[Mlir-commits] [mlir] ba0222c - [mlir][linalg] Add named ops for depthwise 3d convolution
Benjamin Kramer
llvmlistbot at llvm.org
Tue Jun 14 09:29:34 PDT 2022
Author: Benjamin Kramer
Date: 2022-06-14T18:22:47+02:00
New Revision: ba0222cdc6510cd73baf22835e60475ef993a56a
URL: https://github.com/llvm/llvm-project/commit/ba0222cdc6510cd73baf22835e60475ef993a56a
DIFF: https://github.com/llvm/llvm-project/commit/ba0222cdc6510cd73baf22835e60475ef993a56a.diff
LOG: [mlir][linalg] Add named ops for depthwise 3d convolution
Also complete the set by adding a variant of depthwise 1d convolution
with the multiplier != 1.
Differential Revision: https://reviews.llvm.org/D127687
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 cfc8f07cf1c5d..54a9d0539c975 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -1827,6 +1827,91 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv_1d_nwc_wcm
+ cpp_class_name: DepthwiseConv1DNwcWcmOp
+ 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.
+ 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] -> (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, s6] -> (s3, s5, s6)>
+ - !LinalgOperandDefConfig
+ name: O
+ kind: output_tensor
+ type_var: U
+ shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s0, s1, s5, s6)>
+ - !LinalgOperandDefConfig
+ name: strides
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s2)>
+ default_indices:
+ - 1
+ - !LinalgOperandDefConfig
+ name: dilations
+ kind: index_attr
+ index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6] -> (s4)>
+ default_indices:
+ - 1
+ indexing_maps: !LinalgIndexingMapsConfig
+ static_indexing_maps:
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d1 * s2
+ + d4 * s4, d2)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d4, d2, d3)>
+ - affine_map<(d0, d1, d2, d3, d4)[s0, s1, s2, s3, s4, s5, s6] -> (d0, d1, d2,
+ d3)>
+ iterator_types:
+ - parallel
+ - 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_2d_nhwc_hwc
cpp_class_name: DepthwiseConv2DNhwcHwcOp
@@ -2276,6 +2361,205 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: KZp
--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: depthwise_conv_3d_ndhwc_dhwc
+ cpp_class_name: DepthwiseConv3DNdhwcDhwcOp
+ 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] -> (s3, s7, s11, s13)>
+ - !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, s5, s9)>
+ - !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] -> (s2, s6, s10)>
+ 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] -> (s4, s8, s12)>
+ 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, d1 * s2 + d4 * s4, d2 * s6 + d5 * s8, d3
+ * s10 + d6 * s12, d7)>
+ - affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
+ s8, s9, s10, s11, s12, s13] -> (d4, d5, d6, d7)>
+ - 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, d1, d2, d3, d7)>
+ 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
+ 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.
+ 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] -> (s3, s7, s11, s13, s14)>
+ - !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, s1, s5, s9, s14)>
+ - !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] -> (s2, s6, s10)>
+ 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] -> (s4, s8, s12)>
+ 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, d1 * s2 + d5 * s4, d2 * s6 + d6
+ * s8, d3 * s10 + d7 * s12, d8)>
+ - 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] -> (d5, d6, d7, d8, d4)>
+ - 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, d1, d2, d3, d8, d4)>
+ iterator_types:
+ - parallel
+ - 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: pooling_nhwc_sum
cpp_class_name: PoolingNhwcSumOp
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 291a4be22fac2..f553c38809a96 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
@@ -414,6 +414,26 @@ 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_nwc_wcm(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
+ S.IC),
+ K=TensorDef(T2, S.KW, S.IC, S.CM),
+ O=TensorDef(U, S.N, S.OW, S.IC, S.CM,
+ 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.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.ow, D.ic, D.cm, D.kw)
+ O[D.n, D.ow, D.ic, D.cm] += \
+ TypeFn.cast_signed(U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.ic]) * \
+ TypeFn.cast_signed(U, K[D.kw, D.ic, D.cm])
+
+
@linalg_structured_op
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),
@@ -536,6 +556,64 @@ def depthwise_conv_2d_nhwc_hwcm_q(I=TensorDef(T1, S.N,
TypeFn.cast_signed(U, KZp)))
+ at linalg_structured_op
+def depthwise_conv_3d_ndhwc_dhwc(I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD,
+ S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW, S.IC),
+ K=TensorDef(T2, S.KD, S.KH, S.KW, S.IC),
+ O=TensorDef(U, S.N, 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.od, D.oh, D.ow, D.ic] += TypeFn.cast_signed(
+ U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
+ D.ow * S.SW + D.kw * S.DW, D.ic]) * TypeFn.cast_signed(
+ U, K[D.kd, D.kh, D.kw, D.ic])
+
+
+ at linalg_structured_op
+def depthwise_conv_3d_ndhwc_dhwcm(I=TensorDef(T1,
+ S.N, S.OD * S.SD + S.KD * S.DD,
+ S.OH * S.SH + S.KH * S.DH,
+ S.OW * S.SW + S.KW * S.DW, S.IC),
+ K=TensorDef(T2, S.KD, S.KH, S.KW, S.IC, S.CM),
+ O=TensorDef(U, S.N, S.OD, S.OH, S.OW, S.CM,
+ 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.
