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Differential D150066

[mlir][linalg] Add grouped convolution ops for 1D/3D cases
Needs ReviewPublic

Authored by kon72 on May 7 2023, 7:08 AM.

Details

Reviewers
ftynse
nicolasvasilache
Summary

This change adds the following two ops:

  • conv_1d_ngcw_gfcw
  • conv_3d_ngcdhw_gfcdhw

Depends on D150064

Diff Detail

Event Timeline

kon72 created this revision.May 7 2023, 7:08 AM
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kon72 requested review of this revision.May 7 2023, 7:08 AM
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Harbormaster completed remote builds in B230492: Diff 520182.May 7 2023, 7:27 AM

Revision Contents

PathSize
mlir/
include/
mlir/
Dialect/
Linalg/
IR/
196 lines
python/
mlir/
dialects/
linalg/
opdsl/
ops/
61 lines
test/
Dialect/
Linalg/
96 lines

Diff 520182

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

Show First 20 Lines • Show All 1,508 Lines • ▼ Show 20 Lines value: !ScalarExpression
kind: type kind: type
fn_name: cast_signed fn_name: cast_signed
type_var: U type_var: U
operands: operands:
- !ScalarExpression - !ScalarExpression
scalar_arg: K scalar_arg: K
--- !LinalgOpConfig --- !LinalgOpConfig
metadata: !LinalgOpMetadata metadata: !LinalgOpMetadata
name: conv_1d_ngcw_gfcw
cpp_class_name: Conv1DNgcwGfcwOp
doc: |-
Performs 1-D grouped convolution.
Layout:
* Input: NGCW.
* Kernel: GFCW.
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] -> (s0, s1, s2, s3 *
s4 + s5 * s6)>
- !LinalgOperandDefConfig
name: K
kind: input_tensor
type_var: T2
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s1, s7, s2, s5)>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: U
shape_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s0, s1, s7, s3)>
- !LinalgOperandDefConfig
name: strides
kind: index_attr
index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s4)>
default_indices:
- 1
- !LinalgOperandDefConfig
name: dilations
kind: index_attr
index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7] -> (s6)>
default_indices:
- 1
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d0,
d1, d4, d3 * s4 + d5 * s6)>
- affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d1,
d2, d4, d5)>
- affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5, s6, s7] -> (d0,
d1, d2, d3)>
iterator_types:
- parallel
- parallel
- parallel
- parallel
- 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 name: conv_2d_nhwc_hwcf
cpp_class_name: Conv2DNhwcHwcfOp cpp_class_name: Conv2DNhwcHwcfOp
doc: |- doc: |-
Performs 2-D convolution. Performs 2-D convolution.
Layout: Layout:
* Input: NHWC. * Input: NHWC.
* Kernel: HWCF. * Kernel: HWCF.
▲ Show 20 Lines • Show All 753 Lines • ▼ Show 20 Lines value: !ScalarExpression
kind: type kind: type
fn_name: cast_signed fn_name: cast_signed
type_var: U type_var: U
operands: operands:
- !ScalarExpression - !ScalarExpression
scalar_arg: KZp scalar_arg: KZp
--- !LinalgOpConfig --- !LinalgOpConfig
metadata: !LinalgOpMetadata metadata: !LinalgOpMetadata
name: conv_3d_ngcdhw_gfcdhw
cpp_class_name: Conv3DNgcdhwGfcdhwOp
doc: |-
Performs 3-D grouped convolution.
Layout:
* Input: NGCDHW.
* Kernel: GFCDHW.
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, s15] -> (s0, s1, s2, s3 * s4 + s5 * s6, s7 * s8 + s9 * s10, s11 *
s12 + s13 * s14)>
- !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, s15] -> (s1, s15, s2, s5, s9, 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, s14, s15] -> (s0, s1, s15, s3, s7, s11)>
- !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, s15] -> (s4, s8, s12)>
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, s15] -> (s6, s10, s14)>
default_indices:
- 1
- 1
- 1
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9)[s0, s1, s2, s3, s4, s5,
s6, s7, s8, s9, s10, s11, s12, s13, s14, s15] -> (d0, d1, d6, d3 * s4 + d7 *
s6, d4 * s8 + d8 * s10, d5 * s12 + d9 * s14)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9)[s0, s1, s2, s3, s4, s5,
s6, s7, s8, s9, s10, s11, s12, s13, s14, s15] -> (d1, d2, d6, d7, d8, d9)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7, d8, d9)[s0, s1, s2, s3, s4, s5,
s6, s7, s8, s9, s10, s11, s12, s13, s14, s15] -> (d0, d1, d2, d3, d4, d5)>
iterator_types:
- parallel
- 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 name: depthwise_conv_1d_nwc_wc
cpp_class_name: DepthwiseConv1DNwcWcOp cpp_class_name: DepthwiseConv1DNwcWcOp
doc: |- doc: |-
Performs depth-wise 1-D convolution. Performs depth-wise 1-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting 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 them to the same data type as the accumulator/output. Multiplier is set to 1
which is a special case for most depthwise convolutions. which is a special case for most depthwise convolutions.
▲ Show 20 Lines • Show All 2,432 Lines • Show Last 20 Lines

