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Torch dispatch for scipy-like functions and Softplus #1066

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Torch dispatch for scipy-like functions and Softplus #1066

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a7a62f9
Allow for scipy module resolution
Ch0ronomato Nov 2, 2024
f1d7852
Add softplus
Ch0ronomato Nov 2, 2024
f852075
Add tests
Ch0ronomato Nov 7, 2024
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17 changes: 15 additions & 2 deletions pytensor/link/pytorch/dispatch/scalar.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,13 @@
import importlib

import torch

from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
from pytensor.scalar.basic import (
Cast,
ScalarOp,
)
from pytensor.scalar.math import Softplus


@pytorch_funcify.register(ScalarOp)
Expand All @@ -19,9 +22,14 @@ def pytorch_funcify_ScalarOp(op, node, **kwargs):
if nfunc_spec is None:
raise NotImplementedError(f"Dispatch not implemented for Scalar Op {op}")

func_name = nfunc_spec[0]
func_name = nfunc_spec[0].replace("scipy.", "")

pytorch_func = getattr(torch, func_name)
if "." in func_name:
loc = func_name.split(".")
mod = importlib.import_module(".".join(["torch", *loc[:-1]]))
pytorch_func = getattr(mod, loc[-1])
else:
pytorch_func = getattr(torch, func_name)

if len(node.inputs) > op.nfunc_spec[1]:
# Some Scalar Ops accept multiple number of inputs, behaving as a variadic function,
Expand Down Expand Up @@ -49,3 +57,8 @@ def cast(x):
return x.to(dtype=dtype)

return cast


@pytorch_funcify.register(Softplus)
def pytorch_funcify_Softplus(op, node, **kwargs):
return torch.nn.Softplus()
15 changes: 15 additions & 0 deletions tests/link/pytorch/test_basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import numpy as np
import pytest

import pytensor.tensor as pt
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We prefer to use explicit imports as a policy. In your case import expit and softplus directly

import pytensor.tensor.basic as ptb
from pytensor.compile.builders import OpFromGraph
from pytensor.compile.function import function
Expand Down Expand Up @@ -343,3 +344,17 @@ def test_pytorch_OpFromGraph():

f = FunctionGraph([x, y, z], [out])
compare_pytorch_and_py(f, [xv, yv, zv])


def test_pytorch_scipy():
x = vector("a", shape=(3,))
out = pt.expit(x)
f = FunctionGraph([x], [out])
compare_pytorch_and_py(f, [np.random.rand(3)])


def test_pytorch_softplus():
x = vector("a", shape=(3,))
out = pt.softplus(x)
f = FunctionGraph([x], [out])
compare_pytorch_and_py(f, [np.random.rand(3)])
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