mirror of
https://github.com/ROCm/jax.git
synced 2025-04-17 04:16:07 +00:00
fffdb2daa8
* Make check_dtypes, atol, and rtol keyword-only arguments in jax.test_util APIs. Default to check_dtypes=True. Remove explicit usages of check_dtypes=True from tests. This mostly just removes visual noise from tests. Testing for exact type equality is the sensible default, although there are cases where opting out makes sense. No functional changes intended. * Fix a number of lax reference implementations to preserve types.
305 lines
11 KiB
Python
305 lines
11 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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from functools import reduce
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from absl.testing import absltest
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import numpy as np
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from jax 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.experimental 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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from jax.config import config
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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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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 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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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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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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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 = np.random.RandomState(0)
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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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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 = np.random.RandomState(0)
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x = rng.randn(*input_shape).astype("float32")
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primals = (W, b, x)
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series_in1 = [rng.randn(*W.shape).astype("float32") for _ in range(order)]
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series_in2 = [rng.randn(*b.shape).astype("float32") for _ in range(order)]
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series_in3 = [rng.randn(*x.shape).astype("float32") 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):
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dims = 2, 3
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rng = np.random.RandomState(0)
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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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self.check_jet(fun, (primal_in,), (terms_in,), atol=1e-4, rtol=1e-4)
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def binary_check(self, fun, lims=[-2, 2], order=3, finite=True):
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dims = 2, 3
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rng = np.random.RandomState(0)
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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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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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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 expit_check(self, lims=[-2, 2], order=3):
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dims = 2, 3
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rng = np.random.RandomState(0)
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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._expit_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_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_round(self): self.unary_check(jnp.round)
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@jtu.skip_on_devices("tpu")
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def test_sign(self): self.unary_check(jnp.sign)
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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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@jtu.skip_on_devices("tpu")
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def test_expit(self): self.unary_check(jax.scipy.special.expit, lims=[-500, 500], 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_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_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_le(self): self.binary_check(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(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(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(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(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(lambda x, y: x != y)
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@jtu.skip_on_devices("tpu")
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def test_and(self): self.binary_check(lambda x, y: jnp.logical_and(x, y))
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@jtu.skip_on_devices("tpu")
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def test_or(self): self.binary_check(lambda x, y: jnp.logical_or(x, y))
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@jtu.skip_on_devices("tpu")
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def test_xor(self): self.binary_check(lambda x, y: jnp.logical_xor(x, y))
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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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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)
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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)
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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 = np.random.RandomState(0)
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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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self.check_jet(jnp.where, primals, series_in)
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def test_inst_zero(self):
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def f(x):
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return 2.
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def g(x):
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return 2. + 0 * x
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x = jnp.ones(1)
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order = 3
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f_out_primals, f_out_series = jet(f, (x, ), ([jnp.ones_like(x) for _ in range(order)], ))
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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)], ))
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assert g_out_primals == f_out_primals
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assert g_out_series == f_out_series
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if __name__ == '__main__':
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absltest.main()
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