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[mlir][linalg] Reject unsigned pooling on non-integer element types #166070

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[mlir][linalg] Reject unsigned pooling on non-integer element types #166070

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8d5b35a
[mlir][linalg] Reject unsigned pooling on non-integer element types
Men-cotton Nov 2, 2025
bf6ad78
[mlir][linalg][python] Reject unsigned pooling on non-integer element…
Men-cotton Nov 3, 2025
b24dc2b
[mlir][linalg][test] Drop float min_unsigned roundtrip
Men-cotton Dec 19, 2025
3833091
[mlir][linalg][test] Refactor: Move unsigned pooling float check to i…
Men-cotton Nov 21, 2025
2fa6c64
Improve tests
Men-cotton Dec 14, 2025
3e311fd
Remove duplicate newlines at the end
Men-cotton Dec 14, 2025
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18 changes: 14 additions & 4 deletions mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -579,13 +579,23 @@ class RegionBuilderHelper {
return arith::MinSIOp::create(builder, arg0.getLoc(), arg0, arg1);
case BinaryFn::max_unsigned:
assert(!allComplex);
if (allFloatingPoint)
return arith::MaximumFOp::create(builder, arg0.getLoc(), arg0, arg1);
if (!allInteger || allBool) {
if (emitError) {
emitError() << "unsupported operation: unsigned max not on uint";
return nullptr;
}
llvm_unreachable("unsupported operation: unsigned max not on uint");
}
Comment on lines +582 to +588
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@Abhishek-Varma Abhishek-Varma Nov 3, 2025

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Hi @Men-cotton - thanks for taking this up.
Although this addresses the immediate issue of linalg.*_(max|min)_unsigned_* - a better solution might be to indeed fix :-

llvm-project/mlir/python/mlir/dialects/linalg/opdsl/lang/emitter.py

Lines 534 to 539 in f5885de

def _binary_max_unsigned(self, lhs: Value, rhs: Value) -> Value:
if _is_floating_point_type(lhs.type):
return arith.MaximumFOp(lhs, rhs).result
if _is_integer_type(lhs.type) or _is_index_type(lhs.type):
return arith.MaxUIOp(lhs, rhs).result
raise NotImplementedError("Unsupported 'max_unsigned' operands: {lhs}, {rhs}")

Because that'd ensure that in future some other op implementing a (max|min)_unsigned also doesn't warrant a similar fix in their verifier as the one above.

I also see something similar already done in this file for a non-Convolution op : linalg.div_unsigned :-

llvm-project/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

Lines 561 to 568 in 03ef5fc

case BinaryFn::div_unsigned:
if (!allInteger || allBool) {
if (emitError) {
emitError() << "unsupported operation: unsigned div not on uint";
return nullptr;
}
llvm_unreachable("unsupported operation: unsigned div not on uint");
}

Again, these are just my opinion - I'll let @banach-space take a closer look. :)

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@banach-space banach-space Nov 10, 2025

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I'm not really familiar with OPDSL, but in ideal world we should be able to write verification tests and add them in https://github.com/llvm/llvm-project/blob/main/mlir/test/Dialect/Linalg/invalid.mlir - could you try @Men-cotton ?

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@Men-cotton Men-cotton Nov 21, 2025

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Hi @banach-space , thank you for the suggestion.
I actually already added a verification test in mlir/test/Dialect/Linalg/named-ops-fail.mlir. If you prefer invalid.mlir or find the current test insufficient, please let me know.

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@Men-cotton Men-cotton Dec 3, 2025

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@banach-space @Abhishek-Varma
I've moved the verification tests from named-ops-fail.mlir to invalid.mlir. This allows us to use -verify-diagnostics for more precise error location checking, rather than relying on FileCheck.
Please take a look. Thanks!

