1284 lines
51 KiB
Python
1284 lines
51 KiB
Python
# Copyright 2021 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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import dataclasses
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import functools
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import itertools as it
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from typing import (Union, Optional, Callable, Dict, Tuple, TypeVar,
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FrozenSet, Type, Set, List, Sequence, Any)
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import numpy as np
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import jax.numpy as jnp
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from jax import lax
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from jax._src import api
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from jax._src import linear_util as lu
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from jax._src import core
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from jax._src import custom_derivatives
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from jax._src import effects
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from jax._src import pjit
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from jax._src import prng
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from jax._src import sharding_impls
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from jax._src import source_info_util
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from jax._src import traceback_util
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from jax._src import tree_util as jtu
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from jax._src.ad_util import SymbolicZero
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from jax._src.api_util import flatten_fun
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from jax._src.config import config
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from jax._src.interpreters import ad
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from jax._src.interpreters import batching
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from jax._src.interpreters import mlir
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from jax._src.interpreters import partial_eval as pe
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from jax._src.tree_util import tree_flatten
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from jax._src.tree_util import tree_map
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from jax._src.tree_util import tree_unflatten
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from jax._src.typing import Array
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from jax._src.util import (as_hashable_function, split_list, safe_map, safe_zip,
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unzip3, weakref_lru_cache, HashableWrapper)
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source_info_util.register_exclusion(__file__)
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traceback_util.register_exclusion(__file__)
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map, unsafe_map = safe_map, map
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zip, unsafe_zip = safe_zip, zip
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Bool = Union[bool, Array]
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Int = Union[int, Array]
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ErrorCategory = Type['JaxException']
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Payload = List[Union[np.ndarray, Array]]
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PyTreeDef = jtu.PyTreeDef
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Out = TypeVar('Out')
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## Utils
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def popattr(obj, attrname):
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val = getattr(obj, attrname)
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delattr(obj, attrname)
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return val
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def setnewattr(obj, name, val):
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sentinel = object()
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assert getattr(obj, name, sentinel) is sentinel
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setattr(obj, name, val)
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# Concrete errors
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class JaxException(Exception):
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"""Python exception which can contain an error message with JAX run-time info."""
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def __init__(self, traceback_info):
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self.traceback_info = traceback_info
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# TODO(lenamartens): re-enable tracebacks when they don't leak tracers.
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# self.with_traceback(self.traceback_info)
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def __init_subclass__(cls):
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jtu.register_pytree_node_class(cls)
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def tree_flatten(self):
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return ([], self.traceback_info)
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@classmethod
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def tree_unflatten(cls, metadata, payload):
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del payload
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return cls(metadata)
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def get_effect_type(self) -> ErrorEffect:
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raise NotImplementedError
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@functools.total_ordering
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@dataclasses.dataclass(eq=True, frozen=True)
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class ErrorEffect(effects.Effect):
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error_type: Type[JaxException]
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shape_dtypes: Tuple[api.ShapeDtypeStruct, ...]
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def __lt__(self, other: 'ErrorEffect'):
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shape_dtypes = lambda x: tuple((sd.shape, str(sd.dtype)) # dtype is not comparable
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for sd in x.shape_dtypes)
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unpack = lambda x: (str(x.error_type), shape_dtypes(x))
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return (unpack(self) < unpack(other))
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effects.control_flow_allowed_effects.add_type(ErrorEffect)
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effects.lowerable_effects.add_type(ErrorEffect)
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class DivisionByZeroError(JaxException):
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def __str__(self):
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return f'division by zero at:\n\n{self.traceback_info}'
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def get_effect_type(self):
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return ErrorEffect(DivisionByZeroError, ())
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class NaNError(JaxException):
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def __init__(self, traceback_info, primitive_name):
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super().__init__(traceback_info)
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self.prim = primitive_name
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def tree_flatten(self):
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return ([], (self.traceback_info, self.prim))
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@classmethod
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def tree_unflatten(cls, metadata, _):
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return cls(*metadata)
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def get_effect_type(self):
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return ErrorEffect(NaNError, ())
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def __str__(self):
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return f'nan generated by primitive: {self.prim} at:\n\n{self.traceback_info}'
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class OOBError(JaxException):
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def __init__(self, traceback_info, primitive_name, operand_shape, payload):
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super().__init__(traceback_info)
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self.prim = primitive_name
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self.operand_shape = operand_shape
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self._payload = payload
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def tree_flatten(self):
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return ([self._payload], (self.traceback_info, self.prim, self.operand_shape))
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@classmethod
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def tree_unflatten(cls, metadata, payload):
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return cls(*metadata, payload[0])
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def __str__(self):
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return (f'out-of-bounds indexing for array of '
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f'shape {self.operand_shape}: '
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f'index {self._payload[0]} is out of bounds for axis '
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f'{self._payload[1]} with size {self._payload[2]}. '
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f'Failed at:\n\n{self.traceback_info}')
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def get_effect_type(self):
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return ErrorEffect(OOBError, (api.ShapeDtypeStruct((3,), jnp.int32),))
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class FailedCheckError(JaxException):
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def __init__(self, traceback_info, fmt_string, *a, **k):
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super().__init__(traceback_info)
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self.fmt_string = fmt_string
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self.args = a
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self.kwargs = k
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def tree_flatten(self):
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return ((self.args, self.kwargs), # leaves
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(self.traceback_info, self.fmt_string)) # treedef
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@classmethod
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def tree_unflatten(cls, metadata, payload):
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args, kwargs = payload
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return cls(*metadata, *args, **kwargs)
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def __str__(self):
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return (self.fmt_string.format(*self.args, **self.kwargs)
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+ f' `check` failed at:\n\n{self.traceback_info}')
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def get_effect_type(self):
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vals = jtu.tree_leaves((self.args, self.kwargs))
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return ErrorEffect(
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FailedCheckError,
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tuple(api.ShapeDtypeStruct(x.shape, x.dtype) for x in vals))
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@dataclasses.dataclass
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class BatchedError(JaxException):
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error_mapping: Dict[Tuple[int, ...], JaxException]
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def __post_init__(self):
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traceback_info = list(self.error_mapping.values())[0].traceback_info
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super().__init__(traceback_info)
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def __str__(self):
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return '\n'.join(f'at mapped index {", ".join(map(str, idx))}: {e}'
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for idx, e in self.error_mapping.items())
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# Error Value
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@jtu.register_pytree_node_class
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@dataclasses.dataclass(frozen=True)
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class Error:
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_pred: Dict[ErrorEffect, Bool]
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_code: Dict[ErrorEffect, Int]
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_metadata: Dict[Int, PyTreeDef] # mapping of code to JaxException treedef.
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_payload: Dict[ErrorEffect, Payload]
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def get(self) -> Optional[str]:
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"""Returns error message if error happened, None if no error happened."""
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exp = self.get_exception()
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if exp is not None:
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return str(exp)
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return None
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def get_exception(self) -> Optional[JaxException]:
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"""Returns Python exception if error happened, None if no error happened."""
