support multiple inputs that require grad

README.md

@@ -1,6 +1,6 @@
 ## jax2torch
 
-Use Jax functions in Pytorch with DLPack, as outlined <a href="https://gist.github.com/mattjj/e8b51074fed081d765d2f3ff90edf0e9">in a gist</a> by <a href="https://github.com/mattjj">@mattjj</a>. Right now only supports one tensor input (with optional non-tensor input arguments) to one tensor output, for the purposes of <a href="https://github.com/spetti/SMURF">differentiable alignment</a>.
+Use Jax functions in Pytorch with DLPack, as outlined <a href="https://gist.github.com/mattjj/e8b51074fed081d765d2f3ff90edf0e9">in a gist</a> by <a href="https://github.com/mattjj">@mattjj</a>. The repository was made for the purposes of making the <a href="https://github.com/spetti/SMURF">differentiable alignment</a> work here interoperable with Pytorch.
 
 ## Install
 

jax2torch/jax2torch.py

@@ -43,8 +43,9 @@ def backward(ctx, *grad_args):
                     grad_args = tree_t2j(grad_args)
                 else:
                     grad_args = t2j(grad_args[0])
-                grads, *_ = ctx.fun_vjp(grad_args)
-                ret = tree_j2t(grads), *((None,) * (ctx.num_args - 1))
+                grads = ctx.fun_vjp(grad_args)
+                grads = tuple(map(lambda t: t if isinstance(t, jnp.ndarray) else None, grads))
+                ret = tree_j2t(grads)
                 return ret
 
         sig = signature(fn)

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'jax2torch',
   packages = find_packages(exclude=[]),
-  version = '0.0.6',
+  version = '0.0.7',
   license='MIT',
   description = 'Jax 2 Torch',
   author = 'Phil Wang',