torch-mlir/test/Dialect/Torch/canonicalize.mlir

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// RUN: torch-mlir-opt %s -canonicalize --split-input-file | FileCheck %s
// CHECK-LABEL: func.func @torch.aten.__range_length$fold() -> (!torch.int, !torch.int, !torch.int, !torch.int) {
// CHECK-DAG: %[[INT1:.*]] = torch.constant.int 1
// CHECK-DAG: %[[INT2:.*]] = torch.constant.int 2
// CHECK-DAG: %[[INT3:.*]] = torch.constant.int 3
// CHECK-DAG: %[[INTM1:.*]] = torch.constant.int -1
// CHECK: %[[NEG_STEP:.*]] = torch.aten.__range_length %[[INT1]], %[[INT3]], %[[INTM1]] : !torch.int, !torch.int, !torch.int -> !torch.int
// CHECK: return %[[INT2]], %[[INT2]], %[[INT1]], %[[NEG_STEP]] : !torch.int, !torch.int, !torch.int, !torch.int
func.func @torch.aten.__range_length$fold() -> (!torch.int, !torch.int, !torch.int, !torch.int) {
%int3 = torch.constant.int 3
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%int1 = torch.constant.int 1
%int0 = torch.constant.int 0
%int-1 = torch.constant.int -1
%0 = torch.aten.__range_length %int0, %int4, %int2 : !torch.int, !torch.int, !torch.int -> !torch.int
%1 = torch.aten.__range_length %int1, %int4, %int2 : !torch.int, !torch.int, !torch.int -> !torch.int
%2 = torch.aten.__range_length %int1, %int3, %int2 : !torch.int, !torch.int, !torch.int -> !torch.int
%3 = torch.aten.__range_length %int1, %int3, %int-1 : !torch.int, !torch.int, !torch.int -> !torch.int
return %0, %1, %2, %3 : !torch.int, !torch.int, !torch.int, !torch.int
}
// CHECK-LABEL: func.func @torch.runtime.assert
// CHECK-NEXT: return
func.func @torch.runtime.assert() {
%true = torch.constant.bool true
torch.runtime.assert %true, "msg"
return
}
// CHECK-LABEL: func.func @torch.aten.ones_item
// CHECK: %[[CONST:.*]] = torch.constant.int 1
// CHECK: return %[[CONST]] : !torch.int
func.func @torch.aten.ones_item() -> !torch.int {
%int1 = torch.constant.int 1
%int3 = torch.constant.int 3
%none = torch.constant.none
%0 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.ones %0, %int3, %none, %none, %none : !torch.list<int>, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[1],si32>
%2 = torch.aten.item %1 : !torch.vtensor<[1],si32> -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.zeros_item
// CHECK: %[[CONST:.*]] = torch.constant.int 0
// CHECK: return %[[CONST]] : !torch.int
func.func @torch.aten.zeros_item() -> !torch.int {
%int1 = torch.constant.int 1
%int3 = torch.constant.int 3
%none = torch.constant.none
%0 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.zeros %0, %int3, %none, %none, %none : !torch.list<int>, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[1],si32>
%2 = torch.aten.item %1 : !torch.vtensor<[1],si32> -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.full_item
// CHECK: %[[CONST:.*]] = torch.constant.int 1337
// CHECK: return %[[CONST]] : !torch.int
func.func @torch.aten.full_item() -> !torch.int {
%int1 = torch.constant.int 1
%int3 = torch.constant.int 1337
%int5 = torch.constant.int 5
%none = torch.constant.none
%0 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.full %0, %int3, %int5, %none, %none, %none : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[1],si32>
%2 = torch.aten.item %1 : !torch.vtensor<[1],si32> -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.is_floating_point$fold_true
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.is_floating_point$fold_true(%arg0: !torch.vtensor<[], f32>) -> !torch.bool {
%0 = torch.aten.is_floating_point %arg0 : !torch.vtensor<[], f32> -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.is_floating_point$fold_false
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.is_floating_point$fold_false(%arg0: !torch.vtensor<[], si64>) -> !torch.bool {
%0 = torch.aten.is_floating_point %arg0 : !torch.vtensor<[], si64> -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__is__
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.__is__(%arg0: !torch.list<int>, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__is__ %arg0, %arg1 : !torch.list<int>, !torch.none -> !torch.bool
return %0 : !torch.bool
}
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK-LABEL: func.func @torch.aten.__is__$derefine_is_none
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.__is__$derefine_is_none(%arg0: !torch.list<int>, %arg1: !torch.none) -> !torch.bool {
%0 = torch.derefine %arg0 : !torch.list<int> to !torch.optional<list<int>>
%1 = torch.aten.__is__ %0, %arg1 : !torch.optional<list<int>>, !torch.none -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__is__$none_is_none
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.__is__$none_is_none(%arg0: !torch.none, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__is__ %arg0, %arg1 : !torch.none, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__is__$is_none$derefine(
// CHECK-SAME: %{{.*}}: !torch.vtensor) -> !torch.bool {
// CHECK: %[[RESULT:.*]] = torch.constant.bool false
// CHECK: return %[[RESULT]] : !torch.bool
func.func @torch.aten.__is__$is_none$derefine(%arg0: !torch.vtensor) -> !torch.bool {
%none = torch.constant.none
%0 = torch.derefine %arg0 : !torch.vtensor to !torch.optional<vtensor>
%1 = torch.aten.__is__ %0, %none : !torch.optional<vtensor>, !torch.none -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__isnot__
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.__isnot__(%arg0: !torch.list<int>, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__isnot__ %arg0, %arg1 : !torch.list<int>, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__isnot__$none_isnot_none
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.__isnot__$none_isnot_none(%arg0: !torch.none, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__isnot__ %arg0, %arg1 : !torch.none, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.bool() -> !torch.bool {
2021-10-21 23:50:01 +08:00
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ne.bool() -> !torch.bool {
2021-10-21 23:50:01 +08:00
%a = torch.constant.bool true
%b = torch.constant.bool false
%0 = torch.aten.ne.bool %a, %b: !torch.bool, !torch.bool -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.bool$same_operand(
2021-10-21 23:50:01 +08:00
// CHECK-SAME: %[[ARG0:.*]]: !torch.bool) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ne.bool$same_operand(%arg0: !torch.bool) -> !torch.bool {
2021-10-21 23:50:01 +08:00
%0 = torch.aten.ne.bool %arg0, %arg0: !torch.bool, !torch.bool -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.bool$different_operand(
2021-10-21 23:50:01 +08:00
// CHECK-SAME: %[[ARG0:.*]]: !torch.bool) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[RET:.*]] = torch.aten.ne.bool %[[ARG0]], %[[FALSE]] : !torch.bool, !torch.bool -> !torch.bool
// CHECK: return %[[RET]] : !torch.bool
func.func @torch.aten.ne.bool$different_operand(%a: !torch.bool) -> !torch.bool {
2021-10-21 23:50:01 +08:00
%b = torch.constant.bool false
%0 = torch.aten.ne.bool %a, %b: !torch.bool, !torch.bool -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.size$canonicalize_to_list(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3],f32>) -> !torch.list<int> {
// CHECK: %[[C2:.*]] = torch.constant.int 2
// CHECK: %[[C3:.*]] = torch.constant.int 3
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[C2]], %[[C3]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[LIST]] : !torch.list<int>
func.func @torch.aten.size$canonicalize_to_list(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.list<int> {
%0 = torch.aten.size %arg0 : !torch.vtensor<[2,3],f32> -> !torch.list<int>
return %0 : !torch.list<int>
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
}
// One size unknown, so cannot canonicalize.
// TODO: For unknown sizes, insert the equivalent of a "dim" op.
// Then this will only require static rank.
// CHECK-LABEL: func.func @torch.aten.size$unknown_size(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,3],f32>) -> !torch.list<int> {
// CHECK: %[[SIZE:.*]] = torch.aten.size %[[ARG]] : !torch.vtensor<[?,3],f32> -> !torch.list<int>
func.func @torch.aten.size$unknown_size(%arg0: !torch.vtensor<[?,3],f32>) -> !torch.list<int> {
%0 = torch.aten.size %arg0 : !torch.vtensor<[?,3],f32> -> !torch.list<int>
return %0 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.ne.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK-NEXT: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ne.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.ne.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.int$same_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ne.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.ne.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.int$different_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ne.int$different_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.ne.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.eq.int$different_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.eq.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK-NEXT: %[[F:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[F]] : !torch.bool
func.func @torch.aten.eq.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.eq.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.eq.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.eq.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$of_size.int(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.eq.int$of_size.int(%arg0: !torch.tensor) -> !torch.bool {
%int-1 = torch.constant.int -1
%int0 = torch.constant.int 0
%0 = torch.aten.size.int %arg0, %int0 : !torch.tensor, !torch.int -> !torch.int
%1 = torch.aten.eq.int %0, %int-1 : !torch.int, !torch.int -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$of_size.int_lhs_constant(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.eq.int$of_size.int_lhs_constant(%arg0: !torch.tensor) -> !torch.bool {
%int-1 = torch.constant.int -1
%int0 = torch.constant.int 0
%0 = torch.aten.size.int %arg0, %int0 : !torch.tensor, !torch.int -> !torch.int
%1 = torch.aten.eq.int %int-1, %0 : !torch.int, !torch.int -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int$no_change_minus1(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[CM1:.*]] = torch.constant.int -1
// CHECK: %[[RESULT:.*]] = torch.aten.eq.int %[[CM1]], %[[ARG]] : !torch.int, !torch.int -> !torch.bool
// CHECK: return %[[RESULT]] : !torch.bool
func.func @torch.aten.eq.int$no_change_minus1(%arg0: !torch.int) -> !torch.bool {
%int-1 = torch.constant.int -1
%1 = torch.aten.eq.int %int-1, %arg0 : !torch.int, !torch.int -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.int$evaluate_to_true() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.lt.int$evaluate_to_true() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.lt.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.lt.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.lt.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.int$same_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.lt.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.lt.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.le.int$evaluate_to_true() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.le.int$evaluate_to_true() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.le.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.le.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.le.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.le.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.le.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.le.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.le.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.gt.int$evaluate_to_true() -> !torch.bool {
// CHECK-NEXT: %[[T:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[T]] : !torch.bool
func.func @torch.aten.gt.int$evaluate_to_true() -> !torch.bool {
%int2 = torch.constant.int 2
%int4 = torch.constant.int 4
%0 = torch.aten.gt.int %int4, %int2 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.gt.int$evaluate_to_false() -> !torch.bool {
// CHECK-NEXT: %[[T:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[T]] : !torch.bool
func.func @torch.aten.gt.int$evaluate_to_false() -> !torch.bool {
%int2 = torch.constant.int 2
%int4 = torch.constant.int 4
%0 = torch.aten.gt.int %int2, %int4 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ge.int$evaluate_to_false() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ge.int$evaluate_to_false() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.ge.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ge.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ge.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.ge.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ge.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ge.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.ge.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.float$evaluate_to_true() -> !torch.bool {
2022-02-11 05:25:25 +08:00
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.lt.float$evaluate_to_true() -> !torch.bool {
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%float4 = torch.constant.float 4.0
%float5 = torch.constant.float 5.0
%2 = torch.aten.lt.float %float4, %float5 : !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.float$same_operand(
2022-02-11 05:25:25 +08:00
// CHECK-SAME: %{{.*}}: !torch.float) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.lt.float$same_operand(%arg0: !torch.float) -> !torch.bool {
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%2 = torch.aten.lt.float %arg0, %arg0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.lt.float$same_value() -> !torch.bool {
2022-02-11 05:25:25 +08:00
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.lt.float$same_value() -> !torch.bool {
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%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 4.0
%2 = torch.aten.lt.float %float4, %float4_0 : !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.gt.float$evaluate_to_true() -> !torch.bool {
2022-02-11 05:25:25 +08:00
// CHECK-NEXT: %[[T:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[T]] : !torch.bool
func.func @torch.aten.gt.float$evaluate_to_true() -> !torch.bool {
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%float2 = torch.constant.float 2.0
%float4 = torch.constant.float 4.0
%0 = torch.aten.gt.float %float4, %float2 : !torch.float, !torch.float -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.gt.float$evaluate_to_false() -> !torch.bool {
2022-02-11 05:25:25 +08:00
// CHECK-NEXT: %[[T:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[T]] : !torch.bool
func.func @torch.aten.gt.float$evaluate_to_false() -> !torch.bool {
2022-02-11 05:25:25 +08:00
%float2 = torch.constant.float 2.0
%float4 = torch.constant.float 4.0
%0 = torch.aten.gt.float %float2, %float4 : !torch.float, !torch.float -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @comparison_with_torch.aten.size.int(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,2],unk>) -> (!torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool) {
// CHECK: %[[SIZE:.*]] = torch.aten.size.int %[[ARG0]], %int0 : !torch.vtensor<[?,2],unk>, !torch.int -> !torch.int
// CHECK: %[[GE_0_LHS:.*]] = torch.aten.ge.int %int0, %[[SIZE]] : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[LT_0_LHS:.*]] = torch.aten.lt.int %int0, %[[SIZE]] : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[EQ_0_LHS:.*]] = torch.aten.eq.int %int0, %[[SIZE]] : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[NE_0_LHS:.*]] = torch.aten.ne.int %int0, %[[SIZE]] : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[GT_0_RHS:.*]] = torch.aten.gt.int %[[SIZE]], %int0 : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[LE_0_RHS:.*]] = torch.aten.le.int %[[SIZE]], %int0 : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[EQ_0_RHS:.*]] = torch.aten.eq.int %[[SIZE]], %int0 : !torch.int, !torch.int -> !torch.bool
// CHECK: %[[NE_0_RHS:.*]] = torch.aten.ne.int %[[SIZE]], %int0 : !torch.int, !torch.int -> !torch.bool
// CHECK: return %true, %true, %false, %false, %[[GE_0_LHS]], %[[LT_0_LHS]], %[[EQ_0_LHS]], %[[NE_0_LHS]], %[[GT_0_RHS]], %[[LE_0_RHS]], %[[EQ_0_RHS]], %[[NE_0_RHS]] : !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool
func.func @comparison_with_torch.aten.size.int(%arg0: !torch.vtensor<[?,2],unk>) -> (!torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool) {
%int0 = torch.constant.int 0
%0 = torch.aten.size.int %arg0, %int0 : !torch.vtensor<[?,2],unk>, !torch.int -> !torch.int
// Cases we can fold.
%1 = torch.aten.le.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
%2 = torch.aten.ge.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
%3 = torch.aten.lt.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
%4 = torch.aten.gt.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
// Cases we cannot fold.