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if any(map(np.shape, self._pred.values())):
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return self._get_batched_exception()
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else:
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min_code = None
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cur_effect = None
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for error_effect, code in self._code.items():
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if self._pred[error_effect]:
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if min_code is None or code < min_code:
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min_code = code
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cur_effect = error_effect
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if cur_effect is not None:
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return tree_unflatten(self._metadata[int(min_code)], # type: ignore
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self._payload[cur_effect])
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return None
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def throw(self):
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_check_error(self)
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def __str__(self):
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return f'Error({self.get()})'
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# Internal helpers
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def _get_batched_exception(self) -> Optional[BatchedError]:
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shape = np.shape(list(self._pred.values())[0])
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error_mapping = {}
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for idx in np.ndindex(*shape):
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min_code = None
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cur_effect = None
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for error_effect, code in self._code.items():
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if self._pred[error_effect][idx]: # type: ignore
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if min_code is None or code[idx] < min_code:
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min_code = code[idx] # type: ignore
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cur_effect = error_effect
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if cur_effect is not None:
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payload = tree_map(lambda x, i=idx: x[i], self._payload[cur_effect])
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jax_error = tree_unflatten(self._metadata[int(min_code)], payload) # type: ignore
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error_mapping[idx] = jax_error
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if error_mapping:
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return BatchedError(error_mapping)
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else:
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return None
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def _update(self, effect_type: ErrorEffect, pred, code, metadata, payload):
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new_errs = {**self._pred, **{effect_type: pred}} # type: ignore
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new_codes = {**self._code, **{effect_type: code}} # type: ignore
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new_payload = {**self._payload, **{effect_type: payload}} # type: ignore
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new_metadata = {**self._metadata, **metadata}
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return Error(new_errs, new_codes, new_metadata, new_payload)
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def _add_placeholder_effects(self, effects: Set[ErrorEffect]):
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"""Fill out Error with `effects` and np.ones arrays of their payloads."""
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new_err = self._pred.copy()
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new_code = self._code.copy()
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new_payload = self._payload.copy()
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for effect in effects:
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if effect not in self._pred.keys():
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new_err[effect] = False
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new_payload[effect] = list(
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tree_map(lambda a: jnp.ones(a.shape, a.dtype), effect.shape_dtypes))
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# The error value associated with this effect will never become True, so
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# we don't need to set a meaningful code.
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new_code[effect] = -1
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return Error(new_err, new_code, self._metadata, new_payload)
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def _replace(self, *args, **kwargs):
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return dataclasses.replace(self, *args, **kwargs)
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# PyTree methods
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def tree_flatten(self):
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return ((self._pred, self._code, self._payload), (self._metadata))
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@classmethod
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def tree_unflatten(cls, metadata, data):
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pred, code, payload = data
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return cls(pred, code, metadata, payload)
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init_error = Error({}, {}, {}, {}) # value used as initial (empty) error.
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next_code = it.count(1).__next__ # globally unique ids, could be uuid4
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def assert_func(error: Error, pred: Bool, new_error: JaxException) -> Error:
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code = next_code()
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effect_type = new_error.get_effect_type()
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new_payload, new_metadata = tree_flatten(new_error)
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return update_error(error, pred, code, {code: new_metadata}, new_payload, effect_type)
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def update_error(error, pred, code, metadata, payload, effect_type):
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err_of_type = error._pred.get(effect_type, False)
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out_err = err_of_type | pred
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out_code = lax.select(err_of_type, error._code.get(effect_type, -1), code)
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cur_payload = error._payload.get(effect_type, None)
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if cur_payload is not None:
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out_payload = tree_map(functools.partial(lax.select, err_of_type), cur_payload, payload)
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else:
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out_payload = payload
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return error._update(effect_type, out_err, out_code, metadata, out_payload)
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## Checkify transformation for plumbing functional error values.
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@lu.transformation_with_aux
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def _flatten_and_get_error_metadata_thunk(*invals):
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error, out = yield invals, {}
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out_vals, out_tree = jtu.tree_flatten((error, out))
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yield out_vals, (out_tree, set(error._pred.keys()))
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def default_checkify_rule(primitive: core.Primitive, error: Error,
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enabled_errors, *invals: core.Value,
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**params: Any) -> Tuple[Error, Sequence[core.Value]]:
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"""Default rule for primitives in `checkify` interpreter."""
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if 'call_jaxpr' not in params:
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# Default non-HOP case: just call primitive and don't update error.
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return error, primitive.bind(*invals, **params)
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# Code below handles call- and map-primitives, by recursively calling
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# checkify_jaxpr.
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err_vals, err_tree = jtu.tree_flatten(error)
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num_error_vals = len(err_vals)
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if 'donated_invars' in params:
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params = dict(params, donated_invars=(*[False]*num_error_vals,
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*params['donated_invars']))
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# call_jaxpr handling
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call_jaxpr = params.pop('call_jaxpr')
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if isinstance(call_jaxpr, core.ClosedJaxpr): # handle closed_call_p
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jaxpr, consts = call_jaxpr.jaxpr, call_jaxpr.consts
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else:
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jaxpr, consts = call_jaxpr, ()
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consts_ = tuple(HashableWrapper(c) for c in consts)
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partial_checkify = lu.hashable_partial(lu.wrap_init(
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checkify_jaxpr_flat_hashable), jaxpr, consts_, enabled_errors, err_tree)
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partial_checkify, metadata = _flatten_and_get_error_metadata_thunk(
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partial_checkify)
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# map-specific params handling.
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if isinstance(primitive, core.MapPrimitive):
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# Update `in_axes` and `out_axes_thunk` params for map primitive.
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out_val_axes = params.pop('out_axes')
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@as_hashable_function(closure=out_val_axes)
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def out_axes_thunk():
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out_err_num = metadata()[0].num_leaves - len(out_val_axes)
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return (*(0,)*out_err_num, *out_val_axes)
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params = dict(params,
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in_axes=(*(None,)*num_error_vals, *params['in_axes']),
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out_axes_thunk=out_axes_thunk)
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all_vals = primitive.bind(partial_checkify, *err_vals, *invals, **params)
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out_tree, _ = metadata()
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error, out_vals = tree_unflatten(out_tree, all_vals)
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if isinstance(primitive, core.MapPrimitive):
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error = _reduce_any_error(error)
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return error, out_vals
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def get_shaped_aval(val):
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return core.raise_to_shaped(core.get_aval(val))
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def checkify_jaxpr(jaxpr: core.ClosedJaxpr, enabled_errors,
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error: Error, *args) -> Tuple[Error, List[core.Value]]:
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err_vals, err_tree = jtu.tree_flatten(error)
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return checkify_jaxpr_flat(jaxpr.jaxpr, jaxpr.consts,
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enabled_errors, err_tree, *err_vals, *args)
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def checkify_jaxpr_flat(jaxpr: core.Jaxpr, consts: Sequence[core.Value],
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enabled_errors, err_tree: PyTreeDef,
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*args: core.Value) -> Tuple[Error, List[Any]]:
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env: Dict[core.Var, Any] = {}
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err_vals, in_args = split_list(args, [err_tree.num_leaves])
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error = jtu.tree_unflatten(err_tree, err_vals)
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def read_env(var: core.Atom):
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if isinstance(var, core.Literal):
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return var.val
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return env[var]
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def write_env(var: core.Var, val: Any):
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env[var] = val
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map(write_env, jaxpr.constvars, consts)
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map(write_env, jaxpr.invars, in_args)
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# interpreter loop
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for eqn in jaxpr.eqns:
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invals = map(read_env, eqn.invars)
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checkify_rule = error_checks.get(
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eqn.primitive, functools.partial(default_checkify_rule, eqn.primitive))
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name_stack = source_info_util.current_name_stack() + eqn.source_info.name_stack
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with source_info_util.user_context(eqn.source_info.traceback,
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name_stack=name_stack):
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error, outvals = checkify_rule(error, enabled_errors,
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*invals, **eqn.params)
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if eqn.primitive.multiple_results:
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map(write_env, eqn.outvars, outvals)
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else:
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write_env(eqn.outvars[0], outvals)
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return error, map(read_env, jaxpr.outvars)
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def checkify_jaxpr_flat_hashable(jaxpr, hashable_consts, enabled_errors,
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err_tree, *args):
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consts = tuple(c.x for c in hashable_consts)
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return checkify_jaxpr_flat(jaxpr, consts, enabled_errors, err_tree, *args)
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@lu.transformation_with_aux
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def flatten_fun_output(*args):
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ans = yield args, {}
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yield tree_flatten(ans)
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def _reduce_any_error(error: Error):
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out_error = init_error
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for error_effect in error._pred.keys():
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errs, codes, payloads = (error._pred[error_effect],
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error._code[error_effect],
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error._payload[error_effect])
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reduced_idx = jnp.argsort(errs)[-1]
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pred, code, payload = tree_map(lambda x, idx=reduced_idx: x[idx],
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(errs, codes, payloads))
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out_error = out_error._update(error_effect, pred, code, {}, payload)
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out_error = out_error._replace(_metadata=error._metadata)
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return out_error
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## check_p primitive
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check_p = core.Primitive('check')
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check_p.multiple_results = True # zero results
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# TODO(lenamartens): inherit from Exception instead of ValueError.
