mirror of
https://github.com/ROCm/jax.git
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201 lines
7.3 KiB
Python
201 lines
7.3 KiB
Python
# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from absl.testing import absltest, parameterized
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import jax
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from jax.config import config
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import jax.dlpack
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import jax.numpy as jnp
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from jax import test_util as jtu
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config.parse_flags_with_absl()
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FLAGS = config.FLAGS
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try:
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import torch
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import torch.utils.dlpack
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except ImportError:
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torch = None
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try:
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import cupy
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except ImportError:
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cupy = None
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try:
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import tensorflow as tf
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except ImportError:
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tf = None
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dlpack_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
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jnp.uint8, jnp.uint16, jnp.uint32, jnp.uint64,
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jnp.float16, jnp.float32, jnp.float64]
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torch_dtypes = [jnp.int8, jnp.int16, jnp.int32, jnp.int64,
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jnp.uint8, jnp.float16, jnp.float32, jnp.float64]
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nonempty_nonscalar_array_shapes = [(4,), (3, 4), (2, 3, 4)]
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empty_array_shapes = []
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empty_array_shapes += [(0,), (0, 4), (3, 0),]
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nonempty_nonscalar_array_shapes += [(3, 1), (1, 4), (2, 1, 4)]
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nonempty_array_shapes = [()] + nonempty_nonscalar_array_shapes
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all_shapes = nonempty_array_shapes + empty_array_shapes
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class DLPackTest(jtu.JaxTestCase):
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def setUp(self):
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super(DLPackTest, self).setUp()
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if jtu.device_under_test() == "tpu":
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self.skipTest("DLPack not supported on TPU")
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}_take_ownership={}".format(
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jtu.format_shape_dtype_string(shape, dtype),
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take_ownership),
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"shape": shape, "dtype": dtype, "take_ownership": take_ownership}
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for shape in all_shapes
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for dtype in dlpack_dtypes
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for take_ownership in [False, True]))
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def testJaxRoundTrip(self, shape, dtype, take_ownership):
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if jax.lib.version < (0, 1, 57) and not take_ownership:
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raise unittest.SkipTest("Requires jaxlib >= 0.1.57");
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rng = jtu.rand_default(self.rng())
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np = rng(shape, dtype)
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x = jnp.array(np)
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dlpack = jax.dlpack.to_dlpack(x, take_ownership=take_ownership)
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self.assertEqual(take_ownership, x.device_buffer.is_deleted())
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y = jax.dlpack.from_dlpack(dlpack)
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self.assertAllClose(np.astype(x.dtype), y)
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self.assertRaisesRegex(RuntimeError,
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"DLPack tensor may be consumed at most once",
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lambda: jax.dlpack.from_dlpack(dlpack))
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}".format(
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jtu.format_shape_dtype_string(shape, dtype)),
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"shape": shape, "dtype": dtype}
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for shape in all_shapes
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for dtype in dlpack_dtypes))
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@unittest.skipIf(not tf, "Test requires TensorFlow")
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def testTensorFlowToJax(self, shape, dtype):
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if not FLAGS.jax_enable_x64 and dtype in [jnp.int64, jnp.uint64,
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jnp.float64]:
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raise self.skipTest("x64 types are disabled by jax_enable_x64")
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if (jtu.device_under_test() == "gpu" and
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not tf.config.list_physical_devices("GPU")):
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raise self.skipTest("TensorFlow not configured with GPU support")
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rng = jtu.rand_default(self.rng())
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np = rng(shape, dtype)
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with tf.device("/GPU:0" if jtu.device_under_test() == "gpu" else "/CPU:0"):
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x = tf.constant(np)
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dlpack = tf.experimental.dlpack.to_dlpack(x)
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y = jax.dlpack.from_dlpack(dlpack)
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self.assertAllClose(np, y)
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}".format(
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jtu.format_shape_dtype_string(shape, dtype)),
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"shape": shape, "dtype": dtype}
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for shape in all_shapes
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for dtype in dlpack_dtypes))
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@unittest.skipIf(not tf, "Test requires TensorFlow")
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def testJaxToTensorFlow(self, shape, dtype):
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if jax.lib.version < (0, 1, 57):
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raise unittest.SkipTest("Requires jaxlib >= 0.1.57");
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if not FLAGS.jax_enable_x64 and dtype in [jnp.int64, jnp.uint64,
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jnp.float64]:
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self.skipTest("x64 types are disabled by jax_enable_x64")
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if (jtu.device_under_test() == "gpu" and
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not tf.config.list_physical_devices("GPU")):
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raise self.skipTest("TensorFlow not configured with GPU support")
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rng = jtu.rand_default(self.rng())
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np = rng(shape, dtype)
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x = jnp.array(np)
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# TODO(b/171320191): this line works around a missing context initialization
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# bug in TensorFlow.
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_ = tf.add(1, 1)
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dlpack = jax.dlpack.to_dlpack(x)
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y = tf.experimental.dlpack.from_dlpack(dlpack)
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self.assertAllClose(np, y.numpy())
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}".format(
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jtu.format_shape_dtype_string(shape, dtype)),
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"shape": shape, "dtype": dtype}
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for shape in all_shapes
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for dtype in torch_dtypes))
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@unittest.skipIf(not torch, "Test requires PyTorch")
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def testTorchToJax(self, shape, dtype):
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if not FLAGS.jax_enable_x64 and dtype in [jnp.int64, jnp.float64]:
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self.skipTest("x64 types are disabled by jax_enable_x64")
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rng = jtu.rand_default(self.rng())
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np = rng(shape, dtype)
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x = torch.from_numpy(np)
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x = x.cuda() if jtu.device_under_test() == "gpu" else x
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dlpack = torch.utils.dlpack.to_dlpack(x)
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y = jax.dlpack.from_dlpack(dlpack)
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self.assertAllClose(np, y)
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}".format(
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jtu.format_shape_dtype_string(shape, dtype)),
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"shape": shape, "dtype": dtype}
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for shape in all_shapes
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for dtype in torch_dtypes))
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@unittest.skipIf(not torch, "Test requires PyTorch")
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def testJaxToTorch(self, shape, dtype):
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if jax.lib.version < (0, 1, 57):
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raise unittest.SkipTest("Requires jaxlib >= 0.1.57");
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if not FLAGS.jax_enable_x64 and dtype in [jnp.int64, jnp.float64]:
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self.skipTest("x64 types are disabled by jax_enable_x64")
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rng = jtu.rand_default(self.rng())
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np = rng(shape, dtype)
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x = jnp.array(np)
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dlpack = jax.dlpack.to_dlpack(x)
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y = torch.utils.dlpack.from_dlpack(dlpack)
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self.assertAllClose(np, y.numpy())
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class CudaArrayInterfaceTest(jtu.JaxTestCase):
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def setUp(self):
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super(CudaArrayInterfaceTest, self).setUp()
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if jtu.device_under_test() != "gpu":
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self.skipTest("__cuda_array_interface__ is only supported on GPU")
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_{}".format(
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jtu.format_shape_dtype_string(shape, dtype)),
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"shape": shape, "dtype": dtype}
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for shape in all_shapes
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for dtype in dlpack_dtypes))
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@unittest.skipIf(not cupy, "Test requires CuPy")
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def testJaxToCuPy(self, shape, dtype):
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rng = jtu.rand_default(self.rng())
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x = rng(shape, dtype)
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y = jnp.array(x)
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z = cupy.asarray(y)
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self.assertEqual(y.__cuda_array_interface__["data"][0],
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z.__cuda_array_interface__["data"][0])
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self.assertAllClose(x, cupy.asnumpy(z))
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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