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[mlir][linalg] Add linalg.conv_2d_ngchw_gfchw_q to named ops #92136

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May 29, 2024
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[mlir][linalg] Add linalg.conv_2d_ngchw_gfchw_q to named ops #92136

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1e7d870
Add a grouped 2d quantized convolution op
zjgarvey May 14, 2024
160d20b
add unit tests
zjgarvey May 14, 2024
982e7b0
formatting change
zjgarvey May 28, 2024
536058f
Merge remote-tracking branch 'upstream/main' into conv_2d_ngchw_gfchw_q
zjgarvey May 28, 2024
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138 changes: 138 additions & 0 deletions mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
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Expand Up @@ -3421,6 +3421,144 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: K
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: conv_2d_ngchw_gfchw_q
cpp_class_name: Conv2DNgchwGfchwQOp
doc: |-
Performs 2-D grouped convolution with zero-point offsets.

Layout:
* Input: NGCHW.
* Kernel: GFCHW.

Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output. This includes the zero
point offsets common to quantized operations.
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] ->
(s0, s1, s2, s3 * s4 + s5 * s6, s7 * s8 + s9 * s10)>
- !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] ->
(s1, s11, s2, s5, s9)>
- !LinalgOperandDefConfig
name: IZp
kind: scalar
type_var: I32
- !LinalgOperandDefConfig
name: KZp
kind: scalar
type_var: I32
- !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] ->
(s0, s1, s11, s3, s7)>
- !LinalgOperandDefConfig
name: strides
kind: index_attr
index_attr_map: affine_map<()[s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
-> (s4, s8)>
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, s11]
-> (s6, s10)>
default_indices:
- 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] -> (d0, d1, d5, d3 * s4 + d6 * s6, d4 * s8 + d7 * s10)>
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
s8, s9, s10, s11] -> (d1, d2, 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] -> ()>
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
s8, s9, s10, s11] -> ()>
- affine_map<(d0, d1, d2, d3, d4, d5, d6, d7)[s0, s1, s2, s3, s4, s5, s6, s7,
s8, s9, s10, s11] -> (d0, d1, d2, d3, d4)>
iterator_types:
- parallel
- 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: binary
fn_name: sub
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: IZp
- !ScalarExpression
scalar_fn:
kind: binary
fn_name: sub
operands:
- !ScalarExpression
scalar_fn:
kind: type
fn_name: cast_signed
type_var: U
operands:
- !ScalarExpression
scalar_arg: K
- !ScalarExpression
scalar_fn:
kind: type
fn_name: cast_signed
type_var: U
operands:
- !ScalarExpression
scalar_arg: KZp
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: conv_3d_ndhwc_dhwcf
cpp_class_name: Conv3DNdhwcDhwcfOp
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34 changes: 34 additions & 0 deletions mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
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Expand Up @@ -937,6 +937,40 @@ def conv_2d_ngchw_gfchw(
U, I[D.n, D.g, D.c, 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.kh, D.kw])

@linalg_structured_op
def conv_2d_ngchw_gfchw_q(
I=TensorDef(
T1, S.N, S.G, S.C, 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.KH, S.KW),
IZp=ScalarDef(I32),
KZp=ScalarDef(I32),
O=TensorDef(U, S.N, S.G, S.FG, S.OH, S.OW, output=True),
strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1]),
):
"""Performs 2-D grouped convolution with zero-point offsets.

Layout:
* Input: NGCHW.
* Kernel: GFCHW.

Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output. This includes the zero
point offsets common to quantized operations.
"""
implements(ConvolutionOpInterface)
domain(D.n, D.g, D.fg, D.oh, D.ow, D.c, D.kh, D.kw)
O[D.n, D.g, D.fg, D.oh, D.ow] += (
TypeFn.cast_signed(
U, I[D.n, D.g, D.c, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW]
)
- TypeFn.cast_signed(U, IZp)
) * (
TypeFn.cast_signed(U, K[D.g, D.fg, D.c, D.kh, D.kw])
- TypeFn.cast_signed(U, KZp)
)