%5 = torch.aten.ge.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
%6 = torch.aten.lt.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
%7 = torch.aten.eq.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
%8 = torch.aten.ne.int %int0, %0 : !torch.int, !torch.int -> !torch.bool
%9 = torch.aten.gt.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
%10 = torch.aten.le.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
%11 = torch.aten.eq.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
%12 = torch.aten.ne.int %0, %int0 : !torch.int, !torch.int -> !torch.bool
return %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12 : !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool, !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.float$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.eq.float$different_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float5 = torch.constant.float 5.0
%2 = torch.aten.eq.float %float4, %float5 : !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.float$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.eq.float$same_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 4.0
%2 = torch.aten.eq.float %float4, %float4_0 : !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.str$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.eq.str$different_value() -> !torch.bool {
%str4 = torch.constant.str "4"
%str5 = torch.constant.str "5"
%2 = torch.aten.eq.str %str4, %str5 : !torch.str, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.str$same_operand(
// CHECK-SAME: %{{.*}}: !torch.str) -> !torch.bool {
// CHECK-NEXT: %[[TRUE:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[TRUE]] : !torch.bool
func.func @torch.aten.eq.str$same_operand(%arg0: !torch.str) -> !torch.bool {
%0 = torch.aten.eq.str %arg0, %arg0 : !torch.str, !torch.str -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.str$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.eq.str$same_value() -> !torch.bool {
%str4 = torch.constant.str "4"
%str4_0 = torch.constant.str "4"
%2 = torch.aten.eq.str %str4, %str4_0 : !torch.str, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.str$different_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ne.str$different_value() -> !torch.bool {
%str4 = torch.constant.str "4"
%str5 = torch.constant.str "5"
%2 = torch.aten.ne.str %str4, %str5 : !torch.str, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.str$same_operand(
// CHECK-SAME: %{{.*}}: !torch.str) -> !torch.bool {
// CHECK-NEXT: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ne.str$same_operand(%arg0: !torch.str) -> !torch.bool {
%0 = torch.aten.ne.str %arg0, %arg0 : !torch.str, !torch.str -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ne.str$same_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ne.str$same_value() -> !torch.bool {
%str4 = torch.constant.str "4"
%str4_0 = torch.constant.str "4"
%2 = torch.aten.ne.str %str4, %str4_0 : !torch.str, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.len.str() -> !torch.int {
// CHECK: %[[INT7:.*]] = torch.constant.int 7
// CHECK: return %[[INT7]] : !torch.int
func.func @torch.aten.len.str() -> !torch.int {
%str = torch.constant.str "example"
%2 = torch.aten.len.str %str : !torch.str -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.len.str$empty() -> !torch.int {
// CHECK: %[[INT0:.*]] = torch.constant.int 0
// CHECK: return %[[INT0]] : !torch.int
func.func @torch.aten.len.str$empty() -> !torch.int {
%str = torch.constant.str ""
%2 = torch.aten.len.str %str : !torch.str -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.__contains__.str_list$false() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.__contains__.str_list$false() -> !torch.bool {
%str = torch.constant.str "c"
%str_0 = torch.constant.str "b"
%str_1 = torch.constant.str "a"
%1 = torch.prim.ListConstruct %str_1, %str_0 : (!torch.str, !torch.str) -> !torch.list<str>
%2 = torch.aten.__contains__.str_list %1, %str : !torch.list<str>, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__contains__.str_list$true() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.__contains__.str_list$true() -> !torch.bool {
%str = torch.constant.str "aa"
%str_0 = torch.constant.str "aa"
%str_1 = torch.constant.str "ccc"
%1 = torch.prim.ListConstruct %str_1, %str_0 : (!torch.str, !torch.str) -> !torch.list<str>
%2 = torch.aten.__contains__.str_list %1, %str : !torch.list<str>, !torch.str -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__not__
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.__not__() -> !torch.bool {
%false = torch.constant.bool false
%ret = torch.aten.__not__ %false : !torch.bool -> !torch.bool
return %ret: !torch.bool
}
// CHECK-LABEL: func.func @torch.prim.max.int$identity(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.int {
// CHECK: return %[[ARG]] : !torch.int
func.func @torch.prim.max.int$identity(%arg0: !torch.int) -> !torch.int {
%0 = torch.prim.max.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.max.int$constant() -> !torch.int {
// CHECK: %[[INT3:.*]] = torch.constant.int 3
// CHECK: return %[[INT3]] : !torch.int
func.func @torch.prim.max.int$constant() -> !torch.int {
%int-1 = torch.constant.int -1
%int3 = torch.constant.int 3
%0 = torch.prim.max.int %int-1, %int3 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.min.int$identity(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.int {
// CHECK: return %[[ARG]] : !torch.int
func.func @torch.prim.min.int$identity(%arg0: !torch.int) -> !torch.int {
%0 = torch.prim.min.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.min.int$constant() -> !torch.int {
// CHECK: %[[INT1:.*]] = torch.constant.int -1
// CHECK: return %[[INT1]] : !torch.int
func.func @torch.prim.min.int$constant() -> !torch.int {
%int-1 = torch.constant.int -1
%int3 = torch.constant.int 3
%0 = torch.prim.min.int %int-1, %int3 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.min.self_int$basic() -> !torch.int {
// CHECK: %[[M1:.*]] = torch.constant.int -1
// CHECK: return %[[M1]] : !torch.int
func.func @torch.prim.min.self_int$basic() -> !torch.int {
%int-1 = torch.constant.int -1
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int-1, %int0, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.prim.min.self_int %0 : !torch.list<int> -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.min.self_int$nofold$dynamic(
// CHECK: torch.prim.min.self_int
func.func @torch.prim.min.self_int$nofold$dynamic(%arg0: !torch.int) -> !torch.int {
%int-1 = torch.constant.int -1
%int0 = torch.constant.int 0
%0 = torch.prim.ListConstruct %int-1, %int0, %arg0: (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.prim.min.self_int %0 : !torch.list<int> -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.len.t$of_size(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<*,f32>) -> !torch.int {
// CHECK: %[[DIM:.*]] = torch.aten.dim %[[ARG]] : !torch.vtensor<*,f32> -> !torch.int
// CHECK: return %[[DIM]] : !torch.int
func.func @torch.aten.len.t$of_size(%arg0: !torch.vtensor<*,f32>) -> !torch.int {
%0 = torch.aten.size %arg0 : !torch.vtensor<*,f32> -> !torch.list<int>
%1 = torch.aten.len.t %0 : !torch.list<int> -> !torch.int
return %1 : !torch.int
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
}
// CHECK-LABEL: func.func @torch.aten.dim$with_shape(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?,?],f32>) -> !torch.int {
// CHECK: %[[DIM:.*]] = torch.constant.int 3
// CHECK: return %[[DIM]] : !torch.int
func.func @torch.aten.dim$with_shape(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.int {
%0 = torch.aten.dim %arg0 : !torch.vtensor<[?,?,?],f32> -> !torch.int
return %0 : !torch.int
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
}
// CHECK-LABEL: func.func @torch.aten.len.t$of_build_list(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.int {
// CHECK: %[[LEN:.*]] = torch.constant.int 4
// CHECK: return %[[LEN]] : !torch.int
func.func @torch.aten.len.t$of_build_list(%arg0: !torch.int) -> !torch.int {
%0 = torch.prim.ListConstruct %arg0, %arg0, %arg0, %arg0 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.len.t %0 : !torch.list<int> -> !torch.int
return %1 : !torch.int
}
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK-LABEL: func.func @torch.aten.len.t$no_fold_list_mutated()
func.func @torch.aten.len.t$no_fold_list_mutated() -> !torch.int {
%int4 = torch.constant.int 4
%0 = torch.prim.ListConstruct : () -> !torch.list<int>
%1 = torch.aten.append.t %0, %int4 : !torch.list<int>, !torch.int -> !torch.list<int>
// CHECK: torch.aten.len.t
%2 = torch.aten.len.t %0 : !torch.list<int> -> !torch.int
return %2 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.__getitem__.t(
// CHECK: %[[C5:.*]] = torch.constant.int 5
// CHECK: return %[[C5]] : !torch.int
func.func @torch.aten.__getitem__.t() -> !torch.int {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.__getitem__.t %0, %int1 : !torch.list<int>, !torch.int -> !torch.int
return %1 : !torch.int
}
// Not canonicalized because of passed in index
// CHECK-LABEL: func.func @torch.aten.__getitem__.t$no_change_test0(
// CHECK: %[[C5:.*]] = torch.constant.int 5
2022-06-23 11:23:46 +08:00
// CHECK: %[[C4:.*]] = torch.constant.int 4
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[C4]], %[[C5]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[ITEM:.*]] = torch.aten.__getitem__.t %[[LIST]], %arg0 : !torch.list<int>, !torch.int -> !torch.int
// CHECK: return %[[ITEM]] : !torch.int
func.func @torch.aten.__getitem__.t$no_change_test0(%arg0: !torch.int) -> !torch.int {
%int5 = torch.constant.int 5
%int4 = torch.constant.int 4
%0 = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.__getitem__.t %0, %arg0 : !torch.list<int>, !torch.int -> !torch.int
return %1 : !torch.int
}
// Not canonicalized because of passed in list
// CHECK-LABEL: func.func @torch.aten.__getitem__.t$no_change_test1(
// CHECK: %[[C5:.*]] = torch.constant.int 5
// CHECK: %[[ITEM:.*]] = torch.aten.__getitem__.t %arg0, %[[C5]] : !torch.list<int>, !torch.int -> !torch.int
// CHECK: return %[[ITEM]] : !torch.int
func.func @torch.aten.__getitem__.t$no_change_test1(%arg0: !torch.list<int>) -> !torch.int {
%int5 = torch.constant.int 5
%0 = torch.aten.__getitem__.t %arg0, %int5 : !torch.list<int>, !torch.int -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.__getitem__.t$getitem_of_size(
// CHECK-SAME: %[[TENSOR:.*]]: !torch.tensor,
// CHECK-SAME: %[[INDEX:.*]]: !torch.int) -> !torch.int {
// CHECK: %[[RESULT:.*]] = torch.aten.size.int %[[TENSOR]], %[[INDEX]] : !torch.tensor, !torch.int -> !torch.int
// CHECK: return %[[RESULT]] : !torch.int
func.func @torch.aten.__getitem__.t$getitem_of_size(%arg0: !torch.tensor, %arg1: !torch.int) -> !torch.int {
%0 = torch.aten.size %arg0 : !torch.tensor -> !torch.list<int>
%1 = torch.aten.__getitem__.t %0, %arg1 : !torch.list<int>, !torch.int -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.__getitem__.t$negative_index() -> !torch.int {
// CHECK: %[[INT8:.*]] = torch.constant.int 8
// CHECK: return %[[INT8]] : !torch.int
func.func @torch.aten.__getitem__.t$negative_index() -> !torch.int {
%int7 = torch.constant.int 7
%int8 = torch.constant.int 8
%int-1 = torch.constant.int -1
%0 = torch.prim.ListConstruct %int7, %int8 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.__getitem__.t %0, %int-1 : !torch.list<int>, !torch.int -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.__getitem__.t$invalid_index() -> !torch.int {
func.func @torch.aten.__getitem__.t$invalid_index() -> !torch.int {
%int7 = torch.constant.int 7
%int8 = torch.constant.int 8
%int-1 = torch.constant.int -100
%0 = torch.prim.ListConstruct %int7, %int8 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: torch.aten.__getitem__.t
%1 = torch.aten.__getitem__.t %0, %int-1 : !torch.list<int>, !torch.int -> !torch.int
return %1 : !torch.int
}
// Not canonicalized because of mutated lhs list
// CHECK-LABEL: func.func @torch.aten.add.t$no_canonicalize_lhs_mutated()
func.func @torch.aten.add.t$no_canonicalize_lhs_mutated() -> !torch.list<int> {
%int4 = torch.constant.int 4
%0 = torch.prim.ListConstruct : () -> !torch.list<int>
%1 = torch.prim.ListConstruct : () -> !torch.list<int>
%2 = torch.aten.append.t %0, %int4 : !torch.list<int>, !torch.int -> !torch.list<int>
// CHECK: torch.aten.add.t
%3 = torch.aten.add.t %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.list<int>
return %3 : !torch.list<int>
}
// Not canonicalized because of mutated rhs list
// CHECK-LABEL: func.func @torch.aten.add.t$no_canonicalize_rhs_mutated()
func.func @torch.aten.add.t$no_canonicalize_rhs_mutated() -> !torch.list<int> {
%int4 = torch.constant.int 4
%0 = torch.prim.ListConstruct : () -> !torch.list<int>
%1 = torch.prim.ListConstruct : () -> !torch.list<int>
%2 = torch.aten.append.t %1, %int4 : !torch.list<int>, !torch.int -> !torch.list<int>
// CHECK: torch.aten.add.t
%3 = torch.aten.add.t %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.list<int>
return %3 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.add.t$concat(
// CHECK-SAME: %[[ARG0:.*]]: !torch.int,
// CHECK-SAME: %[[ARG1:.*]]: !torch.int) -> !torch.list<int> {
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[ARG0]], %[[ARG1]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[LIST]] : !torch.list<int>
func.func @torch.aten.add.t$concat(%arg0: !torch.int, %arg1: !torch.int) -> !torch.list<int> {
%0 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %arg1 : (!torch.int) -> !torch.list<int>
%2 = torch.aten.add.t %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.add.t$concat_empty(
// CHECK-SAME: %[[ARG0:.*]]: !torch.int) -> !torch.list<int> {
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[ARG0]] : (!torch.int) -> !torch.list<int>
// CHECK: return %[[LIST]] : !torch.list<int>
func.func @torch.aten.add.t$concat_empty(%arg0: !torch.int) -> !torch.list<int> {
%0 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct : () -> !torch.list<int>
%2 = torch.aten.add.t %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.slice.t$basic() -> !torch.list<int> {
// CHECK: %int0 = torch.constant.int 0
// CHECK: %int1 = torch.constant.int 1
// CHECK: %[[RET:.*]] = torch.prim.ListConstruct %int0, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RET]] : !torch.list<int>
func.func @torch.aten.slice.t$basic() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.slice.t %0, %int0, %int-1, %int1 : !torch.list<int>, !torch.int, !torch.int, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.slice.t$none_start() -> !torch.list<int> {
// CHECK: %int0 = torch.constant.int 0
// CHECK: %int1 = torch.constant.int 1
// CHECK: %[[RET:.*]] = torch.prim.ListConstruct %int0, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RET]] : !torch.list<int>
func.func @torch.aten.slice.t$none_start() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%none = torch.constant.none
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.slice.t %0, %none, %int-1, %int1 : !torch.list<int>, !torch.none, !torch.int, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.slice.t$none_end() -> !torch.list<int> {
// CHECK: %int0 = torch.constant.int 0
// CHECK: %int1 = torch.constant.int 1
// CHECK: %int2 = torch.constant.int 2
// CHECK: %[[RET:.*]] = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RET]] : !torch.list<int>
func.func @torch.aten.slice.t$none_end() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%none = torch.constant.none
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.slice.t %0, %int0, %none, %int1 : !torch.list<int>, !torch.int, !torch.none, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.slice.t$start_exceed_range() -> !torch.list<int> {
// CHECK: %int0 = torch.constant.int 0
// CHECK: %int1 = torch.constant.int 1
// CHECK: %[[RET:.*]] = torch.prim.ListConstruct %int0, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RET]] : !torch.list<int>
func.func @torch.aten.slice.t$start_exceed_range() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%int-1000 = torch.constant.int -1000
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.slice.t %0, %int-1000, %int-1, %int1 : !torch.list<int>, !torch.int, !torch.int, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.slice.t$end_exceed_range() -> !torch.list<int> {
// CHECK: %int0 = torch.constant.int 0
// CHECK: %int1 = torch.constant.int 1
// CHECK: %int2 = torch.constant.int 2
// CHECK: %[[RET:.*]] = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RET]] : !torch.list<int>
func.func @torch.aten.slice.t$end_exceed_range() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%int1000 = torch.constant.int 1000
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.slice.t %0, %int0, %int1000, %int1 : !torch.list<int>, !torch.int, !torch.int, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// Not canonicalized because of mutated l list
// CHECK-LABEL: func.func @torch.aten.slice.t$no_canonicalize_l_mutated()
func.func @torch.aten.slice.t$no_canonicalize_l_mutated() -> !torch.list<int> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int-1 = torch.constant.int -1
%0 = torch.prim.ListConstruct %int0, %int1, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: torch.aten.slice.t
%2 = torch.aten.slice.t %0, %int0, %int-1, %int1 : !torch.list<int>, !torch.int, !torch.int, !torch.int -> !torch.list<int>
%3 = torch.aten.append.t %0, %int-1 : !torch.list<int>, !torch.int -> !torch.list<int>
return %2 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.eq.int_list$fold$literals_of_different_sizes
// CHECK: %[[RET:.*]] = torch.constant.bool false
// CHECK: return %[[RET]] : !torch.bool
func.func @torch.aten.eq.int_list$fold$literals_of_different_sizes(%arg0: !torch.int) -> !torch.bool {
