32, i32) outs(%arg2 : tensor<?x?x?x?x1xi32>) -> tensor<?x?x?x?x1xi32>
+  %0 = linalg.depthwise_conv_2d_nhwc_hwcm_q {_someattr, dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1, %arg3, %arg4 : tensor<?x?x?x?xi8>, tensor<?x?x?x1xi8>, i32, i32) outs(%arg2 : tensor<?x?x?x?x1xi32>) -> tensor<?x?x?x?x1xi32>
   return %0 : tensor<?x?x?x?x1xi32>
 }