code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def is_equivalent_to(self: Sharding, other: Sharding, ndim: int) -> bool:
"""Returns ``True`` if two shardings are equivalent.
Two shardings are equivalent if they place the same logical array shards on
the same devices.
"""
try:
return (are_op_shardings_equal(self._to_xla_hlo_sharding(ndim),... | Returns ``True`` if two shardings are equivalent.
Two shardings are equivalent if they place the same logical array shards on
the same devices.
| is_equivalent_to | python | jax-ml/jax | jax/_src/sharding.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding.py | Apache-2.0 |
def unflatten_array(named_sizes, assignment):
"""Recovers the ordering of axis names based on a device assignment.
The device assignments that this function can convert into axis orders
are of the form::
np.arange(np.prod(named_sizes.values())).transpose(...).flatten()
for some transposition ``...``. Thi... | Recovers the ordering of axis names based on a device assignment.
The device assignments that this function can convert into axis orders
are of the form::
np.arange(np.prod(named_sizes.values())).transpose(...).flatten()
for some transposition ``...``. This is satisfied by all OpSharding assignments
gene... | unflatten_array | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def unflatten_superdims(assignment):
"""Unflatten a list of dimension sizes and their strides that generates assignment.
If this function succeeds for a given ``assignment``, then the following property
should be satisfied::
dims_with_strides = unflatten_superdims(assignment)
base_array = np.arange(map(... | Unflatten a list of dimension sizes and their strides that generates assignment.
If this function succeeds for a given ``assignment``, then the following property
should be satisfied::
dims_with_strides = unflatten_superdims(assignment)
base_array = np.arange(map(fst, sorted(dims_with_strides, key=snd, re... | unflatten_superdims | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def explode_superdims(sizes, dims):
"""Explode superdims to fit a known shape.
The unflattening process might mistakenly generate too few too large dimensions.
For example, ``unflatten_superdims(np.arange(n))`` always returns ``[(n, 1)]``.
This function takes a list of such contiguous super-dimensions and spli... | Explode superdims to fit a known shape.
The unflattening process might mistakenly generate too few too large dimensions.
For example, ``unflatten_superdims(np.arange(n))`` always returns ``[(n, 1)]``.
This function takes a list of such contiguous super-dimensions and splits them
into smaller dimensions such th... | explode_superdims | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def get_process_index_and_count(
tensor_sharding: jsharding.Sharding, dim: int, ndims: int) -> tuple[int, int]:
"""Get current process index and number of unique processes for given dimension.
This function facilitates mapping of process-level data to individual
devices. Each process can use its index to obt... | Get current process index and number of unique processes for given dimension.
This function facilitates mapping of process-level data to individual
devices. Each process can use its index to obtain the data corresponding
to that index. If process level data is sharded on multiple dimensions
this function can b... | get_process_index_and_count | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def local_to_global_shape(
sharding: jsharding.Sharding, local_shape: Shape) -> tuple[int | None, ...]:
"""Computes the global shape given the per process if possible.
The returned shape will have the size of the global tensor in that dimension
or None, if it is not computable. The latter can happen when sha... | Computes the global shape given the per process if possible.
The returned shape will have the size of the global tensor in that dimension
or None, if it is not computable. The latter can happen when sharding
is not uniform along that dimension, e.g. different hosts require
different shapes, or if different pro... | local_to_global_shape | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def num_addressable_indices(
tensor_sharding: jsharding.Sharding, dim: int, global_shape: Shape) -> int:
"""Returns the number of indices for given dimension this host has access to.
Each host can have multiple number of devices that are spanning
possibly discontiguous slices of data. This function computes ... | Returns the number of indices for given dimension this host has access to.
Each host can have multiple number of devices that are spanning
possibly discontiguous slices of data. This function computes the
total number of unique indices for dimension `dim` that any of its
addressable devices hold.
