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Differential D154618

[MLIR][Linalg] Add unary named ops to linalg
ClosedPublic

Authored by rengolin on Jul 6 2023, 8:27 AM.

Details

Reviewers
ftynse
nicolasvasilache
harsh
chelini
adam-smnk
Commits
rGfe129311d39b: [MLIR][Linalg] Add unary named ops to linalg
Summary

Following binary arithmetic in previous commits, this patch adds unary
maths ops to linalg.

It also fixes a few of the previous tests, and makes the binary ops call
BinaryFn.<op> directly instead of relying on Python to recognise the
operation.

Diff Detail

Event Timeline

rengolin created this revision.Jul 6 2023, 8:27 AM
Herald added a project: Restricted Project. · View Herald TranscriptJul 6 2023, 8:27 AM
Herald added subscribers: bviyer, Moerafaat, bzcheeseman and 24 others. · View Herald Transcript
rengolin requested review of this revision.Jul 6 2023, 8:27 AM
Herald added a project: Restricted Project. · View Herald TranscriptJul 6 2023, 8:27 AM
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Harbormaster completed remote builds in B243480: Diff 537741.Jul 6 2023, 8:45 AM
nicolasvasilache accepted this revision.Jul 7 2023, 2:20 AM

nice!

This revision is now accepted and ready to land.Jul 7 2023, 2:20 AM
Closed by commit rGfe129311d39b: [MLIR][Linalg] Add unary named ops to linalg (authored by rengolin). · Explain WhyJul 7 2023, 2:38 AM
This revision was automatically updated to reflect the committed changes.
rengolin added a commit: rGfe129311d39b: [MLIR][Linalg] Add unary named ops to linalg.

Revision Contents

PathSize
mlir/
include/
mlir/
Dialect/
Linalg/
IR/
210 lines
python/
mlir/
dialects/
linalg/
opdsl/
ops/
80 lines
test/
Dialect/
Linalg/
152 lines
96 lines
186 lines

Diff 537741

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

Show First 20 Lines • Show All 87 Lines • ▼ Show 20 Lines value: !ScalarExpression
kind: type kind: type
attr_name: cast attr_name: cast
type_var: U type_var: U
operands: operands:
- !ScalarExpression - !ScalarExpression
scalar_arg: I scalar_arg: I
--- !LinalgOpConfig --- !LinalgOpConfig
metadata: !LinalgOpMetadata metadata: !LinalgOpMetadata
name: exp
cpp_class_name: ExpOp
doc: |-
Applies exp(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !ScalarExpression
scalar_fn:
kind: unary
fn_name: exp
operands:
- !ScalarExpression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: log
cpp_class_name: LogOp
doc: |-
Applies log(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !Scalarlogression
scalar_fn:
kind: unary
fn_name: log
operands:
- !Scalarlogression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: abs
cpp_class_name: AbsOp
doc: |-
Applies abs(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !Scalarabsression
scalar_fn:
kind: unary
fn_name: abs
operands:
- !ScalarExpression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: ceil
cpp_class_name: CeilOp
doc: |-
Applies ceil(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !Scalarceilression
scalar_fn:
kind: unary
fn_name: ceil
operands:
- !Scalarceilression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: floor
cpp_class_name: FloorOp
doc: |-
Applies floor(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !Scalarfloorression
scalar_fn:
kind: unary
fn_name: floor
operands:
- !Scalarfloorression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: negf
cpp_class_name: NegFOp
doc: |-
Applies negf(x) elementwise.
No numeric casting is performed on the input operand.
structured_op: !LinalgStructuredOpConfig
args:
- !LinalgOperandDefConfig
name: I
kind: input_tensor
type_var: T1
shape_map: affine_map<() -> ()>
- !LinalgOperandDefConfig
name: O
kind: output_tensor
type_var: T1
shape_map: affine_map<() -> ()>
indexing_maps: !LinalgIndexingMapsConfig
static_indexing_maps:
- affine_map<() -> ()>
- affine_map<() -> ()>
iterator_types: []
assignments:
- !ScalarAssign
arg: O
value: !Scalarnegfression
scalar_fn:
kind: unary
fn_name: negf
operands:
- !ScalarExpression
scalar_arg: I
--- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: elemwise_binary name: elemwise_binary
cpp_class_name: ElemwiseBinaryOp cpp_class_name: ElemwiseBinaryOp
doc: |- doc: |-
Applies the binary function fun elementwise. Applies the binary function fun elementwise.
Numeric casting is performed on the input operand, promoting it to the same Numeric casting is performed on the input operand, promoting it to the same
data type as the accumulator/output. data type as the accumulator/output.
structured_op: !LinalgStructuredOpConfig structured_op: !LinalgStructuredOpConfig
▲ Show 20 Lines • Show All 990 Lines • Show Last 20 Lines

