[mlir][Linalg] Make depthwise convolution naming scheme consistent.

Names should be consistent across all operations otherwise painful bugs will surface.

Reviewed By: rsuderman

Differential Revision: https://reviews.llvm.org/D113762

Author: nicolasvasilache

mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml

@@ -69,7 +69,7 @@ metadata: !LinalgOpMetadata
   name: matmul_unsigned
   cpp_class_name: MatmulUnsignedOp
   doc: |-
-    Performs a unsigned matrix multiplication of two 2D inputs.
+    Performs an unsigned matrix multiplication of two 2D inputs.
 
     Numeric casting is performed on the operands to the inner multiply, promoting
     them to the same data type as the accumulator/output.
@@ -1384,14 +1384,14 @@ structured_op: !LinalgStructuredOpConfig
                 is_unsigned_cast: false
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: depthwise_conv1D_nw
-  cpp_class_name: DepthwiseConv1DNwOp
+  name: depthwise_conv_1d_nwc_wc
+  cpp_class_name: DepthwiseConv1DNwcWcOp
   doc: |-
     Performs depth-wise 1-D convolution.
 
     Numeric casting is performed on the operands to the inner multiply, promoting
     them to the same data type as the accumulator/output. Multiplier is set to 1
-    which is a special case for most dpethwise convolutions.
+    which is a special case for most depthwise convolutions.
   implements:
   - LinalgConvolutionOpInterface
 structured_op: !LinalgStructuredOpConfig
@@ -1461,14 +1461,14 @@ structured_op: !LinalgStructuredOpConfig
                 is_unsigned_cast: false
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: depthwise_conv2D_nhw
-  cpp_class_name: DepthwiseConv2DNhwOp
+  name: depthwise_conv_2d_nhwc_hwc
+  cpp_class_name: DepthwiseConv2DNhwcHwcOp
   doc: |-
     Performs depth-wise 2-D convolution.
 
     Numeric casting is performed on the operands to the inner multiply, promoting
     them to the same data type as the accumulator/output. Multiplier is set to 1
-    which is a special case for most dpethwise convolutions.
+    which is a special case for most depthwise convolutions.
   implements:
   - LinalgConvolutionOpInterface
 structured_op: !LinalgStructuredOpConfig
@@ -1544,8 +1544,8 @@ structured_op: !LinalgStructuredOpConfig
                 is_unsigned_cast: false
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: depthwise_conv2D_nhw_q
-  cpp_class_name: DepthwiseConv2DNhwQOp
+  name: depthwise_conv_2d_nhwc_hwc_q
+  cpp_class_name: DepthwiseConv2DNhwcHwcQOp
   doc: |-
     Performs depth-wise 2-D convolution.
 
@@ -1660,8 +1660,8 @@ structured_op: !LinalgStructuredOpConfig
                     is_unsigned_cast: false
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: depthwise_conv2D_nhwc
-  cpp_class_name: DepthwiseConv2DNhwcOp
+  name: depthwise_conv_2d_nhwc_hwcm
+  cpp_class_name: DepthwiseConv2DNhwcHwcmOp
   doc: |-
     Performs depth-wise 2-D convolution.
 
@@ -1746,8 +1746,8 @@ structured_op: !LinalgStructuredOpConfig
                 is_unsigned_cast: false
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
-  name: depthwise_conv2D_nhwc_q
-  cpp_class_name: DepthwiseConv2DNhwcQOp
+  name: depthwise_conv_2d_nhwc_hwcm_q
+  cpp_class_name: DepthwiseConv2DNhwcHwcmQOp
   doc: |-
     Performs depth-wise 2-D convolution.
 

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

@@ -1230,7 +1230,7 @@ class DepthwiseConvConverter
         loc, resultTy.getShape(), resultETy);
     if (!isQuantized) {
       Value conv = rewriter
-                       .create<linalg::DepthwiseConv2DNhwcOp>(
+                       .create<linalg::DepthwiseConv2DNhwcHwcmOp>(
                            loc, linalgConvTy, ValueRange{input, weight},
                            ValueRange{zeroTensor}, strideAttr, dilationAttr)
                        .getResult(0);
@@ -1254,7 +1254,7 @@ class DepthwiseConvConverter
       auto kZpVal = rewriter.create<arith::ConstantOp>(loc, kZp);
       Value conv =
           rewriter
-              .create<linalg::DepthwiseConv2DNhwcQOp>(
+              .create<linalg::DepthwiseConv2DNhwcHwcmQOp>(
                   loc, linalgConvTy, ValueRange{input, weight, iZpVal, kZpVal},
                   ValueRange{zeroTensor}, strideAttr, dilationAttr)
               .getResult(0);