%0 = torch.prim.ListConstruct : () -> !torch.list<int>
%1 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%2 = torch.aten.eq.int_list %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int_list$fold$same_literal
// CHECK: %[[RET:.*]] = torch.constant.bool true
// CHECK: return %[[RET]] : !torch.bool
func.func @torch.aten.eq.int_list$fold$same_literal(%arg0: !torch.int) -> !torch.bool {
%0 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%2 = torch.aten.eq.int_list %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.eq.int_list$no_fold$different_literals(
func.func @torch.aten.eq.int_list$no_fold$different_literals(%arg0: !torch.int, %arg1: !torch.int) -> !torch.bool {
%0 = torch.prim.ListConstruct %arg0 : (!torch.int) -> !torch.list<int>
%1 = torch.prim.ListConstruct %arg1 : (!torch.int) -> !torch.list<int>
// CHECK: torch.aten.eq.int_list
%2 = torch.aten.eq.int_list %0, %1 : !torch.list<int>, !torch.list<int> -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.Float.Scalar$constant_fold_int_to_float() -> !torch.float {
// CHECK: %[[VAL_0:.*]] = torch.constant.float 3.000000e+00
// CHECK: return %[[VAL_0]] : !torch.float
func.func @torch.aten.Float.Scalar$constant_fold_int_to_float() -> !torch.float {
%0 = torch.constant.int 3
%1 = torch.aten.Float.Scalar %0 : !torch.int -> !torch.float
return %1 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.Float.Scalar$identity(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.float) -> !torch.float {
// CHECK: return %[[VAL_0]] : !torch.float
func.func @torch.aten.Float.Scalar$identity(%arg0: !torch.float) -> !torch.float {
%0 = torch.aten.Float.Scalar %arg0 : !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.constant.none$constantlike() -> (!torch.none, !torch.none) {
// CHECK: %[[C:.*]] = torch.constant.none
// CHECK: return %[[C]], %[[C]] : !torch.none, !torch.none
func.func @torch.constant.none$constantlike() -> (!torch.none, !torch.none) {
%0 = torch.constant.none
%1 = torch.constant.none
return %0, %1 : !torch.none, !torch.none
}
// CHECK-LABEL: func.func @torch.constant.str$constantlike() -> (!torch.str, !torch.str, !torch.str) {
// CHECK: %[[S:.*]] = torch.constant.str "s"
2022-06-23 11:23:46 +08:00
// CHECK: %[[T:.*]] = torch.constant.str "t"
// CHECK: return %[[S]], %[[S]], %[[T]] : !torch.str, !torch.str, !torch.str
func.func @torch.constant.str$constantlike() -> (!torch.str, !torch.str, !torch.str) {
%0 = torch.constant.str "s"
%1 = torch.constant.str "s"
%2 = torch.constant.str "t"
return %0, %1, %2 : !torch.str, !torch.str, !torch.str
}
// CHECK-LABEL: func.func @torch.constant.bool$constantlike() -> (!torch.bool, !torch.bool, !torch.bool) {
// CHECK: %[[T:.*]] = torch.constant.bool true
2022-06-23 11:23:46 +08:00
// CHECK: %[[F:.*]] = torch.constant.bool false
// CHECK: return %[[T]], %[[T]], %[[F]] : !torch.bool, !torch.bool, !torch.bool
func.func @torch.constant.bool$constantlike() -> (!torch.bool, !torch.bool, !torch.bool) {
%0 = torch.constant.bool true
%1 = torch.constant.bool true
%2 = torch.constant.bool false
return %0, %1, %2 : !torch.bool, !torch.bool, !torch.bool
}
// CHECK-LABEL: func.func @torch.prim.If$erase_dead_branch(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.int {
// CHECK-NEXT: %[[RET:.*]] = torch.aten.add.int %[[ARG]], %[[ARG]] : !torch.int, !torch.int -> !torch.int
// CHECK-NEXT: return %[[RET]] : !torch.int
func.func @torch.prim.If$erase_dead_branch(%arg0: !torch.int) -> !torch.int {
%true = torch.constant.bool true
%0 = torch.prim.If %true -> (!torch.int) {
%1 = torch.aten.add.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.int
torch.prim.If.yield %1 : !torch.int
} else {
%1 = torch.aten.mul.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.int
torch.prim.If.yield %1 : !torch.int
}
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.If$no_fold$side_effect(
// CHECK-SAME: %[[ARG0:.*]]: !torch.bool) {
// CHECK: %[[STR:.*]] = torch.constant.str "str"
// CHECK: torch.prim.If %[[ARG0]] -> () {
// CHECK: torch.prim.RaiseException %[[STR]], %[[STR]] : !torch.str, !torch.str
// CHECK: torch.prim.If.yield
// CHECK: } else {
// CHECK: torch.prim.If.yield
// CHECK: }
// CHECK: return
func.func @torch.prim.If$no_fold$side_effect(%arg0: !torch.bool) {
%str = torch.constant.str "str"
torch.prim.If %arg0 -> () {
torch.prim.RaiseException %str, %str : !torch.str, !torch.str
torch.prim.If.yield
} else {
torch.prim.If.yield
}
return
}
// CHECK-LABEL: func.func @torch.prim.If$fold_same_result(
// CHECK-SAME: %[[PRED:.*]]: !torch.bool,
// CHECK-SAME: %[[ARG1:.*]]: !torch.int) -> (!torch.int, !torch.int) {
// CHECK-NEXT: return %[[ARG1]], %[[ARG1]] : !torch.int, !torch.int
func.func @torch.prim.If$fold_same_result(%arg0: !torch.bool, %arg1: !torch.int) -> (!torch.int, !torch.int) {
%0, %1 = torch.prim.If %arg0 -> (!torch.int, !torch.int) {
torch.prim.If.yield %arg1, %arg1 : !torch.int, !torch.int
} else {
torch.prim.If.yield %arg1, %arg1 : !torch.int, !torch.int
}
return %0, %1: !torch.int, !torch.int
}
// CHECK-LABEL: func.func @torch.prim.If$fold_same_result$subset_of_results(
// CHECK-SAME: %[[PRED:.*]]: !torch.bool,
// CHECK-SAME: %[[ARG1:.*]]: !torch.int,
// CHECK-SAME: %[[ARG2:.*]]: !torch.int) -> (!torch.int, !torch.int) {
// CHECK: %[[IF_RESULT:.*]] = torch.prim.If %[[PRED]] -> (!torch.int) {
// CHECK: torch.prim.If.yield %[[ARG1]] : !torch.int
// CHECK: } else {
// CHECK: torch.prim.If.yield %[[ARG2]] : !torch.int
// CHECK: }
// CHECK: return %[[ARG1]], %[[IF_RESULT:.*]] : !torch.int, !torch.int
func.func @torch.prim.If$fold_same_result$subset_of_results(%arg0: !torch.bool, %arg1: !torch.int, %arg2: !torch.int) -> (!torch.int, !torch.int) {
%0, %1 = torch.prim.If %arg0 -> (!torch.int, !torch.int) {
torch.prim.If.yield %arg1, %arg1: !torch.int, !torch.int
} else {
torch.prim.If.yield %arg1, %arg2: !torch.int, !torch.int
}
return %0, %1: !torch.int, !torch.int
}
// CHECK-LABEL: func.func @torch.prim.TupleUnpack(
// CHECK-SAME: %[[ARG0:.*]]: !torch.tensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: return %[[ARG0]] : !torch.tensor
func.func @torch.prim.TupleUnpack(%arg0: !torch.tensor, %arg1: !torch.tensor) -> !torch.tensor{
%123 = torch.prim.TupleConstruct %arg0, %arg1: !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor>
%124:2 = torch.prim.TupleUnpack %123 : !torch.tuple<tensor, tensor> -> !torch.tensor, !torch.tensor
return %124#0 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.prim.TupleUnpack.Derefined(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.optional<tensor> {
// CHECK: %[[DEREFINED:.+]] = torch.derefine %[[ARG]] : !torch.tensor to !torch.optional<tensor>
// CHECK: return %[[DEREFINED]] : !torch.optional<tensor>
func.func @torch.prim.TupleUnpack.Derefined(%arg: !torch.tensor) -> !torch.optional<tensor> {
%tuple = torch.prim.TupleConstruct %arg : !torch.tensor -> !torch.tuple<tensor>
%optional_tensor = torch.prim.TupleUnpack %tuple : !torch.tuple<tensor> -> !torch.optional<tensor>
return %optional_tensor : !torch.optional<tensor>
}
// CHECK-LABEL: func.func @torch.aten.__contains__.str(
// CHECK-SAME: %[[K0:.*]]: !torch.str, %[[V0:.*]]: !torch.tensor,
// CHECK-SAME: %[[K1:.*]]: !torch.str,
// CHECK-SAME: %[[V1:.*]]: !torch.tensor) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: %[[DICT:.*]] = torch.prim.DictConstruct
// CHECK-SAME: keys(%[[K0]], %[[K1]] : !torch.str, !torch.str)
// CHECK-SAME: values(%[[V0]], %[[V1]] : !torch.tensor, !torch.tensor)
// CHECK-SAME: -> !torch.dict<str, tensor>
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.__contains__.str(%k0 : !torch.str, %v0: !torch.tensor, %k1: !torch.str, %v1: !torch.tensor) -> !torch.bool{
%dict = torch.prim.DictConstruct keys(%k0, %k1: !torch.str, !torch.str) values(%v0, %v1: !torch.tensor, !torch.tensor) -> !torch.dict<str, tensor>
%pred = torch.aten.__contains__.str %dict, %k0 : !torch.dict<str, tensor>, !torch.str -> !torch.bool
return %pred : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__contains__.str$with_dict_modified(
// CHECK-SAME: %[[K0:.*]]: !torch.str, %[[V0:.*]]: !torch.tensor,
// CHECK-SAME: %[[K1:.*]]: !torch.str, %[[V1:.*]]: !torch.tensor) -> !torch.bool {
// CHECK: %[[DICT:.*]] = torch.prim.DictConstruct
// CHECK-SAME: keys(%[[K0]], %[[K1]] : !torch.str, !torch.str)
// CHECK-SAME: values(%[[V0]], %[[V1]] : !torch.tensor, !torch.tensor)
// CHECK-SAME: -> !torch.dict<str, tensor>
// CHECK: torch.aten._set_item.str %[[DICT]], %[[K0]], %[[V1]] :
// CHECK-SAME: !torch.dict<str, tensor>, !torch.str, !torch.tensor
// CHECK: %[[RET:.*]] = torch.aten.__contains__.str %[[DICT]], %[[K0]] :
// CHECK-SAME: !torch.dict<str, tensor>, !torch.str -> !torch.bool
// CHECK: return %[[RET]] : !torch.bool
func.func @torch.aten.__contains__.str$with_dict_modified(%k0 : !torch.str, %v0: !torch.tensor, %k1: !torch.str, %v1: !torch.tensor) -> !torch.bool{
%dict = torch.prim.DictConstruct keys(%k0, %k1: !torch.str, !torch.str) values(%v0, %v1: !torch.tensor, !torch.tensor) -> !torch.dict<str, tensor>
torch.aten._set_item.str %dict, %k0, %v1 : !torch.dict<str, tensor>, !torch.str, !torch.tensor
%pred = torch.aten.__contains__.str %dict, %k0 : !torch.dict<str, tensor>, !torch.str -> !torch.bool
return %pred : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.__getitem__.Dict_str(
// CHECK-SAME: %[[K0:.*]]: !torch.str, %[[V0:.*]]: !torch.tensor,
// CHECK-SAME: %[[K1:.*]]: !torch.str, %[[V1:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: %[[DICT:.*]] = torch.prim.DictConstruct
// CHECK-SAME: keys(%[[K0]], %[[K1]] : !torch.str, !torch.str)
// CHECK-SAME: values(%[[V0]], %[[V1]] : !torch.tensor, !torch.tensor)
// CHECK-SAME: -> !torch.dict<str, tensor>
// CHECK: return %[[V0]] : !torch.tensor
func.func @torch.aten.__getitem__.Dict_str(%k0 : !torch.str, %v0: !torch.tensor, %k1: !torch.str, %v1: !torch.tensor) -> !torch.tensor {
%dict = torch.prim.DictConstruct keys(%k0, %k1: !torch.str, !torch.str) values(%v0, %v1: !torch.tensor, !torch.tensor) -> !torch.dict<str, tensor>
%v = torch.aten.__getitem__.Dict_str %dict, %k0 : !torch.dict<str, tensor>, !torch.str -> !torch.tensor
return %v : !torch.tensor
}
// CHECK-LABEL: func.func @torch.aten.add.int() -> !torch.int {
// CHECK: %[[CST9:.*]] = torch.constant.int 9
// CHECK: return %[[CST9]] : !torch.int
func.func @torch.aten.add.int() -> !torch.int {
%cst4 = torch.constant.int 4
%cst5 = torch.constant.int 5
%ret = torch.aten.add.int %cst4, %cst5: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.add.float_int() -> !torch.float {
// CHECK: %[[CST9:.*]] = torch.constant.float 9.000000e+00
// CHECK: return %[[CST9]] : !torch.float
func.func @torch.aten.add.float_int() -> !torch.float {
%cst4 = torch.constant.float 4.0
%cst5 = torch.constant.int 5
%ret = torch.aten.add.float_int %cst4, %cst5: !torch.float, !torch.int -> !torch.float
return %ret : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.sub.int() -> !torch.int {
// CHECK: %[[CST1:.*]] = torch.constant.int 1
// CHECK: return %[[CST1]] : !torch.int
func.func @torch.aten.sub.int() -> !torch.int {
%cst6 = torch.constant.int 6
%cst5 = torch.constant.int 5
%ret = torch.aten.sub.int %cst6, %cst5: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.mul.int() -> !torch.int {
// CHECK: %[[CST30:.*]] = torch.constant.int 30
// CHECK: return %[[CST30]] : !torch.int
func.func @torch.aten.mul.int() -> !torch.int {
%cst6 = torch.constant.int 6
%cst5 = torch.constant.int 5
%ret = torch.aten.mul.int %cst6, %cst5: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.mul.float() -> !torch.float {
// CHECK: %[[CST30:.*]] = torch.constant.float 3.000000e+01
// CHECK: return %[[CST30]] : !torch.float
func.func @torch.aten.mul.float() -> !torch.float {
%cst6 = torch.constant.float 6.0
%cst5 = torch.constant.float 5.0
%ret = torch.aten.mul.float %cst6, %cst5: !torch.float, !torch.float -> !torch.float
return %ret : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.neg.float() -> !torch.float {
// CHECK: %[[CST_6:.*]] = torch.constant.float -6.000000e+00
// CHECK: return %[[CST_6]] : !torch.float
func.func @torch.aten.neg.float() -> !torch.float {
%cst6 = torch.constant.float 6.0
%ret = torch.aten.neg.float %cst6: !torch.float -> !torch.float
return %ret : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.mul.int$with_zero() -> !torch.int {
// CHECK: %[[CST0:.*]] = torch.constant.int 0
// CHECK: return %[[CST0]] : !torch.int
func.func @torch.aten.mul.int$with_zero() -> !torch.int {
%cst6 = torch.constant.int 6
%cst0 = torch.constant.int 0
%ret = torch.aten.mul.int %cst6, %cst0: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.floordiv.int() -> !torch.int {
// CHECK: %[[CST3:.*]] = torch.constant.int 3
// CHECK: return %[[CST3]] : !torch.int
func.func @torch.aten.floordiv.int() -> !torch.int {
%cst18 = torch.constant.int 18
%cst5 = torch.constant.int 5
%ret = torch.aten.floordiv.int %cst18, %cst5: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.remainder.int() -> !torch.int {
// CHECK: %[[CST3:.*]] = torch.constant.int 3
// CHECK: return %[[CST3]] : !torch.int
func.func @torch.aten.remainder.int() -> !torch.int {
%cst18 = torch.constant.int 18
%cst5 = torch.constant.int 5
%ret = torch.aten.remainder.int %cst18, %cst5: !torch.int, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.pow.int_float() -> !torch.float {
// CHECK: %[[FLOAT_8:.*]] = torch.constant.float 8.000000e+00
// CHECK: return %[[FLOAT_8]] : !torch.float
func.func @torch.aten.pow.int_float() -> !torch.float {
%cst2 = torch.constant.int 2
%float3.0 = torch.constant.float 3.0
%ret = torch.aten.pow.int_float %cst2, %float3.0: !torch.int, !torch.float -> !torch.float
return %ret : !torch.float
}
// CHECK-LABEL: func.func @torch.prim.dtype$bfloat16(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<*,bf16>) -> !torch.int {
// CHECK: %[[CST:.*]] = torch.constant.int 15
// CHECK: return %[[CST]] : !torch.int
func.func @torch.prim.dtype$bfloat16(%t : !torch.tensor<*,bf16>) -> !torch.int {
%ret = torch.prim.dtype %t: !torch.tensor<*,bf16> -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.dtype$float(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<*,f32>) -> !torch.int {
// CHECK: %[[CST:.*]] = torch.constant.int 6
// CHECK: return %[[CST]] : !torch.int
func.func @torch.prim.dtype$float(%t : !torch.tensor<*,f32>) -> !torch.int {
%ret = torch.prim.dtype %t: !torch.tensor<*,f32> -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.dtype$bool(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<*,i1>) -> !torch.int {
// CHECK: %[[CST:.*]] = torch.constant.int 11
// CHECK: return %[[CST]] : !torch.int
func.func @torch.prim.dtype$bool(%t : !torch.tensor<*,i1>) -> !torch.int {
%ret = torch.prim.dtype %t: !torch.tensor<*,i1> -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.dtype$int64(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<*,si64>) -> !torch.int {
// CHECK: %[[CST:.*]] = torch.constant.int 4
// CHECK: return %[[CST]] : !torch.int
func.func @torch.prim.dtype$int64(%t : !torch.tensor<*,si64>) -> !torch.int {
%ret = torch.prim.dtype %t: !torch.tensor<*,si64> -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.size.int$neg_dim(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<[2,3],f32>) -> !torch.int {
// CHECK: %[[RET:.*]] = torch.constant.int 2
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.size.int$neg_dim(%t: !torch.tensor<[2,3],f32>) -> !torch.int {
%int-2 = torch.constant.int -2
%ret = torch.aten.size.int %t, %int-2 : !torch.tensor<[2,3],f32>, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.size.int$pos_dim(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<[2,3],f32>) -> !torch.int {
// CHECK: %[[RET:.*]] = torch.constant.int 3
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.size.int$pos_dim(%t: !torch.tensor<[2,3],f32>) -> !torch.int {
%int1 = torch.constant.int 1
%ret = torch.aten.size.int %t, %int1 : !torch.tensor<[2,3],f32>, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.size.int$invalid_dim(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<[2,3],f32>) -> !torch.int {
// CHECK: %[[CST3:.*]] = torch.constant.int 3
// CHECK: %[[RET:.*]] = torch.aten.size.int %[[T]], %[[CST3]] : !torch.tensor<[2,3],f32>, !torch.int -> !torch.int
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.size.int$invalid_dim(%t: !torch.tensor<[2,3],f32>) -> !torch.int {
%int3 = torch.constant.int 3
%ret = torch.aten.size.int %t, %int3 : !torch.tensor<[2,3],f32>, !torch.int -> !torch.int
return %ret : !torch.int
}
// CHECK-LABEL: func.func @torch.prim.unchecked_cast$derefine_identity(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.int {
// CHECK: return %[[ARG]] : !torch.int
func.func @torch.prim.unchecked_cast$derefine_identity(%arg0: !torch.int) -> !torch.int {
%0 = torch.derefine %arg0 : !torch.int to !torch.optional<int>
%1 = torch.prim.unchecked_cast %0 : !torch.optional<int> -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.derefine$of_unchecked_cast(
// CHECK-SAME: %[[ARG:.*]]: !torch.optional<int>) -> !torch.optional<int> {
// CHECK: return %[[ARG]] : !torch.optional<int>
func.func @torch.derefine$of_unchecked_cast(%arg0: !torch.optional<int>) -> !torch.optional<int> {
%0 = torch.prim.unchecked_cast %arg0 : !torch.optional<int> -> !torch.int
%1 = torch.derefine %0 : !torch.int to !torch.optional<int>
return %1 : !torch.optional<int>
}
// CHECK-LABEL: func.func @torch.derefine$use_allows_type_refinement(
// CHECK-SAME: %{{.*}}: !torch.int) -> (!torch.vtensor, !torch.optional<int>) {
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[DEREFINED:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<int>
// For the use that allows type refinement, we replace it with the refined value.
