mirror of https://github.com/llvm/torch-mlir
284 lines
8.8 KiB
MLIR
284 lines
8.8 KiB
MLIR
// RUN: torch-mlir-opt <%s -split-input-file -verify-diagnostics
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// -----
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torch.class_type @c {}
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%0 = torch.nn_module {
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// expected-error @+1 {{'func.func' op is not allowed inside 'torch.nn_module'}}
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func.func @f()
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} : !torch.nn.Module<"c">
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// -----
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torch.class_type @c {}
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%c0 = torch.constant.int 0
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// expected-error @+1 {{number of 'torch.slot's in a 'torch.nn_module' must match number of 'torch.attr's in the corresponding 'torch.class_type'}}
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%0 = torch.nn_module {
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torch.slot "f", %c0 : !torch.int
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} : !torch.nn.Module<"c">
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// -----
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torch.class_type @c {
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// expected-note @+1 {{see torch.attr at corresponding index 0 here}}
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torch.attr "g" : !torch.int
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}
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%c0 = torch.constant.int 0
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%0 = torch.nn_module {
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// expected-error @+1 {{'torch.slot' op is expected to match type and name of '"torch.attr"() {name = "g", type = !torch.int} : () -> ()}}
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torch.slot "f", %c0 : !torch.int
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} : !torch.nn.Module<"c">
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// -----
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torch.class_type @c {
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// expected-error @+1 {{'func.func' op is not allowed inside `torch.class_type`}}
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func.func @f()
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}
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// -----
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// expected-error @+1 {{has duplicate attr/method with name 'a'}}
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torch.class_type @c {
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// expected-note @+1 {{see first conflicting attr/method here}}
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torch.attr "a" : !torch.int
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// expected-note @+1 {{see second conflicting attr/method here}}
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torch.attr "a" : !torch.int
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}
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// -----
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torch.class_type @c {
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// expected-error @+1 {{'@invalidSym' does not reference a valid function}}
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torch.method "f", @invalidSym
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}
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// -----
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torch.class_type @c {
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// expected-error @+1 {{'@f' must reference a private function}}
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torch.method "f", @f
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}
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func.func @f(%arg0: !torch.nn.Module<"c">) {
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return
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}
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// -----
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torch.class_type @c {
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// expected-error @+1 {{'@f' must reference a function that is defined (not merely declared)}}
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torch.method "f", @f
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}
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func.func private @f(%arg0: !torch.nn.Module<"c">)
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// -----
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func.func private @f() {
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return
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}
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torch.class_type @c {
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// expected-error @+1 {{the referenced function 'f' must have a first argument of type '!torch.nn.Module<"c">'}}
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torch.method "f", @f
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}
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// -----
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func.func private @f(%arg0: !torch.nn.Module<"other_c">) {
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return
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}
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torch.class_type @c {
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// expected-error @+1 {{the referenced function 'f' must have a first argument of type '!torch.nn.Module<"c">'}}
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torch.method "f", @f
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}
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// -----
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// expected-error @+1 {{'a' does not reference a valid class type}}
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%m = torch.nn_module {} : !torch.nn.Module<"a">
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// -----
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// expected-error @+1 {{'torch.type_bound' must be attached to an argument of !torch.tensor/!torch.vtensor type}}
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func.func private @f(%arg0: i32 {torch.type_bound = !torch.tensor<*,f32>})
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// -----
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// expected-error @+1 {{'torch.type_bound' must be TypeAttr}}
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func.func private @f(%arg0: i32 {torch.type_bound = 1})
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// -----
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// expected-error @+1 {{'torch.type_bound' must be of !torch.tensor/!torch.vtensor type}}
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func.func private @f(%arg0: i32 {torch.type_bound = i32})
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// -----
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func.func @derefine(%arg0: !torch.optional<tensor>) -> !torch.tensor {
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// expected-error @+1 {{operand type '!torch.optional<tensor>' and result type '!torch.tensor' are cast incompatible}}
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%0 = torch.derefine %arg0 : !torch.optional<tensor> to !torch.tensor
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return %0 : !torch.tensor
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}
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// -----
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func.func @torch.prim.unchecked_cast$invalid_types(%arg0: !torch.tensor) -> !torch.optional<tensor> {
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// expected-error @+1 {{operand type '!torch.tensor' and result type '!torch.optional<tensor>' are cast incompatible}}
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%0 = torch.prim.unchecked_cast %arg0 : !torch.tensor -> !torch.optional<tensor>
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return %0 : !torch.optional<tensor>
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}
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// -----
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// expected-error @+1 {{invalid dtype 'tuple<>' for !torch.tensor type}}
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func.func private @tensor.invalid_dtype() -> !torch.tensor<*,tuple<>>
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// -----
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func.func @torch.tensor() {
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// Incompatible shape.
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// expected-error@+1 {{must be Multi-dimensional array modeling Torch's Tensor type, but got}}
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%0 = torch.tensor.literal(dense<42.0> : tensor<3x2xf32>) : !torch.vtensor<[],f32>
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return
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}
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// -----
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func.func @torch.tensor() {
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// Incompatible dtype.
