Split PyTorch interoperability tests into their own test.

PiperOrigin-RevId: 508722180

tests/BUILD

@@ -623,6 +623,13 @@ jax_test(
     ],
 )
 
+jax_test(
+    name = "pytorch_interoperability_test",
+    srcs = ["pytorch_interoperability_test.py"],
+    disable_backends = ["tpu"],
+    deps = py_deps("torch"),
+)
+
 jax_test(
     name = "qdwh_test",
     srcs = ["qdwh_test.py"],

tests/array_interoperability_test.py

@@ -19,7 +19,6 @@ from absl.testing import absltest
 import jax
 from jax.config import config
 import jax.dlpack
-from jax._src.lib import xla_bridge, xla_client
 import jax.numpy as jnp
 from jax._src import test_util as jtu
 
@@ -29,12 +28,6 @@ numpy_version = jtu.numpy_version()
 
 config.parse_flags_with_absl()
 
-try:
-  import torch
-  import torch.utils.dlpack
-except ImportError:
-  torch = None
-
 try:
   import cupy
 except ImportError:
@@ -50,8 +43,10 @@ except:
 
 dlpack_dtypes = sorted(list(jax.dlpack.SUPPORTED_DTYPES),
                        key=lambda x: x.__name__)
-torch_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
-                jnp.uint8, jnp.float16, jnp.float32, jnp.float64]
+
+numpy_dtypes = sorted(
+    [dt for dt in jax.dlpack.SUPPORTED_DTYPES if dt != jnp.bfloat16],
+    key=lambda x: x.__name__)
 
 nonempty_nonscalar_array_shapes = [(4,), (3, 4), (2, 3, 4)]
 empty_array_shapes = []
@@ -145,60 +140,7 @@ class DLPackTest(jtu.JaxTestCase):
 
   @jtu.sample_product(
     shape=all_shapes,
-    dtype=torch_dtypes,
-  )
-  @unittest.skipIf(not torch, "Test requires PyTorch")
-  def testTorchToJax(self, shape, dtype):
-    if not config.x64_enabled and dtype in [jnp.int64, jnp.float64]:
-      self.skipTest("x64 types are disabled by jax_enable_x64")
-    rng = jtu.rand_default(self.rng())
-    np = rng(shape, dtype)
-    x = torch.from_numpy(np)
-    x = x.cuda() if jtu.device_under_test() == "gpu" else x
-    dlpack = torch.utils.dlpack.to_dlpack(x)
-    y = jax.dlpack.from_dlpack(dlpack)
-    self.assertAllClose(np, y)
-
-  @unittest.skipIf(not torch, "Test requires PyTorch")
-  def testTorchToJaxFailure(self):
-    x = torch.arange(6).reshape((2, 3))
-    y = torch.utils.dlpack.to_dlpack(x[:, :2])
-
-    backend = xla_bridge.get_backend()
-    client = getattr(backend, "client", backend)
-
-    regex_str = (r'UNIMPLEMENTED: Only DLPack tensors with trivial \(compact\) '
-                 r'striding are supported')
-    with self.assertRaisesRegex(RuntimeError, regex_str):
-      xla_client._xla.dlpack_managed_tensor_to_buffer(
-          y, client)
-
-  @jtu.sample_product(
-    shape=all_shapes,
-    dtype=torch_dtypes,
-  )
-  @unittest.skipIf(not torch, "Test requires PyTorch")
-  def testJaxToTorch(self, shape, dtype):
-    if not config.x64_enabled and dtype in [jnp.int64, jnp.float64]:
-      self.skipTest("x64 types are disabled by jax_enable_x64")
-    rng = jtu.rand_default(self.rng())
-    np = rng(shape, dtype)
-    x = jnp.array(np)
-    dlpack = jax.dlpack.to_dlpack(x)
-    y = torch.utils.dlpack.from_dlpack(dlpack)
-    self.assertAllClose(np, y.cpu().numpy())
-
-  @unittest.skipIf(not torch, "Test requires PyTorch")
-  def testTorchToJaxInt64(self):
-    # See https://github.com/google/jax/issues/11895
-    x = jax.dlpack.from_dlpack(
-        torch.utils.dlpack.to_dlpack(torch.ones((2, 3), dtype=torch.int64)))
-    dtype_expected = jnp.int64 if config.x64_enabled else jnp.int32
-    self.assertEqual(x.dtype, dtype_expected)
-
-  @jtu.sample_product(
-    shape=all_shapes,
-    dtype=torch_dtypes,
+    dtype=numpy_dtypes,
   )
   @unittest.skipIf(numpy_version < (1, 22, 0), "Requires numpy 1.22 or newer")
   def testNumpyToJax(self, shape, dtype):
@@ -209,7 +151,7 @@ class DLPackTest(jtu.JaxTestCase):
 