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class JaxRuntimeError(ValueError):
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pass
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@check_p.def_impl
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def check_impl(*args, err_tree, debug):
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if debug:
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# NOOP (check will only trigger when discharged)
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return []
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|
error = tree_unflatten(err_tree, args)
|
|
exc = error.get_exception()
|
|
if exc:
|
|
raise JaxRuntimeError(str(exc)) from exc
|
|
return []
|
|
|
|
@check_p.def_effectful_abstract_eval
|
|
def check_abstract_eval(*args, err_tree, debug):
|
|
del debug
|
|
return [], set(tree_unflatten(err_tree, args)._pred.keys())
|
|
|
|
# TODO(lenamartens) add in-depth error explanation to link to in module docs.
|
|
functionalization_error = ValueError(
|
|
'Cannot abstractly evaluate a checkify.check which was not'
|
|
' functionalized. This probably means you tried to stage'
|
|
' (jit/scan/pmap/...) a `check` without functionalizing it'
|
|
' through `checkify.checkify`.'
|
|
)
|
|
|
|
def check_lowering_rule(ctx, *args, err_tree, debug):
|
|
if debug:
|
|
# NOOP (check will only trigger when discharged)
|
|
return []
|
|
if not config.jax_experimental_unsafe_xla_runtime_errors:
|
|
raise functionalization_error
|
|
|
|
out_op, _, keep_alive = mlir.emit_python_callback(
|
|
ctx, callback=functools.partial(python_err, err_tree),
|
|
token=None,
|
|
operands=args,
|
|
operand_avals=list(ctx.avals_in),
|
|
result_avals=list(ctx.avals_out),
|
|
has_side_effect=True)
|
|
ctx.module_context.add_keepalive(keep_alive)
|
|
return out_op
|
|
|
|
def check_lowering_rule_unsupported(*a, debug, **k):
|
|
if debug:
|
|
return []
|
|
raise functionalization_error
|
|
|
|
def python_err(err_tree, *args):
|
|
error = tree_unflatten(err_tree, args)
|
|
_check_error(error)
|
|
return []
|
|
|
|
mlir.register_lowering(check_p, check_lowering_rule_unsupported,
|
|
platform='tpu')
|
|
mlir.register_lowering(check_p, check_lowering_rule,
|
|
platform='cpu')
|
|
mlir.register_lowering(check_p, check_lowering_rule,
|
|
platform='gpu')
|
|
|
|
def check_batching_rule(batched_args, batch_dims, *, err_tree, debug):
|
|
size = next(x.shape[dim] for x, dim in zip(batched_args, batch_dims)
|
|
if dim is not batching.not_mapped)
|
|
batched_args = (batching.bdim_at_front(a, d, size)
|
|
for a, d in zip(batched_args, batch_dims))
|
|
err = tree_unflatten(err_tree, batched_args)
|
|
_check_error(err, debug=debug)
|
|
return [], []
|
|
batching.primitive_batchers[check_p] = check_batching_rule
|
|
|
|
def check_jvp_rule(primals, _, *, err_tree, debug):
|
|
# Check primals, discard tangents.
|
|
check_p.bind(*primals, err_tree=err_tree, debug=debug)
|
|
return [], []
|
|
ad.primitive_jvps[check_p] = check_jvp_rule
|
|
|
|
## checkify rules
|
|
|
|
ErrorCheckRule = Callable # (Error, FrozenSet[ErrorCategory], *in_vals, **params) -> (Any, Error)
|
|
error_checks: Dict[core.Primitive, ErrorCheckRule] = {}
|
|
|
|
|
|
def summary() -> str:
|
|
return str(source_info_util.summarize(
|
|
source_info_util.current(),
|
|
config.read("jax_tracer_error_num_traceback_frames")))
|
|
|
|
def nan_error_check(prim, error, enabled_errors, *in_vals, **params):
|
|
out = prim.bind(*in_vals, **params)
|
|
err = check_nans(prim, error, enabled_errors, out)
|
|
return err, out
|
|
|
|
def check_nans(prim, error, enabled_errors, out):
|
|
if NaNError not in enabled_errors:
|
|
return error
|
|
|
|
def isnan(x):
|
|
if isinstance(x, prng.PRNGKeyArray):
|
|
return False
|
|
return jnp.any(jnp.isnan(x))
|
|
|
|
any_nans = (jnp.any(jnp.array([isnan(x) for x in out]))
|
|
if prim.multiple_results else isnan(out))
|
|
return assert_func(error, any_nans, NaNError(summary(), prim.name))
