[mlir][linalg] add conv_2d_nhwc_fhwc named op

This operation should be supported as a named op because
when the operands are viewed as having canonical layouts
with decreasing strides, then the "reduction" dimensions
of the filter (h, w, and c) are contiguous relative to each
output channel. When lowered to a matrix multiplication,
this layout is the simplest to deal with, and thus future
transforms/vectorizations of `conv2d` may find using this
named op convenient.

Differential Revision: https://reviews.llvm.org/D126995

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

@@ -1311,6 +1311,104 @@ structured_op: !LinalgStructuredOpConfig
                 - !ScalarExpression
                   scalar_arg: K
 --- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+  name: conv_2d_nhwc_fhwc
+  cpp_class_name: Conv2DNhwcFhwcOp
+  doc: |-
+    Performs 2-D convolution.
+
+    Layout:
+      * Input: NHWC.
+      * Kernel: FHWC.
+
+    Numeric casting is performed on the operands to the inner multiply, promoting
+    them to the same data type as the accumulator/output.
+  implements:
+  - LinalgConvolutionOpInterface
+structured_op: !LinalgStructuredOpConfig
+  args:
+  - !LinalgOperandDefConfig
+    name: I
+    kind: input_tensor
+    type_var: T1
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s0,
+      s1 * s2 + s3 * s4, s5 * s6 + s7 * s8, s9)>
+  - !LinalgOperandDefConfig
+    name: K
+    kind: input_tensor
+    type_var: T2
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] -> (s10, s3,
+      s7, 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] -> (s0,
+      s1, s5, s10)>
+  - !LinalgOperandDefConfig
+    name: strides
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s2, s6)>
+    default_indices:
+    - 1
+    - 1
+  - !LinalgOperandDefConfig
+    name: dilations
+    kind: index_attr
+    index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10] ->
+      (s4, s8)>
+    default_indices:
+    - 1
+    - 1
+  indexing_maps: !LinalgIndexingMapsConfig
+    static_indexing_maps:
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d0, d1 * s2 + d4 * s4, d2 * s6 + d5 * s8, d6)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d3, d4, d5, d6)>
+    - affine_map<(d0, d1, d2, d3, d4, d5, d6)[s0, s1, s2, s3, s4, s5, s6, s7, s8,
+      s9, s10] -> (d0, d1, d2, d3)>
+  iterator_types:
+  - parallel
+  - parallel
+  - parallel
+  - parallel
+  - reduction
+  - reduction
+  - reduction
+  assignments:
+  - !ScalarAssign
+    arg: O
+    value: !ScalarExpression
+      scalar_fn:
+        kind: binary
+        fn_name: add
+        operands:
+        - !ScalarExpression
+          scalar_arg: O
+        - !ScalarExpression
+          scalar_fn:
+            kind: binary
+            fn_name: mul
+            operands:
+            - !ScalarExpression
+              scalar_fn:
+                kind: type
+                fn_name: cast_signed
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: I
+            - !ScalarExpression
+              scalar_fn:
+                kind: type
+                fn_name: cast_signed
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: K
+--- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: conv_2d_nhwc_hwcf_q
   cpp_class_name: Conv2DNhwcHwcfQOp

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

@@ -178,6 +178,38 @@ func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x
 
 // -----
 
+// 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
+  // CHECK-SAME:   dilations = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   strides = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+  // CHECK-SAME:   outs(%{{.+}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+                                 strides = dense<1> : tensor<2xi64>}
+     ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+    outs (%init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_2d_nhwc_fhwc_static
+func.func @conv_2d_nhwc_fhwc_static(%input: tensor<?x128x128x32xf32>, %filter: tensor<64x3x3x32xf32>, %init: tensor<?x126x126x64xf32>) -> tensor<?x126x126x64xf32> {
+  // CHECK:      %{{.+}} = linalg.conv_2d_nhwc_fhwc
+  // CHECK-SAME:   dilations = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   strides = dense<1> : tensor<2xi64>
+  // CHECK-SAME:   ins(%{{.+}}, %{{.+}} : tensor<?x128x128x32xf32>, tensor<64x3x3x32xf32>)
+  // CHECK-SAME:   outs(%{{.+}} : tensor<?x126x126x64xf32>) -> tensor<?x126x126x64xf32>
+  %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
+                                 strides = dense<1> : tensor<2xi64>}
+     ins (%input, %filter: tensor<?x128x128x32xf32>, tensor<64x3x3x32xf32>)
+    outs (%init: tensor<?x126x126x64xf32>) -> tensor<?x126x126x64xf32>
+  return %0 : tensor<?x126x126x64xf32>
+}
+
+// -----
+
 // CHECK-LABEL: func @conv_2d_nhwc_hwcf
 func.func @conv_2d_nhwc_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
   // CHECK:      linalg.conv_2d_nhwc_hwcf