In most ca... | num_addressable_indices | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def make_mesh(axis_shapes: Sequence[int], axis_names: Sequence[str],
*, devices: Sequence[xc.Device] | None = None,
axis_types: tuple[mesh_lib.AxisType, ...] | None = None
) -> mesh_lib.Mesh:
"""Creates an efficient mesh with the shape and axis names specified.
This functi... | Creates an efficient mesh with the shape and axis names specified.
This function attempts to automatically compute a good mapping from a set of
logical axes to a physical mesh. For example, on a TPU v3 with 8 devices:
>>> mesh = jax.make_mesh((8,), ('x')) # doctest: +SKIP
>>> [d.id for d in mesh.devices.flat... | make_mesh | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def set_mesh(mesh: mesh_lib.Mesh | None) -> mesh_lib.Mesh | None:
"""Sets the given concrete mesh globally and returns the previous concrete
mesh."""
if mesh is not None and not isinstance(mesh, mesh_lib.Mesh):
raise ValueError(
f"Expected mesh of type `jax.sharding.Mesh`. Got {type(mesh)}")
asse... | Sets the given concrete mesh globally and returns the previous concrete
mesh. | set_mesh | python | jax-ml/jax | jax/_src/sharding_impls.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_impls.py | Apache-2.0 |
def _sharding_spec_indices(self, shape: tuple[int, ...]) -> np.ndarray:
"""Returns NumPy-style indices corresponding to a sharding spec.
Args:
shape: The shape of the logical array being sharded.
Returns:
An ndarray with the same shape as the logical mesh (as derived form
`mesh_mapping`). Each entry... | Returns NumPy-style indices corresponding to a sharding spec.
Args:
shape: The shape of the logical array being sharded.
Returns:
An ndarray with the same shape as the logical mesh (as derived form
`mesh_mapping`). Each entry is a NumPy-style index selecting the subset of
the data array to be plac... | _sharding_spec_indices | python | jax-ml/jax | jax/_src/sharding_specs.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_specs.py | Apache-2.0 |
def spec_to_indices(shape: Sequence[int],
spec: ShardingSpec) -> tuple[Index, ...]:
"""Returns numpy-style indices corresponding to a sharding spec.
Each index describes a shard of the array. The order of the indices is the
same as the device_buffers of a Array sharded using PmapSharding (i.e... | Returns numpy-style indices corresponding to a sharding spec.
Each index describes a shard of the array. The order of the indices is the
same as the device_buffers of a Array sharded using PmapSharding (i.e. the
data is laid out row-major).
Args:
shape: The shape of the logical array being sharded.
sp... | spec_to_indices | python | jax-ml/jax | jax/_src/sharding_specs.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_specs.py | Apache-2.0 |
def pmap_sharding_spec(nrep, axis_size, sharded_shape: Sequence[int],
map_axis: int | None) -> ShardingSpec:
"""Sharding spec for arguments or results of a pmap.
Args:
nrep: number of local XLA replicas (product of local axis sizes)
axis_size: local axis size for outer pmap
sharde... | Sharding spec for arguments or results of a pmap.
Args:
nrep: number of local XLA replicas (product of local axis sizes)
axis_size: local axis size for outer pmap
sharded_aval: the aval of the value inside the outer pmap, an instance of
a ShapedArray.
map_axis: the axis along which the value is ... | pmap_sharding_spec | python | jax-ml/jax | jax/_src/sharding_specs.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sharding_specs.py | Apache-2.0 |
def shard_map(f=None, /, *, out_specs: Specs, axis_names: Set[AxisName] = set(),
in_specs: Specs | None = None,
mesh: Mesh | AbstractMesh | None = None, check_vma: bool = True):
"""Map a function over shards of data using a mesh of devices.
See the docs at https://docs.jax.dev/en/latest... | Map a function over shards of data using a mesh of devices.
See the docs at https://docs.jax.dev/en/latest/notebooks/shard_map.html.