|
|
|
|
|
|
# All primitives which can generate a NaN.
|
|
nan_primitives = [lax.acos_p, lax.acosh_p, lax.add_p, lax.asin_p, lax.asinh_p,
|
|
lax.atan2_p, lax.atan_p, lax.atanh_p, lax.bessel_i0e_p,
|
|
lax.bessel_i1e_p, lax.cbrt_p, lax.conv_general_dilated_p,
|
|
lax.cos_p, lax.cosh_p, lax.cumlogsumexp_p, lax.cummax_p,
|
|
lax.cummin_p, lax.cumprod_p, lax.cumsum_p, lax.digamma_p,
|
|
lax.dot_general_p, lax.erf_inv_p, lax.erf_p, lax.erfc_p,
|
|
lax.exp_p, lax.expm1_p, lax.fft_p, lax.igamma_grad_a_p,
|
|
lax.igamma_p, lax.igammac_p, lax.integer_pow_p, lax.lgamma_p,
|
|
lax.linear_solve_p, lax.log1p_p, lax.log_p, lax.logistic_p,
|
|
lax.mul_p, lax.pad_p, lax.pow_p, lax.psum_p,
|
|
lax.random_gamma_grad_p, lax.reduce_p, lax.reduce_prod_p,
|
|
lax.reduce_sum_p, lax.reduce_window_p,
|
|
lax.reduce_window_sum_p, lax.regularized_incomplete_beta_p,
|
|
lax.rem_p, lax.rng_uniform_p, lax.rsqrt_p, lax.sin_p,
|
|
lax.sinh_p, lax.sqrt_p, lax.sub_p, lax.tan_p, lax.tanh_p]
|
|
|
|
for _prim in nan_primitives:
|
|
error_checks[_prim] = functools.partial(nan_error_check, _prim)
|
|
|
|
|
|
def dynamic_slice_error_check(error, enabled_errors, operand, *start_indices, slice_sizes):
|
|
out = lax.dynamic_slice_p.bind(operand, *start_indices, slice_sizes=slice_sizes)
|
|
|
|
if OOBError not in enabled_errors:
|
|
return error, out
|
|
|
|
operand_dims = np.array(operand.shape)
|
|
slice_sizes = np.array(slice_sizes)
|
|
start_indices = jnp.array(start_indices)
|
|
oob_mask = (start_indices < 0) | (start_indices + slice_sizes > operand_dims)
|
|
|
|
payload = oob_payload(oob_mask, start_indices, range(operand.ndim), operand.shape)
|
|
error = assert_func(error, jnp.any(oob_mask), OOBError(summary(), "dynamic_slice", operand.shape, payload))
|
|
return error, out
|
|
error_checks[lax.dynamic_slice_p] = dynamic_slice_error_check
|
|
|
|
def dynamic_update_slice_error_check(error, enabled_errors, operand, update, *start_indices):
|
|
out = lax.dynamic_update_slice_p.bind(operand, update, *start_indices)
|
|
|
|
if OOBError not in enabled_errors:
|
|
return error, out
|
|
|
|
operand_dims = np.array(operand.shape)
|
|
update_dims = np.array(update.shape)
|
|
start_indices = jnp.array(start_indices)
|
|
oob_mask = (start_indices < 0) | (start_indices + update_dims > operand_dims)
|
|
|
|
payload = oob_payload(oob_mask, start_indices, range(operand.ndim), operand.shape)
|
|
error = assert_func(error, jnp.any(oob_mask), OOBError(summary(), "dynamic_update_slice", operand.shape, payload))
|
|
return error, out
|
|
error_checks[lax.dynamic_update_slice_p] = dynamic_update_slice_error_check
|
|
|
|
def gather_error_check(error, enabled_errors, operand, start_indices, *,
|
|
dimension_numbers, slice_sizes, unique_indices,
|
|
indices_are_sorted, mode, fill_value):
|
|
out = lax.gather_p.bind(
|
|
operand, start_indices, dimension_numbers=dimension_numbers,
|
|
slice_sizes=slice_sizes, unique_indices=unique_indices,
|
|
indices_are_sorted=indices_are_sorted, mode=mode, fill_value=fill_value)
|
|
|
|
if OOBError not in enabled_errors:
|
|
return error, out
|
|
|
|
# compare to OOB masking logic in lax._gather_translation_rule
|
|
dnums = dimension_numbers
|
|
operand_dims = np.array(operand.shape)
|
|
num_batch_dims = len(start_indices.shape) - 1
|
|
|
|
upper_bound = operand_dims[np.array(dnums.start_index_map)]
|
|
upper_bound -= np.array(slice_sizes)[np.array(dnums.start_index_map)]
|
|
upper_bound = jnp.expand_dims(upper_bound, axis=tuple(range(num_batch_dims)))
|
|
oob_mask = (start_indices < 0) | (start_indices > upper_bound.astype(start_indices.dtype))
|
|
|
|
payload = oob_payload(oob_mask, start_indices, dnums.start_index_map, operand.shape)
|
|
error = assert_func(error, jnp.any(oob_mask), OOBError(summary(), "gather", operand.shape, payload))
|
|
return error, out
|
|
error_checks[lax.gather_p] = gather_error_check
|
|
|
|
def div_error_check(error, enabled_errors, x, y):
|
|
"""Checks for division by zero and NaN."""
|
|
if DivisionByZeroError in enabled_errors:
|
|
any_zero = jnp.any(jnp.equal(y, 0))
|
|
error = assert_func(error, any_zero, DivisionByZeroError(summary()))
|
|
return nan_error_check(lax.div_p, error, enabled_errors, x, y)
|
|
error_checks[lax.div_p] = div_error_check
|
|
|
|
def oob_payload(oob_mask, indices, dims_map, operand_shape):
|
|
# Get first OOB index, axis and axis size so it can be added to the error msg.
|
|
flat_idx = jnp.argmin(jnp.logical_not(oob_mask))
|
|
multi_idx = jnp.unravel_index(flat_idx, indices.shape)
|
|
oob_axis = jnp.array(dims_map)[multi_idx[-1]]
|
|
oob_axis_size = jnp.array(operand_shape)[oob_axis]
|
|
oob_index = jnp.ravel(indices)[flat_idx]
|
|
payload = jnp.array([oob_index, oob_axis, oob_axis_size], dtype=jnp.int32)
|
|
return payload
|
|
|
|
def scatter_oob(operand, indices, updates, dnums):
|
|
# Ref: see clamping code used in scatter_translation_rule
|
|
slice_sizes = []
|
|
pos = 0
|
|
for i in range(len(operand.shape)):
|
|
if i in dnums.inserted_window_dims:
|
|
slice_sizes.append(1)
|
|
else:
|
|
slice_sizes.append(updates.shape[dnums.update_window_dims[pos]])
|
|
pos += 1
|
|
|
|
upper_bound = np.array([operand.shape[i] - slice_sizes[i]
|
|
for i in dnums.scatter_dims_to_operand_dims],
|
|
np.int64)
|
|
upper_bound = np.minimum(upper_bound, np.iinfo(indices.dtype).max)
|
|
upper_bound = lax.broadcast_in_dim(upper_bound, indices.shape,
|
|
(len(indices.shape) - 1,))
|
|
|
|
lower_oob = jnp.less(indices, 0)
|
|
upper_oob = jnp.greater(indices, upper_bound.astype(indices.dtype))
|
|
oob_mask = jnp.logical_or(lower_oob, upper_oob)
|
|
payload = oob_payload(oob_mask, indices,
|
|
dnums.scatter_dims_to_operand_dims, operand.shape)
|
|
return jnp.any(oob_mask), payload
|
|
|
|
def scatter_error_check(prim, error, enabled_errors, operand, indices, updates,
|
|
*, update_jaxpr, update_consts, dimension_numbers,
|
|
indices_are_sorted, unique_indices, mode):
|
|
"""Checks if indices are within bounds and update does not generate NaN."""
|
|
out = prim.bind(
|
|
operand, indices, updates, update_jaxpr=update_jaxpr,
|
|
update_consts=update_consts, dimension_numbers=dimension_numbers,
|
|
indices_are_sorted=indices_are_sorted, unique_indices=unique_indices,
|
|
mode=mode)
|
|
|
|
if OOBError not in enabled_errors:
|
|
return error, out
|
|
|
|
out_of_bounds, payload = scatter_oob(operand, indices, updates, dimension_numbers)
|
|
oob_error = OOBError(summary(), prim.name, operand.shape, payload)
|
|
error = assert_func(error, out_of_bounds, oob_error)
|
|
error = check_nans(prim, error, enabled_errors, out)
|
|
return error, out
|
|
error_checks[lax.scatter_p] = functools.partial(scatter_error_check, lax.scatter_p)
|
|
error_checks[lax.scatter_add_p] = functools.partial(scatter_error_check,
|
|
lax.scatter_add_p)
|
|
error_checks[lax.scatter_mul_p] = functools.partial(scatter_error_check,
|
|
lax.scatter_mul_p)
|
|
error_checks[lax.scatter_min_p] = functools.partial(scatter_error_check,
|
|
lax.scatter_min_p)
|
|
error_checks[lax.scatter_max_p] = functools.partial(scatter_error_check,
|
|
lax.scatter_max_p)
|
|
|
|
# HOP error check rules
|
|
|
|
@weakref_lru_cache
|
|
def jaxpr_to_checkify_jaxpr(
|
|
jaxpr: core.ClosedJaxpr, enabled_errors, err_tree: PyTreeDef,
|
|
*flat_err_and_in_vals) -> Tuple[core.ClosedJaxpr, PyTreeDef, Set[ErrorEffect]]:
|
|
checkify_jaxpr_partial = functools.partial(checkify_jaxpr_flat, jaxpr.jaxpr,
|
|
jaxpr.consts, enabled_errors,
|
|
err_tree)
|
|
fun = lu.wrap_init(checkify_jaxpr_partial)
|
|
fun, metadata = _flatten_and_get_error_metadata_thunk(fun)
|
|
|
|
new_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(fun, flat_err_and_in_vals)
|
|
checked_jaxpr = core.ClosedJaxpr(new_jaxpr, consts)
|
|
out_tree, error_effects = metadata()
|
|
return checked_jaxpr, out_tree, error_effects
|
|
|
|
def cond_error_check(error: Error, enabled_errors, index, *ops, branches, linear):