// CHECK: %[[ARANGE:.*]] = torch.aten.arange.start %{{.*}}, %{{.*}}, %[[NONE]], %{{.*}}, %{{.*}}, %{{.*}} : !torch.int, !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor
// For the use that does not allow type refinement, don't replace.
// CHECK: return %[[ARANGE]], %[[DEREFINED]] : !torch.vtensor, !torch.optional<int>
func.func @torch.derefine$use_allows_type_refinement(%arg0: !torch.int) -> (!torch.vtensor, !torch.optional<int>) {
%none = torch.constant.none
%optional = torch.derefine %none : !torch.none to !torch.optional<int>
%ret = torch.aten.arange.start %arg0, %arg0, %optional, %none, %none, %none: !torch.int, !torch.int, !torch.optional<int>, !torch.none, !torch.none, !torch.none -> !torch.vtensor
return %ret, %optional : !torch.vtensor, !torch.optional<int>
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$downcast_first(
// CHECK-SAME: %[[T:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: return %[[T]] : !torch.tensor
func.func @torch.tensor_static_info_cast$downcast_first(%t: !torch.tensor) -> !torch.tensor {
%downcast = torch.tensor_static_info_cast %t : !torch.tensor to !torch.tensor<[?,?],f64>
%upcast = torch.tensor_static_info_cast %downcast : !torch.tensor<[?,?],f64> to !torch.tensor
return %upcast: !torch.tensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$upcast_first(
// CHECK-SAME: %[[T:.*]]: !torch.tensor<[?,?],f64>) -> !torch.tensor<[?,?],f64> {
// CHECK: return %[[T]] : !torch.tensor<[?,?],f64>
func.func @torch.tensor_static_info_cast$upcast_first(%t: !torch.tensor<[?,?],f64>) -> !torch.tensor<[?,?],f64> {
%upcast = torch.tensor_static_info_cast %t : !torch.tensor<[?,?],f64> to !torch.tensor
%downcast = torch.tensor_static_info_cast %upcast : !torch.tensor to !torch.tensor<[?,?],f64>
return %downcast: !torch.tensor<[?,?],f64>
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$refine(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[],f32>) -> !torch.vtensor {
// CHECK-NEXT: %[[RESULT:.*]] = torch.aten.relu %[[ARG]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK-NEXT: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$refine(%arg0: !torch.vtensor<[], f32>) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],f32> to !torch.vtensor
%1 = torch.aten.relu %0 : !torch.vtensor -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$refine$dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[],f32>) -> !torch.vtensor {
// CHECK-NEXT: %[[RESULT:.*]] = torch.aten.relu %[[ARG]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK-NEXT: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$refine$dtype(%arg0: !torch.vtensor<[], f32>) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],f32> to !torch.vtensor<[],unk>
%1 = torch.aten.relu %0 : !torch.vtensor<[],unk> -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$refine$shape(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[],f32>) -> !torch.vtensor {
// CHECK-NEXT: %[[RESULT:.*]] = torch.aten.relu %[[ARG]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK-NEXT: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$refine$shape(%arg0: !torch.vtensor<[], f32>) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],f32> to !torch.vtensor<*,f32>
%1 = torch.aten.relu %0 : !torch.vtensor<*,f32> -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$no_refine(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor) -> !torch.vtensor {
// CHECK: %[[CAST:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor to !torch.vtensor<[],f32>
// CHECK: %[[RESULT:.*]] = torch.aten.relu %[[CAST]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$no_refine(%arg0: !torch.vtensor) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor to !torch.vtensor<[],f32>
%1 = torch.aten.relu %0 : !torch.vtensor<[],f32> -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$no_refine$dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[],unk>) -> !torch.vtensor {
// CHECK: %[[CAST:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor<[],unk> to !torch.vtensor<[],f32>
// CHECK: %[[RESULT:.*]] = torch.aten.relu %[[CAST]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$no_refine$dtype(%arg0: !torch.vtensor<[],unk>) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],unk> to !torch.vtensor<[],f32>
%1 = torch.aten.relu %0 : !torch.vtensor<[],f32> -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$no_refine$shape(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<*,f32>) -> !torch.vtensor {
// CHECK: %[[CAST:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor<*,f32> to !torch.vtensor<[],f32>
// CHECK: %[[RESULT:.*]] = torch.aten.relu %[[CAST]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK: return %[[RESULT]] : !torch.vtensor
func.func @torch.tensor_static_info_cast$no_refine$shape(%arg0: !torch.vtensor<*,f32>) -> !torch.vtensor {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<*,f32> to !torch.vtensor<[],f32>
%1 = torch.aten.relu %0 : !torch.vtensor<[],f32> -> !torch.vtensor
return %1 : !torch.vtensor
}
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$refine_allowed_ops(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[],f32>) -> !torch.tuple<vtensor, vtensor> {
// CHECK: %[[CAST:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor<[],f32> to !torch.vtensor
// CHECK: %[[RELU:.*]] = torch.aten.relu %[[ARG]] : !torch.vtensor<[],f32> -> !torch.vtensor
// CHECK: %[[RESULT:.*]] = torch.prim.TupleConstruct %[[CAST]], %[[RELU]] : !torch.vtensor, !torch.vtensor -> !torch.tuple<vtensor, vtensor>
// CHECK: return %[[RESULT]] : !torch.tuple<vtensor, vtensor>
func.func @torch.tensor_static_info_cast$refine_allowed_ops(%arg0: !torch.vtensor<[], f32>) -> !torch.tuple<vtensor, vtensor> {
%0 = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[],f32> to !torch.vtensor
%1 = torch.aten.relu %0 : !torch.vtensor -> !torch.vtensor
// prim.TupleConstruct does not allow type refinements
%2 = torch.prim.TupleConstruct %0, %1 : !torch.vtensor, !torch.vtensor -> !torch.tuple<vtensor, vtensor>
return %2 : !torch.tuple<vtensor, vtensor>
}
// CHECK-LABEL: func.func @torch.prim.TupleIndex(
// CHECK-SAME: %[[T0:.*]]: !torch.tensor, %[[T1:.*]]: !torch.tensor, %[[T2:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: return %[[T1]] : !torch.tensor
func.func @torch.prim.TupleIndex(%t0: !torch.tensor, %t1: !torch.tensor, %t2: !torch.tensor) -> !torch.tensor {
%0 = torch.prim.TupleConstruct %t0, %t1, %t2 : !torch.tensor, !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor, tensor>
%int1 = torch.constant.int 1
%1 = torch.prim.TupleIndex %0, %int1 : !torch.tuple<tensor, tensor, tensor>, !torch.int -> !torch.tensor
return %1 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.prim.TupleIndex$out_of_bound(
// CHECK-SAME: %[[T0:.*]]: !torch.tensor, %[[T1:.*]]: !torch.tensor, %[[T2:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: %[[INDEX3:.*]] = torch.constant.int 3
// CHECK: %[[TUPLE:.*]] = torch.prim.TupleConstruct %[[T0]], %[[T1]], %[[T2]] :
// CHECK-SAME: !torch.tensor, !torch.tensor, !torch.tensor ->
// CHECK-SAME: !torch.tuple<tensor, tensor, tensor>
// CHECK: %[[RET:.*]] = torch.prim.TupleIndex %[[TUPLE]], %[[INDEX3]] :
// CHECK-SAME: !torch.tuple<tensor, tensor, tensor>, !torch.int -> !torch.tensor
// CHECK: return %[[RET]] : !torch.tensor
func.func @torch.prim.TupleIndex$out_of_bound(%t0: !torch.tensor, %t1: !torch.tensor, %t2: !torch.tensor) -> !torch.tensor {
%0 = torch.prim.TupleConstruct %t0, %t1, %t2 : !torch.tensor, !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor, tensor>
%int3 = torch.constant.int 3
%1 = torch.prim.TupleIndex %0, %int3 : !torch.tuple<tensor, tensor, tensor>, !torch.int -> !torch.tensor
return %1 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.prim.TupleIndex$adjust_type$tensor(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[7],f32>) -> !torch.tensor {
// CHECK: %[[RETURN:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.tensor<[7],f32> to !torch.tensor
// CHECK: return %[[RETURN]] : !torch.tensor
func.func @torch.prim.TupleIndex$adjust_type$tensor(%arg0: !torch.tensor<[7],f32>) -> !torch.tensor {
%int0 = torch.constant.int 0
%0 = torch.prim.TupleConstruct %arg0 : !torch.tensor<[7],f32> -> !torch.tuple<tensor<[7],f32>>
%1 = torch.prim.TupleIndex %0, %int0 : !torch.tuple<tensor<[7],f32>>, !torch.int -> !torch.tensor
return %1 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.prim.unchecked_cast$derefine
// CHECK-next: return %arg0 : !torch.list<int>
func.func @torch.prim.unchecked_cast$derefine(%arg0: !torch.list<int>) -> !torch.list<int> {
%0 = torch.derefine %arg0 : !torch.list<int> to !torch.optional<list<int>>
%1 = torch.prim.unchecked_cast %0 : !torch.optional<list<int>> -> !torch.list<int>
return %1 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.Int.Tensor(
// CHECK-SAME: %[[NUM:.*]]: !torch.int) -> !torch.int {
// CHECK: %[[T:.*]] = torch.prim.NumToTensor.Scalar %[[NUM]] : !torch.int -> !torch.vtensor<[],si64>
// CHECK: return %[[NUM]] : !torch.int
func.func @torch.aten.Int.Tensor(%arg0: !torch.int) -> !torch.int {
%tensor = torch.prim.NumToTensor.Scalar %arg0: !torch.int -> !torch.vtensor<[],si64>
%scalar = torch.aten.Int.Tensor %tensor : !torch.vtensor<[],si64> -> !torch.int
return %scalar : !torch.int
}
// CHECK-LABEL: @torch.aten.Int.Tensor$canonicalize_0d_const() -> !torch.int {
// CHECK: %[[NUM:.*]] = torch.constant.int 1
// CHECK: return %[[NUM]] : !torch.int
func.func @torch.aten.Int.Tensor$canonicalize_0d_const() -> !torch.int {
%cst = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
%scalar = torch.aten.Int.Tensor %cst : !torch.vtensor<[],si64> -> !torch.int
return %scalar : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.Int.float() -> !torch.int {
// CHECK: %[[NUM:.*]] = torch.constant.int 1
// CHECK: return %[[NUM]] : !torch.int
func.func @torch.aten.Int.float() -> !torch.int {
%float1 = torch.constant.float 1.0
%int1 = torch.aten.Int.float %float1 : !torch.float -> !torch.int
return %int1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.Float.Tensor(
// CHECK-SAME: %[[NUM:.*]]: !torch.float) -> !torch.float {
// CHECK: %[[T:.*]] = torch.prim.NumToTensor.Scalar %[[NUM]] : !torch.float -> !torch.vtensor<[],f64>
// CHECK: return %[[NUM]] : !torch.float
func.func @torch.aten.Float.Tensor(%arg0: !torch.float) -> !torch.float {
%tensor = torch.prim.NumToTensor.Scalar %arg0: !torch.float -> !torch.vtensor<[],f64>
%scalar = torch.aten.Float.Tensor %tensor : !torch.vtensor<[],f64> -> !torch.float
return %scalar : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.squeeze$zero_rank(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.tensor<[],f32>
func.func @torch.aten.squeeze$zero_rank(%arg0: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
%0 = torch.aten.squeeze %arg0 : !torch.tensor<[],f32> -> !torch.tensor<[],f32>
return %0 : !torch.tensor<[],f32>
}
// CHECK-LABEL: func.func @torch.aten.squeeze.dim$zero_rank(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.tensor<[],f32>
func.func @torch.aten.squeeze.dim$zero_rank(%arg0: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
%int0 = torch.constant.int 0
%0 = torch.aten.squeeze.dim %arg0, %int0 : !torch.tensor<[],f32>, !torch.int -> !torch.tensor<[],f32>
return %0 : !torch.tensor<[],f32>
}
// CHECK-LABEL: func.func @torch.aten.tensor$one_elem(
// CHECK-NEXT: torch.vtensor.literal(dense<42> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
func.func @torch.aten.tensor$one_elem() -> (!torch.vtensor<[1],si64>) {
%none = torch.constant.none
%false = torch.constant.bool false
%int42 = torch.constant.int 42
%66 = torch.prim.ListConstruct %int42 : (!torch.int) -> !torch.list<int>
%67 = torch.aten.tensor %66, %none, %none, %false : !torch.list<int>, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[1],si64>
return %67 : !torch.vtensor<[1],si64>
}
// CHECK-LABEL: func.func @torch.aten.tensor.float(
// CHECK-NEXT: torch.vtensor.literal(dense<1.000000e+01> : tensor<f32>) : !torch.vtensor<[],f32>
func.func @torch.aten.tensor.float() -> !torch.vtensor<[],f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%float1.000000e01 = torch.constant.float 1.000000e+01
%67 = torch.aten.tensor.float %float1.000000e01, %none, %none, %false : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],f32>
return %67 : !torch.vtensor<[],f32>
}
// CHECK-LABEL: func.func @torch.aten.tensor.int(
// CHECK-NEXT: torch.vtensor.literal(dense<45> : tensor<si32>) : !torch.vtensor<[],si32>
func.func @torch.aten.tensor.int() -> !torch.vtensor<[],si32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int45 = torch.constant.int 45
%67 = torch.aten.tensor.int %int45, %none, %none, %false : !torch.int, !torch.none, !torch.none, !torch.bool -> !torch.vtensor<[],si32>
return %67 : !torch.vtensor<[],si32>
}
// CHECK-LABEL: func.func @torch.aten.to.dtype$same_dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<*,f32>) -> !torch.tensor<*,f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.tensor<*,f32>
func.func @torch.aten.to.dtype$same_dtype(%arg0: !torch.tensor<*,f32>) -> !torch.tensor<*,f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int6 = torch.constant.int 6
%0 = torch.aten.to.dtype %arg0, %int6, %false, %false, %none : !torch.tensor<*,f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<*,f32>
return %0 : !torch.tensor<*,f32>
}
// CHECK-LABEL: func.func @torch.aten.to.dtype$no_fold$unk_dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: %[[RESULT:.*]] = torch.aten.to.dtype %[[ARG]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}} : !torch.tensor, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor
// CHECK: return %[[RESULT]] : !torch.tensor
func.func @torch.aten.to.dtype$no_fold$unk_dtype(%arg0: !torch.tensor) -> !torch.tensor {
%none = torch.constant.none
%false = torch.constant.bool false
%int6 = torch.constant.int 6
%0 = torch.aten.to.dtype %arg0, %int6, %false, %false, %none : !torch.tensor, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor
return %0 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.aten.to.other$basic(
// CHECK-SAME: %[[ARG_0:.*]]: !torch.tensor, %[[ARG_1:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[CPU:.*]] = torch.constant.device "cpu"
// CHECK: %[[VAR_0:.*]] = torch.prim.dtype %[[ARG_1]] : !torch.tensor -> !torch.int
// CHECK: %[[VAR_1:.*]] = torch.aten.to.device %[[ARG_0]], %[[CPU]], %[[VAR_0]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.tensor, !torch.Device, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor
// CHECK: return %[[VAR_1]] : !torch.tensor
func.func @torch.aten.to.other$basic(%arg0 : !torch.tensor, %arg1 : !torch.tensor) -> !torch.tensor {
%none = torch.constant.none
%false = torch.constant.bool false
%0 = torch.aten.to.other %arg0, %arg1, %false, %false, %none : !torch.tensor, !torch.tensor, !torch.bool, !torch.bool, !torch.none -> !torch.tensor
return %0 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.aten.view$1D(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[?],f32>) -> !torch.tensor<[?],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.tensor<[?],f32>
func.func @torch.aten.view$1D(%arg0: !torch.tensor<[?],f32>) -> !torch.tensor<[?],f32> {
%int-1 = torch.constant.int -1
%0 = torch.prim.ListConstruct %int-1 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.view %arg0, %0 : !torch.tensor<[?],f32>, !torch.list<int> -> !torch.tensor<[?],f32>
return %1 : !torch.tensor<[?],f32>
}
// CHECK-LABEL: func.func @torch.aten.div.float$fold_zero_dividend(
// CHECK: %[[CST0:.*]] = torch.constant.float 0.000000e+00
// CHECK: return %[[CST0]] : !torch.float
func.func @torch.aten.div.float$fold_zero_dividend() -> !torch.float {