Args:
f: callable to be mapped. Each application of ``f``, or "instance" of ``f``,
takes as input a shard of the mapped-over arguments and produces a shard
of the ou... | shard_map | python | jax-ml/jax | jax/_src/shard_map.py | https://github.com/jax-ml/jax/blob/master/jax/_src/shard_map.py | Apache-2.0 |
def decode_vlq(enc: Iterable[int]) -> int:
"""Decode a Base-64-VLQ into an integer."""
enc_iter = iter(enc)
d = VLQ_DECODE_TABLE[next(enc_iter)]
sign = bool(d & VLQ_SIGN_MASK)
value = (d & VLQ_VALUE_MASK) >> 1
# Compensate for first quantum containing sign as LSB:
shift = -1
while d & VLQ_MORE_MASK:
... | Decode a Base-64-VLQ into an integer. | decode_vlq | python | jax-ml/jax | jax/_src/sourcemap.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sourcemap.py | Apache-2.0 |
def encode_vlq(value: int) -> bytes:
"""Encode an integer into a Base-64-VLQ."""
# Move sign to LSB
value = ((-value) << 1 | 1) if value < 0 else value << 1
buf = []
while True:
d = value & VLQ_VALUE_MASK
value >>= VLQ_VALUE_BITWIDTH
more = value > 0
if more:
d |= VLQ_MORE_MASK
buf.... | Encode an integer into a Base-64-VLQ. | encode_vlq | python | jax-ml/jax | jax/_src/sourcemap.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sourcemap.py | Apache-2.0 |
def decode_segment(enc: Iterable[int]) -> Segment:
"""Decode a sequence of VLQs into a segment."""
enc_iter = iter(enc)
col = decode_vlq(enc_iter)
try:
source = decode_vlq(enc_iter)
except StopIteration:
# Stopping here is fine (1-segment).
return (col,)
source_line = decode_vlq(enc_iter)
sour... | Decode a sequence of VLQs into a segment. | decode_segment | python | jax-ml/jax | jax/_src/sourcemap.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sourcemap.py | Apache-2.0 |
def serialize_mappings(mappings: Mappings) -> str:
"""Encode mappings into a string of TC39 mapping data."""
enc = b";".join(
b",".join(encode_segment(seg) for seg in segs) for segs in mappings
)
return enc.decode("ascii") | Encode mappings into a string of TC39 mapping data. | serialize_mappings | python | jax-ml/jax | jax/_src/sourcemap.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sourcemap.py | Apache-2.0 |
def new_segment(self, *seg):
"""Start a new source mapping segment in the current group.
Args:
*seg: A segment as in TC39, but all indices are absolute. See
https://tc39.es/source-map/#mappings-structure for details.
Raises:
RuntimeError: If no current group exists.
"""
assert ... | Start a new source mapping segment in the current group.
Args:
*seg: A segment as in TC39, but all indices are absolute. See
https://tc39.es/source-map/#mappings-structure for details.
Raises:
RuntimeError: If no current group exists.
| new_segment | python | jax-ml/jax | jax/_src/sourcemap.py | https://github.com/jax-ml/jax/blob/master/jax/_src/sourcemap.py | Apache-2.0 |
def is_user_filename(filename: str) -> bool:
"""Heuristic that guesses the identity of the user's code in a stack trace."""
return (_include_path_regex().search(filename) is not None
or _exclude_path_regex().search(filename) is None) | Heuristic that guesses the identity of the user's code in a stack trace. | is_user_filename | python | jax-ml/jax | jax/_src/source_info_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/source_info_util.py | Apache-2.0 |
def user_frames(source_info: SourceInfo) -> Iterator[Frame]:
"""Iterator over the user's frames, filtering jax-internal frames."""
# Guess the user's frame is the innermost frame not in the jax source tree or
# Python stdlib. We don't use traceback_util.path_starts_with because that
# incurs filesystem access, ... | Iterator over the user's frames, filtering jax-internal frames. | user_frames | python | jax-ml/jax | jax/_src/source_info_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/source_info_util.py | Apache-2.0 |
def input_shardings(self) -> Sequence[sharding_lib.Sharding]:
"""Flat sequence of input shardings.