   @jtu.sample_product(
     shape=all_shapes,
-    dtype=torch_dtypes,
+    dtype=numpy_dtypes,
   )
   @unittest.skipIf(numpy_version < (1, 23, 0), "Requires numpy 1.23 or newer")
   @jtu.skip_on_devices("gpu") #NumPy only accepts cpu DLPacks

tests/pytorch_interoperability_test.py

@@ -0,0 +1,97 @@
+# Copyright 2020 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from absl.testing import absltest
+
+import jax
+from jax.config import config
+import jax.dlpack
+from jax._src.lib import xla_bridge, xla_client
+import jax.numpy as jnp
+from jax._src import test_util as jtu
+
+config.parse_flags_with_absl()
+
+try:
+  import torch
+  import torch.utils.dlpack
+except ImportError:
+  torch = None
+
+
+torch_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
+                jnp.uint8, jnp.float16, jnp.float32, jnp.float64,
+                jnp.bfloat16, jnp.complex64, jnp.complex128]
+
+nonempty_nonscalar_array_shapes = [(4,), (3, 4), (2, 3, 4)]
+empty_array_shapes = []
+empty_array_shapes += [(0,), (0, 4), (3, 0),]
+nonempty_nonscalar_array_shapes += [(3, 1), (1, 4), (2, 1, 4)]
+
+nonempty_array_shapes = [()] + nonempty_nonscalar_array_shapes
+all_shapes = nonempty_array_shapes + empty_array_shapes
+
+class DLPackTest(jtu.JaxTestCase):
+  def setUp(self):
+    super().setUp()
+    if jtu.device_under_test() == "tpu":
+      self.skipTest("DLPack not supported on TPU")
+
+  @unittest.skipIf(not torch, "Test requires PyTorch")
+  def testTorchToJaxFailure(self):
+    x = torch.arange(6).reshape((2, 3))
+    x = x.cuda() if jtu.device_under_test() == "gpu" else x
+    y = torch.utils.dlpack.to_dlpack(x[:, :2])
+
+    backend = xla_bridge.get_backend()
+    client = getattr(backend, "client", backend)
+
+    regex_str = (r'UNIMPLEMENTED: Only DLPack tensors with trivial \(compact\) '
+                 r'striding are supported')
+    with self.assertRaisesRegex(RuntimeError, regex_str):
+      xla_client._xla.dlpack_managed_tensor_to_buffer(
+          y, client)
+
+  @jtu.sample_product(
+    shape=all_shapes,
+    dtype=torch_dtypes,
+  )
+  @unittest.skipIf(not torch, "Test requires PyTorch")
+  def testJaxToTorch(self, shape, dtype):
+    if not config.x64_enabled and dtype in [jnp.int64, jnp.float64]:
+      self.skipTest("x64 types are disabled by jax_enable_x64")
+    rng = jtu.rand_default(self.rng())
+    np = rng(shape, dtype)
+    x = jnp.array(np)
+    dlpack = jax.dlpack.to_dlpack(x)
+    y = torch.utils.dlpack.from_dlpack(dlpack)
+    if dtype == jnp.bfloat16:
+      # .numpy() doesn't work on Torch bfloat16 tensors.
+      self.assertAllClose(np,
+                          y.cpu().view(torch.int16).numpy().view(jnp.bfloat16))
+    else:
+      self.assertAllClose(np, y.cpu().numpy())
+
+  @unittest.skipIf(not torch, "Test requires PyTorch")
+  def testTorchToJaxInt64(self):
+    # See https://github.com/google/jax/issues/11895
+    x = jax.dlpack.from_dlpack(
+        torch.utils.dlpack.to_dlpack(torch.ones((2, 3), dtype=torch.int64)))
+    dtype_expected = jnp.int64 if config.x64_enabled else jnp.int32
+    self.assertEqual(x.dtype, dtype_expected)
+
+if __name__ == "__main__":
+  absltest.main(testLoader=jtu.JaxTestLoader())