%float0 = torch.constant.float 0.0
%float5 = torch.constant.float 5.0
%0 = torch.aten.div.float %float0, %float5 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.div.float$fold_one_divisor(
// CHECK: %[[CST4:.*]] = torch.constant.float 4.000000e+00
// CHECK: return %[[CST4]] : !torch.float
func.func @torch.aten.div.float$fold_one_divisor() -> !torch.float {
%float4 = torch.constant.float 4.0
%float1 = torch.constant.float 1.0
%0 = torch.aten.div.float %float4, %float1 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.div.float$fold_cst_operands(
// CHECK: %[[CST2:.*]] = torch.constant.float 2.000000e+00
// CHECK: return %[[CST2]] : !torch.float
func.func @torch.aten.div.float$fold_cst_operands() -> !torch.float {
%float4 = torch.constant.float 4.0
%float2 = torch.constant.float 2.0
%0 = torch.aten.div.float %float4, %float2 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.div.int$fold_cst_operands(
// CHECK: %[[CST:.*]] = torch.constant.float 5.000000e-01
// CHECK: return %[[CST]] : !torch.float
func.func @torch.aten.div.int$fold_cst_operands() -> !torch.float {
%int2 = torch.constant.int 2
%int4 = torch.constant.int 4
%0 = torch.aten.div.int %int2, %int4 : !torch.int, !torch.int -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.to.dtype_layout$same_dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.tensor<[?,?],f32>
func.func @torch.aten.to.dtype_layout$same_dtype(%arg0: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int6 = torch.constant.int 6
%0 = torch.aten.to.dtype_layout %arg0, %int6, %none, %none, %none, %false, %false, %none : !torch.tensor<[?,?],f32>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f32>
return %0 : !torch.tensor<[?,?],f32>
}
// CHECK-LABEL: func.func @torch.aten.to.dtype_layout$to_device(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f32> {
// CHECK-NEXT: %[[INT6:.*]] = torch.constant.int 6
// CHECK-NEXT: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-NEXT: %[[NONE:.*]] = torch.constant.none
// CHECK-NEXT: %[[CPU:.*]] = torch.constant.device "cpu"
// CHECK-NEXT: %[[RESULT:.*]] = torch.aten.to.device %[[ARG]], %[[CPU]], %[[INT6]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.tensor<[?,?],f32>, !torch.Device, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f32>
// CHECK-NEXT: return %[[RESULT]] : !torch.tensor<[?,?],f32>
func.func @torch.aten.to.dtype_layout$to_device(%arg0: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f32> {
%none = torch.constant.none
%device = torch.constant.device "cpu"
%false = torch.constant.bool false
%int6 = torch.constant.int 6
%0 = torch.aten.to.dtype_layout %arg0, %int6, %none, %device, %none, %false, %false, %none : !torch.tensor<[?,?],f32>, !torch.int, !torch.none, !torch.Device, !torch.none, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f32>
return %0 : !torch.tensor<[?,?],f32>
}
// CHECK-LABEL: func.func @torch.aten.to.dtype_layout$to_dtype(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f16> {
// CHECK-NEXT: %[[NONE:.*]] = torch.constant.none
// CHECK-NEXT: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-NEXT: %[[INT5:.*]] = torch.constant.int 5
// CHECK-NEXT: %[[RESULT:.*]] = torch.aten.to.dtype %[[ARG]], %[[INT5]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.tensor<[?,?],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f16>
// CHECK-NEXT: return %[[RESULT]] : !torch.tensor<[?,?],f16>
func.func @torch.aten.to.dtype_layout$to_dtype(%arg0: !torch.tensor<[?,?],f32>) -> !torch.tensor<[?,?],f16> {
%none = torch.constant.none
%false = torch.constant.bool false
%int5 = torch.constant.int 5
%0 = torch.aten.to.dtype_layout %arg0, %int5, %none, %none, %none, %false, %false, %none : !torch.tensor<[?,?],f32>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f16>
return %0 : !torch.tensor<[?,?],f16>
}
// CHECK-LABEL: func.func @torch.aten.ge.float$same_operand(
// CHECK-SAME: %{{.*}}: !torch.float) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ge.float$same_operand(%arg0: !torch.float) -> !torch.bool {
%2 = torch.aten.ge.float %arg0, %arg0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ge.float$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func.func @torch.aten.ge.float$same_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 4.0
%2 = torch.aten.ge.float %float4, %float4_0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ge.float$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func.func @torch.aten.ge.float$different_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 5.0
%2 = torch.aten.ge.float %float4, %float4_0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.ceil.float$fold_cst() -> !torch.int {
// CHECK: %[[CST2:.*]] = torch.constant.int 2
// CHECK: return %[[CST2]] : !torch.int
func.func @torch.aten.ceil.float$fold_cst() -> !torch.int {
%float = torch.constant.float 1.5
%1 = torch.aten.ceil.float %float : !torch.float -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.ceil.float$no_fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[RESULT:.*]] = torch.aten.ceil.float %[[ARG]] : !torch.float -> !torch.int
// CHECK: return %[[RESULT]] : !torch.int
func.func @torch.aten.ceil.float$no_fold(%arg0 : !torch.float) -> !torch.int {
%1 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.sqrt.int$fold_cst() -> !torch.float {
// CHECK: %[[CST:.*]] = torch.constant.float 2.2360679774997898
// CHECK: return %[[CST]] : !torch.float
func.func @torch.aten.sqrt.int$fold_cst() -> !torch.float {
%int = torch.constant.int 5
%0 = torch.aten.sqrt.int %int : !torch.int -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.sqrt.int$no_fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.float {
// CHECK: %[[RESULT:.*]] = torch.aten.sqrt.int %[[ARG]] : !torch.int -> !torch.float
// CHECK: return %[[RESULT]] : !torch.float
func.func @torch.aten.sqrt.int$no_fold(%arg0 : !torch.int) -> !torch.float {
%0 = torch.aten.sqrt.int %arg0 : !torch.int -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.Bool.float$fold_cst() -> !torch.bool {
// CHECK: %[[CST2:.*]] = torch.constant.bool true
// CHECK: return %[[CST2]] : !torch.bool
func.func @torch.aten.Bool.float$fold_cst() -> !torch.bool {
%float = torch.constant.float 1.5
%1 = torch.aten.Bool.float %float : !torch.float -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.Bool.int$fold_cst() -> !torch.bool {
// CHECK: %[[CST2:.*]] = torch.constant.bool true
// CHECK: return %[[CST2]] : !torch.bool
func.func @torch.aten.Bool.int$fold_cst() -> !torch.bool {
%int = torch.constant.int 2
%1 = torch.aten.Bool.int %int : !torch.int -> !torch.bool
return %1 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.add.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.*]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.add.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int0 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.add.Tensor %0, %1, %int3 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: @torch.aten.add.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.*]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.add.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.add.Tensor %0, %1, %int3 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: @torch.aten.size$copy(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3],f32>) -> !torch.list<int> {
// CHECK: %[[TWO:.*]] = torch.constant.int 2
// CHECK: %[[THREE:.*]] = torch.constant.int 3
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[TWO]], %[[THREE]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[LIST]] : !torch.list<int>
// CHECK: }
func.func @torch.aten.size$copy(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.list<int> {
%cast = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[2,3],f32> to !torch.vtensor
%non_value_tensor = torch.copy.to_tensor %cast : !torch.tensor
%value_tensor = torch.copy.to_vtensor %non_value_tensor : !torch.vtensor
%size = torch.aten.size %value_tensor : !torch.vtensor -> !torch.list<int>
return %size : !torch.list<int>
}
// CHECK-LABEL: @torch.aten.size.int$copy(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3],f32>) -> !torch.int {
// CHECK: %[[TWO:.*]] = torch.constant.int 2
// CHECK: return %[[TWO]] : !torch.int
// CHECK: }
func.func @torch.aten.size.int$copy(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.int {
%cast = torch.tensor_static_info_cast %arg0 : !torch.vtensor<[2,3],f32> to !torch.vtensor
%non_value_tensor = torch.copy.to_tensor %cast : !torch.tensor
%value_tensor = torch.copy.to_vtensor %non_value_tensor : !torch.vtensor
%zero = torch.constant.int 0
%size = torch.aten.size.int %value_tensor, %zero : !torch.vtensor, !torch.int -> !torch.int
return %size : !torch.int
}
// CHECK-LABEL: func.func @prim.ListUnpack$fold_list(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[2,3],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[2,3],f32>) -> (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>) {
// CHECK: return %[[ARG0]], %[[ARG1]] : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>
func.func @prim.ListUnpack$fold_list(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !torch.vtensor<[2,3],f32>) -> (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>) {
%0 = torch.prim.ListConstruct %arg0, %arg1 : (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>) -> !torch.list<vtensor>
%1:2 = torch.prim.ListUnpack %0 : !torch.list<vtensor> -> !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>
return %1#0, %1#1 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>
}
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.*]] = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.div.Tensor_mode$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int6 = torch.constant.int 6
%str = torch.constant.str "floor"
%0 = torch.vtensor.literal(dense<2> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int6 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.div.Tensor_mode %1, %0, %str : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.str -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.div.Tensor_mode$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int6 = torch.constant.int 6
%int2 = torch.constant.int 2
%str = torch.constant.str "floor"
%0 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int6 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.div.Tensor_mode %1, %0, %str : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.str -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.add.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.add.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int0 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.add.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.add.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.add.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%2 = torch.aten.add.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.sub.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.sub.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int0 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.sub.Tensor %0, %1, %int3 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: @torch.aten.sub.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]]
func.func @torch.aten.sub.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.sub.Tensor %0, %1, %int3 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.sub.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.sub.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int0 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.sub.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.sub.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.sub.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%2 = torch.aten.sub.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.sub.float$fold() -> !torch.float {
// CHECK: %[[FLOAT_1:.*]] = torch.constant.float -1.000000e+00
// CHECK: return %[[FLOAT_1]] : !torch.float
func.func @torch.aten.sub.float$fold() -> !torch.float {
%float1 = torch.constant.float 1.0
%float2 = torch.constant.float 2.0
%0 = torch.aten.sub.float %float1, %float2 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.mul.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.mul.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<2> : tensor<si64>) : !torch.vtensor<[],si64>
%2 = torch.aten.mul.Scalar %0, %int3 : !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.mul.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.mul.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.mul.Scalar %0, %int3 : !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.mul.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.mul.Tensor$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%0 = torch.vtensor.literal(dense<2> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
%2 = torch.aten.mul.Tensor %0, %1 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64> -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.mul.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<6> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.mul.Tensor$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int3 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.mul.Tensor %0, %1 : !torch.vtensor<[],si64>, !torch.vtensor<[],si64> -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$canonicalize_numtotensor_0d_trunc() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.div.Tensor_mode$canonicalize_numtotensor_0d_trunc() -> !torch.vtensor<[],si64> {
%int6 = torch.constant.int 6
%int2 = torch.constant.int 2
%str = torch.constant.str "trunc"
%0 = torch.prim.NumToTensor.Scalar %int2 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int6 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.div.Tensor_mode %1, %0, %str : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.str -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// CHECK-LABEL: func.func @torch.aten.div.Tensor_mode$canonicalize_literal_0d_trunc() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.div.Tensor_mode$canonicalize_literal_0d_trunc() -> !torch.vtensor<[],si64> {
%int6 = torch.constant.int 6
%str = torch.constant.str "trunc"
%0 = torch.vtensor.literal(dense<2> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.prim.NumToTensor.Scalar %int6 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.div.Tensor_mode %1, %0, %str : !torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.str -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
2022-09-13 06:59:12 +08:00
}
// CHECK-LABEL: func.func @torch.aten.sort.int$reverse_false() -> !torch.list<int> {
// CHECK: %[[INT0:.*]] = torch.constant.int 0
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[INT2:.*]] = torch.constant.int 2
// CHECK: %[[INT3:.*]] = torch.constant.int 3
// CHECK: %[[RESULT:.*]] = torch.prim.ListConstruct %[[INT0]], %[[INT1]], %[[INT2]], %[[INT3]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RESULT]] : !torch.list<int>
func.func @torch.aten.sort.int$reverse_false() -> !torch.list<int> {
%false = torch.constant.bool false
%int1 = torch.constant.int 1
%int0 = torch.constant.int 0
%int3 = torch.constant.int 3
%int2 = torch.constant.int 2
%0 = torch.prim.ListConstruct %int1, %int0, %int3, %int2 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
torch.aten.sort.int %0, %false : !torch.list<int>, !torch.bool
return %0 : !torch.list<int>
}
// CHECK-LABEL: func.func @torch.aten.sort.int$reverse_true() -> !torch.list<int> {
// CHECK: %[[INT3:.*]] = torch.constant.int 3
// CHECK: %[[INT2:.*]] = torch.constant.int 2
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[INT0:.*]] = torch.constant.int 0