May raise ``NotImplementedError`` if unavailable, e.g. based on backend,
compiler, or runtime.
"""
raise NotImplementedError(
"compiled executable carries no input sharding information") | Flat sequence of input shardings.
May raise ``NotImplementedError`` if unavailable, e.g. based on backend,
compiler, or runtime.
| input_shardings | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def output_shardings(self) -> Sequence[sharding_lib.Sharding]:
"""Flat sequence of output shardings.
May raise ``NotImplementedError`` if unavailable, e.g. based on backend,
compiler, or runtime.
"""
raise NotImplementedError(
"compiled executable carries no output sharding information") | Flat sequence of output shardings.
May raise ``NotImplementedError`` if unavailable, e.g. based on backend,
compiler, or runtime.
| output_shardings | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def as_text(self) -> str:
"""A human-readable text representation of this executable.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization. It is relayed directly to external callers.
May raise ``NotImplementedError`` if unavailable, e.g. based on back... | A human-readable text representation of this executable.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization. It is relayed directly to external callers.
May raise ``NotImplementedError`` if unavailable, e.g. based on backend,
compiler, or runtime.
... | as_text | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def cost_analysis(self) -> Any:
"""A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
stru... | A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
structure can be arbitrary: it need not be ... | cost_analysis | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def memory_analysis(self) -> Any:
"""A summary of estimated memory requirements.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
... | A summary of estimated memory requirements.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
structure can be arbitrary: it need no... | memory_analysis | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def hlo(self) -> xc.XlaComputation:
"""Return an HLO representation of this computation."""
hlo = self.stablehlo()
m: str | bytes
m = mlir.module_to_bytecode(hlo)
return _jax.mlir.mlir_module_to_xla_computation(
m, use_tuple_args=self.compile_args["tuple_args"]) | Return an HLO representation of this computation. | hlo | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def stablehlo(self) -> ir.Module:
"""Return a StableHLO representation of this computation."""
raise NotImplementedError(
f"cost analysis unsupported on XLA computation: {type(self)}") | Return a StableHLO representation of this computation. | stablehlo | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def as_text(self, dialect: str | None = None,
*,
debug_info: bool = False) -> str:
"""A human-readable text representation of this lowering.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization. It is relayed directly to external... | A human-readable text representation of this lowering.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization. It is relayed directly to external callers.
| as_text | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def compiler_ir(self, dialect: str | None = None) -> Any:
"""An arbitrary object representation of this lowering.
Intended for debugging purposes. This need not be a valid nor reliable
serialization. It is relayed directly to external callers, with no
guarantee on type, structure, or consistency across... | An arbitrary object representation of this lowering.
Intended for debugging purposes. This need not be a valid nor reliable
serialization. It is relayed directly to external callers, with no
guarantee on type, structure, or consistency across invocations.
May raise ``NotImplementedError`` if unavailab... | compiler_ir | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def cost_analysis(self) -> Any:
"""A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
stru... | A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
structure can be arbitrary: it need not be ... | cost_analysis | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def donate_argnums(self):
"""Flat tuple of donated argument indices."""
return tuple(
i for i, x in enumerate(tree_util.tree_leaves(self.args_info))
if x.donated) | Flat tuple of donated argument indices. | donate_argnums | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def as_text(self) -> str | None:
"""A human-readable text representation of this executable.
Intended for visualization and debugging purposes. This is not a valid nor
reliable serialization.
Returns ``None`` if unavailable, e.g. based on backend, compiler, or
runtime.
"""
try:
retur... | A human-readable text representation of this executable.
Intended for visualization and debugging purposes. This is not a valid nor
reliable serialization.
Returns ``None`` if unavailable, e.g. based on backend, compiler, or
runtime.
| as_text | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def cost_analysis(self) -> Any | None:
"""A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
... | A summary of execution cost estimates.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
structure can be arbitrary: it may be incon... | cost_analysis | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def memory_analysis(self) -> Any | None:
"""A summary of estimated memory requirements.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However... | A summary of estimated memory requirements.