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

Show All 30 Lines):
Numeric casting is performed on the input operand, promoting it to the same Numeric casting is performed on the input operand, promoting it to the same
data type as the accumulator/output. data type as the accumulator/output.
""" """
O[None] = fun(cast(U, I[None])) O[None] = fun(cast(U, I[None]))
@linalg_structured_op @linalg_structured_op
def exp(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies exp(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.exp(I[None])
@linalg_structured_op
def log(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies log(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.log(I[None])
@linalg_structured_op
def abs(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies abs(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.abs(I[None])
@linalg_structured_op
def ceil(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies ceil(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.ceil(I[None])
@linalg_structured_op
def floor(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies floor(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.floor(I[None])
@linalg_structured_op
def negf(
I=TensorDef(T1),
O=TensorDef(T1, output=True),
):
"""Applies negf(x) elementwise.
No numeric casting is performed on the input operand.
"""
O[None] = UnaryFn.negf(I[None])
@linalg_structured_op
def elemwise_binary( def elemwise_binary(
lhs=TensorDef(T1), lhs=TensorDef(T1),
rhs=TensorDef(T2), rhs=TensorDef(T2),
O=TensorDef(U, output=True), O=TensorDef(U, output=True),
fun=BinaryFnAttrDef(default=BinaryFn.add), fun=BinaryFnAttrDef(default=BinaryFn.add),
cast=TypeFnAttrDef(default=TypeFn.cast_signed), cast=TypeFnAttrDef(default=TypeFn.cast_signed),
): ):
"""Applies the binary function fun elementwise. """Applies the binary function fun elementwise.
Show All 15 Lines):
The shapes and element types must be identical. The appropriate casts, The shapes and element types must be identical. The appropriate casts,
broadcasts and reductions should be done previously to calling this op. broadcasts and reductions should be done previously to calling this op.
This means reduction/broadcast/element cast semantics is explicit. Further This means reduction/broadcast/element cast semantics is explicit. Further
passes can take that into account when lowering this code. For example, passes can take that into account when lowering this code. For example,
a `linalg.broadcast` + `linalg.add` sequence can be lowered to a a `linalg.broadcast` + `linalg.add` sequence can be lowered to a
`linalg.generic` with different affine maps for the two operands. `linalg.generic` with different affine maps for the two operands.
""" """
O[None] = lhs[None] + rhs[None] O[None] = BinaryFn.add(lhs[None], rhs[None])
@linalg_structured_op @linalg_structured_op
def sub( def sub(
lhs=TensorDef(T1), lhs=TensorDef(T1),
rhs=TensorDef(T1), rhs=TensorDef(T1),
O=TensorDef(T1, output=True), O=TensorDef(T1, output=True),
): ):
"""Subtracts two tensors elementwise. """Subtracts two tensors elementwise.
The shapes and element types must be identical. The appropriate casts, The shapes and element types must be identical. The appropriate casts,
broadcasts and reductions should be done previously to calling this op. broadcasts and reductions should be done previously to calling this op.