// CHECK: %[[RESULT:.*]] = torch.prim.ListConstruct %[[INT3]], %[[INT2]], %[[INT1]], %[[INT0]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: return %[[RESULT]] : !torch.list<int>
func.func @torch.aten.sort.int$reverse_true() -> !torch.list<int> {
%true = torch.constant.bool true
%int1 = torch.constant.int 1
%int0 = torch.constant.int 0
%int3 = torch.constant.int 3
%int2 = torch.constant.int 2
%0 = torch.prim.ListConstruct %int1, %int0, %int3, %int2 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
torch.aten.sort.int %0, %true : !torch.list<int>, !torch.bool
return %0 : !torch.list<int>
}
// CHECK-LABEL: @torch.aten.sort$unary_element
// CHECK : %[[INDICES:.*]] = torch.vtensor.literal(dense<0> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK-NOT : torch.aten.sort %arg
// CHECK : return %arg0, %[[INDICES]] : !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>
func.func @torch.aten.sort$unary_element(%arg0 : !torch.vtensor<[1],si64>, %arg1 : !torch.int, %arg2 : !torch.bool) -> (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) {
%0, %1 = torch.aten.sort %arg0, %arg1, %arg2 : !torch.vtensor<[1],si64>, !torch.int, !torch.bool -> !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>
return %0, %1 : !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>
}
// CHECK-LABEL: @torch.aten.sort$unary_dim
// CHECK : %[[INDICES:.*]] = torch.vtensor.literal(dense<1> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK-NOT : torch.aten.sort %arg
// CHECK : return %arg0, %[[INDICES]] : !torch.vtensor<[3, 1,4],si64>, !torch.vtensor<[1],si64>
func.func @torch.aten.sort$unary_dim(%arg0 : !torch.vtensor<[3, 1, 4],si64>, %arg1 : !torch.bool) -> (!torch.vtensor<[3, 1, 4],si64>, !torch.vtensor<[1],si64>) {
%dim = torch.constant.int 1
%0, %1 = torch.aten.sort %arg0, %dim, %arg1 : !torch.vtensor<[3, 1, 4],si64>, !torch.int, !torch.bool -> !torch.vtensor<[3, 1, 4],si64>, !torch.vtensor<[1],si64>
return %0, %1 : !torch.vtensor<[3, 1,4],si64>, !torch.vtensor<[1],si64>
}
// CHECK-LABEL: @torch.aten.sort$nofold
// CHECK : torch.aten.sort %arg
func.func @torch.aten.sort$nofold (%arg0 : !torch.vtensor<[3, 1, 4],si64>, %arg1 : !torch.bool) -> (!torch.vtensor<[3, 1, 4],si64>, !torch.vtensor<[3],si64>) {
%dim = torch.constant.int 0
%0, %1 = torch.aten.sort %arg0, %dim, %arg1 : !torch.vtensor<[3, 1, 4],si64>, !torch.int, !torch.bool -> !torch.vtensor<[3, 1, 4],si64>, !torch.vtensor<[3],si64>
return %0, %1 : !torch.vtensor<[3, 1, 4],si64>, !torch.vtensor<[3],si64>
}
// -----
// CHECK-LABEL: @torch.aten.cat$fold_single_operand
// CHECK-SAME: %[[ARG0:.+]]: !torch.tensor
// CHECK: return %[[ARG0]] : !torch.tensor
func.func @torch.aten.cat$fold_single_operand(%arg0: !torch.tensor) -> !torch.tensor {
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %arg0 : (!torch.tensor) -> !torch.list<tensor>
%1 = torch.aten.cat %0, %int1 : !torch.list<tensor>, !torch.int -> !torch.tensor
return %1: !torch.tensor
}
// -----
// CHECK-LABEL: @torch.aten.cat$fold_zero_dim_operand
// CHECK: %[[FOLD:.+]] = torch.vtensor.literal(dense<[1, 3, 2, 2]> : tensor<4xsi32>)
// CHECK: return %[[FOLD]] : !torch.vtensor
func.func @torch.aten.cat$fold_zero_dim_operand() -> !torch.vtensor<[4],si32> {
%0 = torch.vtensor.literal(dense<[1, 3]> : tensor<2xsi32>) : !torch.vtensor<[2],si32>
%1 = torch.vtensor.literal(dense<2> : tensor<2xsi32>) : !torch.vtensor<[2],si32>
%int0 = torch.constant.int 0
%list = torch.prim.ListConstruct %0, %1 : (!torch.vtensor<[2],si32>, !torch.vtensor<[2],si32>) -> !torch.list<vtensor>
%cat = torch.aten.cat %list, %int0 : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[4],si32>
return %cat: !torch.vtensor<[4],si32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.broadcast_to$fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,4,2],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.vtensor<[3,4,2],f32>
func.func @torch.aten.broadcast_to$fold(%arg0: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,4,2],f32> {
%int3 = torch.constant.int 3
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%list = torch.prim.ListConstruct %int3, %int4, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%0 = torch.aten.broadcast_to %arg0, %list : !torch.vtensor<[3,4,2],f32>, !torch.list<int> -> !torch.vtensor<[3,4,2],f32>
return %0 : !torch.vtensor<[3,4,2],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.broadcast_to$fold_splat
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<3.000000e+00> : tensor<3x4x2xf32>) : !torch.vtensor<[3,4,2],f32>
// CHECK: return %[[CST]]
func.func @torch.aten.broadcast_to$fold_splat() -> !torch.vtensor<[3,4,2],f32> {
%tensor = torch.vtensor.literal(dense<3.0> : tensor<1x4x1xf32>) : !torch.vtensor<[1,4,1],f32>
%int3 = torch.constant.int 3
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%list = torch.prim.ListConstruct %int3, %int4, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%0 = torch.aten.broadcast_to %tensor, %list : !torch.vtensor<[1,4,1],f32>, !torch.list<int> -> !torch.vtensor<[3,4,2],f32>
return %0 : !torch.vtensor<[3,4,2],f32>
}
// -----
// CHECK-LABEL: @torch.aten.slice.tensor$fold_full_domain_slice
// CHECK-SAME: %[[ARG0:.+]]: !torch.vtensor<[4],f32>
// CHECK: return %[[ARG0]] : !torch.vtensor<[4],f32>
func.func @torch.aten.slice.tensor$fold_full_domain_slice(%arg0: !torch.vtensor<[4],f32>) -> !torch.vtensor<[4],f32> {
%int1 = torch.constant.int 1
%int-1 = torch.constant.int -1
%int0 = torch.constant.int 0
%0 = torch.aten.slice.Tensor %arg0, %int0, %int0, %int-1, %int1 : !torch.vtensor<[4], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[4], f32>
return %0 : !torch.vtensor<[4],f32>
}
// CHECK-LABEL: @torch.aten.slice.tensor$fold_full_slice
// CHECK-SAME: %[[ARG0:.+]]: !torch.vtensor<[?],f32>
// CHECK: return %[[ARG0]] : !torch.vtensor<[?],f32>
func.func @torch.aten.slice.tensor$fold_full_slice(%arg0: !torch.vtensor<[?],f32>, %dim: !torch.int) -> !torch.vtensor<[?],f32> {
%int1 = torch.constant.int 1
%int9223372036854775807 = torch.constant.int 9223372036854775807
%int0 = torch.constant.int 0
%0 = torch.aten.slice.Tensor %arg0, %dim, %int0, %int9223372036854775807, %int1 : !torch.vtensor<[?], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?], f32>
return %0 : !torch.vtensor<[?],f32>
}
// CHECK-LABEL: @torch.aten.slice.tensor$no_fold_step
// CHECK: torch.aten.slice.Tensor
func.func @torch.aten.slice.tensor$no_fold_step(%arg0: !torch.vtensor<[?],f32>, %dim: !torch.int) -> !torch.vtensor<[?],f32> {
%int2 = torch.constant.int 2
%int9223372036854775807 = torch.constant.int 9223372036854775807
%int0 = torch.constant.int 0
%0 = torch.aten.slice.Tensor %arg0, %dim, %int0, %int9223372036854775807, %int2 : !torch.vtensor<[?], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?], f32>
return %0 : !torch.vtensor<[?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.slice.tensor$fold_dim_1() -> (!torch.vtensor<[1,1],si64>, !torch.vtensor<[1,1],si64>) {
// CHECK-NOT: torch.aten.slice.Tensor
// CHECK: %[[RET_0:.*]] = torch.vtensor.literal(dense<50> : tensor<1x1xsi64>) : !torch.vtensor<[1,1],si64>
// CHECK-NOT: torch.aten.slice.Tensor
// CHECK: %[[RET_1:.*]] = torch.vtensor.literal(dense<70> : tensor<1x1xsi64>) : !torch.vtensor<[1,1],si64>
// CHECK-NOT: torch.aten.slice.Tensor
// CHECK: return %[[RET_0]], %[[RET_1]]
func.func @torch.aten.slice.tensor$fold_dim_1() -> (!torch.vtensor<[1, 1],si64>, !torch.vtensor<[1, 1],si64>) {
%tensor = torch.vtensor.literal(dense<[[10,20,30,40,50,60,70,80,90,100]]> : tensor<1x10xsi64>) : !torch.vtensor<[1, 10],si64>
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%int6 = torch.constant.int 6
%int7 = torch.constant.int 7
%dim = torch.constant.int 1
%0 = torch.aten.slice.Tensor %tensor, %dim, %int4, %int5, %int1 : !torch.vtensor<[1, 10], si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], si64>
%1 = torch.aten.slice.Tensor %tensor, %dim, %int6, %int7, %int1 : !torch.vtensor<[1, 10], si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], si64>
return %0, %1 : !torch.vtensor<[1,1],si64>, !torch.vtensor<[1,1],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.slice.tensor$fold_small() -> !torch.vtensor<[2],si32> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<[3, 5]> : tensor<2xsi32>) : !torch.vtensor<[2],si32>
// CHECK: return %[[CST]]
func.func @torch.aten.slice.tensor$fold_small() -> (!torch.vtensor<[2],si32>) {
%tensor = torch.vtensor.literal(dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]> : tensor<10xsi32>) : !torch.vtensor<[10],si32>
%dim = torch.constant.int 0
%int3 = torch.constant.int 3
%int2 = torch.constant.int 2
%int7 = torch.constant.int 7
%0 = torch.aten.slice.Tensor %tensor, %dim, %int3, %int7, %int2 : !torch.vtensor<[10], si32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2], si32>
return %0 : !torch.vtensor<[2],si32>
}
// -----
func.func @torch.aten.slice.tensor$fold_dim_0() -> (!torch.vtensor<[1, 1],f32>, !torch.vtensor<[1, 1],f32>) {
%tensor = torch.vtensor.literal(dense<[[2.0],[4.0],[8.0],[16.0],[32.0],[64.0],[128.0],[256.0],[512.0],[1024.0]]> : tensor<10x1xf32>) : !torch.vtensor<[10, 1],f32>
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%intn7 = torch.constant.int -7
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%int6 = torch.constant.int 6
%dim = torch.constant.int 0
%0 = torch.aten.slice.Tensor %tensor, %dim, %intn7, %int4, %int1 : !torch.vtensor<[10, 1], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], f32>
%1 = torch.aten.slice.Tensor %tensor, %dim, %int5, %int6, %int1 : !torch.vtensor<[10, 1], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], f32>
return %0, %1 : !torch.vtensor<[1, 1],f32>, !torch.vtensor<[1, 1], f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsub.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-1> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.rsub.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
%2 = torch.aten.rsub.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsub.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<-1> : tensor<si64>) : !torch.vtensor<[],si64>
// CHECK: return %[[CST]] : !torch.vtensor<[],si64>
func.func @torch.aten.rsub.Scalar$canonicalize_numtotensor_0d() -> !torch.vtensor<[],si64> {
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.NumToTensor.Scalar %int1 : !torch.int -> !torch.vtensor<[],si64>
%2 = torch.aten.rsub.Scalar %0, %int2, %int3 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
return %2 : !torch.vtensor<[],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.ScalarImplicit$canonicalize_numtotensor_0d() -> !torch.number {
// CHECK: %int1 = torch.constant.int 1
// CHECK: %[[VAL_1:.*]] = torch.derefine %int1 : !torch.int to !torch.number
// CHECK: return %[[VAL_1]] : !torch.number
func.func @torch.aten.ScalarImplicit$canonicalize_numtotensor_0d() -> !torch.number {
%int1 = torch.constant.int 1
%0 = torch.prim.NumToTensor.Scalar %int1 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.aten.ScalarImplicit %0 : !torch.vtensor<[],si64> -> !torch.number
return %1 : !torch.number
}
// -----
// CHECK-LABEL: func.func @torch.aten.ScalarImplicit$canonicalize_literal_0d() -> !torch.number {
// CHECK: %int1 = torch.constant.int 1
// CHECK: %[[VAL_0:.*]] = torch.derefine %int1 : !torch.int to !torch.number
// CHECK: return %[[VAL_0]] : !torch.number
func.func @torch.aten.ScalarImplicit$canonicalize_literal_0d() -> !torch.number {
%0 = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.aten.ScalarImplicit %0 : !torch.vtensor<[],si64> -> !torch.number
return %1 : !torch.number
}
// -----
// CHECK-LABEL: func.func @torch.aten.ScalarImplicit$canonicalize_literal_0d_float() -> !torch.number {
// CHECK: %float1.000000e00 = torch.constant.float 1.000000e+00
// CHECK: %[[VAL_0:.*]] = torch.derefine %float1.000000e00 : !torch.float to !torch.number
// CHECK: return %[[VAL_0]] : !torch.number
func.func @torch.aten.ScalarImplicit$canonicalize_literal_0d_float() -> !torch.number {
%0 = torch.vtensor.literal(dense<1.0> : tensor<f64>) : !torch.vtensor<[],f64>
%1 = torch.aten.ScalarImplicit %0 : !torch.vtensor<[],f64> -> !torch.number
return %1 : !torch.number
}
// -----
// CHECK-LABEL: func.func @torch.aten.FloatImplicit$canonicalize_numtotensor_0d() -> !torch.float {
// CHECK: %[[FLOAT1:.*]] = torch.constant.float 1.000000e+00
// CHECK: return %[[FLOAT1]] : !torch.float
func.func @torch.aten.FloatImplicit$canonicalize_numtotensor_0d() -> !torch.float {
%float1 = torch.constant.float 1.0
%0 = torch.prim.NumToTensor.Scalar %float1 : !torch.float -> !torch.vtensor<[],f64>
%1 = torch.aten.FloatImplicit %0 : !torch.vtensor<[],f64> -> !torch.float
return %1 : !torch.float
}
// -----
// CHECK-LABEL: func.func @torch.aten.FloatImplicit$canonicalize_literal_0d() -> !torch.float {
// CHECK: %[[FLOAT1:.*]] = torch.constant.float 1.000000e+00
// CHECK: return %[[FLOAT1]] : !torch.float
func.func @torch.aten.FloatImplicit$canonicalize_literal_0d() -> !torch.float {
%0 = torch.vtensor.literal(dense<1.0> : tensor<f64>) : !torch.vtensor<[],f64>
%1 = torch.aten.FloatImplicit %0 : !torch.vtensor<[],f64> -> !torch.float
return %1 : !torch.float
}
// -----
// CHECK-LABEL: func.func @torch.aten.IntImplicit$canonicalize_numtotensor_0d() -> !torch.int {
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: return %[[INT1]] : !torch.int
func.func @torch.aten.IntImplicit$canonicalize_numtotensor_0d() -> !torch.int {
%int1 = torch.constant.int 1
%0 = torch.prim.NumToTensor.Scalar %int1 : !torch.int -> !torch.vtensor<[],si64>
%1 = torch.aten.IntImplicit %0 : !torch.vtensor<[],si64> -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.IntImplicit$canonicalize_literal_0d() -> !torch.int {
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: return %[[INT1]] : !torch.int
func.func @torch.aten.IntImplicit$canonicalize_literal_0d() -> !torch.int {
%0 = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
%1 = torch.aten.IntImplicit %0 : !torch.vtensor<[],si64> -> !torch.int
return %1 : !torch.int
}
// -----
// CHECK-LABEL: func.func @torch.prims.view_of$fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,4,2],f32> {
// CHECK-NEXT: return %[[ARG]] : !torch.vtensor<[3,4,2],f32>
func.func @torch.prims.view_of$fold(%arg0: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,4,2],f32> {
%0 = torch.prims.view_of %arg0 : !torch.vtensor<[3,4,2],f32> -> !torch.vtensor<[3,4,2],f32>
return %0 : !torch.vtensor<[3,4,2],f32>
}
// CHECK-LABEL: func.func @torch.aten.cuda$canonicalize
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor
// CHECK-NEXT: return %[[ARG]] : !torch.tensor
func.func @torch.aten.cuda$canonicalize(%arg0: !torch.tensor) -> !torch.tensor {
%0 = torch.aten.cuda %arg0 : !torch.tensor -> !torch.tensor
return %0 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.aten.device.with_index$canonicalize
// CHECK-NEXT: %[[VAL:.*]] = torch.constant.device "cuda:0"
// CHECK-NEXT: return %[[VAL]] : !torch.Device
func.func @torch.aten.device.with_index$canonicalize() -> !torch.Device {
%str = torch.constant.str "cuda"
%int0 = torch.constant.int 0
%0 = torch.aten.device.with_index %str, %int0 : !torch.str, !torch.int -> !torch.Device
return %0 : !torch.Device
}
// CHECK-LABEL: func.func @torch.aten.add$fold() -> !torch.float {
// CHECK: %[[FLOAT_1:.*]] = torch.constant.float 3.000000e+00
// CHECK: return %[[FLOAT_1]] : !torch.float
func.func @torch.aten.add$fold() -> !torch.float {
%float1 = torch.constant.float 1.0
%float2 = torch.constant.float 2.0
%0 = torch.aten.add %float1, %float2 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func.func @torch.aten.any.bool$fold() -> !torch.bool {
// CHECK: %[[CST_TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[CST_TRUE]] : !torch.bool