return arith::MaxUIOp::create(builder, arg0.getLoc(), arg0, arg1);
case BinaryFn::min_unsigned:
assert(!allComplex);
if (allFloatingPoint)
return arith::MinimumFOp::create(builder, arg0.getLoc(), arg0, arg1);
if (!allInteger || allBool) {
if (emitError) {
emitError() << "unsupported operation: unsigned min not on uint";
return nullptr;
}
llvm_unreachable("unsupported operation: unsigned min not on uint");
}
return arith::MinUIOp::create(builder, arg0.getLoc(), arg0, arg1);
case BinaryFn::powf:
assert(allFloatingPoint);
Expand Down
18 changes: 12 additions & 6 deletions mlir/python/mlir/dialects/linalg/opdsl/lang/emitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -532,9 +532,9 @@ def _binary_max_signed(self, lhs: Value, rhs: Value) -> Value:
raise NotImplementedError("Unsupported 'max' operands: {lhs}, {rhs}")

def _binary_max_unsigned(self, lhs: Value, rhs: Value) -> Value:
if _is_floating_point_type(lhs.type):
return arith.MaximumFOp(lhs, rhs).result
if _is_integer_type(lhs.type) or _is_index_type(lhs.type):
if (
_is_integer_type(lhs.type) and not _is_bool_type(lhs.type)
) or _is_index_type(lhs.type):
return arith.MaxUIOp(lhs, rhs).result
raise NotImplementedError("Unsupported 'max_unsigned' operands: {lhs}, {rhs}")

Expand All @@ -546,9 +546,9 @@ def _binary_min_signed(self, lhs: Value, rhs: Value) -> Value:
raise NotImplementedError("Unsupported 'min' operands: {lhs}, {rhs}")

def _binary_min_unsigned(self, lhs: Value, rhs: Value) -> Value:
if _is_floating_point_type(lhs.type):
return arith.MinimumFOp(lhs, rhs).result
if _is_integer_type(lhs.type) or _is_index_type(lhs.type):
if (
_is_integer_type(lhs.type) and not _is_bool_type(lhs.type)
) or _is_index_type(lhs.type):
return arith.MinUIOp(lhs, rhs).result
raise NotImplementedError("Unsupported 'min_unsigned' operands: {lhs}, {rhs}")

Expand Down Expand Up @@ -634,6 +634,12 @@ def _is_index_type(t: Type) -> bool:
return IndexType.isinstance(t)


def _is_bool_type(t: Type) -> bool:
if not IntegerType.isinstance(t):
return False
return IntegerType(t).width == 1


def _get_floating_point_width(t: Type) -> int:
# TODO: Create a FloatType in the Python API and implement the switch
# there.
Expand Down
13 changes: 0 additions & 13 deletions mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -481,19 +481,6 @@ func.func @pooling_nhwc_min_unsigned_integer(%input: tensor<?x?x?x?xi32>, %filte

// -----

func.func @pooling_nhwc_min_unsigned_float(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%0 = linalg.pooling_nhwc_min_unsigned
{dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<?x?x?x?xf32>, tensor<?x?xf32>)
outs (%output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}
// CHECK: @pooling_nhwc_min_unsigned_float
// CHECK: linalg.pooling_nhwc_min
// CHECK-SAME: dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>

// -----

func.func @pooling_nchw_sum(%input: tensor<?x?x?x?xf32>, %filter: tensor<?x?xf32>, %output: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%0 = linalg.pooling_nchw_sum
{dilations = dense<2> : tensor<2xi64>, strides = dense<3> : tensor<2xi64>}
Expand Down
119 changes: 119 additions & 0 deletions mlir/test/Dialect/Linalg/invalid.mlir
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Expand Up @@ -1931,6 +1931,125 @@ func.func @reduce_non_operation_name(%arg0: tensor<4xf32>, %arg1: tensor<f32>) -

// -----

//===----------------------------------------------------------------------===//
// linalg.pooling_nhwc_*
//===----------------------------------------------------------------------===//

func.func @pooling_nhwc_max_unsigned_float_type(
%input: tensor<1x4x4x1xf32>,
%filter: tensor<2x2xf32>,
%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> {
// expected-error @+1 {{unsupported operation: unsigned max not on uint}}
%0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<1x4x4x1xf32>, tensor<2x2xf32>)
outs (%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32>
return %0 : tensor<1x2x2x1xf32>
}

// -----

func.func @pooling_nhwc_max_unsigned_i1(
%input: tensor<1x4x4x1xi1>,
%filter: tensor<2x2xi1>,
%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> {
// expected-error @+1 {{unsupported operation: unsigned max not on uint}}
%0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<1x4x4x1xi1>, tensor<2x2xi1>)
outs (%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1>
return %0 : tensor<1x2x2x1xi1>
}