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

@@ -3037,16 +3037,16 @@ LogicalResult matchAndReplaceDepthwiseConv(Operation *operation, Value input,
       loc, newInitTy, init, collapsedInitDims);
 
   Value newConv;
-  if (isa<DepthwiseConv2DNhwcOp>(operation)) {
+  if (isa<DepthwiseConv2DNhwcHwcmOp>(operation)) {
     newConv = rewriter
-                  .create<DepthwiseConv2DNhwOp>(
+                  .create<DepthwiseConv2DNhwcHwcOp>(
                       loc, newInitTy, ValueRange{input, collapsedKernel},
                       ValueRange{collapsedInit}, stride, dilation)
                   .getResult(0);
-  } else if (isa<DepthwiseConv2DNhwcQOp>(operation)) {
+  } else if (isa<DepthwiseConv2DNhwcHwcmQOp>(operation)) {
     newConv =
         rewriter
-            .create<DepthwiseConv2DNhwQOp>(
+            .create<DepthwiseConv2DNhwcHwcQOp>(
                 loc, newInitTy, ValueRange{input, collapsedKernel, iZp, kZp},
                 ValueRange{collapsedInit}, stride, dilation)
             .getResult(0);
@@ -3062,10 +3062,10 @@ LogicalResult matchAndReplaceDepthwiseConv(Operation *operation, Value input,
 }
 
 struct SimplifyDepthwiseConvOp
-    : public OpRewritePattern<DepthwiseConv2DNhwcOp> {
-  using OpRewritePattern<DepthwiseConv2DNhwcOp>::OpRewritePattern;
+    : public OpRewritePattern<DepthwiseConv2DNhwcHwcmOp> {
+  using OpRewritePattern<DepthwiseConv2DNhwcHwcmOp>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(DepthwiseConv2DNhwcOp op,
+  LogicalResult matchAndRewrite(DepthwiseConv2DNhwcHwcmOp op,
                                 PatternRewriter &rewriter) const override {
     Operation *operation = op.getOperation();
     Value input = op.getInputOperand(0)->get();
@@ -3082,10 +3082,10 @@ struct SimplifyDepthwiseConvOp
 };
 
 struct SimplifyDepthwiseConvQOp
-    : public OpRewritePattern<DepthwiseConv2DNhwcQOp> {
-  using OpRewritePattern<DepthwiseConv2DNhwcQOp>::OpRewritePattern;
+    : public OpRewritePattern<DepthwiseConv2DNhwcHwcmQOp> {
+  using OpRewritePattern<DepthwiseConv2DNhwcHwcmQOp>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(DepthwiseConv2DNhwcQOp op,
+  LogicalResult matchAndRewrite(DepthwiseConv2DNhwcHwcmQOp op,
                                 PatternRewriter &rewriter) const override {
     Operation *operation = op.getOperation();
     Value input = op.getInputOperand(0)->get();

mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py

@@ -310,7 +310,7 @@ def conv_3d_ndhwc_dhwcf(
            ]) * cast(U, K[D.kd, D.kh, D.kw, D.c, D.f])
 
 @linalg_structured_op
-def depthwise_conv1D_nw(
+def depthwise_conv_1d_nwc_wc(
     I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW, S.IC),
     K=TensorDef(T2, S.KW, S.IC),
     O=TensorDef(U, S.N, S.OW, S.IC, output=True),
@@ -320,7 +320,7 @@ def depthwise_conv1D_nw(
 
   Numeric casting is performed on the operands to the inner multiply, promoting
   them to the same data type as the accumulator/output. Multiplier is set to 1
-  which is a special case for most dpethwise convolutions.
+  which is a special case for most depthwise convolutions.
   """
   implements(ConvolutionOpInterface)
   domain(D.n, D.ow, D.ic, D.kw)
@@ -329,7 +329,7 @@ def depthwise_conv1D_nw(
       cast(U, K[D.kw, D.ic])
 
