mirror of
https://github.com/ROCm/jax.git
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70b4823348
Description: - Updated jnp.ceil/floor/trunc to preserve int dtypes - Updated tests - For integral dtypes but we can't yet today compare types vs numpy as numpy 2.0.0rc2 is not yet array api compliant in this case
437 lines
17 KiB
Python
437 lines
17 KiB
Python
# Copyright 2020 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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from functools import reduce, partial
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from absl.testing import absltest
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import numpy as np
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import unittest
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import jax
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from jax._src import test_util as jtu
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import jax.numpy as jnp
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import jax.scipy.special
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from jax import random
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from jax import jacfwd, jit
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from jax.example_libraries import stax
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from jax.experimental.jet import jet, fact, zero_series
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from jax import lax
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jax.config.parse_flags_with_absl()
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def jvp_taylor(fun, primals, series):
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# Computes the Taylor series the slow way, with nested jvp.
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order, = set(map(len, series))
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primals = tuple(jnp.asarray(p) for p in primals)
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def composition(eps):
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taylor_terms = [sum(eps ** (i+1) * terms[i] / fact(i + 1)
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for i in range(len(terms))) for terms in series]
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nudged_args = [(x + t).astype(x.dtype) for x, t in zip(primals, taylor_terms)]
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return fun(*nudged_args)
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primal_out = fun(*primals)
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terms_out = [repeated(jacfwd, i+1)(composition)(0.) for i in range(order)]
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return primal_out, terms_out
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def repeated(f, n):
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def rfun(p):
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return reduce(lambda x, _: f(x), range(n), p)
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return rfun
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def transform(lims, x):
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return x * (lims[1] - lims[0]) + lims[0]
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class JetTest(jtu.JaxTestCase):
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def check_jet(self, fun, primals, series, atol=1e-5, rtol=1e-5,
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check_dtypes=True):
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# Convert to jax arrays to ensure dtype canonicalization.
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primals = jax.tree.map(jnp.asarray, primals)
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series = jax.tree.map(jnp.asarray, series)
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y, terms = jet(fun, primals, series)
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expected_y, expected_terms = jvp_taylor(fun, primals, series)
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self.assertAllClose(y, expected_y, atol=atol, rtol=rtol,
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check_dtypes=check_dtypes)
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self.assertAllClose(terms, expected_terms, atol=atol, rtol=rtol,
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check_dtypes=check_dtypes)
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def check_jet_finite(self, fun, primals, series, atol=1e-5, rtol=1e-5,
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check_dtypes=True):
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# Convert to jax arrays to ensure dtype canonicalization.
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primals = jax.tree.map(jnp.asarray, primals)
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series = jax.tree.map(jnp.asarray, series)
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y, terms = jet(fun, primals, series)
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expected_y, expected_terms = jvp_taylor(fun, primals, series)
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def _convert(x):
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return jnp.where(jnp.isfinite(x), x, jnp.nan)
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y = _convert(y)
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expected_y = _convert(expected_y)
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terms = _convert(jnp.asarray(terms))
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expected_terms = _convert(jnp.asarray(expected_terms))
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self.assertAllClose(y, expected_y, atol=atol, rtol=rtol,
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check_dtypes=check_dtypes)
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self.assertAllClose(terms, expected_terms, atol=atol, rtol=rtol,
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check_dtypes=check_dtypes)
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@jtu.skip_on_devices("tpu")
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# Default tolerance too tight on A100 after openxla/xla@a58070090
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@jax.default_matmul_precision("float32")
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def test_dot(self):
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M, K, N = 2, 3, 4
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order = 3
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rng = self.rng()
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x1 = rng.randn(M, K)
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x2 = rng.randn(K, N)
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primals = (x1, x2)
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terms_in1 = [rng.randn(*x1.shape) for _ in range(order)]
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terms_in2 = [rng.randn(*x2.shape) for _ in range(order)]
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series_in = (terms_in1, terms_in2)
