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ac560c0d90
This function provides sensible defaults for custom call lowering rules with the goal of reducing the amount of boilerplate required for implementing custom calls. Co-authored-by: Sergei Lebedev <slebedev@google.com>
125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
# Copyright 2023 The JAX Authors.
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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 os
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import unittest
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import numpy as np
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from absl.testing import absltest, parameterized
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import jax
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import jax.extend as jex
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import jax.numpy as jnp
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from jax._src import abstract_arrays
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from jax._src import api
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from jax._src import core
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from jax._src import linear_util
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from jax._src import prng
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from jax._src import test_util as jtu
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from jax._src.interpreters import mlir
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from jax._src.lib.mlir import ir
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from jax._src.extend import ffi
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from jax._src.lib import xla_extension_version
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jax.config.parse_flags_with_absl()
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class ExtendTest(jtu.JaxTestCase):
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def test_symbols(self):
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# Assume these are tested in random_test.py, only check equivalence
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self.assertIs(jex.random.seed_with_impl, prng.seed_with_impl)
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self.assertIs(jex.random.threefry2x32_p, prng.threefry2x32_p)
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self.assertIs(jex.random.threefry_2x32, prng.threefry_2x32)
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self.assertIs(jex.random.threefry_prng_impl, prng.threefry_prng_impl)
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self.assertIs(jex.random.rbg_prng_impl, prng.rbg_prng_impl)
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self.assertIs(jex.random.unsafe_rbg_prng_impl, prng.unsafe_rbg_prng_impl)
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# Assume these are tested elsewhere, only check equivalence
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self.assertIs(jex.backend.clear_backends, api.clear_backends)
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self.assertIs(jex.core.array_types, abstract_arrays.array_types)
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self.assertIs(jex.linear_util.StoreException, linear_util.StoreException)
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self.assertIs(jex.linear_util.WrappedFun, linear_util.WrappedFun)
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self.assertIs(jex.linear_util.cache, linear_util.cache)
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self.assertIs(jex.linear_util.merge_linear_aux, linear_util.merge_linear_aux)
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self.assertIs(jex.linear_util.transformation, linear_util.transformation)
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self.assertIs(jex.linear_util.transformation_with_aux, linear_util.transformation_with_aux)
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self.assertIs(jex.linear_util.wrap_init, linear_util.wrap_init)
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class RandomTest(jtu.JaxTestCase):
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def test_key_make_with_custom_impl(self):
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shape = (4, 2, 7)
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def seed_rule(_):
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return jnp.ones(shape, dtype=jnp.dtype('uint32'))
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def no_rule(*args, **kwargs):
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assert False, 'unreachable'
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impl = jex.random.define_prng_impl(
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key_shape=shape, seed=seed_rule, split=no_rule, fold_in=no_rule,
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random_bits=no_rule)
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k = jax.random.key(42, impl=impl)
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self.assertEqual(k.shape, ())
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self.assertEqual(impl, jax.random.key_impl(k))
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def test_key_wrap_with_custom_impl(self):
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def no_rule(*args, **kwargs):
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assert False, 'unreachable'
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shape = (4, 2, 7)
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impl = jex.random.define_prng_impl(
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key_shape=shape, seed=no_rule, split=no_rule, fold_in=no_rule,
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random_bits=no_rule)
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data = jnp.ones((3, *shape), dtype=jnp.dtype('uint32'))
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k = jax.random.wrap_key_data(data, impl=impl)
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self.assertEqual(k.shape, (3,))
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self.assertEqual(impl, jax.random.key_impl(k))
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class FfiTest(jtu.JaxTestCase):
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@unittest.skipIf(xla_extension_version < 265, "Requires jaxlib 0.4.29")
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def testHeadersExist(self):
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base_dir = os.path.join(jex.ffi.include_dir(), "xla", "ffi", "api")
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for header in ["c_api.h", "api.h", "ffi.h"]:
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self.assertTrue(os.path.exists(os.path.join(base_dir, header)))
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@parameterized.parameters(
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[True, int(1), float(5.0),
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np.int32(-5), np.float32(0.5)])
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def testIrAttribute(sel, value):
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with mlir.make_ir_context(), ir.Location.unknown():
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const = mlir.ir_constant(value)
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attr = ffi._ir_attribute(value)
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assert const.type.element_type == attr.type
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@parameterized.parameters([True, 1, 5.0, "param", np.float32(0.5)])
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def testParams(self, param):
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prim = core.Primitive("test_ffi")
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prim.def_abstract_eval(lambda *args, **kwargs: args[0])
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mlir.register_lowering(prim, jex.ffi.ffi_lowering("test_ffi"))
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# TODO(dfm): Currently testing that lowering works with different types of
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# parameters, but we should probably actually check the emitted HLO.
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func = jax.jit(lambda *args: prim.bind(*args, param=param))
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func.lower(jnp.linspace(0, 5, 10))
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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