This means reduction/broadcast/element cast semantics is explicit. Further This means reduction/broadcast/element cast semantics is explicit. Further
passes can take that into account when lowering this code. For example, passes can take that into account when lowering this code. For example,
a `linalg.broadcast` + `linalg.sub` sequence can be lowered to a a `linalg.broadcast` + `linalg.sub` sequence can be lowered to a
`linalg.generic` with different affine maps for the two operands. `linalg.generic` with different affine maps for the two operands.
""" """
O[None] = lhs[None] - rhs[None] O[None] = BinaryFn.sub(lhs[None], rhs[None])
@linalg_structured_op @linalg_structured_op
def mul( def mul(
lhs=TensorDef(T1), lhs=TensorDef(T1),
rhs=TensorDef(T1), rhs=TensorDef(T1),
O=TensorDef(T1, output=True), O=TensorDef(T1, output=True),
): ):
"""Multiplies two tensors elementwise. """Multiplies two tensors elementwise.
The shapes and element types must be identical. The appropriate casts, The shapes and element types must be identical. The appropriate casts,
broadcasts and reductions should be done previously to calling this op. broadcasts and reductions should be done previously to calling this op.
This means reduction/broadcast/element cast semantics is explicit. Further This means reduction/broadcast/element cast semantics is explicit. Further
passes can take that into account when lowering this code. For example, passes can take that into account when lowering this code. For example,
a `linalg.broadcast` + `linalg.mul` sequence can be lowered to a a `linalg.broadcast` + `linalg.mul` sequence can be lowered to a
`linalg.generic` with different affine maps for the two operands. `linalg.generic` with different affine maps for the two operands.
""" """
O[None] = lhs[None] * rhs[None] O[None] = BinaryFn.mul(lhs[None], rhs[None])
@linalg_structured_op @linalg_structured_op
def div( def div(
lhs=TensorDef(T1), lhs=TensorDef(T1),
rhs=TensorDef(T1), rhs=TensorDef(T1),
O=TensorDef(T1, output=True), O=TensorDef(T1, output=True),
): ):
"""Divides the first tensor by the second tensor, elementwise. """Divides the first tensor by the second tensor, elementwise.
The shapes and element types must be identical. The appropriate casts, The shapes and element types must be identical. The appropriate casts,
broadcasts and reductions should be done previously to calling this op. broadcasts and reductions should be done previously to calling this op.
This means reduction/broadcast/element cast semantics is explicit. Further This means reduction/broadcast/element cast semantics is explicit. Further
passes can take that into account when lowering this code. For example, passes can take that into account when lowering this code. For example,
a `linalg.broadcast` + `linalg.div` sequence can be lowered to a a `linalg.broadcast` + `linalg.div` sequence can be lowered to a
`linalg.generic` with different affine maps for the two operands. `linalg.generic` with different affine maps for the two operands.
""" """
O[None] = lhs[None] / rhs[None] O[None] = BinaryFn.div(lhs[None], rhs[None])
@linalg_structured_op @linalg_structured_op
def div_unsigned( def div_unsigned(
lhs=TensorDef(T1), lhs=TensorDef(T1),
rhs=TensorDef(T1), rhs=TensorDef(T1),
O=TensorDef(T1, output=True), O=TensorDef(T1, output=True),
): ):
▲ Show 20 Lines • Show All 991 Lines • Show Last 20 Lines