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self.check_jet(jnp.dot, primals, series_in)
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@jtu.skip_on_devices("tpu")
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@jax.legacy_prng_key('allow')
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def test_conv(self):
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order = 3
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input_shape = (1, 5, 5, 1)
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key = random.PRNGKey(0)
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# TODO(duvenaud): Check all types of padding
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init_fun, apply_fun = stax.Conv(3, (2, 2), padding='VALID')
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_, (W, b) = init_fun(key, input_shape)
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rng = self.rng()
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x = rng.randn(*input_shape).astype(W.dtype)
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primals = (W, b, x)
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series_in1 = [rng.randn(*W.shape).astype(W.dtype) for _ in range(order)]
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series_in2 = [rng.randn(*b.shape).astype(W.dtype) for _ in range(order)]
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series_in3 = [rng.randn(*x.shape).astype(W.dtype) for _ in range(order)]
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series_in = (series_in1, series_in2, series_in3)
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def f(W, b, x):
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return apply_fun((W, b), x)
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self.check_jet(f, primals, series_in, check_dtypes=False)
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def unary_check(self, fun, lims=(-2, 2), order=3, dtype=None, atol=1e-3,
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rtol=1e-3):
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dims = 2, 3
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rng = self.rng()
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if dtype is None:
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primal_in = transform(lims, rng.rand(*dims))
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terms_in = [rng.randn(*dims) for _ in range(order)]
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else:
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rng = jtu.rand_uniform(rng, *lims)
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primal_in = rng(dims, dtype)
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terms_in = [rng(dims, dtype) for _ in range(order)]
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self.check_jet(fun, (primal_in,), (terms_in,), atol, rtol)
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def binary_check(self, fun, lims=None, order=3, finite=True, dtype=None):
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lims = lims or [-2, 2]
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dims = 2, 3
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rng = self.rng()
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if isinstance(lims, tuple):
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x_lims, y_lims = lims
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else:
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x_lims, y_lims = lims, lims
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if dtype is None:
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primal_in = (transform(x_lims, rng.rand(*dims)),
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transform(y_lims, rng.rand(*dims)))
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series_in = ([rng.randn(*dims) for _ in range(order)],
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[rng.randn(*dims) for _ in range(order)])
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else:
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rng = jtu.rand_uniform(rng, *lims)
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primal_in = (rng(dims, dtype),
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rng(dims, dtype))
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series_in = ([rng(dims, dtype) for _ in range(order)],
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[rng(dims, dtype) for _ in range(order)])
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if finite:
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self.check_jet(fun, primal_in, series_in, atol=1e-4, rtol=1e-4)
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else:
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self.check_jet_finite(fun, primal_in, series_in, atol=1e-4, rtol=1e-4)
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def unary_check_float0(self, fun, lims=(-2, 2), order=3, dtype=None):
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# like unary_check but for functions that output integers (so their tangent
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# type is float0 arrays)
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raise unittest.SkipTest("jet tests must be adapted for integer-output functions")
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def binary_check_float0(self, fun, lims=(-2, 2), order=3, finite=True, dtype=None):
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# like binary_check but for functions that output integers (so their tangent
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# type is float0 arrays)
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raise unittest.SkipTest("jet tests must be adapted for integer-output functions")
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def expit_check(self, lims=(-2, 2), order=3):
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dims = 2, 3
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rng = self.rng()
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primal_in = transform(lims, rng.rand(*dims))
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terms_in = [rng.randn(*dims) for _ in range(order)]
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primals = (primal_in, )
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series = (terms_in, )
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y, terms = jax.experimental.jet._logistic_taylor(primals, series)
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expected_y, expected_terms = jvp_taylor(jax.scipy.special.expit, primals, series)
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atol = 1e-4
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rtol = 1e-4
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self.assertAllClose(y, expected_y, atol=atol, rtol=rtol)
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self.assertAllClose(terms, expected_terms, atol=atol, rtol=rtol)
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@jtu.skip_on_devices("tpu")