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

Show First 20 Lines • Show All 303 Lines • ▼ Show 20 Linesdef conv_1d_ncw_fcw(I=TensorDef(T1, S.N, S.C, S.OW * S.SW + S.KW * S.DW),
implements(ConvolutionOpInterface) implements(ConvolutionOpInterface)
domain(D.n, D.f, D.ow, D.c, D.kw) domain(D.n, D.f, D.ow, D.c, D.kw)
O[D.n, D.f, D.ow] += TypeFn.cast_signed( O[D.n, D.f, D.ow] += TypeFn.cast_signed(
U, I[D.n, D.c, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed( U, I[D.n, D.c, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
U, K[D.f, D.c, D.kw]) U, K[D.f, D.c, D.kw])
@linalg_structured_op @linalg_structured_op
def conv_1d_ngcw_gfcw(I=TensorDef(T1, S.N, S.G, S.C, S.OW * S.SW + S.KW * S.DW),
K=TensorDef(T2, S.G, S.FG, S.C, S.KW),
O=TensorDef(U, S.N, S.G, S.FG, S.OW, output=True),
strides=IndexAttrDef(S.SW, default=[1]),
dilations=IndexAttrDef(S.DW, default=[1])):
"""Performs 1-D grouped convolution.
Layout:
* Input: NGCW.
* Kernel: GFCW.
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.g, D.fg, D.ow, D.c, D.kw)
O[D.n, D.g, D.fg, D.ow] += TypeFn.cast_signed(
U, I[D.n, D.g, D.c, D.ow * S.SW + D.kw * S.DW]) * TypeFn.cast_signed(
U, K[D.g, D.fg, D.c, D.kw])
@linalg_structured_op
def conv_2d_nhwc_hwcf(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, def conv_2d_nhwc_hwcf(I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH,
S.OW * S.SW + S.KW * S.DW, S.C), S.OW * S.SW + S.KW * S.DW, S.C),
K=TensorDef(T2, S.KH, S.KW, S.C, S.F), K=TensorDef(T2, S.KH, S.KW, S.C, S.F),
O=TensorDef(U, S.N, S.OH, S.OW, S.F, output=True), O=TensorDef(U, S.N, S.OH, S.OW, S.F, output=True),
strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]), strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1])): dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1])):
"""Performs 2-D convolution. """Performs 2-D convolution.
▲ Show 20 Lines • Show All 169 Lines • ▼ Show 20 Linesdef conv_3d_ndhwc_dhwcf_q(I=TensorDef(T1, S.N, S.OD * S.SD + S.KD * S.DD,
O[D.n, D.od, D.oh, D.ow, D.f] += (TypeFn.cast_signed( O[D.n, D.od, D.oh, D.ow, D.f] += (TypeFn.cast_signed(
U, I[D.n, D.od * S.SD + D.kd * S.DD, D.oh * S.SH + D.kh * S.DH, 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.c]) - TypeFn.cast_signed(U, IZp)) * ( D.ow * S.SW + D.kw * S.DW, D.c]) - TypeFn.cast_signed(U, IZp)) * (
TypeFn.cast_signed(U, K[D.kd, D.kh, D.kw, D.c, D.f]) - TypeFn.cast_signed(U, K[D.kd, D.kh, D.kw, D.c, D.f]) -
TypeFn.cast_signed(U, KZp)) TypeFn.cast_signed(U, KZp))
@linalg_structured_op @linalg_structured_op
def conv_3d_ngcdhw_gfcdhw(I=TensorDef(T1, S.N, S.G, 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.G, S.FG, S.C, S.KD, S.KH, S.KW),
O=TensorDef(U,
S.N,
S.G,
S.FG,
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 grouped convolution.
Layout:
* Input: NGCDHW.
* Kernel: GFCDHW.
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.g, D.fg, D.od, D.oh, D.ow, D.c, D.kd, D.kh, D.kw)
O[D.n, D.g, D.fg, D.od, D.oh, D.ow] += TypeFn.cast_signed(
U, I[D.n, D.g, 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.g, D.fg, 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, def depthwise_conv_1d_nwc_wc(I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW,
S.IC), S.IC),
K=TensorDef(T2, S.KW, S.IC), K=TensorDef(T2, S.KW, S.IC),
O=TensorDef(U, S.N, S.OW, S.IC, output=True), O=TensorDef(U, S.N, S.OW, S.IC, output=True),
strides=IndexAttrDef(S.SW, default=[1]), strides=IndexAttrDef(S.SW, default=[1]),
dilations=IndexAttrDef(S.DW, default=[1])): dilations=IndexAttrDef(S.DW, default=[1])):
"""Performs depth-wise 1-D convolution. """Performs depth-wise 1-D convolution.
▲ Show 20 Lines • Show All 652 Lines • Show Last 20 Lines

