[Mlir-commits] [mlir] c8e0560 - [mlir][linalg] Add channel-first variants of convolution
Alex Zinenko
llvmlistbot at llvm.org
Fri May 12 07:42:54 PDT 2023
Author: kon72
Date: 2023-05-12T16:42:47+02:00
New Revision: c8e056065898bba2d51fb655da40bd4ee7beedcf
URL: https://github.com/llvm/llvm-project/commit/c8e056065898bba2d51fb655da40bd4ee7beedcf
DIFF: https://github.com/llvm/llvm-project/commit/c8e056065898bba2d51fb655da40bd4ee7beedcf.diff
LOG: [mlir][linalg] Add channel-first variants of convolution
This change adds the following three operations and unit tests for them:
- conv_3d_ncdhw_fcdhw
- depthwise_conv_1d_ncw_cw
- depthwise_conv_3d_ncdhw_cdhw
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D150054
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/named-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index cbe40fcec5e04..52ab6c7b7fdda 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -2282,6 +2282,106 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: KZp
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: conv_3d_ncdhw_fcdhw
+ cpp_class_name: Conv3DNcdhwFcdhwOp
+ doc: |-
+ Performs 3-D convolution.
+
+ 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, s11, s12,
+ s13, s14] -> (s0, s1, s2 * s3 + s4 * s5, s6 * s7 + s8 * s9, s10 * s11 + s12
+ * s13)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13, s14] -> (s14, s1, s4, s8, s12)>
+ - !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, s12,
+ s13, s14] -> (s0, s14, s2, s6, s10)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14] -> (s3, s7, s11)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13, s14] -> (s5, s9, s13)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d8, d1 * s3 + d5 * s5, d2 * s7
+ + d6 * s9, d3 * s11 + d7 * s13)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d4, d8, d5, d6, d7)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8)[s0, s1, s2, s3, s4, s5, s6,
+ s7, s8, s9, s10, s11, s12, s13, s14] -> (d0, d4, d1, d2, d3)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - 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: depthwise_conv_1d_nwc_wc
cpp_class_name: DepthwiseConv1DNwcWcOp
@@ -2365,6 +2465,89 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv_1d_ncw_cw
+ cpp_class_name: DepthwiseConv1DNcwCwOp
+ 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 depthwise convolutions.
+ 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] -> (s0, s1, s2 * s3 + s4 * s5)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s1, s4)>
+ - !LinalgOperandDefConfig
+ name: O
+ kind: output_tensor
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s0, s1, s2)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s3)>
+ default_indices:
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s5)>
+ default_indices:
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d0, d2, d1 * s3 + d3
+ * s5)>
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d2, d3)>
+ - affine_map<(d0, d1, d2, d3)[s0, s1, s2, s3, s4, s5] -> (d0, d2, d1)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - 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: depthwise_conv_1d_nwc_wcm
cpp_class_name: DepthwiseConv1DNwcWcmOp
@@ -3091,6 +3274,105 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv_3d_ncdhw_cdhw
+ cpp_class_name: DepthwiseConv3DNcdhwCdhwOp
+ doc: |-
+ Performs depth-wise 3-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 depthwise convolutions.
+ 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, s11, s12,
+ s13] -> (s0, s1, s2 * s3 + s4 * s5, s6 * s7 + s8 * s9, s10 * s11 + s12 * s13)>
+ - !LinalgOperandDefConfig
+ name: K
+ kind: input_tensor
+ type_var: T2
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12,
+ s13] -> (s1, s4, s8, s12)>
+ - !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, s12,
+ s13] -> (s0, s1, s2, s6, s10)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13] -> (s3, s7, s11)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11,
+ s12, s13] -> (s5, s9, s13)>
+ default_indices:
+ - 1
+ - 1
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11, s12, s13] -> (d0, d7, d1 * s3 + d4 * s5, d2 * s7 + d5 * s9,
+ d3 * s11 + d6 * s13)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11, s12, s13] -> (d7, d4, d5, d6)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11, s12, s13] -> (d0, d7, d1, d2, d3)>
+ iterator_types:
+ - parallel
+ - parallel
+ - parallel
+ - parallel
+ - reduction
+ - reduction
+ - reduction
+ - parallel
+ 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: depthwise_conv_3d_ndhwc_dhwcm
cpp_class_name: DepthwiseConv3DNdhwcDhwcmOp
@@ -4722,4 +5004,3 @@ structured_op: !LinalgStructuredOpConfig
scalar_const: '2.3283063999999999E-10 : f64'
- !ScalarExpression
scalar_arg: min
-
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
index 4402624c174b2..9c96868c10f1a 100644
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -493,6 +493,33 @@ def conv_3d_ndhwc_dhwcf_q(I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD,
TypeFn.cast_signed(U, KZp))
+ at linalg_structured_op
+def conv_3d_ncdhw_fcdhw(I=TensorDef(T1, S.N, S.C, S.OD * S.SD + S.KD * S.DD,
+ S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW),
+ K=TensorDef(T2, S.F, S.C, S.KD, S.KH, S.KW),
+ O=TensorDef(U, S.N, S.F, S.OD, S.OH, S.OW, output=True),
+ strides=IndexAttrDef(S.SD,
+ S.SH,
+ S.SW,
+ default=[1, 1, 1]),
+ dilations=IndexAttrDef(S.DD,
+ S.DH,
+ S.DW,
+ default=[1, 1, 1])):
+ """Performs 3-D convolution.