|
|
# Get the error-effects out of all branches so the cond can be called with
|
|
# a merged error with all these effects.
|
|
err_vals, err_tree = jtu.tree_flatten(error)
|
|
in_avals = map(get_shaped_aval, [*err_vals, *ops])
|
|
def get_error_effects_from_jaxpr(jxpr):
|
|
_, _, effects = jaxpr_to_checkify_jaxpr(jxpr, enabled_errors, err_tree,
|
|
*in_avals)
|
|
return effects
|
|
effects = [get_error_effects_from_jaxpr(jxpr) for jxpr in branches]
|
|
merged_error = error._add_placeholder_effects(set().union(*effects))
|
|
err_vals, err_tree = jtu.tree_flatten(merged_error)
|
|
new_linear = (*[False] * len(err_vals), *linear)
|
|
|
|
# Update branch jaxprs to be checkified jaxprs.
|
|
in_avals = map(get_shaped_aval, [*err_vals, *ops])
|
|
new_branches, out_trees, _ = unzip3(
|
|
jaxpr_to_checkify_jaxpr(
|
|
jxpr, enabled_errors, err_tree, *in_avals) for jxpr in branches)
|
|
|
|
err_and_outs = lax.cond_p.bind(
|
|
index, *err_vals, *ops,
|
|
branches=tuple(new_branches), linear=new_linear)
|
|
|
|
# we need to merge metadata across out_trees (a tuple)
|
|
err0, out = tree_unflatten(out_trees[0], err_and_outs)
|
|
merged_metadata = err0._metadata
|
|
for tr in out_trees[1:]:
|
|
err, _ = tree_unflatten(tr, err_and_outs)
|
|
merged_metadata = {**merged_metadata, **err._metadata}
|
|
return err0._replace(_metadata=merged_metadata), out
|
|
error_checks[lax.cond_p] = cond_error_check
|
|
|
|
def scan_error_check(error, enabled_errors, *in_flat, reverse, length, jaxpr,
|
|
num_consts, num_carry, linear, unroll):
|
|
|
|
consts, carry, xs = split_list(in_flat, [num_consts, num_carry])
|
|
xs_mapped = [core.mapped_aval(length, 0, get_shaped_aval(val)) for val in xs]
|
|
# Query body effects to create a merged error containing all effects (such
|
|
# that in and out carried error are of the same type).
|
|
err_vals, err_tree = jtu.tree_flatten(error)
|
|
new_in_aval = map(get_shaped_aval, [*err_vals, *consts, *carry]) + xs_mapped
|
|
_, _, effects = jaxpr_to_checkify_jaxpr(jaxpr, enabled_errors,
|
|
err_tree, *new_in_aval)
|
|
|
|
merged_error = error._add_placeholder_effects(effects)
|
|
err_vals, err_tree = jtu.tree_flatten(merged_error)
|
|
|
|
# Create checked-jaxpr, with the needed pre-processing on the inputs.
|
|
new_in_aval = map(get_shaped_aval, [*err_vals, *consts, *carry]) + xs_mapped
|
|
checked_jaxpr_, out_tree, _ = jaxpr_to_checkify_jaxpr(jaxpr, enabled_errors,
|
|
err_tree, *new_in_aval)
|
|
|
|
new_in_flat = [*consts, *err_vals, *carry, *xs]
|
|
new_linear = (*[False] * len(err_vals), *linear)
|
|
tomove = ([False] * len(err_vals) + [True] * len(consts)
|
|
+ [False] * (len(carry) + len(xs)))
|
|
checked_jaxpr = pe.move_binders_to_front(checked_jaxpr_, tomove)
|
|
new_in_flat = [*consts, *err_vals, *carry, *xs]
|
|
err_and_out = lax.scan_p.bind(
|
|
*new_in_flat, reverse=reverse, length=length, jaxpr=checked_jaxpr,
|
|
num_consts=len(consts), num_carry=len(carry)+len(err_vals),
|
|
linear=new_linear, unroll=unroll)
|
|
err, out = tree_unflatten(out_tree, err_and_out)
|
|
return err, out
|
|
|
|
error_checks[lax.scan_p] = scan_error_check
|
|
|
|
def checkify_while_body_jaxpr(
|
|
cond_jaxpr: core.ClosedJaxpr, body_jaxpr: core.ClosedJaxpr,
|
|
enabled_errors, error: Error,
|
|
c_consts_num: int) -> Tuple[core.ClosedJaxpr, PyTreeDef, Set[ErrorEffect]]:
|
|
cond_f = core.jaxpr_as_fun(cond_jaxpr)
|
|
body_f = core.jaxpr_as_fun(body_jaxpr)
|
|
def new_body_f(*c_consts_and_vals):
|
|
c_consts, vals = split_list(c_consts_and_vals, [c_consts_num])
|
|
out = body_f(*vals)
|
|
# This checks if the next cond application will error
|
|
_ = cond_f(*c_consts, *out)
|
|
return out
|
|
new_body_f_ = lu.wrap_init(new_body_f)
|
|
c_consts_avals = cond_jaxpr.in_avals[:c_consts_num]
|
|
jaxpr, _, () = pe.trace_to_jaxpr_dynamic(new_body_f_, [*c_consts_avals,
|
|
*body_jaxpr.in_avals])
|
|
closed_jaxpr = core.ClosedJaxpr(jaxpr, ())
|
|
err_vals, err_tree = jtu.tree_flatten(error)
|
|
err_vals = map(get_shaped_aval, err_vals)
|
|
flat_err_and_in_vals = [*err_vals, *c_consts_avals, *body_jaxpr.in_avals]
|
|
jaxpr, out_tree, error_effects = jaxpr_to_checkify_jaxpr(
|
|
closed_jaxpr, enabled_errors, err_tree, *flat_err_and_in_vals)
|
|
return jaxpr, out_tree, error_effects
|
|
|
|
def ignore_error_output_jaxpr(jaxpr, num_error_vals):
|
|
"""Constructs a checked jaxpr which does not output its error value."""
|
|
consts = jaxpr.consts
|
|
jaxpr = jaxpr.jaxpr
|
|
new_jaxpr = jaxpr.replace(outvars=jaxpr.outvars[num_error_vals:])
|
|
return core.ClosedJaxpr(new_jaxpr, consts)
|
|
|
|
def while_loop_error_check(error, enabled_errors, *in_flat, cond_nconsts,
|
|
cond_jaxpr, body_nconsts, body_jaxpr):
|
|
if cond_jaxpr.out_avals[0].shape:
|
|
# TODO(lenamartens, sharadmv): support batched while.
|
|
raise ValueError('Checkify does not support batched while-loops '
|
|
'(checkify-of-vmap-of-while). \nHint: if possible, move '
|
|
'the vmap to the outer level to get '
|
|
'vmap-of-checkify-of-while.')