// -----

func.func @pooling_nhwc_min_unsigned_float_type(
%input: tensor<1x4x4x1xf32>,
%filter: tensor<2x2xf32>,
%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32> {
// expected-error @+1 {{unsupported operation: unsigned min not on uint}}
%0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<1x4x4x1xf32>, tensor<2x2xf32>)
outs (%init_val: tensor<1x2x2x1xf32>) -> tensor<1x2x2x1xf32>
return %0 : tensor<1x2x2x1xf32>
}

// -----

func.func @pooling_nhwc_min_unsigned_i1(
%input: tensor<1x4x4x1xi1>,
%filter: tensor<2x2xi1>,
%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1> {
// expected-error @+1 {{unsupported operation: unsigned min not on uint}}
%0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<1x4x4x1xi1>, tensor<2x2xi1>)
outs (%init_val: tensor<1x2x2x1xi1>) -> tensor<1x2x2x1xi1>
return %0 : tensor<1x2x2x1xi1>
}

// -----

//===----------------------------------------------------------------------===//
// linalg.pooling_nwc_*
//===----------------------------------------------------------------------===//

func.func @pooling_nwc_max_unsigned_float_type(
%input: tensor<1x4x1xf32>,
%filter: tensor<2xf32>,
%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> {
// expected-error @+1 {{unsupported operation: unsigned max not on uint}}
%0 = linalg.pooling_nwc_max_unsigned {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: tensor<1x4x1xf32>, tensor<2xf32>)
outs (%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32>
return %0 : tensor<1x2x1xf32>
}

// -----

func.func @pooling_nwc_max_unsigned_i1(
%input: tensor<1x4x1xi1>,
%filter: tensor<2xi1>,
%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> {
// expected-error @+1 {{unsupported operation: unsigned max not on uint}}
%0 = linalg.pooling_nwc_max_unsigned {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: tensor<1x4x1xi1>, tensor<2xi1>)
outs (%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1>
return %0 : tensor<1x2x1xi1>
}

// -----

func.func @pooling_nwc_min_unsigned_float_type(
%input: tensor<1x4x1xf32>,
%filter: tensor<2xf32>,
%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32> {
// expected-error @+1 {{unsupported operation: unsigned min not on uint}}
%0 = linalg.pooling_nwc_min_unsigned {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: tensor<1x4x1xf32>, tensor<2xf32>)
outs (%init_val: tensor<1x2x1xf32>) -> tensor<1x2x1xf32>
return %0 : tensor<1x2x1xf32>
}

// -----

func.func @pooling_nwc_min_unsigned_i1(
%input: tensor<1x4x1xi1>,
%filter: tensor<2xi1>,
%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1> {
// expected-error @+1 {{unsupported operation: unsigned min not on uint}}
%0 = linalg.pooling_nwc_min_unsigned {dilations = dense<1> : tensor<1xi64>,
strides = dense<1> : tensor<1xi64>}
ins (%input, %filter: tensor<1x4x1xi1>, tensor<2xi1>)
outs (%init_val: tensor<1x2x1xi1>) -> tensor<1x2x1xi1>
return %0 : tensor<1x2x1xi1>
}

// -----

//===----------------------------------------------------------------------===//
// Tests for generic infrastructure for named Ops. The actual Ops used are
Expand Down
1 change: 0 additions & 1 deletion mlir/test/Dialect/Linalg/named-ops-fail.mlir
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Expand Up @@ -349,4 +349,3 @@ func.func @select_wrong_condition_type(%arg0: memref<4x8x16xf32>, %arg1: memref<
linalg.select ins(%arg0, %arg1, %arg2 : memref<4x8x16xf32>, memref<4x8x16xf32>, memref<4x8x16xf32>) outs(%arg3: memref<4x8x16xf32>)
return
}

72 changes: 72 additions & 0 deletions mlir/test/Dialect/Linalg/named-ops.mlir
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Expand Up @@ -705,6 +705,42 @@ func.func @pooling_nhwc_max_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x
return %res : tensor<1x2x2x1xf32>
}

