diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -1945,14 +1945,14 @@
                   scalar_arg: K
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: conv_2d_ngchw_fgchw
-  cpp_class_name: Conv2DNgchwFgchwOp
+  name: conv_2d_ngchw_gfchw
+  cpp_class_name: Conv2DNgchwGfchwOp
   doc: |-
     Performs 2-D grouped convolution.
 
     Layout:
       * Input: NGCHW.
-      * Kernel: FGCHW.
+      * Kernel: GFCHW.
 
     Numeric casting is performed on the operands to the inner multiply, promoting
     them to the same data type as the accumulator/output.
@@ -1971,13 +1971,13 @@
     kind: input_tensor
     type_var: T2
     shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
-      (s11, s1, s2, s5, s9)>
+      (s1, s11, s2, s5, s9)>
   - !LinalgOperandDefConfig
     name: O
     kind: output_tensor
     type_var: U
     shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] ->
-      (s0, s11, s1, s3, s7)>
+      (s0, s1, s11, s3, s7)>
   - !LinalgOperandDefConfig
     name: strides
     kind: index_attr
diff --git a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -406,18 +406,18 @@
 
 
 @linalg_structured_op
-def conv_2d_ngchw_fgchw(I=TensorDef(T1, S.N, S.G, S.C,
+def conv_2d_ngchw_gfchw(I=TensorDef(T1, S.N, S.G, S.C,
                                     S.OH * S.SH + S.KH * S.DH,
                                     S.OW * S.SW + S.KW * S.DW),
-                        K=TensorDef(T2, S.FG, S.G, S.C, S.KH, S.KW),
-                        O=TensorDef(U, S.N, S.FG, S.G, S.OH, S.OW, output=True),
+                        K=TensorDef(T2, S.G, S.FG, S.C, S.KH, S.KW),
+                        O=TensorDef(U, S.N, S.G, S.FG, S.OH, S.OW, output=True),
                         strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
                         dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1])):
   """Performs 2-D grouped convolution.
 
   Layout:
     * Input: NGCHW.
-    * Kernel: FGCHW.
+    * Kernel: GFCHW.
 
   Numeric casting is performed on the operands to the inner multiply, promoting
   them to the same data type as the accumulator/output.
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -301,14 +301,14 @@
 
 // -----
 
-// CHECK-LABEL: func @conv_2d_ngchw_fgchw
-func.func @conv_2d_ngchw_fgchw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
-  // CHECK:      %{{.+}} = linalg.conv_2d_ngchw_fgchw
+// CHECK-LABEL: func @conv_2d_ngchw_gfchw
+func.func @conv_2d_ngchw_gfchw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+  // CHECK:      %{{.+}} = linalg.conv_2d_ngchw_gfchw
   // CHECK-SAME:   dilations = dense<1> : tensor<2xi64>
   // CHECK-SAME:   strides = dense<1> : tensor<2xi64>
   // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
   // CHECK-SAME:   outs(%{{.+}} : tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
-  %0 = linalg.conv_2d_ngchw_fgchw {dilations = dense<1> : tensor<2xi64>,
+  %0 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : tensor<2xi64>,
                                               strides = dense<1> : tensor<2xi64>}
      ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
     outs (%init: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
@@ -317,6 +317,22 @@
 
 // -----
 
+// CHECK-LABEL: func @conv_2d_ngchw_gfchw_static
+func.func @conv_2d_ngchw_gfchw_static(%input: tensor<1x2x32x128x256xf32>, %filter: tensor<2x64x32x3x5xf32>, %init: tensor<1x2x64x126x252xf32>) -> tensor<1x2x64x126x252xf32> {
+  // CHECK:      %{{.+}} = linalg.conv_2d_ngchw_gfchw
+  // CHECK-SAME:   dilations = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   strides = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : tensor<1x2x32x128x256xf32>, tensor<2x64x32x3x5xf32>)
+  // CHECK-SAME:   outs(%{{.+}} : tensor<1x2x64x126x252xf32>) -> tensor<1x2x64x126x252xf32>
+  %0 = linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : tensor<2xi64>,
+                                              strides = dense<1> : tensor<2xi64>}
+     ins (%input, %filter: tensor<1x2x32x128x256xf32>, tensor<2x64x32x3x5xf32>)
+    outs (%init: tensor<1x2x64x126x252xf32>) -> tensor<1x2x64x126x252xf32>
+  return %0 : tensor<1x2x64x126x252xf32>
+}
+
+// -----
+
 // CHECK-LABEL: func @conv_2d_nhwc_fhwc
 func.func @conv_2d_nhwc_fhwc(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
   // CHECK:      %{{.+}} = linalg.conv_2d_nhwc_fhwc
@@ -365,14 +381,14 @@
 
 // -----
 
-// CHECK-LABEL: func @conv_2d_ngchw_fgchw
-func.func @conv_2d_ngchw_fgchw(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
-  // CHECK:      linalg.conv_2d_ngchw_fgchw
+// CHECK-LABEL: func @conv_2d_ngchw_gfchw
+func.func @conv_2d_ngchw_gfchw(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
+  // CHECK:      linalg.conv_2d_ngchw_gfchw
   // CHECK-SAME:   dilations = dense<1> : tensor<2xi64>
   // CHECK-SAME:   strides = dense<1> : tensor<2xi64>
   // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
   // CHECK-SAME:   outs(%{{.+}} : memref<?x?x?x?x?xf32>)
-  linalg.conv_2d_ngchw_fgchw {dilations = dense<1> : tensor<2xi64>,
+  linalg.conv_2d_ngchw_gfchw {dilations = dense<1> : tensor<2xi64>,
                                          strides = dense<1> : tensor<2xi64>}
      ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
     outs (%output: memref<?x?x?x?x?xf32>)