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add specs for triangular_solve and linalg_solve_triangular #59

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c2e429c
add specs for triangular_solve.default and linalg_solve_triangular.de…
rsdcastro Jan 31, 2026
0848323
Fix lint formatting issues
rsdcastro Feb 6, 2026
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112 changes: 112 additions & 0 deletions facto/specdb/db.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5084,6 +5084,75 @@
OutArg(ArgType.Tensor),
],
),
Spec(
op="linalg_solve_triangular.default", # (Tensor A, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="A",
deps=[1, 3], # B, left
constraints=[
# Same dtype as A
cp.Dtype.Eq(lambda deps: deps[0].dtype),
# Matrix or batch of matrices
cp.Rank.Ge(lambda deps: 2),
# Must be square: A.size(-1) == A.size(-2)
# Fix both last dimensions to same value to ensure square matrix
# cp.Size.Eq(lambda deps, r, d: 8 if d >= r - 2 else None),
# B.size(-2) must equal A.size(-1) for left=True (simplified: assume left=True)
# For simplicity, match the last two dims with A
cp.Size.Eq(
lambda deps, r, d: (
fn.safe_size(deps[0], -2 if deps[1] else -1)
if d == r - 2 or d == r - 1
else None
)
),
# Batch dimensions must be broadcastable with B
cp.Size.In(
lambda deps, r, d: (
fn.broadcast_with(deps[0].shape[:-2], r - 2, d)
if d < r - 2
else None
)
),
],
),
InPosArg(
ArgType.Tensor,
name="B",
constraints=[
# Support both real and complex dtypes
cp.Dtype.In(
lambda deps: [
torch.float32,
torch.float64,
torch.complex64,
torch.complex128,
]
),
# Must be at least 2D (matrix or batch of matrices)
cp.Rank.Ge(lambda deps: 2),
],
),
InKwArg(
ArgType.Bool,
name="upper",
# Required: whether A is upper triangular
),
InKwArg(
ArgType.Bool,
name="left",
# Default True: solve AX = B (True) or XA = B (False)
),
InKwArg(
ArgType.Bool,
name="unitriangular",
# Default False: whether to assume diagonal is 1
),
],
outspec=[OutArg(ArgType.Tensor, name="X")],
),
Spec(
op="t_copy.default", # (Tensor self) -> Tensor
inspec=[
Expand Down Expand Up @@ -5171,6 +5240,49 @@
OutArg(ArgType.Tensor),
],
),
Spec(
op="triangular_solve.default", # (Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: [torch.float, torch.double]),
cp.Rank.Ge(lambda deps: 2),
],
),
InPosArg(
ArgType.Tensor,
name="A",
deps=[0],
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
cp.Rank.Ge(lambda deps: 2),
cp.Size.Eq(
lambda deps, r, d: (
fn.safe_size(deps[0], -2)
if d == r - 1 or d == r - 2
else None
)
),
cp.Size.In(
lambda deps, r, d: (
fn.broadcast_with(deps[0].shape, r, d)
if d != r - 1 and d != r - 2
else None
)
),
],
),
InPosArg(ArgType.Bool, name="upper"),
InPosArg(ArgType.Bool, name="transpose"),
InPosArg(ArgType.Bool, name="unitriangular"),
],
outspec=[
OutArg(ArgType.Tensor, name="solution"),
OutArg(ArgType.Tensor, name="cloned_coefficient"),
],
),
Spec(
op="tril.default", # (Tensor self, int diagonal=0) -> Tensor
inspec=[
Expand Down
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