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def test_int_pow(self):
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for p in range(6):
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self.unary_check(lambda x: x ** p, lims=[-2, 2])
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self.unary_check(lambda x: x ** 10, lims=[0, 0])
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@jtu.skip_on_devices("tpu")
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def test_is_finite(self): self.unary_check_float0(lax.is_finite)
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@jtu.skip_on_devices("tpu")
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def test_and(self): self.binary_check_float0(lax.bitwise_and, dtype=np.bool_)
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@jtu.skip_on_devices("tpu")
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def test_or(self): self.binary_check_float0(lax.bitwise_or, dtype=np.bool_)
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@jtu.skip_on_devices("tpu")
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def test_xor(self): self.binary_check_float0(jnp.bitwise_xor, dtype=np.bool_)
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@jtu.skip_on_devices("tpu")
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def test_shift_left(self): self.binary_check_float0(lax.shift_left, dtype=np.int32)
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@jtu.skip_on_devices("tpu")
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def test_shift_right_a(self): self.binary_check_float0(lax.shift_right_arithmetic, dtype=np.int32)
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@jtu.skip_on_devices("tpu")
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def test_shift_right_l(self): self.binary_check_float0(lax.shift_right_logical, dtype=np.int32)
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@jtu.skip_on_devices("tpu")
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def test_le(self): self.binary_check_float0(lambda x, y: x <= y)
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@jtu.skip_on_devices("tpu")
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def test_gt(self): self.binary_check_float0(lambda x, y: x > y)
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@jtu.skip_on_devices("tpu")
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def test_lt(self): self.binary_check_float0(lambda x, y: x < y)
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@jtu.skip_on_devices("tpu")
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def test_ge(self): self.binary_check_float0(lambda x, y: x >= y)
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@jtu.skip_on_devices("tpu")
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def test_eq(self): self.binary_check_float0(lambda x, y: x == y)
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@jtu.skip_on_devices("tpu")
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def test_ne(self): self.binary_check_float0(lambda x, y: x != y)
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@jtu.skip_on_devices("tpu")
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def test_not(self): self.unary_check_float0(lax.bitwise_not, dtype=np.bool_)
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@jtu.skip_on_devices("tpu")
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def test_exp(self): self.unary_check(jnp.exp)
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@jtu.skip_on_devices("tpu")
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def test_neg(self): self.unary_check(jnp.negative)
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@jtu.skip_on_devices("tpu")
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def test_floor(self): self.unary_check(jnp.floor)
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@jtu.skip_on_devices("tpu")
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def test_ceil(self): self.unary_check(jnp.ceil)
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@jtu.skip_on_devices("tpu")
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def test_trunc(self): self.unary_check(jnp.trunc)
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@jtu.skip_on_devices("tpu")
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def test_round(self): self.unary_check(lax.round)
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@jtu.skip_on_devices("tpu")
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def test_sign(self): self.unary_check(lax.sign)
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@jtu.skip_on_devices("tpu")
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def test_real(self): self.unary_check(lax.real, dtype=np.complex64)
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@jtu.skip_on_devices("tpu")
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def test_conj(self): self.unary_check(lax.conj, dtype=np.complex64)
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@jtu.skip_on_devices("tpu")
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def test_imag(self): self.unary_check(lax.imag, dtype=np.complex64)
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@jtu.skip_on_devices("tpu")
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def test_log(self): self.unary_check(jnp.log, lims=[0.8, 4.0])
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@jtu.skip_on_devices("tpu")
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def test_gather(self): self.unary_check(lambda x: x[1:])
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@jtu.skip_on_devices("tpu")
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def test_reduce_max(self): self.unary_check(lambda x: x.max(axis=1))
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@jtu.skip_on_devices("tpu")
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def test_reduce_min(self): self.unary_check(lambda x: x.min(axis=1))
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@jtu.skip_on_devices("tpu")
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def test_all_max(self): self.unary_check(jnp.max)
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@jtu.skip_on_devices("tpu")
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def test_all_min(self): self.unary_check(jnp.min)
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@jtu.skip_on_devices("tpu")
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def test_stopgrad(self): self.unary_check(lax.stop_gradient)
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@jtu.skip_on_devices("tpu")
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def test_abs(self): self.unary_check(jnp.abs)
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@jtu.skip_on_devices("tpu")
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def test_fft(self): self.unary_check(jnp.fft.fft)
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@jtu.skip_on_devices("tpu")
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def test_log1p(self): self.unary_check(jnp.log1p, lims=[0, 4.])