// -----

// CHECK-LABEL: func @pooling_nhwc_max_unsigned_i32
// CHECK: %{{.+}} = linalg.pooling_nhwc_max_unsigned
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xi32>, tensor<3x3xi32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
func.func @pooling_nhwc_max_unsigned_i32(%input: tensor<1x4x4x1xi32>) -> tensor<1x2x2x1xi32> {
%fake = tensor.empty() : tensor<3x3xi32>
%init = tensor.empty() : tensor<1x2x2x1xi32>
%cst = arith.constant 0 : i32
%fill = linalg.fill ins(%cst : i32) outs(%init : tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
%res = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
ins(%input, %fake: tensor<1x4x4x1xi32>, tensor<3x3xi32>)
outs(%fill: tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
return %res : tensor<1x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @pooling_nwc_max_unsigned_i32
// CHECK: %{{.+}} = linalg.pooling_nwc_max_unsigned
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
// CHECK-SAME: strides = dense<1> : tensor<1xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x1xi32>, tensor<3xi32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
func.func @pooling_nwc_max_unsigned_i32(%input: tensor<1x4x1xi32>) -> tensor<1x2x1xi32> {
%fake = tensor.empty() : tensor<3xi32>
%init = tensor.empty() : tensor<1x2x1xi32>
%cst = arith.constant 0 : i32
%fill = linalg.fill ins(%cst : i32) outs(%init : tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
%res = linalg.pooling_nwc_max_unsigned {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
ins(%input, %fake: tensor<1x4x1xi32>, tensor<3xi32>)
outs(%fill: tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
return %res : tensor<1x2x1xi32>
}

// -----
// CHECK-LABEL: func @pooling_nwc_max_tensor
// CHECK: %{{.+}} = linalg.pooling_nwc_max
Expand Down Expand Up @@ -1017,6 +1053,42 @@ func.func @pooling_nhwc_min_tensor(%input: tensor<1x4x4x1xf32>) -> tensor<1x2x2x

// -----

// CHECK-LABEL: func @pooling_nhwc_min_unsigned_i32
// CHECK: %{{.+}} = linalg.pooling_nhwc_min_unsigned
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x4x1xi32>, tensor<3x3xi32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
func.func @pooling_nhwc_min_unsigned_i32(%input: tensor<1x4x4x1xi32>) -> tensor<1x2x2x1xi32> {
%fake = tensor.empty() : tensor<3x3xi32>
%init = tensor.empty() : tensor<1x2x2x1xi32>
%cst = arith.constant 0 : i32
%fill = linalg.fill ins(%cst : i32) outs(%init : tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
%res = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
ins(%input, %fake: tensor<1x4x4x1xi32>, tensor<3x3xi32>)
outs(%fill: tensor<1x2x2x1xi32>) -> tensor<1x2x2x1xi32>
return %res : tensor<1x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @pooling_nwc_min_unsigned_i32
// CHECK: %{{.+}} = linalg.pooling_nwc_min_unsigned
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
// CHECK-SAME: strides = dense<1> : tensor<1xi64>
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<1x4x1xi32>, tensor<3xi32>)
// CHECK-SAME: outs(%{{.+}} : tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
func.func @pooling_nwc_min_unsigned_i32(%input: tensor<1x4x1xi32>) -> tensor<1x2x1xi32> {
%fake = tensor.empty() : tensor<3xi32>
%init = tensor.empty() : tensor<1x2x1xi32>
%cst = arith.constant 0 : i32
%fill = linalg.fill ins(%cst : i32) outs(%init : tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
%res = linalg.pooling_nwc_min_unsigned {dilations = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
ins(%input, %fake: tensor<1x4x1xi32>, tensor<3xi32>)
outs(%fill: tensor<1x2x1xi32>) -> tensor<1x2x1xi32>
return %res : tensor<1x2x1xi32>
}

// -----

// CHECK-LABEL: func @pooling_nwc_min_tensor
// CHECK: %{{.+}} = linalg.pooling_nwc_min
// CHECK-SAME: dilations = dense<1> : tensor<1xi64>
Expand Down
28 changes: 14 additions & 14 deletions mlir/test/Dialect/Linalg/transform-op-decompose.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -131,21 +131,21 @@ func.func @pooling_nhwc_max(%input: tensor<?x1x?x?xf32>, %filter: tensor<1x?xf32
}