@linalg_structured_op
def conv_3d_ndhwc_dhwcf(
Expand Down
31 changes: 31 additions & 0 deletions mlir/test/Dialect/Linalg/generalize-named-ops.mlir
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Expand Up @@ -204,6 +204,37 @@ func.func @conv_1d_ncw_fcw(%input: memref<?x?x?xf32>, %filter: memref<?x?x?xf32>

// -----

func.func @conv_2d_ngchw_gfchw_q(%input: memref<?x?x?x?x?xi8>, %filter: memref<?x?x?x?x?xi8>, %inputzp: i32, %filterzp: i32, %output: memref<?x?x?x?x?xi32>) {
linalg.conv_2d_ngchw_gfchw_q {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter, %inputzp, %filterzp: memref<?x?x?x?x?xi8>, memref<?x?x?x?x?xi8>, i32, i32)
outs (%output: memref<?x?x?x?x?xi32>)
return
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d5, d3 + d6, d4 + d7)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d1, d2, d5, d6, d7)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> ()>
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6, d7) -> (d0, d1, d2, d3, d4)>

// CHECK: func @conv_2d_ngchw_gfchw_q

// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP2]], #[[MAP3]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"]}
// CHECK-SAME: ins(%{{.+}}, %{{.+}}, %{{.+}}, %{{.+}} : memref<?x?x?x?x?xi8>, memref<?x?x?x?x?xi8>, i32, i32)
// CHECK-SAME: outs(%{{.+}} : memref<?x?x?x?x?xi32>)

// CHECK: ^{{.+}}(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8, %[[BBARG2:.+]]: i32, %[[BBARG3:.+]]: i32, %[[BBARG4:.+]]: i32)
// CHECK-NEXT: %[[EXTSI0:.+]] = arith.extsi %[[BBARG0]] : i8 to i32
// CHECK-NEXT: %[[SUB0:.+]] = arith.subi %[[EXTSI0]], %[[BBARG2]] : i32
// CHECK-NEXT: %[[EXTSI1:.+]] = arith.extsi %[[BBARG1]] : i8 to i32
// CHECK-NEXT: %[[SUB1:.+]] = arith.subi %[[EXTSI1]], %[[BBARG3]] : i32
// CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[SUB0]], %[[SUB1]] : i32
// CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[BBARG4]], %[[MUL]] : i32
// CHECK-NEXT: linalg.yield %[[ADD]] : i32

// -----

func.func @generalize_fill(%output: memref<?x?xf32>, %value : f32) {
linalg.fill ins(%value : f32) outs(%output : memref<?x?xf32>)
return
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15 changes: 15 additions & 0 deletions mlir/test/Dialect/Linalg/named-ops.mlir
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Expand Up @@ -441,6 +441,21 @@ func.func @conv_2d_ngchw_gfchw(%input: tensor<1x5x3x32x32xf32>, %filter: tensor<

// -----

// CHECK-LABEL: func @conv_2d_ngchw_gfchw_q
func.func @conv_2d_ngchw_gfchw_q(%input: tensor<1x5x3x32x32xi8>, %filter: tensor<5x2x3x3x3xi8>, %inputzp: i32, %filterzp: i32, %init: tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32> {
// CHECK: linalg.conv_2d_ngchw_gfchw_q
// CHECK-SAME: dilations = dense<1> : tensor<2xi64>
// CHECK-SAME: strides = dense<1> : tensor<2xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x5x3x32x32xi8>, tensor<5x2x3x3x3xi8>, i32, i32)
// CHECK-SAME: outs(%{{.+}} : tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32>
%0 = linalg.conv_2d_ngchw_gfchw_q {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter, %inputzp, %filterzp: tensor<1x5x3x32x32xi8>, tensor<5x2x3x3x3xi8>, i32, i32)
outs (%init: tensor<1x5x2x30x30xi32>) -> tensor<1x5x2x30x30xi32>
return %0 : tensor<1x5x2x30x30xi32>
}
// -----

// CHECK-LABEL: func @conv_3d_ndhwc_dhwcf
func.func @conv_3d_ndhwc_dhwcf(%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_ndhwc_dhwcf
Expand Down
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