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

Show First 20 Lines • Show All 290 Lines • ▼ Show 20 Lines
func.func @generalize_add(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>, func.func @generalize_add(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
%out: memref<7x14x21xf32>) { %out: memref<7x14x21xf32>) {
linalg.add ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>) linalg.add ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
outs(%out : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return return
} }
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> // CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_add // CHECK: func @generalize_add
// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>, // CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>) // CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic // CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]] // CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]} // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>) // CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>) // CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) // CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
// CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[BBARG0]], %[[BBARG1]] : f32 // CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[BBARG0]], %[[BBARG1]] : f32
// CHECK-NEXT: linalg.yield %[[SUM]] : f32 // CHECK-NEXT: linalg.yield %[[SUM]] : f32
// ----- // -----
func.func @generalize_sub(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>, func.func @generalize_sub(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
%out: memref<7x14x21xf32>) { %out: memref<7x14x21xf32>) {
linalg.sub ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>) linalg.sub ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
outs(%out : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return return
} }
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> // CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_sub // CHECK: func @generalize_sub
// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>, // CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>) // CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic // CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]] // CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]} // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>) // CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>) // CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) // CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
// CHECK-NEXT: %[[SUM:.+]] = arith.subf %[[BBARG0]], %[[BBARG1]] : f32 // CHECK-NEXT: %[[SUB:.+]] = arith.subf %[[BBARG0]], %[[BBARG1]] : f32
// CHECK-NEXT: linalg.yield %[[SUM]] : f32 // CHECK-NEXT: linalg.yield %[[SUB]] : f32
// ----- // -----
func.func @generalize_mul(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>, func.func @generalize_mul(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
%out: memref<7x14x21xf32>) { %out: memref<7x14x21xf32>) {
linalg.mul ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>) linalg.mul ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
outs(%out : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return return
} }
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> // CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_mul // CHECK: func @generalize_mul
// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>, // CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>) // CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic // CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]] // CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]} // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>) // CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>) // CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) // CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
// CHECK-NEXT: %[[SUM:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32 // CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32
// CHECK-NEXT: linalg.yield %[[SUM]] : f32 // CHECK-NEXT: linalg.yield %[[MUL]] : f32
// ----- // -----
func.func @generalize_div(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>, func.func @generalize_div(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
%out: memref<7x14x21xf32>) { %out: memref<7x14x21xf32>) {
linalg.div ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>) linalg.div ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
outs(%out : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return return
} }
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> // CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_div // CHECK: func @generalize_div
// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>, // CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>) // CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic // CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]] // CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]} // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>) // CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>) // CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32) // CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
// CHECK-NEXT: %[[SUM:.+]] = arith.divf %[[BBARG0]], %[[BBARG1]] : f32 // CHECK-NEXT: %[[DIV:.+]] = arith.divf %[[BBARG0]], %[[BBARG1]] : f32
// CHECK-NEXT: linalg.yield %[[SUM]] : f32 // CHECK-NEXT: linalg.yield %[[DIV]] : f32
// ----- // -----
func.func @generalize_divu(%lhs: memref<7x14x21xi32>, %rhs: memref<7x14x21xi32>, func.func @generalize_divu(%lhs: memref<7x14x21xi32>, %rhs: memref<7x14x21xi32>,
%out: memref<7x14x21xi32>) { %out: memref<7x14x21xi32>) {
linalg.div_unsigned ins(%lhs, %rhs : memref<7x14x21xi32>, memref<7x14x21xi32>) linalg.div_unsigned ins(%lhs, %rhs : memref<7x14x21xi32>, memref<7x14x21xi32>)
outs(%out : memref<7x14x21xi32>) outs(%out : memref<7x14x21xi32>)
return return
} }
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> // CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_divu // CHECK: func @generalize_divu
// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xi32>, %[[RHS:.+]]: memref<7x14x21xi32>, // CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xi32>, %[[RHS:.+]]: memref<7x14x21xi32>,
// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xi32>) // CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xi32>)
// CHECK: linalg.generic // CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]] // CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]} // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xi32>, memref<7x14x21xi32>) // CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xi32>, memref<7x14x21xi32>)
// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xi32>) // CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xi32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: i32, %[[BBARG2:.+]]: i32) // CHECK: ^{{.+}}(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: i32, %[[BBARG2:.+]]: i32)
// CHECK-NEXT: %[[SUM:.+]] = arith.divui %[[BBARG0]], %[[BBARG1]] : i32 // CHECK-NEXT: %[[DIVU:.+]] = arith.divui %[[BBARG0]], %[[BBARG1]] : i32
// CHECK-NEXT: linalg.yield %[[SUM]] : i32 // CHECK-NEXT: linalg.yield %[[DIVU]] : i32
// -----
func.func @generalize_exp(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.exp ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_exp
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[EXP:.+]] = math.exp %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[EXP]] : f32
// -----
func.func @generalize_log(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.log ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_log
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[log:.+]] = math.log %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[log]] : f32
// -----
func.func @generalize_abs(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.abs ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_abs
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[abs:.+]] = math.absf %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[abs]] : f32
// -----
func.func @generalize_ceil(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.ceil ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_ceil
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[ceil:.+]] = math.ceil %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[ceil]] : f32
// -----
func.func @generalize_floor(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.floor ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_floor
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[floor:.+]] = math.floor %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[floor]] : f32
// -----
func.func @generalize_negf(%arg: memref<7x14x21xf32>, %out: memref<7x14x21xf32>) {
linalg.negf ins(%arg : memref<7x14x21xf32>) outs(%out : memref<7x14x21xf32>)
return
}
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generalize_negf
// CHECK-SAME: (%[[ARG:.+]]: memref<7x14x21xf32>, %[[OUT:.+]]: memref<7x14x21xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[LHS]] : memref<7x14x21xf32>) outs(%[[OUT]] : memref<7x14x21xf32>)
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
// CHECK-NEXT: %[[negf:.+]] = arith.negf %[[BBARG0]] : f32
// CHECK-NEXT: linalg.yield %[[negf]] : f32