 @linalg_structured_op
-def depthwise_conv2D_nhw(
+def depthwise_conv_2d_nhwc_hwc(
     I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.IC),
     K=TensorDef(T2, S.KH, S.KW, S.IC),
     O=TensorDef(U, S.N, S.OH, S.OW, S.IC, output=True),
@@ -339,7 +339,7 @@ def depthwise_conv2D_nhw(
 
   Numeric casting is performed on the operands to the inner multiply, promoting
   them to the same data type as the accumulator/output. Multiplier is set to 1
-  which is a special case for most dpethwise convolutions.
+  which is a special case for most depthwise convolutions.
   """
   implements(ConvolutionOpInterface)
   domain(D.n, D.oh, D.ow, D.ic, D.kh, D.kw)
@@ -348,7 +348,7 @@ def depthwise_conv2D_nhw(
            D.ic]) * cast(U, K[D.kh, D.kw, D.ic])
 
 @linalg_structured_op
-def depthwise_conv2D_nhw_q(
+def depthwise_conv_2d_nhwc_hwc_q(
     I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.IC),
     K=TensorDef(T2, S.KH, S.KW, S.IC),
     IZp=ScalarDef(I32),
@@ -369,7 +369,7 @@ def depthwise_conv2D_nhw_q(
       (cast(U, K[D.kh, D.kw, D.ic]) - cast(U, KZp)))
 
 @linalg_structured_op
-def depthwise_conv2D_nhwc(
+def depthwise_conv_2d_nhwc_hwcm(
     I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.IC),
     K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
     O=TensorDef(U, S.N, S.OH, S.OW, S.IC, S.CM, output=True),
@@ -387,7 +387,7 @@ def depthwise_conv2D_nhwc(
            D.ic]) * cast(U, K[D.kh, D.kw, D.ic, D.cm])
 
 @linalg_structured_op
-def depthwise_conv2D_nhwc_q(
+def depthwise_conv_2d_nhwc_hwcm_q(
     I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW, S.IC),
     K=TensorDef(T2, S.KH, S.KW, S.IC, S.CM),
     IZp=ScalarDef(I32),

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir

@@ -1592,7 +1592,7 @@ func @depthwise_conv(%arg0 : tensor<1x7x5x3xf32>, %arg1 : tensor<3x1x3x11xf32>,
   // CHECK: [[CST0:%.+]] = arith.constant 0
   // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]])
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 5, 5, 33]
-  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
+  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
   // CHECK: [[COLLAPSED:%.+]] = linalg.tensor_collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<33xf32>, tensor<1x5x5x33xf32>) outs([[OUT]] : tensor<1x5x5x33xf32>) {
   // CHECK: ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):  // no predecessors
@@ -1614,7 +1614,7 @@ func @depthwise_conv_strides(%arg0 : tensor<1x11x9x3xf32>, %arg1 : tensor<3x1x3x
   // CHECK: [[CST0:%.+]] = arith.constant 0
   // CHECK: [[FILL:%.+]] = linalg.fill([[CST0]], [[INIT]])
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 5, 5, 33]
-  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
+  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
   // CHECK: [[COLLAPSED:%.+]] = linalg.tensor_collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<33xf32>, tensor<1x5x5x33xf32>) outs([[OUT]] : tensor<1x5x5x33xf32>) {
   // CHECK: ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):  // no predecessors
@@ -1642,7 +1642,7 @@ func @depthwise_conv_quant(%arg0 : tensor<1x12x12x4xi8>, %arg1 : tensor<3x3x4x12
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 12, 12, 512]
   // CHECK: [[C128:%.+]] = arith.constant -128
   // CHECK: [[C42:%.+]] = arith.constant 42
-  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x12x12x4x128xi32>)
+  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x12x12x4x128xi32>)
   // CHECK: [[COLLAPSED:%.+]] = linalg.tensor_collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<512xi32>, tensor<1x12x12x512xi32>) outs([[OUT]] : tensor<1x12x12x512xi32>) {
   // CHECK: ^bb0(%arg3: i32, %arg4: i32, %arg5: i32):  // no predecessors
@@ -1666,7 +1666,7 @@ func @depthwise_conv_quant_dilations(%arg0 : tensor<1x14x14x4xi8>, %arg1 : tenso
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 10, 10, 512]
   // CHECK: [[C128:%.+]] = arith.constant -128
   // CHECK: [[C42:%.+]] = arith.constant 42
-  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv2D_nhwc_q {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x10x10x4x128xi32>)
+  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x10x10x4x128xi32>)
   // CHECK: [[COLLAPSED:%.+]] = linalg.tensor_collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<512xi32>, tensor<1x10x10x512xi32>) outs([[OUT]] : tensor<1x10x10x512xi32>) {
   // CHECK: ^bb0(%arg3: i32, %arg4: i32, %arg5: i32):  // no predecessors