func.func @torch.aten.any.bool$fold() -> !torch.bool {
%false = torch.constant.bool false
%true = torch.constant.bool true
%input = torch.prim.ListConstruct %false, %true, %false : (!torch.bool, !torch.bool, !torch.bool) -> !torch.list<bool>
%0 = torch.aten.any.bool %input : !torch.list<bool> -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.floor$canonicalize
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],si64>
// CHECK-NEXT: return %[[ARG]] : !torch.vtensor<[?,?],si64>
func.func @torch.aten.floor$canonicalize(%arg0: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
%0 = torch.aten.floor %arg0 : !torch.vtensor<[?,?],si64> -> !torch.vtensor<[?,?],si64>
return %0 : !torch.vtensor<[?,?],si64>
}
// CHECK-LABEL: func.func @torch.aten.trunc$canonicalize
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],si64>
// CHECK-NEXT: return %[[ARG]] : !torch.vtensor<[?,?],si64>
func.func @torch.aten.trunc$canonicalize(%arg0: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
%0 = torch.aten.trunc %arg0 : !torch.vtensor<[?,?],si64> -> !torch.vtensor<[?,?],si64>
return %0 : !torch.vtensor<[?,?],si64>
}
// CHECK-LABEL: func.func @torch.aten.numel$canonicalize
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[3,4],f32>
// CHECK-NEXT: %int12 = torch.constant.int 12
// CHECK-NEXT: return %int12 : !torch.int
func.func @torch.aten.numel$canonicalize(%arg0: !torch.vtensor<[3,4],f32>) -> !torch.int {
%0 = torch.aten.numel %arg0 : !torch.vtensor<[3,4],f32> -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func.func @torch.aten.masked_fill.Tensor$canonicalize
// CHECK-NEXT: torch.constant.float -1.000000e+09
// CHECK-NEXT: torch.aten.masked_fill.Scalar
// CHECK-NEXT: return
func.func @torch.aten.masked_fill.Tensor$canonicalize(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.vtensor.literal(dense<-1.000000e+09> : tensor<f32>) : !torch.vtensor<[],f32>
%1 = torch.aten.masked_fill.Tensor %arg0, %arg1, %0 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],i1>, !torch.vtensor<[],f32> -> !torch.vtensor<[?,?],f32>
return %1 : !torch.vtensor<[?,?],f32>
}
// CHECK-LABEL: func.func @torch.aten.detach$canonicalize
// CHECK-NEXT: torch.aten.detach
func.func @torch.aten.detach$canonicalize(%arg0: !torch.tensor<[1],f32>) -> !torch.tensor {
%1 = torch.aten.detach %arg0 : !torch.tensor<[1],f32> -> !torch.tensor
return %1 : !torch.tensor
}
// CHECK-LABEL: func.func @torch.aten.index_select$noop(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[1,2,3],si64>
// CHECK-NEXT: return %[[ARG]] : !torch.vtensor<[1,2,3],si64>
func.func @torch.aten.index_select$noop(%arg0 : !torch.vtensor<[1,2,3],si64>, %arg1 : !torch.int, %arg2 : !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,2,3],si64> {
%0 = torch.aten.index_select %arg0, %arg1, %arg2 : !torch.vtensor<[1,2,3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1,2,3],si64>
return %0 : !torch.vtensor<[1,2,3],si64>
}
// CHECK-LABEL: func.func @torch.aten.index_select$const_si_si(
// CHECK-NEXT: %[[RES:.*]] = torch.vtensor.literal(dense<60> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK-NEXT: return %[[RES]] : !torch.vtensor<[1],si64>
func.func @torch.aten.index_select$const_si_si() -> !torch.vtensor<[1],si64> {
%tensor = torch.vtensor.literal(dense<[10,20,30,40,50,60,70,80,90,100]> : tensor<10xsi64>) : !torch.vtensor<[10],si64>
%dim = torch.constant.int 0
%index = torch.vtensor.literal(dense<5> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
%0 = torch.aten.index_select %tensor, %dim, %index : !torch.vtensor<[10],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
return %0 : !torch.vtensor<[1],si64>
}
// CHECK-LABEL: func.func @torch.aten.index_select$const_si_ui(
// CHECK-NEXT: %[[RES:.*]] = torch.vtensor.literal(dense<60> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK-NEXT: return %[[RES]] : !torch.vtensor<[1],si64>
func.func @torch.aten.index_select$const_si_ui() -> !torch.vtensor<[1],si64> {
%tensor = torch.vtensor.literal(dense<[10,20,30,40,50,60,70,80,90,100]> : tensor<10xsi64>) : !torch.vtensor<[10],si64>
%dim = torch.constant.int 0
%index = torch.vtensor.literal(dense<5> : tensor<1xui64>) : !torch.vtensor<[1],ui64>
%0 = torch.aten.index_select %tensor, %dim, %index : !torch.vtensor<[10],si64>, !torch.int, !torch.vtensor<[1],ui64> -> !torch.vtensor<[1],si64>
return %0 : !torch.vtensor<[1],si64>
}
// CHECK-LABEL: func.func @torch.aten.index_select$const_f32_ui(
// CHECK-NEXT: %[[RES:.*]] = torch.vtensor.literal(dense<6.6{{.*}}> : tensor<1xf32>) : !torch.vtensor<[1],f32>
// CHECK-NEXT: return %[[RES]] : !torch.vtensor<[1],f32>
func.func @torch.aten.index_select$const_f32_ui() -> !torch.vtensor<[1],f32> {
%tensor = torch.vtensor.literal(dense<[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.0]> : tensor<10xf32>) : !torch.vtensor<[10],f32>
%dim = torch.constant.int 0
%index = torch.vtensor.literal(dense<5> : tensor<1xui64>) : !torch.vtensor<[1],ui64>
%0 = torch.aten.index_select %tensor, %dim, %index : !torch.vtensor<[10],f32>, !torch.int, !torch.vtensor<[1],ui64> -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}
// CHECK-LABEL: func.func @torch.aten.index_select$const_f32_si_neg(
// CHECK-NEXT: %[[RES:.*]] = torch.vtensor.literal(dense<7.{{.*}}> : tensor<1xf32>) : !torch.vtensor<[1],f32>
// CHECK-NEXT: return %[[RES]] : !torch.vtensor<[1],f32>
func.func @torch.aten.index_select$const_f32_si_neg() -> !torch.vtensor<[1],f32> {
%tensor = torch.vtensor.literal(dense<[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.0]> : tensor<10xf32>) : !torch.vtensor<[10],f32>
%dim = torch.constant.int -1
%index = torch.vtensor.literal(dense<-4> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
%0 = torch.aten.index_select %tensor, %dim, %index : !torch.vtensor<[10],f32>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_attr
func.func @fold_aten_where_true_attr() -> !torch.vtensor<[4],si64> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<11> : tensor<si64>) : !torch.vtensor<[],si64>
%where = torch.aten.where.self %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.vtensor<[4],si64>, !torch.vtensor<[],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_prim_numtotensor_scalar
func.func @fold_prim_numtotensor_scalar() -> !torch.vtensor<[1],si64> {
%int42 = torch.constant.int 42
// CHECK: %[[TENSOR:.+]] = torch.vtensor.literal(dense<42> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK: return %[[TENSOR]]
%0 = torch.prim.NumToTensor.Scalar %int42 : !torch.int -> !torch.vtensor<[1],si64>
return %0 : !torch.vtensor<[1],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_attr
func.func @fold_aten_where_false_attr() -> !torch.vtensor<[4],si64> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<11> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<11> : tensor<si64>) : !torch.vtensor<[],si64>
%where = torch.aten.where.self %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.vtensor<[4],si64>, !torch.vtensor<[],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_value
func.func @fold_aten_where_true_value(%arg0 : !torch.vtensor<[4],si64>, %arg1 : !torch.vtensor<[4],si64>) -> !torch.vtensor<[4],si64> {
// CHECK: return %arg0
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%where = torch.aten.where.self %bool, %arg0, %arg1 : !torch.vtensor<[4],i1>, !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_value
func.func @fold_aten_where_false_value(%arg0 : !torch.vtensor<[4],si64>, %arg1 : !torch.vtensor<[4],si64>) -> !torch.vtensor<[4],si64> {
// CHECK: return %arg1
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%where = torch.aten.where.self %bool, %arg0, %arg1 : !torch.vtensor<[4],i1>, !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_value_nofold
func.func @fold_aten_where_true_value_nofold(%arg0 : !torch.vtensor<[],si64>, %arg1 : !torch.vtensor<[4],si64>) -> !torch.vtensor<[4],si64> {
// CHECK: torch.aten.where.self
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%where = torch.aten.where.self %bool, %arg0, %arg1 : !torch.vtensor<[4],i1>, !torch.vtensor<[],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_scalar_int
func.func @fold_aten_where_true_scalar_int() -> !torch.vtensor<[4],si64> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.int 7
%rhs = torch.constant.int 11
%where = torch.aten.where.Scalar %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.int, !torch.int -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_scalar_int
func.func @fold_aten_where_false_scalar_int() -> !torch.vtensor<[4],ui8> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<11> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.int 7
%rhs = torch.constant.int 11
%where = torch.aten.where.Scalar %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.int, !torch.int -> !torch.vtensor<[4],ui8>
return %where : !torch.vtensor<[4],ui8>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_scalar_fp
func.func @fold_aten_where_false_scalar_fp() -> !torch.vtensor<[4],f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<1.100000e+01> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.float 7.0
%rhs = torch.constant.float 11.0
%where = torch.aten.where.Scalar %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.float, !torch.float -> !torch.vtensor<[4],f32>
return %where : !torch.vtensor<[4],f32>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_sother_int
func.func @fold_aten_where_true_sother_int() -> !torch.vtensor<[4],si64> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
// CHECK: %[[RET]]
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.constant.int 11
%where = torch.aten.where.ScalarOther %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.vtensor<[4],si64>, !torch.int -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_sother_int
func.func @fold_aten_where_false_sother_int() -> !torch.vtensor<[4],ui8> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<11> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.vtensor.literal(dense<7> : tensor<ui8>) : !torch.vtensor<[],ui8>
%rhs = torch.constant.int 11
%where = torch.aten.where.ScalarOther %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.vtensor<[],ui8>, !torch.int -> !torch.vtensor<[4],ui8>
return %where : !torch.vtensor<[4],ui8>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_sother_fp
func.func @fold_aten_where_false_sother_fp() -> !torch.vtensor<[4],f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<1.100000e+01> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.vtensor.literal(dense<7.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%rhs = torch.constant.float 11.0
%where = torch.aten.where.ScalarOther %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],f32>
return %where : !torch.vtensor<[4],f32>
}
// -----
// CHECK-LABEL: @fold_aten_where_true_sself_int
func.func @fold_aten_where_true_sself_int() -> !torch.vtensor<[4],si64> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<7> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
// CHECK: %[[RET]]
%bool = torch.vtensor.literal(dense<1> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.int 7
%rhs = torch.vtensor.literal(dense<11> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%where = torch.aten.where.ScalarSelf %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.int, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],si64>
return %where : !torch.vtensor<[4],si64>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_sself_int
func.func @fold_aten_where_false_sself_int() -> !torch.vtensor<[4],ui8> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<11> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
// CHECK: return %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.int 7
%rhs = torch.vtensor.literal(dense<11> : tensor<ui8>) : !torch.vtensor<[],ui8>
%where = torch.aten.where.ScalarSelf %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.int, !torch.vtensor<[],ui8> -> !torch.vtensor<[4],ui8>
return %where : !torch.vtensor<[4],ui8>
}
// -----
// CHECK-LABEL: @fold_aten_where_false_sself_fp
func.func @fold_aten_where_false_sself_fp() -> !torch.vtensor<[4],f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<1.100000e+01> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: %[[RET]]
%bool = torch.vtensor.literal(dense<0> : tensor<4xi1>) : !torch.vtensor<[4],i1>
%lhs = torch.constant.float 7.0
%rhs = torch.vtensor.literal(dense<11.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%where = torch.aten.where.ScalarSelf %bool, %lhs, %rhs : !torch.vtensor<[4],i1>, !torch.float, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],f32>
return %where : !torch.vtensor<[4],f32>
}
// -----
// CHECK-LABEL: @aten_select_int_fold_splat
func.func @aten_select_int_fold_splat(%arg0 : !torch.int, %arg1 : !torch.int) -> !torch.vtensor<[1],si64> {
%splat = torch.vtensor.literal(dense<4> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%select = torch.aten.select.int %splat, %arg0, %arg1 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<4> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK: return %[[RET]]
return %select : !torch.vtensor<[1],si64>
}
// -----
// CHECK-LABEL: @aten_select_int_fold_1D
func.func @aten_select_int_fold_1D() -> !torch.vtensor<[1],si64> {
%index = torch.constant.int 1
%dim = torch.constant.int 0
%splat = torch.vtensor.literal(dense<[5,6,7,8]> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%select = torch.aten.select.int %splat, %dim, %index : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<6> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
// CHECK: return %[[RET]]
return %select : !torch.vtensor<[1],si64>
}
// -----
// CHECK-LABEL: @aten_select_int_fold_3D
func.func @aten_select_int_fold_3D() -> !torch.vtensor<[1, 1, 1],si64> {
%index = torch.constant.int 2
%dim = torch.constant.int 2
%splat = torch.vtensor.literal(dense<[[[5,6,7,8]]]> : tensor<1x1x4xsi64>) : !torch.vtensor<[1,1,4],si64>
%select = torch.aten.select.int %splat, %dim, %index : !torch.vtensor<[1,1,4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1,1,1],si64>
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<7> : tensor<1x1x1xsi64>) : !torch.vtensor<[1,1,1],si64>
// CHECK: return %[[RET]]
return %select : !torch.vtensor<[1,1,1],si64>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_args
func.func @aten_eq_tensor_args(%arg0 : !torch.vtensor<[4],si64>) -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%0 = torch.aten.eq.Tensor %arg0, %arg0 : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splats_int_false
func.func @aten_eq_tensor_splats_int_false() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<false> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<4> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<5> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splats_int_true
func.func @aten_eq_tensor_splats_int_true() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<5> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<5> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splats_fp_false
func.func @aten_eq_tensor_splats_fp_false() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<false> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<4.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%rhs = torch.vtensor.literal(dense<5.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splats_fp_true
func.func @aten_eq_tensor_splats_fp_true() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<5.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%rhs = torch.vtensor.literal(dense<5.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splat_dense_fp