// CHECK-LABEL: @pooling_nhwc_max_unsigned
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x?xf32>,
// CHECK-SAME: %[[ARG1:.+]]: tensor<1x?xf32>
// CHECK-SAME: %[[ARG2:.+]]: tensor<?x1x?x?xf32>
func.func @pooling_nhwc_max_unsigned(%input: tensor<?x1x?x?xf32>, %filter: tensor<1x?xf32>, %init: tensor<?x1x?x?xf32>) -> tensor<?x1x?x?xf32> {
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x?xi32>,
// CHECK-SAME: %[[ARG1:.+]]: tensor<1x?xi32>
// CHECK-SAME: %[[ARG2:.+]]: tensor<?x1x?x?xi32>
func.func @pooling_nhwc_max_unsigned(%input: tensor<?x1x?x?xi32>, %filter: tensor<1x?xi32>, %init: tensor<?x1x?x?xi32>) -> tensor<?x1x?x?xi32> {
// CHECK: %[[SLICE0:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK: %[[SLICE1:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG2]]
// CHECK: %[[SLICERES:.+]] = linalg.pooling_nwc_max_unsigned
// CHECK: %[[RES:.+]] = tensor.insert_slice %[[SLICERES]] into %[[ARG2]]
%0 = linalg.pooling_nhwc_max_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<?x1x?x?xf32>, tensor<1x?xf32>)
outs (%init: tensor<?x1x?x?xf32>) -> tensor<?x1x?x?xf32>
ins (%input, %filter: tensor<?x1x?x?xi32>, tensor<1x?xi32>)
outs (%init: tensor<?x1x?x?xi32>) -> tensor<?x1x?x?xi32>
// CHECK: return %[[RES]]
return %0 : tensor<?x1x?x?xf32>
return %0 : tensor<?x1x?x?xi32>
}

// CHECK-LABEL: @pooling_nhwc_min
Expand All @@ -167,21 +167,21 @@ func.func @pooling_nhwc_min(%input: tensor<?x1x?x?xf32>, %filter: tensor<1x?xf32
}

// CHECK-LABEL: @pooling_nhwc_min_unsigned
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x?xf32>,
// CHECK-SAME: %[[ARG1:.+]]: tensor<1x?xf32>
// CHECK-SAME: %[[ARG2:.+]]: tensor<?x1x?x?xf32>
func.func @pooling_nhwc_min_unsigned(%input: tensor<?x1x?x?xf32>, %filter: tensor<1x?xf32>, %init: tensor<?x1x?x?xf32>) -> tensor<?x1x?x?xf32> {
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x?xi32>,
// CHECK-SAME: %[[ARG1:.+]]: tensor<1x?xi32>
// CHECK-SAME: %[[ARG2:.+]]: tensor<?x1x?x?xi32>
func.func @pooling_nhwc_min_unsigned(%input: tensor<?x1x?x?xi32>, %filter: tensor<1x?xi32>, %init: tensor<?x1x?x?xi32>) -> tensor<?x1x?x?xi32> {
// CHECK: %[[SLICE0:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK: %[[SLICE1:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG2]]
// CHECK: %[[SLICERES:.+]] = linalg.pooling_nwc_min_unsigned
// CHECK: %[[RES:.+]] = tensor.insert_slice %[[SLICERES]] into %[[ARG2]]
%0 = linalg.pooling_nhwc_min_unsigned {dilations = dense<1> : tensor<2xi64>,
strides = dense<1> : tensor<2xi64>}
ins (%input, %filter: tensor<?x1x?x?xf32>, tensor<1x?xf32>)
outs (%init: tensor<?x1x?x?xf32>) -> tensor<?x1x?x?xf32>
ins (%input, %filter: tensor<?x1x?x?xi32>, tensor<1x?xi32>)
outs (%init: tensor<?x1x?x?xi32>) -> tensor<?x1x?x?xi32>
// CHECK: return %[[RES]]
return %0 : tensor<?x1x?x?xf32>
return %0 : tensor<?x1x?x?xi32>
}

// CHECK-LABEL: @pooling_nchw_max
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