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@jtu.skip_on_devices("tpu")
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def test_expm1(self): self.unary_check(jnp.expm1)
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@jtu.skip_on_devices("tpu")
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def test_sin(self): self.unary_check(jnp.sin)
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@jtu.skip_on_devices("tpu")
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def test_cos(self): self.unary_check(jnp.cos)
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@jtu.skip_on_devices("tpu")
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def test_sinh(self): self.unary_check(jnp.sinh)
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@jtu.skip_on_devices("tpu")
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def test_cosh(self): self.unary_check(jnp.cosh)
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@jtu.skip_on_devices("tpu")
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def test_tanh(self): self.unary_check(jnp.tanh, lims=[-500, 500], order=5,
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atol=5e-3)
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@jtu.skip_on_devices("tpu")
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def test_logistic(self): self.unary_check(lax.logistic, lims=[-100, 100], order=5)
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@jtu.skip_on_devices("tpu")
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def test_expit2(self): self.expit_check(lims=[-500, 500], order=5)
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@jtu.skip_on_devices("tpu")
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def test_sqrt(self): self.unary_check(jnp.sqrt, lims=[0, 5.])
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@jtu.skip_on_devices("tpu")
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def test_rsqrt(self): self.unary_check(lax.rsqrt, lims=[0, 5000.])
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@jtu.skip_on_devices("tpu")
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def test_asinh(self): self.unary_check(lax.asinh, lims=[-100, 100])
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@jtu.skip_on_devices("tpu")
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def test_acosh(self): self.unary_check(lax.acosh, lims=[-100, 100])
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@jtu.skip_on_devices("tpu")
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def test_atanh(self): self.unary_check(lax.atanh, lims=[-1, 1])
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@jtu.skip_on_devices("tpu")
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def test_erf(self): self.unary_check(lax.erf)
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@jtu.skip_on_devices("tpu")
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def test_erfc(self): self.unary_check(lax.erfc)
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@jtu.skip_on_devices("tpu")
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def test_erf_inv(self): self.unary_check(lax.erf_inv, lims=[-1, 1])
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@jtu.skip_on_devices("tpu")
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def test_cumsum(self): self.unary_check(jnp.cumsum)
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@jtu.skip_on_devices("tpu")
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def test_cumprod(self): self.unary_check(jnp.cumprod)
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@jtu.skip_on_devices("tpu")
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def test_cummax(self): self.unary_check(partial(lax.cummax, axis=0))
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@jtu.skip_on_devices("tpu")
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def test_cummin(self): self.unary_check(partial(lax.cummin, axis=0))
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@jtu.skip_on_devices("tpu")
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def test_dynamic_slice(self): self.unary_check(partial(lax.dynamic_slice, start_indices=(1,2), slice_sizes=(1,1)))
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@jtu.skip_on_devices("tpu")
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def test_dynamic_update_slice(self): self.unary_check(partial(lax.dynamic_update_slice, start_indices=(1,2), update=np.arange(6.0).reshape(2, 3)))
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@jtu.skip_on_devices("tpu")
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def test_div(self): self.binary_check(lambda x, y: x / y, lims=[0.8, 4.0])
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@jtu.skip_on_devices("tpu")
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def test_rem(self): self.binary_check(lax.rem, lims=[0.8, 4.0])
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@jtu.skip_on_devices("tpu")
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def test_complex(self): self.binary_check(lax.complex)
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@jtu.skip_on_devices("tpu")
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def test_sub(self): self.binary_check(lambda x, y: x - y)
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@jtu.skip_on_devices("tpu")
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def test_add(self): self.binary_check(lambda x, y: x + y)
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@jtu.skip_on_devices("tpu")
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def test_mul(self): self.binary_check(lambda x, y: x * y)
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@jtu.skip_on_devices("tpu")
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def test_max(self): self.binary_check(lax.max)
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@jtu.skip_on_devices("tpu")
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def test_min(self): self.binary_check(lax.min)
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@jtu.skip_on_devices("tpu")
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@jtu.ignore_warning(message="overflow encountered in power")
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def test_pow(self): self.binary_check(lambda x, y: x ** y, lims=([0.2, 500], [-500, 500]), finite=False)
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@jtu.skip_on_devices("tpu")
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def test_atan2(self): self.binary_check(lax.atan2, lims=[-40, 40])
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@jtu.skip_on_devices("tpu")
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def test_clamp(self):
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lims = [-1, 1]
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order = 3
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dims = 2, 3
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# TODO(jakevdp): This test is very sensitive to the inputs, so we use a known
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# working seed. We should instead use self.rng(), and make sure that the primal
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# points lie outside an epsilon ball of the two critical points in the function.
