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jax_jit_test.py
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# 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.
from functools import partial
import inspect
from absl.testing import absltest
from absl.testing import parameterized
import jax
from jax._src import api
from jax import dtypes
from jax._src import lib as jaxlib
from jax import numpy as jnp
from jax._src import test_util as jtu
from jax.config import config
import numpy as np
config.parse_flags_with_absl()
def _cpp_device_put(value, device):
return jaxlib.jax_jit.device_put(value, config.x64_enabled, device)
class JaxJitTest(jtu.JaxTestCase):
def test_is_float_0(self):
self.assertTrue(
jaxlib.jax_jit._is_float0(np.zeros((5, 5), dtype=jax.float0)))
self.assertFalse(jaxlib.jax_jit._is_float0(np.zeros((5, 5))))
@parameterized.parameters([jax.device_put, _cpp_device_put])
def test_device_put_on_numpy_scalars(self, device_put_function):
device = jax.devices()[0]
for dtype in jtu.supported_dtypes():
value = dtype(0)
output_buffer = device_put_function(value, device=device)
self.assertFalse(output_buffer.aval.weak_type)
dtype = dtypes.canonicalize_dtype(dtype)
self.assertEqual(output_buffer.aval, jax.core.ShapedArray((), dtype))
self.assertEqual(output_buffer.dtype, dtype)
@parameterized.parameters([jax.device_put, _cpp_device_put])
def test_device_put_on_numpy_arrays(self, device_put_function):
device = jax.devices()[0]
for dtype in jtu.supported_dtypes():
value = np.zeros((3, 4), dtype=dtype)
output_buffer = device_put_function(value, device=device)
self.assertFalse(output_buffer.aval.weak_type)
dtype = dtypes.canonicalize_dtype(dtype)
self.assertEqual(output_buffer.aval, jax.core.ShapedArray((3, 4), dtype))
self.assertEqual(output_buffer.dtype, dtype)
np.testing.assert_array_equal(output_buffer, np.zeros((3, 4),
dtype=dtype))
@parameterized.parameters([jax.device_put, _cpp_device_put])
def test_device_put_on_buffers(self, device_put_function):
device = jax.devices()[0]
jitted_f = jax.jit(lambda x: x + 1)
# We run it twice, to cover `_DeviceArray` and the C++ `Buffer`.
for value in range(2):
buffer = jitted_f(value)
output_buffer = device_put_function(buffer, device=device)
self.assertEqual(output_buffer.dtype, buffer.dtype)
self.assertEqual(output_buffer.aval, buffer.aval)
np.testing.assert_array_equal(output_buffer, np.array(value + 1))
@parameterized.parameters([jax.device_put, _cpp_device_put])
def test_device_put_on_sharded_device_array(self, device_put_function):
device = jax.devices()[0]
pmaped_f = jax.pmap(lambda x: x + 1)
for _ in range(2):
sda = pmaped_f(np.asarray([[1]]))
output_buffer = device_put_function(sda, device=device)
self.assertNotIsInstance(output_buffer,
jax.interpreters.pxla.ShardedDeviceArray)
self.assertEqual(output_buffer.dtype, sda.dtype)
self.assertEqual(output_buffer.aval, sda.aval)
np.testing.assert_array_equal(output_buffer, np.asarray(sda))
def test_device_put_on_python_scalars(self):
device = jax.devices()[0]
int_type = dtypes.canonicalize_dtype(np.int64)
float_type = dtypes.canonicalize_dtype(np.float64)
complex_type = dtypes.canonicalize_dtype(np.complex128)
# int
res = np.asarray(_cpp_device_put(1, device))
self.assertEqual(res, 1)
self.assertEqual(res.dtype, int_type)
# We also compare to the Python Jax API, to make sure we have the exact
# same behavior. When Jax removes the flag and removes this feature, this
# test will fail.
self.assertEqual(jnp.asarray(1).dtype, res.dtype)
# float
res = np.asarray(_cpp_device_put(1.0, device))
self.assertEqual(res, 1.0)
self.assertEqual(res.dtype, float_type)
self.assertEqual(jnp.asarray(1.0).dtype, res.dtype)
# bool
for bool_value in [True, False]:
res = np.asarray(_cpp_device_put(bool_value, device))
self.assertEqual(res, np.asarray(bool_value))
self.assertEqual(res.dtype, np.bool_)
self.assertEqual(jnp.asarray(bool_value).dtype, res.dtype)
# Complex
if not (config.x64_enabled and jtu.device_under_test() == "tpu"):
# No TPU support for complex128.
res = np.asarray(_cpp_device_put(1 + 1j, device))
self.assertEqual(res, 1 + 1j)
self.assertEqual(res.dtype, complex_type)
self.assertEqual(jnp.asarray(1 + 1j).dtype, res.dtype)
def test_convert_int_overflow(self):
with self.assertRaisesRegex(
RuntimeError,
"(Python int too large|Unable to convert Python scalar).*"):
jaxlib.jax_jit.device_put(int(1e100), True, jax.devices()[0])
def test_arg_signature_of_value(self):
"""Tests the C++ code-path."""
jax_enable_x64 = config.x64_enabled
# 1. Numpy scalar types
for dtype in jtu.supported_dtypes():
value = dtype(0)
signature = jaxlib.jax_jit._ArgSignatureOfValue(value, jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(value).dtype)
self.assertEqual(signature.shape, ())
self.assertFalse(signature.weak_type)
# 2. Numpy arrays
for dtype in jtu.supported_dtypes():
value = np.zeros((3, 4), dtype=dtype)
signature = jaxlib.jax_jit._ArgSignatureOfValue(value, jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(value).dtype)
self.assertEqual(signature.shape, (3, 4))
self.assertFalse(signature.weak_type)
int_type = dtypes.canonicalize_dtype(np.int64)
float_type = dtypes.canonicalize_dtype(np.float64)
complex_type = dtypes.canonicalize_dtype(np.complex128)
# 3. Python scalar types
# int
signature = jaxlib.jax_jit._ArgSignatureOfValue(1, jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(1).dtype)
self.assertEqual(signature.dtype, int_type)
self.assertEqual(signature.shape, ())
self.assertTrue(signature.weak_type)
# float
signature = jaxlib.jax_jit._ArgSignatureOfValue(1.0, jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(1.0).dtype)
self.assertEqual(signature.dtype, float_type)
self.assertEqual(signature.shape, ())
self.assertTrue(signature.weak_type)
# bool
for bool_value in [True, False]:
signature = jaxlib.jax_jit._ArgSignatureOfValue(bool_value,
jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(bool_value).dtype)
self.assertEqual(signature.dtype, np.bool_)
self.assertEqual(signature.shape, ())
self.assertTrue(signature.weak_type)
# Complex
if not (jax_enable_x64 and jtu.device_under_test() == "tpu"):
# No TPU support for complex128.
signature = jaxlib.jax_jit._ArgSignatureOfValue(1 + 1j, jax_enable_x64)
self.assertEqual(signature.dtype, jax.device_put(1 + 1j).dtype)
self.assertEqual(signature.dtype, complex_type)
self.assertEqual(signature.shape, ())
self.assertTrue(signature.weak_type)
def test_signature_support(self):
jit = partial(api._jit, True)
def f(a, b, c):
return a + b + c
jitted_f = jit(f)
self.assertEqual(inspect.signature(f), inspect.signature(jitted_f))
if __name__ == "__main__":
absltest.main(testLoader=jtu.JaxTestLoader())