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

Show First 20 Lines • Show All 71 Lines • ▼ Show 20 Lines
// ----- // -----
func.func @divu_broadcast(%arg0: memref<8x16xi32>, %arg1: memref<4x8x16xi32>, %arg2: memref<4x8x16xi32>) { func.func @divu_broadcast(%arg0: memref<8x16xi32>, %arg1: memref<4x8x16xi32>, %arg2: memref<4x8x16xi32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3) // CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.div_unsigned ins(%arg0, %arg1 : memref<8x16xi32>, memref<4x8x16xi32>) outs(%arg2: memref<4x8x16xi32>) linalg.div_unsigned ins(%arg0, %arg1 : memref<8x16xi32>, memref<4x8x16xi32>) outs(%arg2: memref<4x8x16xi32>)
return return
} }
// -----
func.func @exp_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.exp ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @exp_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.exp ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @log_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.log ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @log_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.log ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @abs_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.abs ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @abs_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.abs ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @ceil_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.ceil ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @ceil_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.ceil ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @floor_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.floor ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @floor_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.floor ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @negf_type_cast(%arg: memref<4x8x16xf16>, %out: memref<4x8x16xf32>) {
// CHECK: operand 1 ('f16') doesn't match the element type of the enclosing linalg.generic op ('f32')
linalg.negf ins(%arg : memref<4x8x16xf16>) outs(%out: memref<4x8x16xf32>)
return
}
// -----
func.func @negf_broadcast(%arg: memref<8x16xf32>, %out: memref<4x8x16xf32>) {
// CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
linalg.negf ins(%arg : memref<8x16xf32>) outs(%out: memref<4x8x16xf32>)
return
}

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

Show First 20 Lines • Show All 991 Lines • ▼ Show 20 Lines
func.func @div_unsigned_tensor(%arg0: tensor<4x8x16xi32>, %arg1: tensor<4x8x16xi32>) -> tensor<4x8x16xi32> { func.func @div_unsigned_tensor(%arg0: tensor<4x8x16xi32>, %arg1: tensor<4x8x16xi32>) -> tensor<4x8x16xi32> {
%0 = tensor.empty() : tensor<4x8x16xi32> %0 = tensor.empty() : tensor<4x8x16xi32>
// CHECK: linalg.div_unsigned // CHECK: linalg.div_unsigned
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xi32>, tensor<4x8x16xi32>) // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xi32>, tensor<4x8x16xi32>)
// CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xi32>) // CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xi32>)
%1 = linalg.div_unsigned ins(%arg0, %arg1 : tensor<4x8x16xi32>, tensor<4x8x16xi32>) outs(%0: tensor<4x8x16xi32>) -> tensor<4x8x16xi32> %1 = linalg.div_unsigned ins(%arg0, %arg1 : tensor<4x8x16xi32>, tensor<4x8x16xi32>) outs(%0: tensor<4x8x16xi32>) -> tensor<4x8x16xi32>
return %1 : tensor<4x8x16xi32> return %1 : tensor<4x8x16xi32>
} }
// -----
// CHECK-LABEL: func @exp_dynamic
func.func @exp_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.exp
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.exp ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @exp_static
func.func @exp_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.exp
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.exp ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @exp_tensor
func.func @exp_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.exp
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.exp ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: func @log_dynamic
func.func @log_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.log
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.log ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @log_static
func.func @log_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.log
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.log ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @log_tensor
func.func @log_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.log
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.log ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: func @abs_dynamic
func.func @abs_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.abs
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.abs ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @abs_static
func.func @abs_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.abs
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.abs ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @abs_tensor
func.func @abs_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.abs
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.abs ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: func @ceil_dynamic
func.func @ceil_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.ceil
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.ceil ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @ceil_static
func.func @ceil_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.ceil
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.ceil ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @ceil_tensor
func.func @ceil_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.ceil
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.ceil ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: func @floor_dynamic
func.func @floor_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.floor
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.floor ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @floor_static
func.func @floor_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.floor
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.floor ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @floor_tensor
func.func @floor_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.floor
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.floor ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
// -----
// CHECK-LABEL: func @negf_dynamic
func.func @negf_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>) {
// CHECK: linalg.negf
// CHECK-SAME: ins(%{{.+}} : memref<?x?x?xf32>) outs(%{{.+}} : memref<?x?x?xf32>)
linalg.negf ins(%arg0 : memref<?x?x?xf32>) outs(%arg1: memref<?x?x?xf32>)
return
}
// -----
// CHECK-LABEL: func @negf_static
func.func @negf_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>) {
// CHECK: linalg.negf
// CHECK-SAME: ins(%{{.+}} : memref<4x8x16xf32>) outs(%{{.+}} : memref<4x8x16xf32>)
linalg.negf ins(%arg0 : memref<4x8x16xf32>) outs(%arg1: memref<4x8x16xf32>)
return
}
// -----
// CHECK-LABEL: func @negf_tensor
func.func @negf_tensor(%arg0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
%0 = tensor.empty() : tensor<4x8x16xf32>
// CHECK: linalg.negf
// CHECK-SAME: ins(%{{.+}} : tensor<4x8x16xf32>) outs(%{{.+}} : tensor<4x8x16xf32>)
%1 = linalg.negf ins(%arg0 : tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
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