mlir/test/Dialect/Linalg/canonicalize.mlir

@@ -1095,9 +1095,9 @@ func @dim_of_tiled_loop_result_no_canonicalize(%arg0: tensor<?x?xf32>, %arg1: te
 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:.+]] = linalg.tensor_collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = linalg.tensor_collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv2D_nhw {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 {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:.+]] = linalg.tensor_expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv2D_nhwc {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 {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>
 }
 
@@ -1108,8 +1108,8 @@ func @depthwise_conv(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x1xf32>, %ar
 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:.+]] = linalg.tensor_collapse_shape %arg1 {{\[\[}}0], [1], [2, 3]]
   // CHECK-DAG: %[[INIT:.+]] = linalg.tensor_collapse_shape %arg2 {{\[\[}}0], [1], [2], [3, 4]]
-  // CHECK-DAG: %[[CONV:.+]] = linalg.depthwise_conv2D_nhw_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 {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:.+]] = linalg.tensor_expand_shape %[[CONV]] {{\[\[}}0], [1], [2], [3, 4]]
-  %0 = linalg.depthwise_conv2D_nhwc_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 {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>
 }

mlir/test/Dialect/Linalg/generalize-named-ops.mlir

@@ -49,8 +49,8 @@ func @generalize_matmul_tensor(%A : tensor<16x8xf32>, %B: tensor<8x32xf32>, %C:
 
 // -----
 
-func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
-  linalg.depthwise_conv2D_nhwc
+func @depthwise_conv_2d_nhwc_hwcm(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x3x4x2x3xf32>) {
+  linalg.depthwise_conv_2d_nhwc_hwcm
      { dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
      ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
     outs(%output : memref<2x3x4x2x3xf32>)
@@ -61,7 +61,7 @@ func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3
 // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d3, d4)>
 // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3, d4)>
 
-// CHECK: func @depthwise_conv2D_nhwc
+// CHECK: func @depthwise_conv_2d_nhwc_hwcm
 
 // CHECK: linalg.generic
 // CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
@@ -76,8 +76,8 @@ func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3
 
 // -----
 
-func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x2x3x2x3xf32>) {
-  linalg.depthwise_conv2D_nhwc
+func @depthwise_conv_2d_nhwc_hwcm(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3xf32>, %output: memref<2x2x3x2x3xf32>) {
+  linalg.depthwise_conv_2d_nhwc_hwcm
      { dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
      ins(%input, %filter : memref<2x4x5x2xf32>, memref<2x2x2x3xf32>)
     outs(%output : memref<2x2x3x2x3xf32>)
@@ -88,7 +88,7 @@ func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3
 // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d3, d4)>
 // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3, d4)>
 
-// CHECK: func @depthwise_conv2D_nhwc
+// CHECK: func @depthwise_conv_2d_nhwc_hwcm
 
 // CHECK: linalg.generic
 // CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
@@ -103,8 +103,8 @@ func @depthwise_conv2D_nhwc(%input: memref<2x4x5x2xf32>, %filter: memref<2x2x2x3
 
 // -----
 
-func @depthwise_conv2D_nhw(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
-  linalg.depthwise_conv2D_nhw {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
+func @depthwise_conv_2d_nhwc_hwc(%input: memref<1x113x113x96xf32>, %filter: memref<3x3x96xf32>, %output: memref<1x56x56x96xf32>) {
+  linalg.depthwise_conv_2d_nhwc_hwc {dilations = dense<1> : vector<2xi64>, strides = dense<2> : vector<2xi64>}
     ins(%input, %filter: memref<1x113x113x96xf32>, memref<3x3x96xf32>)
     outs(%output: memref<1x56x56x96xf32>)
   return
@@ -114,7 +114,7 @@ func @depthwise_conv2D_nhw(%input: memref<1x113x113x96xf32>, %filter: memref<3x3
 // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5, d3)>
 // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3)>
 
-// CHECK: func @depthwise_conv2D_nhw
+// CHECK: func @depthwise_conv_2d_nhwc_hwc
 
 // CHECK: linalg.generic
 // CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]