func.func @aten_eq_tensor_splat_dense_fp() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<[false, true, false, true]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<5.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%rhs = torch.vtensor.literal(dense<[4.0, 5.0, 6.0, 5.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_dense_fp
func.func @aten_eq_tensor_dense_fp() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<[true, false, true, false]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<[4.0, 5.5, 6.0, 6.4]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%rhs = torch.vtensor.literal(dense<[4.0, 5.0, 6.0, 5.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_splat_dense_int
func.func @aten_eq_tensor_splat_dense_int() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<[false, true, false, true]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<5> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<[4, 5, 6, 5]> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_eq_tensor_dense_int
func.func @aten_eq_tensor_dense_int() -> !torch.vtensor<[4],i1> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<[true, true, true, false]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[RET]]
%lhs = torch.vtensor.literal(dense<[4, 5, 6, 6]> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%rhs = torch.vtensor.literal(dense<[4, 5, 6, 5]> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
%0 = torch.aten.eq.Tensor %lhs, %rhs : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64> -> !torch.vtensor<[4],i1>
return %0 : !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_shape_to_tensor
func.func @aten_shape_to_tensor(%arg0 : !torch.vtensor<[4,5,6],f32>) -> !torch.vtensor<[3],si32> {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<[4, 5, 6]> : tensor<3xsi32>) : !torch.vtensor<[3],si32>
%0 = torch.aten._shape_as_tensor %arg0 : !torch.vtensor<[4,5,6],f32> -> !torch.vtensor<[3],si32>
// CHECK: return %[[CST]]
return %0 : !torch.vtensor<[3],si32>
}
// -----
// CHECK-LABEL: @aten_cat_zero
func.func @aten_cat_zero(%arg0 : !torch.vtensor<[4,5,6],f32>, %arg1 : !torch.vtensor<[4,0,6],f32>) -> !torch.vtensor<[4,5,6],f32> {
// CHECK: return %arg0 : !torch.vtensor<[4,5,6],f32>
%list = torch.prim.ListConstruct %arg0, %arg1 : (!torch.vtensor<[4,5,6],f32>, !torch.vtensor<[4,0,6],f32>) -> !torch.list<vtensor>
%dim = torch.constant.int -2
%0 = torch.aten.cat %list, %dim : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[4,5,6],f32>
return %0 : !torch.vtensor<[4,5,6],f32>
}
// -----
// CHECK-LABEL: @aten_tensor_scalar_lt
func.func @aten_tensor_scalar_lt() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[CST:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[CST]], %[[CST]] : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
%intTensor = torch.vtensor.literal(dense<1> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%fpTensor = torch.vtensor.literal(dense<1.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 2
%fpScalar = torch.constant.float 2.0
%intBool = torch.aten.lt.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.lt.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_lt
func.func @aten_tensor_tensor_lt() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[true, false, false, false]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.lt.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.lt.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.lt.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_le
func.func @aten_tensor_tensor_le() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[true, true, false, false]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.le.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.le.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.le.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_ge
func.func @aten_tensor_tensor_ge() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<false> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[false, true, true, true]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.ge.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.ge.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.ge.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_gt
func.func @aten_tensor_tensor_gt() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<false> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[false, false, true, true]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.gt.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.gt.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.gt.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_eq
func.func @aten_tensor_tensor_eq() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<false> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[false, true, false, false]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.eq.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.eq.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.eq.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
// -----
// CHECK-LABEL: @aten_tensor_tensor_ne
func.func @aten_tensor_tensor_ne() -> (!torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>) {
// CHECK: %[[UNSIGN:.+]] = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: %[[SIGNED:.+]] = torch.vtensor.literal(dense<[true, false, true, true]> : tensor<4xi1>) : !torch.vtensor<[4],i1>
// CHECK: return %[[UNSIGN]], %[[SIGNED]], %[[SIGNED]]
%intTensor = torch.vtensor.literal(dense<[127, -128, -127, -126]> : tensor<4xsi8>) : !torch.vtensor<[4],si8>
%uintTensor = torch.vtensor.literal(dense<[127, 128, 129, 130]> : tensor<4xui8>) : !torch.vtensor<[4],ui8>
%fpTensor = torch.vtensor.literal(dense<[127.0, 128.0, 129.0, 130.0]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%intScalar = torch.constant.int 128
%fpScalar = torch.constant.float 128.0
%intBool = torch.aten.ne.Scalar %intTensor, %intScalar : !torch.vtensor<[4],si8>, !torch.int -> !torch.vtensor<[4],i1>
%uintBool = torch.aten.ne.Scalar %uintTensor, %intScalar : !torch.vtensor<[4],ui8>, !torch.int -> !torch.vtensor<[4],i1>
%fpBool = torch.aten.ne.Scalar %fpTensor, %fpScalar : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],i1>
return %intBool, %uintBool, %fpBool : !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>, !torch.vtensor<[4],i1>
}
2024-04-26 10:10:02 +08:00
// -----
// CHECK-LABEL: @aten_log$fold_splat_i1
func.func @aten_log$fold_splat_i1() -> !torch.vtensor<[4], f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<0.000000e+00> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: return %[[RET]] : !torch.vtensor<[4],f32>
%cst = torch.vtensor.literal(dense<true> : tensor<4xi1>) : !torch.vtensor<[4], i1>
%result = torch.aten.log %cst : !torch.vtensor<[4], i1> -> !torch.vtensor<[4], f32>
return %result : !torch.vtensor<[4], f32>
}
// -----
// CHECK-LABEL: @aten_log$fold_splat_si32
func.func @aten_log$fold_splat_si32() -> !torch.vtensor<[4], f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<1.09861231> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: return %[[RET]] : !torch.vtensor<[4],f32>
%cst = torch.vtensor.literal(dense<3> : tensor<4xsi32>) : !torch.vtensor<[4], si32>
%result = torch.aten.log %cst : !torch.vtensor<[4], si32> -> !torch.vtensor<[4], f32>
return %result : !torch.vtensor<[4], f32>
}
// -----
// CHECK-LABEL: @aten_log$fold_splat_f32
func.func @aten_log$fold_splat_f32() -> !torch.vtensor<[4], f32> {
// CHECK: %[[RET:.+]] = torch.vtensor.literal(dense<1.09861231> : tensor<4xf32>) : !torch.vtensor<[4],f32>
// CHECK: return %[[RET]] : !torch.vtensor<[4],f32>
%cst = torch.vtensor.literal(dense<3.0> : tensor<4xf32>) : !torch.vtensor<[4], f32>
%result = torch.aten.log %cst : !torch.vtensor<[4], f32> -> !torch.vtensor<[4], f32>
return %result : !torch.vtensor<[4], f32>
}
// -----
// CHECK-LABEL: func.func @torch.prims.convert_element_type$fold(
// CHECK: %[[ARG:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[64],f32> {
// CHECK: return %[[ARG]] : !torch.vtensor<[64],f32>
func.func @torch.prims.convert_element_type$fold(%arg0: !torch.vtensor<[64],f32>) -> !torch.vtensor<[64],f32> {
%int6 = torch.constant.int 6
%0 = torch.prims.convert_element_type %arg0, %int6 : !torch.vtensor<[64],f32>, !torch.int -> !torch.vtensor<[64],f32>
return %0 : !torch.vtensor<[64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.prims.convert_element_type$no_fold(
// CHECK: %[[ARG:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[64],si32> {
// CHECK: %[[RET:.*]] = torch.prims.convert_element_type %[[ARG]], %{{.*}} : !torch.vtensor<[64],f32>, !torch.int -> !torch.vtensor<[64],si32>
// CHECK: return %[[RET]] : !torch.vtensor<[64],si32>
func.func @torch.prims.convert_element_type$no_fold(%arg0: !torch.vtensor<[64],f32>) -> !torch.vtensor<[64],si32> {
%int6 = torch.constant.int 6
%0 = torch.prims.convert_element_type %arg0, %int6 : !torch.vtensor<[64],f32>, !torch.int -> !torch.vtensor<[64],si32>
return %0 : !torch.vtensor<[64],si32>
}
// -----
// CHECK-LABEL: @torch.aten.max_pool2d_with_indices$canonicalize(
// CHECK: %[[ARG:.*]]: !torch.vtensor<[10,64,112,112],f32>) -> !torch.vtensor<[10,64,56,56],f32> {
// CHECK: %[[RET:.*]] = torch.aten.max_pool2d %[[ARG]]
// CHECK: return %[[RET]] : !torch.vtensor<[10,64,56,56],f32>
func.func @torch.aten.max_pool2d_with_indices$canonicalize(%arg0: !torch.vtensor<[10,64,112,112],f32>) -> !torch.vtensor<[10,64,56,56],f32> {
%false = torch.constant.bool false
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%29 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
%30 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%31 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%result0, %result1 = torch.aten.max_pool2d_with_indices %arg0, %29, %30, %31, %31, %false : !torch.vtensor<[10,64,112,112],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[10,64,56,56],f32>, !torch.vtensor<[10,64,56,56],si64>
return %result0 : !torch.vtensor<[10,64,56,56],f32>
}
// -----
// CHECK-LABEL: @torch.aten.clone$no_fold(
func.func @torch.aten.clone$no_fold(%arg0: !torch.vtensor<[1,2,50,4],f32>) -> (!torch.tensor) {
// CHECK: %{{.*}} = torch.aten.clone %{{.*}}, %{{.*}} : !torch.vtensor<[1,2,50,4],f32>, !torch.none -> !torch.vtensor
%none = torch.constant.none
%0 = torch.aten.clone %arg0, %none : !torch.vtensor<[1,2,50,4],f32>, !torch.none -> !torch.vtensor
%1 = torch.copy.to_tensor %0 : !torch.tensor
return %1 : !torch.tensor
}
Representing Symbolic Shape Expressions in Torch Dialect (#3372) Torch Dialect with symbolic shape expressions: ```ll module { func.func @main(%arg0: !torch.vtensor<[?,?,3],f32>, %arg1: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> { %0 = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int %1 = torch.symbolic_int "s1" {min_val = 0, max_val = 100} : !torch.int %2 = torch.symbolic_int "s3" {min_val = 0, max_val = 50} : !torch.int torch.bind_symbolic_shape %arg0, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32> torch.bind_symbolic_shape %arg1, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32> %3 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32> torch.bind_symbolic_shape %3, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32> %4 = torch.aten.sigmoid %arg1 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32> torch.bind_symbolic_shape %4, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32> %5 = torch.prim.ListConstruct %3, %3, %4 : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.list<vtensor> %int1 = torch.constant.int 1 %6 = torch.aten.cat %5, %int1 : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[?,?,3],f32> torch.bind_symbolic_shape %6, [%0, %1, %2], #affine_map<()[s0, s1, s2] -> (s0, s1 * 2 + s2, 3)> : !torch.vtensor<[?,?,3],f32> return %6 : !torch.vtensor<[?,?,3],f32> } } ``` For reference, this is the TorchDynamo exported program with symbolic shape expressions that the above Torch dialect program is imported from: ```py ExportedProgram: class GraphModule(torch.nn.Module): def forward(self, x: "f32[s0, s1, 3]", y: "f32[s0, s3, 3]"): # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:31 in forward, code: a = torch.tanh(x) tanh: "f32[s0, s1, 3]" = torch.ops.aten.tanh.default(x); x = None # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:32 in forward, code: b = torch.sigmoid(y) sigmoid: "f32[s0, s3, 3]" = torch.ops.aten.sigmoid.default(y); y = None # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:33 in forward, code: return torch.cat((a, a, b), dim=1) cat: "f32[s0, 2*s1 + s3, 3]" = torch.ops.aten.cat.default([tanh, tanh, sigmoid], 1); tanh = sigmoid = None return (cat,) Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='cat'), target=None)]) Range constraints: {s0: ValueRanges(lower=5, upper=10, is_bool=False), s1: ValueRanges(lower=0, upper=100, is_bool=False), s3: ValueRanges(lower=0, upper=50, is_bool=False)} ``` Huge credit to @stellaraccident for the inputs that helped evaluate the various design options and arrive at the representation of choice. - [x] Op definitions for symbolic_int and bind_symbolic_shape ops - [x] fx_importer updates to import range constraints + create symbolic_int ops - [x] fx_importer changes for AffineMapAttr building + adding bind_symbolic_shape ops - [x] custom printer/parser for inlined AffineMap expressions in mlir assembly - [x] Dialect lit test - [x] fx_importer python lit tests - [ ] Cleanup pass to remove these ops (can add in a follow-on)
2024-06-07 19:04:03 +08:00
// -----
// CHECK-LABEL: @torch.symbolic_int$canonicalize(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
// CHECK: %[[S0:.*]] = torch.symbolic_int "s0" {min_val = 3, max_val = 6} : !torch.int
// CHECK-NOT: %[[S1:.*]] = torch.symbolic_int "s0 + 1" {min_val = 4, max_val = 7} : !torch.int
// CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
// CHECK: torch.bind_symbolic_shape %[[ARG1]], [%[[S0]]], affine_map<()[s0] -> (s0 + 1)> : !torch.vtensor<[?],f32>
// CHECK: %[[V1:.*]] = torch.aten.slice.Tensor %[[ARG1]], {{.*}}, {{.*}}, {{.*}}, {{.*}} : !torch.vtensor<[?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?],f32>
// CHECK: torch.bind_symbolic_shape %[[V1]], [%[[S0]]], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
// CHECK: %[[V2:.*]] = torch.aten.add.Tensor %[[ARG0]], {{.*}}, {{.*}} : !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>, !torch.int -> !torch.vtensor<[?],f32>
// CHECK: torch.bind_symbolic_shape %[[V2]], [%[[S0]]], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
// CHECK: return %[[V2]] : !torch.vtensor<[?],f32>
func.func @torch.symbolic_int$canonicalize(%arg0: !torch.vtensor<[?],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
%0 = torch.symbolic_int "s0" {min_val = 3, max_val = 6} : !torch.int
%1 = torch.symbolic_int "s0 + 1" {min_val = 4, max_val = 7} : !torch.int
torch.bind_symbolic_shape %arg0, [%0], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
torch.bind_symbolic_shape %arg1, [%0], affine_map<()[s0] -> (s0 + 1)> : !torch.vtensor<[?],f32>
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int9223372036854775807 = torch.constant.int 9223372036854775807
%int1_0 = torch.constant.int 1
%2 = torch.aten.slice.Tensor %arg1, %int0, %int1, %int9223372036854775807, %int1_0 : !torch.vtensor<[?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?],f32>
torch.bind_symbolic_shape %2, [%0], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
%int1_1 = torch.constant.int 1
%3 = torch.aten.add.Tensor %arg0, %2, %int1_1 : !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>, !torch.int -> !torch.vtensor<[?],f32>
torch.bind_symbolic_shape %3, [%0], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
return %3 : !torch.vtensor<[?],f32>
}