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Add constraints for split_copy test #14870

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Merged
merged 1 commit into from
Oct 8, 2025
Merged

Add constraints for split_copy test #14870

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75 changes: 75 additions & 0 deletions backends/cadence/utils/facto_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,6 +222,34 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
cp.Value.Le(lambda deps, dtype, struct: 2),
]
)
case "transpose_copy.int":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
]
)
case "permute_copy.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32, torch.int8, torch.uint8]),
cp.Rank.Le(
lambda deps: 5
), # xa_nn_transpose only supports up to 5D
cp.Rank.Ge(lambda deps: 1), # Must have at least 1 dimension
]
)
case "sqrt.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
]
)
case "clamp.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
]
)
case "rsqrt.default":
tensor_constraints.extend(
[
Expand All @@ -232,6 +260,12 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
cp.Value.Le(lambda deps, dtype, struct: 2**2),
]
)
case "relu.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32]),
]
)
case "mean.dim":
tensor_constraints.extend(
[
Expand All @@ -241,10 +275,17 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
case "exp.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32]),
cp.Value.Ge(lambda deps, dtype, struct: -(2**2)),
cp.Value.Le(lambda deps, dtype, struct: 2**2),
]
)
case "tanh.default":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32]),
]
)
case "slice_copy.Tensor":
tensor_constraints.extend(
[
Expand All @@ -253,6 +294,34 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
cp.Value.Le(lambda deps, dtype, struct: 2),
]
)
case "div.Scalar" | "add.Tensor" | "mul.Tensor" | "sub.Tensor":
tensor_constraints.extend(
[
cp.Dtype.In(
lambda deps: [
torch.int32,
torch.int64,
torch.float32,
]
),
]
)
case "split_copy.Tensor":
tensor_constraints.extend(
[
cp.Dtype.In(
lambda deps: [
torch.int32,
torch.int64,
torch.float32,
]
),
cp.Value.Ge(lambda deps, dtype, struct: 1),
cp.Value.Le(lambda deps, dtype, struct: 2**3),
cp.Rank.Le(lambda deps: 3),
cp.Size.Le(lambda deps, r, d: 2**2),
]
)
case "constant_pad_nd.default":
tensor_constraints.extend(
[
Expand Down Expand Up @@ -283,6 +352,12 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
cp.Rank.Le(lambda deps: 2**2),
]
)
case "pow.Tensor_Scalar":
tensor_constraints.extend(
[
cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
]
)
case "div.Tensor_mode" | "minimum.default":
if index == 0:
tensor_constraints = [
Expand Down
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