|
|
|
|
c_consts, b_consts, carry = split_list(in_flat, [cond_nconsts, body_nconsts])
|
|
# Check if the first cond application will error.
|
|
error, _ = checkify_jaxpr(cond_jaxpr, enabled_errors, error, *c_consts, *carry)
|
|
|
|
_, _, error_effects = checkify_while_body_jaxpr(cond_jaxpr, body_jaxpr,
|
|
enabled_errors, error,
|
|
cond_nconsts)
|
|
# merged error!
|
|
error = error._add_placeholder_effects(error_effects)
|
|
err_vals, err_tree = jtu.tree_flatten(error)
|
|
checked_body_jaxpr_, body_out_tree, _ = checkify_while_body_jaxpr(
|
|
cond_jaxpr, body_jaxpr, enabled_errors, error, cond_nconsts)
|
|
num_error_vals = len(err_vals)
|
|
to_move = ([False] * num_error_vals + [True] * cond_nconsts
|
|
+ [True] * body_nconsts + [False] * len(carry))
|
|
checked_body_jaxpr = pe.move_binders_to_front(checked_body_jaxpr_, to_move)
|
|
|
|
cond_in_flat = [*err_vals, *c_consts, *carry]
|
|
cond_in_flat = map(get_shaped_aval, cond_in_flat)
|
|
checked_cond_jaxpr, _, _ = jaxpr_to_checkify_jaxpr(cond_jaxpr, enabled_errors,
|
|
err_tree, *cond_in_flat)
|
|
compat_cond_jaxpr_ = ignore_error_output_jaxpr(checked_cond_jaxpr, num_error_vals)
|
|
to_move = [False] * num_error_vals + [True] * cond_nconsts + [False] * len(carry)
|
|
compat_cond_jaxpr = pe.move_binders_to_front(compat_cond_jaxpr_, to_move)
|
|
|
|
new_in_flat = [*c_consts, *c_consts, *b_consts, *err_vals, *carry]
|
|
all_out_vals = lax.while_p.bind(
|
|
*new_in_flat, cond_nconsts=cond_nconsts, cond_jaxpr=compat_cond_jaxpr,
|
|
body_nconsts=cond_nconsts+body_nconsts, body_jaxpr=checked_body_jaxpr)
|
|
# body_out_tree will have all the metadata of cond because it executes a cond!
|
|
error, out = tree_unflatten(body_out_tree, all_out_vals)
|
|
return error, out
|
|
error_checks[lax.while_p] = while_loop_error_check
|
|
|
|
def pjit_error_check(error, enabled_errors, *vals_in, jaxpr,
|
|
in_shardings, out_shardings, resource_env,
|
|
donated_invars, name,
|
|
inline, keep_unused):
|
|
# jaxpr to checked_jaxpr
|
|
err_vals, err_tree = jtu.tree_flatten(error)
|
|
new_vals_in = [*err_vals, *vals_in]
|
|
in_avals = tuple(map(get_shaped_aval, new_vals_in))
|
|
checked_jaxpr, out_tree, _ = jaxpr_to_checkify_jaxpr(jaxpr, enabled_errors,
|
|
err_tree, *in_avals)
|
|
|
|
# Update pjit params to account for extra error values.
|
|
num_error_vals = len(err_vals)
|
|
num_out_error_vals = out_tree.num_leaves - len(out_shardings)
|
|
sharding = sharding_impls.UNSPECIFIED
|
|
|
|
new_in_shardings = (*[sharding] * num_error_vals, *in_shardings)
|
|
new_out_shardings = (*[sharding] * num_out_error_vals, *out_shardings)
|
|
new_donated_invars = (*[False] * num_error_vals, *donated_invars)
|
|
|
|
err_and_out = pjit.pjit_p.bind(
|
|
*new_vals_in,
|
|
jaxpr=checked_jaxpr,
|
|
in_shardings=new_in_shardings,
|
|
out_shardings=new_out_shardings,
|
|
resource_env=resource_env,
|
|
donated_invars=new_donated_invars,
|
|
name=name,
|
|
inline=inline,
|
|
keep_unused=keep_unused,
|
|
)
|
|
return tree_unflatten(out_tree, err_and_out)
|
|
error_checks[pjit.pjit_p] = pjit_error_check
|
|
|
|
def custom_jvp_call_rule(in_err, enabled_errors, *in_vals, num_consts,
|
|
jvp_jaxpr_thunk, call_jaxpr, **params):
|
|
# The types to have in mind are:
|
|
# jvp : (a -> b) -> (a, T a) -> (b, T b)
|
|
# checkify : (a -> b) -> a -> Err b
|
|
# jvp-of-checkify : (a -> b) -> (a, T a) -> (Err b, T (Err b))
|
|
# where because Err is a pytree, we necessarily have T (Err b) = Err' (T b)
|
|
# where the other Err' components are trivial (of float0 dtype).
|
|
# Semantically, we don't add checks to the JVP rule. To check the result of a
|
|
# JVP rule, one must instead use checkify-of-jvp. Thus this implementation
|
|
# just forwards the input error and code (and trivial tangents) to the output.
|
|
err_vals, err_tree = jtu.tree_flatten(in_err)
|
|
partial_checkify = lu.wrap_init(
|
|
functools.partial(checkify_jaxpr_flat, call_jaxpr.jaxpr,
|
|
call_jaxpr.consts, enabled_errors, err_tree))
|
|
partial_checkify, f_metadata = _flatten_and_get_error_metadata_thunk(
|
|
partial_checkify)
|
|
jvp = lift_jvp(err_tree.num_leaves, num_consts, jvp_jaxpr_thunk)
|
|
jvp, jvp_out_tree = flatten_fun_output(jvp)
|
|
all_outs = custom_derivatives.custom_jvp_call_p.bind(
|
|
partial_checkify, jvp, *err_vals, *in_vals, **params)
|
|
fst, out_metadata = lu.merge_linear_aux(f_metadata, jvp_out_tree)
|
|
if fst:
|
|
err_and_out_tree, _ = out_metadata
|
|
out_err, out_vals = tree_unflatten(err_and_out_tree, all_outs)
|
|
else:
|
|
err_vals, out_vals = split_list(all_outs, [len(err_vals)])
|
|
# forward input error to output
|
|
out_err = jtu.tree_unflatten(err_tree, err_vals)
|
|
return out_err, out_vals
|
|
error_checks[custom_derivatives.custom_jvp_call_p] = custom_jvp_call_rule
|
|
|
|
# Compared to custom_derivatives.lift_jvp, we're handling the extra inputs and
|
|
# outputs that checkify adds (just forwarding the error data's primal and
|
|
# tangent components). The jaxpr in jvp_jaxpr_thunk doesn't expect those.
|
|
# TODO(mattjj): can we simplify this, or dedup with custom_derivatives.lift_jvp?
|
|
# Adding another layer of lu.transformation was tricky, though maybe doable.
|
|
def lift_jvp(num_errs, num_consts, jvp_jaxpr_thunk):
|
|
@lu.wrap_init
|
|
def jvp(*xs):
|
|
n, ragged = divmod(len(xs), 2)
|
|
assert not ragged
|
|
primals, tangents = xs[num_consts+num_errs:n], xs[n+num_consts+num_errs:]
|
|
zeros = [type(t) is SymbolicZero for t in tangents]
|
|
jvp_jaxpr, jvp_consts, out_zeros = jvp_jaxpr_thunk(*zeros)
|
|
nonzero_tangents = [t for t in tangents if type(t) is not SymbolicZero]
|
|
out = core.eval_jaxpr(jvp_jaxpr, jvp_consts, *primals, *nonzero_tangents)
|
|
out_primals, nz_out_tangents = split_list(out, [len(out_zeros)])
|
|
nz_out_tangents_ = iter(nz_out_tangents)
|
|
out_tangents = [SymbolicZero(core.get_aval(p).at_least_vspace())
|
|
if z else next(nz_out_tangents_)
|
|
for p, z in zip(out_primals, out_zeros)]
|
|
assert next(nz_out_tangents_, None) is None
|
|
primal_errs = xs[num_consts:num_consts+num_errs]
|
|
tangent_errs = xs[n+num_consts:n+num_consts+num_errs]
|
|
return [*primal_errs, *out_primals, *tangent_errs, *out_tangents]
|
|
return jvp
|
|
|
|
def custom_vjp_call_jaxpr_rule(in_err, enabled_errors, *in_vals, fun_jaxpr,
|
|
fwd_jaxpr_thunk, num_consts, bwd, out_trees,
|
|
symbolic_zeros):
|
|
err_vals, err_tree = jtu.tree_flatten(in_err)
|
|
num_errs = err_tree.num_leaves
|
|
checkified_fun = lu.wrap_init(
|
|
functools.partial(checkify_jaxpr_flat, fun_jaxpr.jaxpr,
|
|
fun_jaxpr.consts, enabled_errors, err_tree))
|
|
checkified_fun, fun_metadata = _flatten_and_get_error_metadata_thunk(
|
|
checkified_fun)
|
|
|
|
@lu.wrap_init
|
|
def checkified_fwd(*args):