+ """
+ implements(ConvolutionOpInterface)
+ domain(D.n, D.od, D.oh, D.ow, D.cm, D.kd, D.kh, D.kw, D.ic)
+ O[D.n, D.od, D.oh, D.ow, D.ic, D.cm] += TypeFn.cast_signed(
+ U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH,
+ 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 b984a1baaa94e..e1ade46716533 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -1,5 +1,33 @@
// RUN: mlir-opt -split-input-file -verify-diagnostics %s | FileCheck %s
+// CHECK-LABEL: func @depthwise_conv_1d_nwc_wcm
+func.func @depthwise_conv_1d_nwc_wcm(%input: tensor<1x12x8xf32>, %filter: tensor<3x8x8xf32>) -> tensor<1x10x8x8xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.init_tensor [1, 10, 8, 8] : tensor<1x10x8x8xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
+ // CHECK: depthwise_conv_1d_nwc_wcm
+ %0 = linalg.depthwise_conv_1d_nwc_wcm {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
+ ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8x8xf32>)
+ outs(%fill : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>
+ return %0 : tensor<1x10x8x8xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @depthwise_conv_1d_nwc_wc
+func.func @depthwise_conv_1d_nwc_wc(%input: tensor<1x12x8xf32>, %filter: tensor<3x8xf32>) -> tensor<1x10x8xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.init_tensor [1, 10, 8] : tensor<1x10x8xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8xf32>) -> tensor<1x10x8xf32>
+ // CHECK: depthwise_conv_1d_nwc_wc
+ %0 = linalg.depthwise_conv_1d_nwc_wc {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
+ ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8xf32>)
+ outs(%fill : tensor<1x10x8xf32>) -> tensor<1x10x8xf32>
+ return %0 : tensor<1x10x8xf32>
+}
+
+// -----
+
// 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
@@ -130,6 +158,34 @@ func.func @depthwise_conv_2d_input_nhwc_filter_wrong_stride_size(%input: memref<
// -----
+// CHECK-LABEL: func @depthwise_conv_3d_ndhwc_dhwcm
+func.func @depthwise_conv_3d_ndhwc_dhwcm(%input: tensor<2x6x13x12x6xf32>, %filter: tensor<2x1x3x6x6xf32>) -> tensor<2x3x13x4x6x6xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.init_tensor [2, 3, 13, 4, 6, 6] : tensor<2x3x13x4x6x6xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x3x13x4x6x6xf32>) -> tensor<2x3x13x4x6x6xf32>
+ // CHECK: depthwise_conv_3d_ndhwc_dhwcm
+ %0 = linalg.depthwise_conv_3d_ndhwc_dhwcm {dilations = dense<1> : tensor<3xi64>, strides = dense<[2, 1, 3]> : tensor<3xi64>}
+ ins(%input, %filter : tensor<2x6x13x12x6xf32>, tensor<2x1x3x6x6xf32>)
+ outs(%fill : tensor<2x3x13x4x6x6xf32>) -> tensor<2x3x13x4x6x6xf32>
+ return %0 : tensor<2x3x13x4x6x6xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @depthwise_conv_3d_ndhwc_dhwc
+func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<2x6x13x12x6xf32>, %filter: tensor<2x1x3x6xf32>) -> tensor<2x3x13x4x6xf32> {
+ %zero = arith.constant 0.000000e+00 : f32
+ %init = linalg.init_tensor [2, 3, 13, 4, 6] : tensor<2x3x13x4x6xf32>
+ %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<2x3x13x4x6xf32>) -> tensor<2x3x13x4x6xf32>
+ // CHECK: depthwise_conv_3d_ndhwc_dhwc
+ %0 = linalg.depthwise_conv_3d_ndhwc_dhwc {dilations = dense<1> : tensor<3xi64>, strides = dense<[2, 1, 3]> : tensor<3xi64>}
+ ins(%input, %filter : tensor<2x6x13x12x6xf32>, tensor<2x1x3x6xf32>)
+ outs(%fill : tensor<2x3x13x4x6xf32>) -> tensor<2x3x13x4x6xf32>
+ return %0 : tensor<2x3x13x4x6xf32>
+}
+
+// -----
+
// 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
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