traits.mlir
5.81 KB
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// RUN: mlir-opt %s -split-input-file -verify-diagnostics
// Verify that ops with broadcastable trait verifies operand and result type
// combinations and emits an error for invalid combinations.
func @broadcast_scalar_scalar_scalar(tensor<i32>, tensor<i32>) -> tensor<i32> {
^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
return %0 : tensor<i32>
}
// -----
func @broadcast_tensor_scalar_tensor(tensor<4xi32>, tensor<i32>) -> tensor<4xi32> {
^bb0(%arg0: tensor<4xi32>, %arg1: tensor<i32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4xi32>, tensor<i32>) -> tensor<4xi32>
return %0 : tensor<4xi32>
}
// -----
// Check only one dimension has size 1
func @broadcast_tensor_tensor_tensor(tensor<4x3x2xi32>, tensor<3x1xi32>) -> tensor<4x3x2xi32> {
^bb0(%arg0: tensor<4x3x2xi32>, %arg1: tensor<3x1xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4x3x2xi32>, tensor<3x1xi32>) -> tensor<4x3x2xi32>
return %0 : tensor<4x3x2xi32>
}
// -----
// Check multiple dimensions have size 1
func @broadcast_tensor_tensor_tensor(tensor<8x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x5xi32> {
^bb0(%arg0: tensor<8x1x6x1xi32>, %arg1: tensor<7x1x5xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<8x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x5xi32>
return %0 : tensor<8x7x6x5xi32>
}
// -----
// Check leading unknown dimension
func @broadcast_tensor_tensor_tensor(tensor<?x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<?x7x6x5xi32> {
^bb0(%arg0: tensor<?x1x6x1xi32>, %arg1: tensor<7x1x5xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<?x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<?x7x6x5xi32>
return %0 : tensor<?x7x6x5xi32>
}
// -----
// Check unknown dimension in the middle
func @broadcast_tensor_tensor_tensor(tensor<8x1x?x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x?x5xi32> {
^bb0(%arg0: tensor<8x1x?x1xi32>, %arg1: tensor<7x1x5xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<8x1x?x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x?x5xi32>
return %0 : tensor<8x7x?x5xi32>
}
// -----
// Check incompatible vector and tensor result type
func @broadcast_scalar_vector_vector(tensor<4xf32>, tensor<4xf32>) -> vector<4xf32> {
^bb0(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>):
// expected-error @+1 {{cannot broadcast vector with tensor}}
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> vector<4xf32>
return %0 : vector<4xf32>
}
// -----
// Check incompatible operand types with known dimension
func @broadcast_tensor_tensor_tensor(tensor<4x3x2xi32>, tensor<3x3xi32>) -> tensor<4x3x2xi32> {
^bb0(%arg0: tensor<4x3x2xi32>, %arg1: tensor<3x3xi32>):
// expected-error @+1 {{operands don't have broadcast-compatible shapes}}
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4x3x2xi32>, tensor<3x3xi32>) -> tensor<4x3x2xi32>
return %0 : tensor<4x3x2xi32>
}
// -----
// Check incompatible result type with known dimension
func @broadcast_tensor_tensor_tensor(tensor<4x3x2xi32>, tensor<3x1xi32>) -> tensor<4x3x3xi32> {
^bb0(%arg0: tensor<4x3x2xi32>, %arg1: tensor<3x1xi32>):
// expected-error @+1 {{does not have shape compatible with the one computed}}
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4x3x2xi32>, tensor<3x1xi32>) -> tensor<4x3x3xi32>
return %0 : tensor<4x3x3xi32>
}
// -----
// Check incompatible result type with known dimension
func @broadcast_tensor_tensor_tensor(tensor<8x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x1xi32> {
^bb0(%arg0: tensor<8x1x6x1xi32>, %arg1: tensor<7x1x5xi32>):
// expected-error @+1 {{does not have shape compatible with the one computed}}
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<8x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x1xi32>
return %0 : tensor<8x7x6x1xi32>
}
// -----
func @broadcast_tensor_tensor_tensor(tensor<2xi32>, tensor<2xi32>) -> tensor<*xi32> {
^bb0(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<*xi32>
return %0 : tensor<*xi32>
}
// -----
func @broadcast_tensor_tensor_tensor(tensor<4x3x2xi32>, tensor<?xi32>) -> tensor<4x3x2xi32> {
^bb0(%arg0: tensor<4x3x2xi32>, %arg1: tensor<?xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4x3x2xi32>, tensor<?xi32>) -> tensor<4x3x2xi32>
return %0 : tensor<4x3x2xi32>
}
// -----
// Unranked operands but ranked result
func @broadcast_tensor_tensor_tensor(tensor<*xi32>, tensor<*xi32>) -> tensor<2xi32> {
^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<*xi32>, tensor<*xi32>) -> tensor<2xi32>
return %0 : tensor<2xi32>
}
// -----
// Unranked operand and compatible ranked result
func @broadcast_tensor_tensor_tensor(tensor<3x2xi32>, tensor<*xi32>) -> tensor<4x3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>, %arg1: tensor<*xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<3x2xi32>, tensor<*xi32>) -> tensor<4x3x2xi32>
return %0 : tensor<4x3x2xi32>
}
// -----
func @broadcast_tensor_tensor_tensor(tensor<3x2xi32>, tensor<*xi32>) -> tensor<2xi32> {
^bb0(%arg0: tensor<3x2xi32>, %arg1: tensor<*xi32>):
// expected-error @+1 {{shape incompatible with a ranked operand type}}
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<3x2xi32>, tensor<*xi32>) -> tensor<2xi32>
return %0 : tensor<2xi32>
}
// -----
func @broadcast_tensor_tensor_tensor(tensor<?x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x5xi32> {
^bb0(%arg0: tensor<?x1x6x1xi32>, %arg1: tensor<7x1x5xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<?x1x6x1xi32>, tensor<7x1x5xi32>) -> tensor<8x7x6x5xi32>
return %0 : tensor<8x7x6x5xi32>
}
// -----
func @broadcastDifferentResultType(tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1> {
^bb0(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>):
%0 = "test.broadcastable"(%arg0, %arg1) : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1>
return %0 : tensor<4xi1>
}