+
+ Numeric casting is performed on the operands to the inner multiply, promoting
+ them to the same data type as the accumulator/output.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.od, D.oh, D.ow, D.f, D.kd, D.kh, D.kw, D.c)
+ O[D.n, D.f, D.od, D.oh, D.ow] += TypeFn.cast_signed(
+ U, I[D.n, D.c, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
+ D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
+ U, K[D.f, D.c, D.kd, D.kh, D.kw])
+
+
@linalg_structured_op
def depthwise_conv_1d_nwc_wc(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
S.IC),
@@ -513,6 +540,26 @@ def depthwise_conv_1d_nwc_wc(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
TypeFn.cast_signed(U, K[D.kw, D.ic])
+ at linalg_structured_op
+def depthwise_conv_1d_ncw_cw(I=TensorDef(T1, S.N, S.IC,
+ S.OW * S.SW + S.KW * S.DW),
+ K=TensorDef(T2, S.IC, S.KW),
+ O=TensorDef(U, S.N, S.IC, S.OW, output=True),
+ strides=IndexAttrDef(S.SW, default=[1]),
+ dilations=IndexAttrDef(S.DW, default=[1])):
+ """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 depthwise convolutions.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.ow, D.ic, D.kw)
+ O[D.n, D.ic, D.ow] += \
+ TypeFn.cast_signed(U, I[D.n, D.ic, D.ow * S.SW + D.kw * S.DW]) * \
+ TypeFn.cast_signed(U, K[D.ic, D.kw])
+
+
@linalg_structured_op
def depthwise_conv_1d_nwc_wcm(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
S.IC),
@@ -716,6 +763,41 @@ def depthwise_conv_3d_ndhwc_dhwc(I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD,
U, K[D.kd, D.kh, D.kw, D.ic])
+ at linalg_structured_op
+def depthwise_conv_3d_ncdhw_cdhw(I=TensorDef(T1, S.N, S.IC,
+ S.OD * S.SD + S.KD * S.DD,
+ S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW),
+ K=TensorDef(T2, S.IC, S.KD, S.KH, S.KW),
+ O=TensorDef(U,
+ S.N,
+ S.IC,
+ S.OD,
+ S.OH,
+ S.OW,
+ output=True),
+ strides=IndexAttrDef(S.SD,
+ S.SH,
+ S.SW,
+ default=[1, 1, 1]),
+ dilations=IndexAttrDef(S.DD,
+ S.DH,
+ S.DW,
+ default=[1, 1, 1])):
+ """Performs depth-wise 3-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 depthwise convolutions.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.od, D.oh, D.ow, D.kd, D.kh, D.kw, D.ic)
+ O[D.n, D.ic, D.od, D.oh, D.ow] += TypeFn.cast_signed(
+ U, I[D.n, D.ic, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
+ D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
+ U, K[D.ic, D.kd, D.kh, D.kw])
+
+
@linalg_structured_op
def depthwise_conv_3d_ndhwc_dhwcm(I=TensorDef(T1, S.N,
S.OD * S.SD + S.KD * S.DD,
@@ -749,6 +831,7 @@ def depthwise_conv_3d_ndhwc_dhwcm(I=TensorDef(T1, S.N,
D.ow * S.SW + D.kw * S.DW, D.ic]) * TypeFn.cast_signed(
U, K[D.kd, D.kh, D.kw, D.ic, D.cm])
+
@linalg_structured_op
def pooling_nhwc_sum(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
S.OW * S.SW + S.KW * S.DW, S.C),
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index f5e266ea7f6c2..e89fb8d682e22 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -28,6 +28,20 @@ func.func @depthwise_conv_1d_nwc_wc(%input: tensor<1x12x8xf32>, %filter: tensor<
// -----
+// CHECK-LABEL: func @depthwise_conv_1d_ncw_cw