Intended for visualization and debugging purposes. The object output by
this is some simple data structure that can easily be printed or serialized
(e.g. nested dicts, lists, and tuples with numeric leaves). However, its
structure can be arbitrary: it may be ... | memory_analysis | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def from_flat_info(cls,
lowering: Lowering,
in_tree: tree_util.PyTreeDef,
in_avals,
donate_argnums: tuple[int, ...],
out_tree: tree_util.PyTreeDef,
no_kwargs: bool = False):
"""Initialize fr... | Initialize from flat info (``in_avals`` etc.) and an input PyTreeDef.
Args:
in_tree: The ``PyTreeDef`` of (args, kwargs).
out_tree: The ``PyTreeDef`` of the outputs.
no_kwargs: If ``True`` the transformation, and the
``Compiled`` returned from this object will not support keyword
... | from_flat_info | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def as_text(self, dialect: str | None = None, *,
debug_info: bool = False) -> str:
"""A human-readable text representation of this lowering.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization.
Use `jax.export` if you want reliable and po... | A human-readable text representation of this lowering.
Intended for visualization and debugging purposes. This need not be a valid
nor reliable serialization.
Use `jax.export` if you want reliable and portable serialization.
Args:
dialect: Optional string specifying a lowering dialect (e.g. "sta... | as_text | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def compiler_ir(self, dialect: str | None = None) -> Any | None:
"""An arbitrary object representation of this lowering.
Intended for debugging purposes. This is not a valid nor reliable
serialization. The output has no guarantee of consistency across
invocations.
Use `jax.export` if you want relia... | An arbitrary object representation of this lowering.
Intended for debugging purposes. This is not a valid nor reliable
serialization. The output has no guarantee of consistency across
invocations.
Use `jax.export` if you want reliable and portable serialization.
Returns ``None`` if unavailable, e.... | compiler_ir | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def lower(self, *, lowering_platforms: tuple[str, ...] | None = None,
_private_parameters: mlir.LoweringParameters | None = None):
"""Lower to compiler input, returning a ``Lowered`` instance."""
if _private_parameters is None:
_private_parameters = mlir.LoweringParameters()
new_callable =... | Lower to compiler input, returning a ``Lowered`` instance. | lower | python | jax-ml/jax | jax/_src/stages.py | https://github.com/jax-ml/jax/blob/master/jax/_src/stages.py | Apache-2.0 |
def thread_unsafe_test():
"""Decorator for tests that are not thread-safe.
Note: this decorator (naturally) only applies to what it wraps, not to, say,
code in separate setUp() or tearDown() methods.
"""
if TEST_NUM_THREADS.value <= 0:
yield
return
_test_rwlock.assert_reader_held()
_test_rwlock.... | Decorator for tests that are not thread-safe.
Note: this decorator (naturally) only applies to what it wraps, not to, say,
code in separate setUp() or tearDown() methods.
| thread_unsafe_test | python | jax-ml/jax | jax/_src/test_loader.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_loader.py | Apache-2.0 |
def thread_unsafe_test_class():
"""Decorator that marks a TestCase class as thread-hostile."""
def f(klass):
assert issubclass(klass, unittest.TestCase), type(klass)
klass.thread_hostile = True
return klass
return f | Decorator that marks a TestCase class as thread-hostile. | thread_unsafe_test_class | python | jax-ml/jax | jax/_src/test_loader.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_loader.py | Apache-2.0 |
def run_test(test):
"""Recursively runs tests in a test suite or test case."""
if isinstance(test, unittest.TestSuite):
for subtest in test:
run_test(subtest)
else:
test_result = ThreadSafeTestResult(lock, result)
futures.append(executor.submit(_run_one_test, test, te... | Recursively runs tests in a test suite or test case. | run_test | python | jax-ml/jax | jax/_src/test_loader.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_loader.py | Apache-2.0 |
def to_default_dtype(arr: ArrayLike) -> np.ndarray:
"""Convert a value to an array with JAX's default dtype.
