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954cb9983b
See https://github.com/google/jax/pull/8043 for context as to why we are making this change. The upshot for most users is that the values returned by iteration over a JAX array are now themselves JAX arrays, with the semantics of JAX arrays, which sometimes differ from the semantics of NumPy scalars and arrays. In particular: * Unlike NumPy scalars 0-dimensional JAX arrays are not hashable. This change updates users to call `.tolist()` or `np.asarray(...)` when the output of iterating over a JAX array is hashed, used as a dictionary key, or passed to `set(...)`. In some instances, we can just call `numpy` functions instead of `jax.numpy` functions to build the array in the first place. * This change confuses Pandas and PIL when a JAX array is converted to a Pandas dataframe or a PIL image. For now, cast JAX arrays to a NumPy array first before passing them into those libraries. * We now need to use `numpy.testing.assert_array_equal` instead of `numpy.testing.assert_equal` to compare JAX arrays. PiperOrigin-RevId: 406247725
110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
# Copyright 2021 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 absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from jax import lax
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from jax.experimental import ann
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from jax._src import test_util as jtu
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from jax._src.util import prod
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from jax.config import config
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config.parse_flags_with_absl()
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class AnnTest(jtu.JaxTestCase):
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@parameterized.named_parameters(
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jtu.cases_from_list({
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"testcase_name":
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"_qy={}_db={}_k={}_recall={}".format(
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jtu.format_shape_dtype_string(qy_shape, dtype),
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jtu.format_shape_dtype_string(db_shape, dtype), k, recall),
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"qy_shape": qy_shape, "db_shape": db_shape, "dtype": dtype,
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"k": k, "recall": recall }
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for qy_shape in [(200, 128), (128, 128)]
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for db_shape in [(128, 500), (128, 3000)]
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for dtype in jtu.dtypes.all_floating
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for k in [1, 10, 50] for recall in [0.9, 0.95]))
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def test_approx_max_k(self, qy_shape, db_shape, dtype, k, recall):
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rng = jtu.rand_default(self.rng())
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qy = rng(qy_shape, dtype)
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db = rng(db_shape, dtype)
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scores = lax.dot(qy, db)
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_, gt_args = lax.top_k(scores, k)
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_, ann_args = ann.approx_max_k(scores, k, recall_target=recall)
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self.assertEqual(k, len(ann_args[0]))
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gt_args_sets = [set(np.asarray(x)) for x in gt_args]
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hits = sum(
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len(list(x
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for x in ann_args_per_q
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if x.item() in gt_args_sets[q]))
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for q, ann_args_per_q in enumerate(ann_args))
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self.assertGreater(hits / (qy_shape[0] * k), recall)
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@parameterized.named_parameters(
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jtu.cases_from_list({
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"testcase_name":
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"_qy={}_db={}_k={}_recall={}".format(
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jtu.format_shape_dtype_string(qy_shape, dtype),
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jtu.format_shape_dtype_string(db_shape, dtype), k, recall),
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"qy_shape": qy_shape, "db_shape": db_shape, "dtype": dtype,
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"k": k, "recall": recall }
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for qy_shape in [(200, 128), (128, 128)]
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for db_shape in [(128, 500), (128, 3000)]
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for dtype in jtu.dtypes.all_floating
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for k in [1, 10, 50] for recall in [0.9, 0.95]))
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def test_approx_min_k(self, qy_shape, db_shape, dtype, k, recall):
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rng = jtu.rand_default(self.rng())
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qy = rng(qy_shape, dtype)
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db = rng(db_shape, dtype)
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scores = lax.dot(qy, db)
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_, gt_args = lax.top_k(-scores, k)
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_, ann_args = ann.approx_min_k(scores, k, recall_target=recall)
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self.assertEqual(k, len(ann_args[0]))
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gt_args_sets = [set(np.asarray(x)) for x in gt_args]
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hits = sum(
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len(list(x
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for x in ann_args_per_q
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if x.item() in gt_args_sets[q]))
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for q, ann_args_per_q in enumerate(ann_args))
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self.assertGreater(hits / (qy_shape[0] * k), recall)
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@parameterized.named_parameters(
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jtu.cases_from_list({
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"testcase_name":
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"_shape={}_k={}_max_k={}".format(
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jtu.format_shape_dtype_string(shape, dtype), k, is_max_k),
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"shape": shape, "dtype": dtype, "k": k, "is_max_k": is_max_k }
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for dtype in [np.float32]
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for shape in [(4,), (5, 5), (2, 1, 4)]
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for k in [1, 3]
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for is_max_k in [True, False]))
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def test_autodiff(self, shape, dtype, k, is_max_k):
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vals = np.arange(prod(shape), dtype=dtype)
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vals = self.rng().permutation(vals).reshape(shape)
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if is_max_k:
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fn = lambda vs: ann.approx_max_k(vs, k=k)[0]
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else:
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fn = lambda vs: ann.approx_min_k(vs, k=k)[0]
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jtu.check_grads(fn, (vals,), 2, ["fwd", "rev"], eps=1e-2)
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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