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// RUN: mlir-opt %s -split-input-file -verify-diagnostics --tosa-validate=strict-op-spec-alignment
func.func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error@+1 {{expect both input and weight to be float or not together, got 'f32' and 'i8'}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
// -----
func.func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error@+1 {{expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error@+1 {{'tosa.conv2d' op operand #1 must be 4D tensor of 4-bit signless integer or 8-bit signless integer or Quint8 type or Qint4 type or Qint8 type or Qint16 type or Qint32 type or 32-bit float or 16-bit float or bfloat16 type values, but got 'tensor<*xi8>'}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
// -----
func.func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
// expected-error@+1 {{'tosa.conv2d' op quantizationattr is required for quantized type, and not allowed for float type}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
return %0 : tensor<1x27x27x16xi8>
}
// -----
func.func @test_concat(%arg0 : tensor<2x1xf32>, %arg1 : tensor<2x2xf32>) -> tensor<?x?xf32> {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{Cannot concat tensors with different sizes on the non-axis dimension 1}}
%0 = "tosa.concat"(%arg0, %arg1) {axis = 0 : i64} : (tensor<2x1xf32>, tensor<2x2xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
// -----
func.func @test_concat_element_type_mismatch(%arg0 : tensor<1x2xf32>, %arg1 : tensor<2x2xf32>) -> tensor<?x?xi8> {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.concat' op inferred type(s) 'tensor<3x2xf32>' are incompatible with return type(s) of operation 'tensor<?x?xi8>}}
%0 = "tosa.concat"(%arg0, %arg1) {axis = 0 : i64} : (tensor<1x2xf32>, tensor<2x2xf32>) -> tensor<?x?xi8>
return %0 : tensor<?x?xi8>
}
// -----
func.func @test_pad_non_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3x2xi32>) -> tensor<13x21x3xf32> {
// expected-error@+1 {{'tosa.pad' op padding of pad is not constant}}
%0 = "tosa.pad"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<3x2xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
// -----
func.func @test_pad_non_const(%arg0: tensor<13x21x3xi8>, %arg1: tensor<i8>) -> tensor<13x21x3xi8> {
%0 = "tosa.const"() {value = dense<[[0, 0], [0, 1], [0, 1]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// expected-error@+1 {{'tosa.pad' op pad_const of pad is not constant}}
%1 = "tosa.pad"(%arg0, %0, %arg1) : (tensor<13x21x3xi8>, tensor<3x2xi32>, tensor<i8>) -> tensor<13x21x3xi8>
return %1 : tensor<13x21x3xi8>
}
// -----
func.func @test_transpose_non_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3xi32>) -> tensor<3x13x21xf32> {
// expected-error@+1 {{'tosa.transpose' op perms of transpose is not constant}}
%0 = "tosa.transpose"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21xf32>
return %0 : tensor<3x13x21xf32>
}
// -----
func.func @test_fully_connected_non_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<2x3xf32>) -> tensor<273x2xf32> {
%0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
%1 = "tosa.reshape"(%arg0) {new_shape = array<i64: 273, 3>} : (tensor<13x21x3xf32>) -> tensor<273x3xf32>
// expected-error@+1 {{'tosa.fully_connected' op weight of fully_connected is not constant}}
%2 = "tosa.fully_connected"(%1, %arg1, %0) : (tensor<273x3xf32>, tensor<2x3xf32>, tensor<2xf32>) -> tensor<273x2xf32>
return %2 : tensor<273x2xf32>
}
// -----
func.func @test_fully_connected_non_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<2xf32>) -> tensor<273x2xf32> {
%0 = "tosa.const"() {value = dense<[[-0.613216758, -0.63714242, -0.73500061], [0.180762768, 0.773053169, -0.933686495]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
%1 = "tosa.reshape"(%arg0) {new_shape = array<i64: 273, 3>} : (tensor<13x21x3xf32>) -> tensor<273x3xf32>
// expected-error@+1 {{'tosa.fully_connected' op bias of fully_connected is not constant}}
%2 = "tosa.fully_connected"(%1, %0, %arg1) : (tensor<273x3xf32>, tensor<2x3xf32>, tensor<2xf32>) -> tensor<273x2xf32>
return %2 : tensor<273x2xf32>
}
// -----
func.func @test_reduce_sum_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.reduce_sum' op inferred type(s) 'tensor<1x3x4x5xf32>' are incompatible with return type(s) of operation 'tensor<1x3x4x5xi32>'}}
%0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} : (tensor<2x3x4x5xf32>) -> tensor<1x3x4x5xi32>
return
}
// -----
func.func @test_reduce_max_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.reduce_max' op inferred type(s) 'tensor<2x3x4x1xf32>' are incompatible with return type(s) of operation 'tensor<2x3x4x1xi32>'}}
%0 = "tosa.reduce_max"(%arg0) {axis = 3 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x1xi32>
return
}
// -----
func.func @test_reduce_min_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.reduce_min' op inferred type(s) 'tensor<2x1x4x5xf32>' are incompatible with return type(s) of operation 'tensor<2x1x4x5xi32>'}}
%0 = "tosa.reduce_min"(%arg0) {axis = 1 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x1x4x5xi32>
return
}
// -----
func.func @test_reduce_prod_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.reduce_prod' op inferred type(s) 'tensor<2x1x4x5xf32>' are incompatible with return type(s) of operation 'tensor<2x3x4x5xf32>'}}
%0 = "tosa.reduce_prod"(%arg0) {axis = 1 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32>
return
}
// -----
func.func @test_reshape_type_mismatch(%arg0 : tensor<13x21x3xf32>) -> () {
// expected-error@+2 {{failed to infer returned types}}
// expected-error@+1 {{'tosa.reshape' op inferred type(s) 'tensor<13x21x3x1xf32>' are incompatible with return type(s) of operation 'tensor<13x21x3x1xi32>'}}
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3, 1>} : (tensor<13x21x3xf32>) -> tensor<13x21x3x1xi32>
return
}
// -----
func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
// expected-error@+1 {{'tosa.const' op failed to verify that all of {value, output} have same shape}}
%0 = "tosa.const"() {value = dense<0.000000e+00> : tensor<1x1xf32>} : () -> tensor<100x100xf32>
return %0 : tensor<100x100xf32>
}
// -----
func.func @test_reshape_static_zero_dim_input(%arg0 : tensor<13x0x3xf32>) -> () {
// expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<13x0x3xf32>) -> tensor<13x0x3xf32>
return
}
// -----
func.func @test_reshape_zero_dim_input(%arg0 : tensor<?x0x3xf32>) -> () {
// expected-error@+1 {{'tosa.reshape' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.reshape"(%arg0) {new_shape = array<i64: 13, 21, 3>} : (tensor<?x0x3xf32>) -> tensor<13x0x3xf32>
return
}
// -----
func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
// expected-error@+1 {{'tosa.conv2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
// -----
func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
// expected-error@+1 {{'tosa.conv2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
}
// -----
func.func @test_avg_pool2d_static_zero_dim_input(%arg0: tensor<1x0x7x9xf32>) -> tensor<1x7x7x9xf32> {
// expected-error@+1 {{'tosa.avg_pool2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
: (tensor<1x0x7x9xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
// -----
func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>) -> tensor<1x7x7x9xf32> {
// expected-error@+1 {{'tosa.avg_pool2d' op tensor has a dimension with size zero. Each dimension of a tensor must have size >= 1}}
%0 = "tosa.avg_pool2d"(%arg0) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
: (tensor<1x0x?x9xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
}
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