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

Show First 20 Lines • Show All 279 Lines • ▼ Show 20 Lines linalg.conv_1d_ncw_fcw {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>} strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>) ins (%input, %filter: memref<?x?x?xf32>, memref<?x?x?xf32>)
outs (%output: memref<?x?x?xf32>) outs (%output: memref<?x?x?xf32>)
return return
} }
// ----- // -----
// CHECK-LABEL: func @conv_1d_ngcw_gfcw
func.func @conv_1d_ngcw_gfcw(%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_1d_ngcw_gfcw
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
// CHECK-SAME: strides = dense<1> : tensor<1xi64>
// 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_1d_ngcw_gfcw {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
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_1d_ngcw_gfcw_static
func.func @conv_1d_ngcw_gfcw_static(%input: tensor<1x2x32x128xf32>, %filter: tensor<2x64x32x3xf32>, %init: tensor<1x2x64x126xf32>) -> tensor<1x2x64x126xf32> {
// CHECK: %{{.+}} = linalg.conv_1d_ngcw_gfcw
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
// CHECK-SAME: strides = dense<1> : tensor<1xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x2x32x128xf32>, tensor<2x64x32x3xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x64x126xf32>) -> tensor<1x2x64x126xf32>
%0 = linalg.conv_1d_ngcw_gfcw {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: tensor<1x2x32x128xf32>, tensor<2x64x32x3xf32>)
outs (%init: tensor<1x2x64x126xf32>) -> tensor<1x2x64x126xf32>
return %0 : tensor<1x2x64x126xf32>
}
// -----
// CHECK-LABEL: func @conv_1d_ngcw_gfcw
func.func @conv_1d_ngcw_gfcw(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) {
// CHECK: linalg.conv_1d_ngcw_gfcw
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
// CHECK-SAME: strides = dense<1> : tensor<1xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?xf32>)
linalg.conv_1d_ngcw_gfcw {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: memref<?x?x?x?xf32>, memref<?x?x?x?xf32>)
outs (%output: memref<?x?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @conv_2d_nhwc_hwcf // CHECK-LABEL: func @conv_2d_nhwc_hwcf
func.func @conv_2d_nhwc_hwcf(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %init: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> { func.func @conv_2d_nhwc_hwcf(%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_hwcf // CHECK: %{{.+}} = linalg.conv_2d_nhwc_hwcf
// CHECK-SAME: dilations = dense<1> : tensor<2xi64> // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
// CHECK-SAME: strides = 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: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : 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_hwcf {dilations = dense<1> : tensor<2xi64>, %0 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>,
▲ Show 20 Lines • Show All 128 Lines • ▼ Show 20 Lines linalg.conv_3d_ndhwc_dhwcf {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>} strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>) ins (%input, %filter: memref<?x?x?x?x?xf32>, memref<?x?x?x?x?xf32>)
outs (%output: memref<?x?x?x?x?xf32>) outs (%output: memref<?x?x?x?x?xf32>)
return return
} }
// ----- // -----
// CHECK-LABEL: func @conv_3d_ngcdhw_gfcdhw