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rng = np.random.RandomState(0)
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primal_in = (transform(lims, rng.rand(*dims)),
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transform(lims, rng.rand(*dims)),
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transform(lims, rng.rand(*dims)))
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series_in = ([rng.randn(*dims) for _ in range(order)],
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[rng.randn(*dims) for _ in range(order)],
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[rng.randn(*dims) for _ in range(order)])
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self.check_jet(lax.clamp, primal_in, series_in, atol=1e-4, rtol=1e-4)
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def test_process_call(self):
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def f(x):
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return jit(lambda x: x * x)(x)
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self.unary_check(f, rtol=2e-4)
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def test_post_process_call(self):
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def f(x):
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return jit(lambda y: x * y)(2.)
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self.unary_check(f, rtol=5e-4)
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|
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def test_select(self):
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M, K = 2, 3
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order = 3
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rng = self.rng()
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b = rng.rand(M, K) < 0.5
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x = rng.randn(M, K)
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y = rng.randn(M, K)
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primals = (b, x, y)
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terms_b = [rng.randn(*b.shape) for _ in range(order)]
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terms_x = [rng.randn(*x.shape) for _ in range(order)]
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terms_y = [rng.randn(*y.shape) for _ in range(order)]
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series_in = (terms_b, terms_x, terms_y)
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# Since this nudges bool inputs, we need to allow promotion to float.
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with jax.numpy_dtype_promotion('standard'):
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self.check_jet(jnp.where, primals, series_in, rtol=5e-4)
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|
|
|
def test_inst_zero(self):
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|
def f(x):
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|
return jnp.full_like(x, 2.)
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|
def g(x):
|
|
return 2. + 0 * x
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|
x = jnp.ones(1)
|
|
order = 3
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|
f_out_primals, f_out_series = jet(f, (x, ), ([jnp.ones_like(x) for _ in range(order)], ))
|
|
assert f_out_series is not zero_series
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|
|
|
g_out_primals, g_out_series = jet(g, (x, ), ([jnp.ones_like(x) for _ in range(order)], ))
|
|
|
|
self.assertArraysEqual(g_out_primals, f_out_primals)
|
|
self.assertArraysEqual(g_out_series, f_out_series)
|
|
|
|
def test_add_any(self):
|
|
# https://github.com/google/jax/issues/5217
|
|
f = lambda x, eps: x * eps + eps + x
|
|
def g(eps):
|
|
x = jnp.array(1.)
|
|
return jax.grad(f)(x, eps)
|
|
jet(g, (1.,), ([1.],)) # doesn't crash
|
|
|
|
def test_scatter_add(self):
|
|
# very basic test from https://github.com/google/jax/issues/5365
|
|
def f(x):
|
|
x0 = x[0]
|
|
x1 = x[1]
|
|
return (x0**5 + x1**5).sum()
|
|
|
|
def h(eps):
|
|
from jax import jacfwd, grad
|
|
|
|
x = jnp.array([1., 1.])
|
|
μ = eps * x
|
|
|
|
def F(t):
|
|
return f(x + t * μ)
|
|
|
|
return grad(jacfwd(F))(0.)
|
|
|
|
self.check_jet(h, (0.,), ([1., 2., 3.],), rtol=1e-3)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
absltest.main(testLoader=jtu.JaxTestLoader())
|