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Use vectorized jacobian in Minimize Op #1582

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Use vectorized jacobian in Minimize Op #1582

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d5290b8
added identity as alias for tensor_copy and defined No-Op for TensorF…
Michal-Novomestsky Aug 12, 2025
f20abb9
refactor: jacobian should use tensorize
Michal-Novomestsky Aug 12, 2025
30a71c4
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 13, 2025
8fe62c6
removed redundant pprint
Michal-Novomestsky Aug 15, 2025
35df4b2
refactor: added vectorize=True to all jacobians
Michal-Novomestsky Aug 15, 2025
ee37c24
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 15, 2025
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7 changes: 7 additions & 0 deletions pytensor/tensor/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -664,6 +664,11 @@ def c_code_cache_version(self):
tensor_from_scalar = TensorFromScalar()


@_vectorize_node.register(TensorFromScalar)
def vectorize_tensor_from_scalar(op, node, batch_x):
return identity(batch_x).owner


class ScalarFromTensor(COp):
__props__ = ()

Expand Down Expand Up @@ -2046,6 +2051,7 @@ def register_transfer(fn):
"""Create a duplicate of `a` (with duplicated storage)"""
tensor_copy = Elemwise(ps.identity)
pprint.assign(tensor_copy, printing.IgnorePrinter())
identity = tensor_copy


class Default(Op):
Expand Down Expand Up @@ -4603,6 +4609,7 @@ def ix_(*args):
"matrix_transpose",
"default",
"tensor_copy",
"identity",
"transfer",
"alloc",
"identity_like",
Expand Down
19 changes: 15 additions & 4 deletions pytensor/tensor/optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -560,7 +560,10 @@ def L_op(self, inputs, outputs, output_grads):
implicit_f = grad(inner_fx, inner_x)

df_dx, *df_dtheta_columns = jacobian(
implicit_f, [inner_x, *inner_args], disconnected_inputs="ignore"
implicit_f,
[inner_x, *inner_args],
disconnected_inputs="ignore",
vectorize=True,
)
grad_wrt_args = implict_optimization_grads(
df_dx=df_dx,
Expand Down Expand Up @@ -816,7 +819,9 @@ def __init__(
self.fgraph = FunctionGraph([variables, *args], [equations])

if jac:
jac_wrt_x = jacobian(self.fgraph.outputs[0], self.fgraph.inputs[0])
jac_wrt_x = jacobian(
self.fgraph.outputs[0], self.fgraph.inputs[0], vectorize=True
)
self.fgraph.add_output(atleast_2d(jac_wrt_x))

self.jac = jac
Expand Down Expand Up @@ -896,8 +901,14 @@ def L_op(
inner_x, *inner_args = self.fgraph.inputs
inner_fx = self.fgraph.outputs[0]

df_dx = jacobian(inner_fx, inner_x) if not self.jac else self.fgraph.outputs[1]
df_dtheta_columns = jacobian(inner_fx, inner_args, disconnected_inputs="ignore")
df_dx = (
jacobian(inner_fx, inner_x, vectorize=True)
if not self.jac
else self.fgraph.outputs[1]
)
df_dtheta_columns = jacobian(
inner_fx, inner_args, disconnected_inputs="ignore", vectorize=True
)

grad_wrt_args = implict_optimization_grads(
df_dx=df_dx,
Expand Down