85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# Copyright 2018 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 __future__ import annotations
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from functools import partial
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import numpy as np
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from jax._src import core
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from jax._src import dtypes
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from jax._src import traceback_util
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traceback_util.register_exclusion(__file__)
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ShapedArray = core.ShapedArray
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AbstractToken = core.AbstractToken
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abstract_token = core.abstract_token
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canonicalize_shape = core.canonicalize_shape
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numpy_scalar_types: set[type] = { # pylint: disable=g-bare-generic
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dtypes.int4, np.int8, np.int16, np.int32, np.int64,
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dtypes.uint4, np.uint8, np.uint16, np.uint32, np.uint64,
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np.complex64, np.complex128,
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np.bool_, np.longlong, np.intc,
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} | {np.dtype(dt).type for dt in dtypes._float_types}
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if dtypes.int2 is not None:
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assert dtypes.uint2 is not None
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numpy_scalar_types.add(dtypes.int2)
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numpy_scalar_types.add(dtypes.uint2)
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array_types: set[type] = {np.ndarray} | numpy_scalar_types # pylint: disable=g-bare-generic
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def masked_array_error(*args, **kwargs):
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raise ValueError(
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"numpy masked arrays are not supported as direct inputs to JAX functions."
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" Use arr.filled() to convert the value to a standard numpy array.")
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core.pytype_aval_mappings[np.ma.MaskedArray] = masked_array_error
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def _make_shaped_array_for_numpy_array(x: np.ndarray) -> ShapedArray:
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dtype = x.dtype
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dtypes.check_valid_dtype(dtype)
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return ShapedArray(x.shape, dtypes.canonicalize_dtype(dtype), sharding=None)
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core.pytype_aval_mappings[np.ndarray] = _make_shaped_array_for_numpy_array
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def _make_shaped_array_for_numpy_scalar(x: np.generic) -> ShapedArray:
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dtype = np.dtype(x)
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dtypes.check_valid_dtype(dtype)
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shape = np.shape(x)
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return ShapedArray(shape, dtypes.canonicalize_dtype(dtype), sharding=None)
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for t in numpy_scalar_types:
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core.pytype_aval_mappings[t] = _make_shaped_array_for_numpy_scalar
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core.literalable_types.update(array_types)
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def _make_abstract_python_scalar(typ, val):
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# Note: all python scalar types are weak except bool, because bool only
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# comes in a single width.
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return ShapedArray((), dtypes._scalar_type_to_dtype(typ, val),
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weak_type=typ is not bool, sharding=None)
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for t in dtypes.python_scalar_dtypes:
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core.pytype_aval_mappings[t] = partial(_make_abstract_python_scalar, t)
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core.literalable_types.update(dtypes.python_scalar_dtypes.keys())
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