func.func @conv_3d_ngcdhw_gfcdhw(%input: tensor<?x?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?x?xf32>, %init: tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32> {
// CHECK: %{{.+}} = linalg.conv_3d_ngcdhw_gfcdhw
// CHECK-SAME: dilations = dense<1> : tensor<3xi64>
// CHECK-SAME: strides = dense<1> : tensor<3xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x?x?x?x?x?xf32>, tensor<?x?x?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32>
%0 = linalg.conv_3d_ngcdhw_gfcdhw {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: tensor<?x?x?x?x?x?xf32>, tensor<?x?x?x?x?x?xf32>)
outs (%init: tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32>
return %0 : tensor<?x?x?x?x?x?xf32>
}
// -----
// CHECK-LABEL: func @conv_3d_ngcdhw_gfcdhw_static
func.func @conv_3d_ngcdhw_gfcdhw_static(%input: tensor<1x2x32x128x256x512xf32>, %filter: tensor<2x64x32x3x5x7xf32>, %init: tensor<1x2x64x126x252x506xf32>) -> tensor<1x2x64x126x252x506xf32> {
// CHECK: %{{.+}} = linalg.conv_3d_ngcdhw_gfcdhw
// CHECK-SAME: dilations = dense<1> : tensor<3xi64>
// CHECK-SAME: strides = dense<1> : tensor<3xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x2x32x128x256x512xf32>, tensor<2x64x32x3x5x7xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x64x126x252x506xf32>) -> tensor<1x2x64x126x252x506xf32>
%0 = linalg.conv_3d_ngcdhw_gfcdhw {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: tensor<1x2x32x128x256x512xf32>, tensor<2x64x32x3x5x7xf32>)
outs (%init: tensor<1x2x64x126x252x506xf32>) -> tensor<1x2x64x126x252x506xf32>
return %0 : tensor<1x2x64x126x252x506xf32>
}
// -----
// CHECK-LABEL: func @conv_3d_ngcdhw_gfcdhw
func.func @conv_3d_ngcdhw_gfcdhw(%input: memref<?x?x?x?x?x?xf32>, %filter: memref<?x?x?x?x?x?xf32>, %output: memref<?x?x?x?x?x?xf32>) {
// CHECK: linalg.conv_3d_ngcdhw_gfcdhw
// CHECK-SAME: dilations = dense<1> : tensor<3xi64>
// CHECK-SAME: strides = dense<1> : tensor<3xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?x?x?x?xf32>, memref<?x?x?x?x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?x?xf32>)
linalg.conv_3d_ngcdhw_gfcdhw {dilations = dense<1> : tensor<3xi64>,
strides = dense<1> : tensor<3xi64>}
ins (%input, %filter: memref<?x?x?x?x?x?xf32>, memref<?x?x?x?x?x?xf32>)
outs (%output: memref<?x?x?x?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @pooling_nhwc_sum_tensor // CHECK-LABEL: func @pooling_nhwc_sum_tensor
// CHECK: %{{.+}} = linalg.pooling_nhwc_sum // CHECK: %{{.+}} = linalg.pooling_nhwc_sum
// CHECK-SAME: dilations = dense<1> : tensor<2xi64> // CHECK-SAME: dilations = dense<1> : tensor<2xi64>
// CHECK-SAME: strides = dense<1> : tensor<2xi64> // CHECK-SAME: strides = dense<1> : tensor<2xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xf32>, tensor<3x3xf32>) // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xf32>, tensor<3x3xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> // CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32>
func.func @pooling_nhwc_sum_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> { func.func @pooling_nhwc_sum_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> {
%fake = tensor.empty() : tensor<3x3xf32> %fake = tensor.empty() : tensor<3x3xf32>
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