Fix case when both device & dtype are given in .to

torchax/test/test_misc.py

@@ -0,0 +1,47 @@
+"""If you don't know which file a test should go, and don't want to make a new file
+for a small test. PUt it here
+"""
+import torch
+import unittest
+import torchax
+import jax
+import jax.numpy as jnp
+
+
+class MiscTest(unittest.TestCase):
+
+  def test_extract_jax_kwargs(self):
+
+    class M(torch.nn.Module):
+
+      def forward(self, a, b):
+        return torch.sin(a) + torch.cos(b)
+
+    weights, func = torchax.extract_jax(M())
+    res = func(
+        weights,
+        args=(),
+        kwargs={
+            'a': jnp.array([1, 2, 3]),
+            'b': jnp.array([3, 4, 5])
+        })
+    self.assertTrue(
+        jnp.allclose(
+            res,
+            jnp.sin(jnp.array([1, 2, 3])) + jnp.cos(jnp.array([3, 4, 5]))))
+
+  def test_to_device(self):
+    env = torchax.default_env()
+    env.config.debug_print_each_op = True
+    with env:
+      step1 = torch.ones(
+          100,
+          100,
+      )
+      step2 = torch.triu(step1, diagonal=1)
+      step3 = step2.to(dtype=torch.bool, device='jax')
+      self.assertEqual(step3.device.type, 'jax')
+
+
+if __name__ == '__main__':
+  unittest.main()

torchax/torchax/tensor.py

@@ -469,12 +469,12 @@ def _to_copy(self, the_tensor, new_dtype, new_device):
         arr = self.t2j_copy(the_tensor)
         res = Tensor(arr, self, the_tensor.requires_grad)
 
-    if new_dtype is not None and new_dtype != the_tensor.dtype:
-      if isinstance(the_tensor, Tensor):
+    if new_dtype is not None and new_dtype != res.dtype:
+      if isinstance(res, Tensor):
         res = res.apply_jax(jnp.astype, mappings.t2j_dtype(new_dtype))
       else:
         with mode_utils.no_dispatch(), torch._C.DisableTorchFunction():
-          return the_tensor.to(device=new_device, dtype=new_dtype)
+          return res.to(device=new_device, dtype=new_dtype)
     return res
 
   def get_and_rotate_prng_key(self,