|
|
# TODO(lenamartens, sharadmv): why not checkify here?
|
|
xs, zeros = args[::2], args[1::2]
|
|
xs, zeros = xs[num_errs:], zeros[num_errs:]
|
|
fwd_jaxpr, fwd_consts = fwd_jaxpr_thunk(*zeros)
|
|
xs_without_consts = xs[num_consts:]
|
|
return core.eval_jaxpr(fwd_jaxpr, fwd_consts, *xs_without_consts)
|
|
|
|
bwd_ = lambda *args: (*(None,)*num_errs, *bwd(*args))
|
|
checkified_fwd, fwd_out_tree = flatten_fun_output(checkified_fwd)
|
|
all_outs = custom_derivatives.custom_vjp_call_p.bind(
|
|
checkified_fun, checkified_fwd, bwd_, *err_vals, *in_vals, out_trees=out_trees,
|
|
symbolic_zeros=symbolic_zeros)
|
|
fst, out_metadata = lu.merge_linear_aux(fun_metadata, fwd_out_tree)
|
|
if fst:
|
|
err_and_out_tree, _ = out_metadata
|
|
out_err, out_vals = tree_unflatten(err_and_out_tree, all_outs)
|
|
else:
|
|
out_err, out_vals = in_err, all_outs
|
|
return out_err, out_vals
|
|
error_checks[custom_derivatives.custom_vjp_call_jaxpr_p] = custom_vjp_call_jaxpr_rule
|
|
|
|
|
|
def check_discharge_rule(error, enabled_errors, *args, err_tree, debug):
|
|
del debug
|
|
new_error = tree_unflatten(err_tree, args)
|
|
# Split up new_error into error to be functionalized if it's included in
|
|
# enabled_errors (=discharged_error) and an error to be defunctionalized if
|
|
# it's not included (=recharged_error)
|
|
discharged_error = error
|
|
recharged_error = init_error
|
|
for error_effect in new_error._pred.keys():
|
|
pred = new_error._pred[error_effect]
|
|
code = new_error._code[error_effect]
|
|
payload = new_error._payload[error_effect]
|
|
if error_effect.error_type in enabled_errors:
|
|
discharged_error = update_error(discharged_error, pred, code, {}, payload,
|
|
error_effect)
|
|
else:
|
|
recharged_error = update_error(recharged_error, pred, code, {}, payload,
|
|
error_effect)
|
|
|
|
discharged_error = discharged_error._replace(
|
|
_metadata={**new_error._metadata, **discharged_error._metadata})
|
|
recharged_error = recharged_error._replace(_metadata=new_error._metadata)
|
|
# TODO(lenamartens): we actually need to recharge, but this would be a
|
|
# breaking API change so leaving for a follow-up.
|
|
# check_error(recharged_error)
|
|
return discharged_error, []
|
|
error_checks[check_p] = check_discharge_rule
|
|
|
|
|
|
## checkify public api
|
|
|
|
user_checks = frozenset({FailedCheckError})
|
|
nan_checks = frozenset({NaNError})
|
|
index_checks = frozenset({OOBError})
|
|
div_checks = frozenset({DivisionByZeroError})
|
|
float_checks = nan_checks | div_checks
|
|
automatic_checks = float_checks | index_checks
|
|
all_checks = automatic_checks | user_checks
|
|
|
|
|
|
def checkify(f: Callable[..., Out],
|
|
errors: FrozenSet[ErrorCategory] = user_checks
|
|
) -> Callable[..., Tuple[Error, Out]]:
|
|
"""Functionalize `check` calls in `fun`, and optionally add run-time error checks.
|
|
|
|
Run-time errors are either user-added :func:`~check` assertions, or
|
|
automatically added checks like NaN checks, depending on the ``errors``
|
|
argument.
|
|
|
|
The returned function will return an Error object `err` along with the output
|
|
of the original function. ``err.get()`` will either return ``None`` (if no
|
|
error occurred) or a string containing an error message. This error message
|
|
will correspond to the first error which occurred. ``err.throw()`` will raise
|
|
a ValueError with the error message if an error occurred.
|
|
|
|
By default only user-added :func:`~check` assertions are enabled. You can
|
|
enable automatic checks through the ``errors`` argument.
|
|
|
|
The automatic check sets which can be enabled, and when an error is generated:
|
|
- ``user_checks``: a :func:`~check` evaluated to False.
|
|
- ``nan_checks``: a floating-point operation generated a NaN value
|
|
as output.
|
|
- ``div_checks``: a division by zero.
|
|
- ``index_checks``: an index was out-of-bounds.
|
|
|
|
Multiple categories can be enabled together by passing in an error `Set` (eg.
|
|
``errors=nan_checks``). Multiple sets can be re-combined (eg.
|
|
``errors=float_checks|user_checks``)
|
|
|
|
Args:
|
|
fun: Callable which can contain user checks (see :func:`~check`).
|
|
errors: A set of ErrorCategory values which defines the set of enabled
|
|
checks. By default only explicit ``checks`` are enabled
|
|
(``user_checks``). You can also for example enable NAN and
|
|
DIV errors by passing the ``float_checks`` set, or for
|
|
example combine multiple sets through set operations
|
|
(``float_checks | user_checks``)
|
|
Returns:
|
|
A function which accepts the same arguments as ``fun`` and returns as output
|
|
a pair where the first element is an ``Error`` value, representing the first
|
|
failed :func:`~check`, and the second element is the original output of
|
|
``fun``.
|
|
|
|
For example:
|
|
|
|
>>> import jax
|
|
>>> import jax.numpy as jnp
|
|
>>> from jax.experimental import checkify
|
|
>>>
|
|
>>> @jax.jit
|
|
... def f(x):
|
|
... y = jnp.sin(x)
|
|
... return x+y
|
|
>>> err, out = checkify.checkify(f, errors=checkify.float_checks)(jnp.inf)
|
|
>>> err.throw() # doctest: +IGNORE_EXCEPTION_DETAIL
|
|
Traceback (most recent call last):
|
|
...
|
|
jax._src.checkify.JaxRuntimeError: nan generated by primitive: sin
|
|
"""
|
|
@traceback_util.api_boundary
|
|
def checked_fun(*args, **kwargs):
|
|
# close over all arguments so they're not turned into abstract values.
|
|
in_tree = jtu.tree_structure(((), {}))
|
|
closed_f = lambda: f(*args, **kwargs)
|
|
# stage:
|
|
fun_, out_tree = flatten_fun(lu.wrap_init(closed_f), in_tree)
|
|
debug = pe.debug_info(closed_f, in_tree, out_tree, False, 'checkify')
|
|
jaxpr_, _, consts = pe.trace_to_jaxpr_dynamic(fun_, (), debug)
|
|
jaxpr = pe.close_jaxpr(pe.convert_constvars_jaxpr(jaxpr_))
|
|
# checkify:
|
|
error, out_flat = checkify_jaxpr(jaxpr, errors, init_error, *consts)
|
|
return error, jtu.tree_unflatten(out_tree(), out_flat)
|
|
return checked_fun
|
|
|
|
def check(pred: Bool, msg: str, *fmt_args, **fmt_kwargs) -> None:
|
|
"""Check a predicate, add an error with msg if predicate is False.