+func.func @depthwise_conv_1d_ncw_cw(%input: tensor<1x8x12xf32>, %filter: tensor<8x3xf32>) -> tensor<1x8x10xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = tensor.empty() : tensor<1x8x10xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x8x10xf32>) -> tensor<1x8x10xf32>
+ // CHECK: depthwise_conv_1d_ncw_cw
+ %0 = linalg.depthwise_conv_1d_ncw_cw {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
+ ins(%input, %filter : tensor<1x8x12xf32>, tensor<8x3xf32>)
+ outs(%fill : tensor<1x8x10xf32>) -> tensor<1x8x10xf32>
+ return %0 : tensor<1x8x10xf32>
+}
+
+// -----
+
// CHECK-LABEL: func @depthwise_conv_2d_nhwc_hwcm_tensor
func.func @depthwise_conv_2d_nhwc_hwcm_tensor(%input: tensor<2x4x5x2xf32>, %filter: tensor<2x2x2x3xf32>) -> tensor<2x3x4x2x3xf32> {
%zero = arith.constant 0.000000e+00 : f32
@@ -221,6 +235,20 @@ func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<2x6x13x12x6xf32>, %filter
// -----
+// CHECK-LABEL: func @depthwise_conv_3d_ncdhw_cdhw
+func.func @depthwise_conv_3d_ncdhw_cdhw(%input: tensor<2x6x6x13x12xf32>, %filter: tensor<6x2x1x3xf32>) -> tensor<2x6x3x13x4xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = tensor.empty() : tensor<2x6x3x13x4xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x6x3x13x4xf32>) -> tensor<2x6x3x13x4xf32>
+ // CHECK: depthwise_conv_3d_ncdhw_cdhw
+ %0 = linalg.depthwise_conv_3d_ncdhw_cdhw {dilations = dense<1> : tensor<3xi64>, strides = dense<[2, 1, 3]> : tensor<3xi64>}
+ ins(%input, %filter : tensor<2x6x6x13x12xf32>, tensor<6x2x1x3xf32>)
+ outs(%fill : tensor<2x6x3x13x4xf32>) -> tensor<2x6x3x13x4xf32>
+ return %0 : tensor<2x6x3x13x4xf32>
+}
+
+// -----
+
// CHECK-LABEL: func @conv_1d_nwc_wcf
func.func @conv_1d_nwc_wcf(%input: tensor<?x?x?xf32>, %filter: tensor<?x?x?xf32>, %init: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
// CHECK: %{{.+}} = linalg.conv_1d_nwc_wcf
@@ -413,6 +441,38 @@ func.func @conv_3d_ndhwc_dhwcf(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x
// -----
+// CHECK-LABEL: func @conv_3d_ncdhw_fcdhw
+func.func @conv_3d_ncdhw_fcdhw(%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_3d_ncdhw_fcdhw
+ // CHECK-SAME: dilations = dense<1> : tensor<3xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<3xi64>
+ // 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_3d_ncdhw_fcdhw {dilations = dense<1> : tensor<3xi64>,
+ strides = dense<1> : tensor<3xi64>}
+ 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>
+ return %0 : tensor<?x?x?x?x?xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @conv_3d_ncdhw_fcdhw
+func.func @conv_3d_ncdhw_fcdhw(%input: memref<?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?xf32>, %output: memref<?x?x?x?x?xf32>) {
+ // CHECK: linalg.conv_3d_ncdhw_fcdhw
+ // CHECK-SAME: dilations = dense<1> : tensor<3xi64>
+ // CHECK-SAME: strides = dense<1> : tensor<3xi64>
+ // 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_3d_ncdhw_fcdhw {dilations = dense<1> : tensor<3xi64>,
+ strides = dense<1> : tensor<3xi64>}
+ ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
+ outs (%output: memref<?x?x?x?x?xf32>)
+ return
+}
+
+// -----
+
// CHECK-LABEL: func @pooling_nhwc_sum_tensor
// CHECK: %{{.+}} = linalg.pooling_nhwc_sum
// CHECK-SAME: dilations = dense<1> : tensor<2xi64>
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