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// expected-error@+1 {{must be Multi-dimensional array modeling Torch's Tensor type, but got}}
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%0 = torch.tensor.literal(dense<42.0> : tensor<f32>) : !torch.vtensor<[],f64>
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return
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}
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// -----
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func.func @torch.tensor() {
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// Incompatible type.
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// expected-error@+1 {{must be Multi-dimensional array modeling Torch's Tensor type, but got}}
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%0 = torch.tensor.literal(dense<42.0> : tensor<f32>) : i1
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return
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}
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// -----
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func.func @torch.prim.ListConstruct() {
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%int2 = torch.constant.int 2
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// expected-error@+1 {{operand types should have the same type as the list contained type}}
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torch.prim.ListConstruct %int2 : (!torch.int) -> !torch.list<tensor>
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return
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}
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// -----
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func.func @torch.overwrite.tensor.contents(%arg0: !torch.vtensor<[1],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[1],f32> {
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%0 = torch.copy.to_tensor %arg0 : !torch.tensor<[1],f32>
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// expected-error@+1 {{'torch.overwrite.tensor.contents' op failed to verify that overwritten tensor type is corresponding !torch.tensor of value tensor type}}
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torch.overwrite.tensor.contents %arg1 overwrites %0 : !torch.vtensor<[?],f32>, !torch.tensor<[1],f32>
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%1 = torch.copy.to_vtensor %0 : !torch.vtensor<[1],f32>
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return %1 : !torch.vtensor<[1],f32>
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}
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// -----
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// There must be only one module initialize.
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torch.global_slot.module_initializer {
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torch.initialize.global_slots [
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]
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}
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// expected-error @+1 {{there must be only one global slot initializer}}
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torch.global_slot.module_initializer {
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torch.initialize.global_slots [
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]
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}
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// -----
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// Initialized slot missing, or or non-existent slots initialized.
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// expected-note @+1 {{missing global slot initializer for @slot0}}
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torch.global_slot @slot0 : !torch.int
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// expected-note @+1 {{missing global slot initializer for @slot1}}
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torch.global_slot @slot1 : !torch.int
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torch.global_slot.module_initializer {
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%0 = torch.constant.int 1
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%1 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor
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%2 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor<[],unk>
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// expected-error @below {{must have one initializer for each global slot in the module}}
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// expected-note @below {{unexpected global slot initializer for non-existent global slot @nonexistent_slot0}}
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// expected-note @below {{unexpected global slot initializer for non-existent global slot @nonexistent_slot1}}
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torch.initialize.global_slots [
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@nonexistent_slot0(%0 : !torch.int)
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@nonexistent_slot1(%0 : !torch.int)
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]
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}
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// -----
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// Duplicate initialization of global slot.
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torch.global_slot @slot0 : !torch.int
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torch.global_slot.module_initializer {
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%0 = torch.constant.int 1
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// expected-error @+1 {{duplicate initialization of global slot: @slot0}}
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torch.initialize.global_slots [
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@slot0(%0 : !torch.int)
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@slot0(%0 : !torch.int)
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]
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}
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// -----
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// Subtyping checks.
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torch.global_slot @tensor : !torch.tensor
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torch.global_slot @initialized_with_refined : !torch.tensor
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torch.global_slot @error_initialized_with_derefined : !torch.tensor<[],unk>
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torch.global_slot.module_initializer {
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%1 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor
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%2 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor<[],unk>
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// expected-error @below {{initial value for global slot @error_initialized_with_derefined has type '!torch.tensor' which is not within the bound '!torch.tensor<[],unk>'}}
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torch.initialize.global_slots [
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@tensor(%1 : !torch.tensor)
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@initialized_with_refined(%2 : !torch.tensor<[],unk>)
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@error_initialized_with_derefined(%1 : !torch.tensor)
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]
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}
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// -----
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// Restricted set of ops in the module initializer.
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torch.global_slot @tensor : !torch.tensor
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torch.global_slot.module_initializer {
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%0 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor
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// expected-error @+1 {{'torch.aten.mul.Tensor' op is not allowed in a module initializer}}
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%1 = torch.aten.mul.Tensor %0, %0 : !torch.tensor, !torch.tensor -> !torch.tensor
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torch.initialize.global_slots [
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@tensor(%1 : !torch.tensor)
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]
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}
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// -----
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func.func @torch.tensor_static_info_cast$shape_mismatch(%arg0: !torch.vtensor<[],unk>) -> !torch.vtensor<[?],unk> {
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// expected-error@+1 {{'torch.tensor_static_info_cast' op operand type '!torch.vtensor<[],unk>' and result type '!torch.vtensor<[?],unk>' are cast incompatible}}
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%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],unk> to !torch.vtensor<[?],unk>
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return %0 : !torch.vtensor<[?],unk>
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}
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// -----
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func.func @torch.tensor_static_info_cast$dtype_mismatch(%arg0: !torch.vtensor<*,f32>) -> !torch.vtensor<*,f64> {
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// expected-error@+1 {{'torch.tensor_static_info_cast' op operand type '!torch.vtensor<*,f32>' and result type '!torch.vtensor<*,f64>' are cast incompatible}}
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%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<*,f32> to !torch.vtensor<*,f64>
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return %0 : !torch.vtensor<*,f64>
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}
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