|
|
|
|
This is an effectful operation, and can't be staged (jitted/scanned/...).
|
|
Before staging a function with checks, :func:`~checkify` it!
|
|
|
|
Args:
|
|
pred: if False, a FailedCheckError error is added.
|
|
msg: error message if error is added. Can be a format string.
|
|
fmt_args, fmt_kwargs: Positional and keyword formatting arguments for
|
|
`msg`, eg.:
|
|
``check(.., "check failed on values {} and {named_arg}", x, named_arg=y)``
|
|
Note that these arguments can be traced values allowing you to add
|
|
run-time values to the error message.
|
|
Note that tracking these run-time arrays will increase your memory usage,
|
|
even if no error happens.
|
|
|
|
For example:
|
|
|
|
>>> import jax
|
|
>>> import jax.numpy as jnp
|
|
>>> from jax.experimental import checkify
|
|
>>> def f(x):
|
|
... checkify.check(x>0, "{x} needs to be positive!", x=x)
|
|
... return 1/x
|
|
>>> checked_f = checkify.checkify(f)
|
|
>>> err, out = jax.jit(checked_f)(-3.)
|
|
>>> err.throw() # doctest: +IGNORE_EXCEPTION_DETAIL
|
|
Traceback (most recent call last):
|
|
...
|
|
jax._src.checkify.JaxRuntimeError: -3. needs to be positive!
|
|
|
|
"""
|
|
_check(pred, msg, False, *fmt_args, **fmt_kwargs)
|
|
|
|
def _check(pred, msg, debug, *fmt_args, **fmt_kwargs):
|
|
if not is_scalar_pred(pred):
|
|
prim_name = 'debug_check' if debug else 'check'
|
|
raise TypeError(f'{prim_name} takes a scalar pred as argument, got {pred}')
|
|
for arg in jtu.tree_leaves((fmt_args, fmt_kwargs)):
|
|
if not isinstance(arg, (Array, np.ndarray)):
|
|
raise TypeError('Formatting arguments to checkify.check need to be '
|
|
'PyTrees of arrays, but got '
|
|
f'{repr(arg)} of type {type(arg)}.')
|
|
new_error = FailedCheckError(summary(), msg, *fmt_args, **fmt_kwargs)
|
|
error = assert_func(init_error, jnp.logical_not(pred), new_error)
|
|
_check_error(error, debug=debug)
|
|
|
|
def _check_error(error, *, debug=False):
|
|
if any(map(np.shape, error._pred.values())):
|
|
error = _reduce_any_error(error)
|
|
err_args, tree_def = tree_flatten(error)
|
|
|
|
return check_p.bind(*err_args, err_tree=tree_def, debug=debug)
|
|
|
|
|
|
def is_scalar_pred(pred) -> bool:
|
|
return (isinstance(pred, bool) or
|
|
isinstance(pred, Array) and pred.shape == () and
|
|
pred.dtype == jnp.dtype('bool'))
|
|
|
|
|
|
def debug_check(pred: Bool, msg: str, *fmt_args, **fmt_kwargs) -> None:
|
|
"""Check a predicate when running under checkify, otherwise is a no-op.
|
|
|
|
A `debug_check` will only be run if it is transformed by :func:`~checkify`,
|
|
otherwise the check will be dropped.
|
|
|
|
Args:
|
|
pred: if False, a FailedCheckError error is added.
|
|
msg: error message if error is added.
|
|
fmt_args, fmt_kwargs: Positional and keyword formatting arguments for
|
|
`msg`, eg.:
|
|
``debug_check(.., "check failed on values {} and {named}", x, named=y)``
|
|
Note that these arguments can be traced values allowing you to add
|
|
run-time values to the error message.
|
|
Note that tracking these run-time arrays will increase your memory usage,
|
|
even if no error happens.
|
|
|
|
For example:
|
|
|
|
>>> import jax
|
|
>>> import jax.numpy as jnp
|
|
>>> from jax.experimental import checkify
|
|
>>> def f(x):
|
|
... checkify.debug_check(x!=0, "cannot be zero!")
|
|
... return x
|
|
>>> _ = f(0) # running without checkify means no debug_check is run.
|
|
>>> checked_f = checkify.checkify(f)
|
|
>>> err, out = jax.jit(checked_f)(0) # running with checkify runs debug_check.
|
|
>>> err.throw() # doctest: +IGNORE_EXCEPTION_DETAIL
|
|
Traceback (most recent call last):
|
|
...
|
|
jax._src.checkify.JaxRuntimeError: cannot be zero!
|
|
|
|
"""
|
|
_check(pred, msg, True, *fmt_args, **fmt_kwargs)
|
|
|
|
|
|
def check_error(error: Error) -> None:
|
|
"""Raise an Exception if ``error`` represents a failure. Functionalized by :func:`~checkify`.
|
|
|
|
The semantics of this function are equivalent to:
|
|
|
|
>>> def check_error(err: Error) -> None:
|
|
... err.throw() # can raise ValueError
|
|
|
|
But unlike that implementation, ``check_error`` can be functionalized using
|
|
the :func:`~checkify` transformation.
|
|
|
|
This function is similar to :func:`~check` but with a different signature: whereas
|
|
:func:`~check` takes as arguments a boolean predicate and a new error message
|
|
string, this function takes an ``Error`` value as argument. Both :func:`~check`
|
|
and this function raise a Python Exception on failure (a side-effect), and
|
|
thus cannot be staged out by :func:`~jax.jit`, :func:`~jax.pmap`,
|
|
:func:`~jax.lax.scan`, etc. Both also can
|
|
be functionalized by using :func:`~checkify`.
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But unlike :func:`~check`, this function is like a direct inverse of
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:func:`~checkify`:
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whereas :func:`~checkify` takes as input a function which
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can raise a Python
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Exception and produces a new function without that effect but which produces
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an ``Error`` value as output, this ``check_error`` function can accept an
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``Error`` value as input and can produce the side-effect of raising an
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Exception. That is, while :func:`~checkify` goes from
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functionalizable Exception
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effect to error value, this ``check_error`` goes from error value to
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functionalizable Exception effect.
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``check_error`` is useful when you want to turn checks represented by an
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``Error`` value (produced by functionalizing ``checks`` via
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:func:`~checkify`) back into Python Exceptions.
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Args:
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error: Error to check.
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For example, you might want to functionalize part of your program through
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checkify, stage out your functionalized code through :func:`~jax.jit`, then
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re-inject your error value outside of the :func:`~jax.jit`:
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>>> import jax
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>>> from jax.experimental import checkify
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>>> def f(x):
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... checkify.check(x>0, "must be positive!")
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... return x
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>>> def with_inner_jit(x):
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... checked_f = checkify.checkify(f)
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... # a checkified function can be jitted
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... error, out = jax.jit(checked_f)(x)
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... checkify.check_error(error)
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... return out
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>>> _ = with_inner_jit(1) # no failed check
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>>> with_inner_jit(-1) # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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jax._src.JaxRuntimeError: must be positive!
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>>> # can re-checkify
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>>> error, _ = checkify.checkify(with_inner_jit)(-1)
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"""
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if not isinstance(error, Error):
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raise ValueError('check_error takes an Error as argument, '
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|
f'got type {type(error)} instead.')
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_check_error(error, debug=False)
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