This is generally used for type conversions of values returned by numpy functions,
to make their dtypes take into account the state of the ``jax_enable_x64`` and
``jax_default_dtype_bits`` flags.
"""
... | Convert a value to an array with JAX's default dtype.
This is generally used for type conversions of values returned by numpy functions,
to make their dtypes take into account the state of the ``jax_enable_x64`` and
``jax_default_dtype_bits`` flags.
| to_default_dtype | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def with_jax_dtype_defaults(func: Callable[..., Any], use_defaults: bool = True):
"""Return a version of a function with outputs that match JAX's default dtypes.
This is generally used to wrap numpy functions within tests, in order to make
their default output dtypes match those of corresponding JAX functions, t... | Return a version of a function with outputs that match JAX's default dtypes.
This is generally used to wrap numpy functions within tests, in order to make
their default output dtypes match those of corresponding JAX functions, taking
into account the state of the ``jax_enable_x64`` and ``jax_default_dtype_bits``... | with_jax_dtype_defaults | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def _capture_output(fp: TextIO) -> Generator[Callable[[], str], None, None]:
"""Context manager to capture all output written to a given file object.
Unlike ``contextlib.redirect_stdout``, this context manager works for
any file object and also for both pure Python and native code.
Example::
with capture... | Context manager to capture all output written to a given file object.
Unlike ``contextlib.redirect_stdout``, this context manager works for
any file object and also for both pure Python and native code.
Example::
with capture_output(sys.stdout) as get_output:
print(42)
print("Captured": get_outpu... | _capture_output | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def count_events(event):
"Returns a context-manager that yields a function that counts a test event."
@contextmanager
def count_event():
before = thread_local_state.counts.get(event, 0)
yield lambda: thread_local_state.counts.get(event, 0) - before
return count_event | Returns a context-manager that yields a function that counts a test event. | count_events | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def collect_lowered_jaxprs() -> Generator[Sequence[tuple[core.ClosedJaxpr,
mlir.ir.Module]]]:
"""
Collects all the pairs of (jaxpr, mlir_module) that are lowered.
"""
assert thread_local_state.collect_lowered_jaxprs is None
collection: list[tuple[core.C... |
Collects all the pairs of (jaxpr, mlir_module) that are lowered.
| collect_lowered_jaxprs | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def _get_device_tags():
"""returns a set of tags defined for the device under test"""
if is_device_rocm():
device_tags = {device_under_test(), "rocm"}
elif is_device_cuda():
device_tags = {device_under_test(), "cuda"}
elif device_under_test() == "METAL":
device_tags = {device_under_test(), "gpu"}
... | returns a set of tags defined for the device under test | _get_device_tags | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def device_supports_buffer_donation():
"""A decorator for test methods to run the test only on devices that support
buffer donation."""
return _device_filter(
lambda: test_device_matches(mlir._platforms_with_donation)
) | A decorator for test methods to run the test only on devices that support
buffer donation. | device_supports_buffer_donation | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def request_cpu_devices(nr_devices: int):
"""Requests at least `nr_devices` CPU devices.
request_cpu_devices should be called at the top-level of a test module before
main() runs.
It is not guaranteed that the number of CPU devices will be exactly
`nr_devices`: it may be more or less, depending on how exact... | Requests at least `nr_devices` CPU devices.
request_cpu_devices should be called at the top-level of a test module before
main() runs.
It is not guaranteed that the number of CPU devices will be exactly
`nr_devices`: it may be more or less, depending on how exactly the test is
invoked. Test cases that requi... | request_cpu_devices | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def pytest_mark_if_available(marker: str):
"""A decorator for test classes or methods to pytest.mark if installed."""
def wrap(func_or_class):
try:
import pytest
except ImportError:
return func_or_class
return getattr(pytest.mark, marker)(func_or_class)
return wrap | A decorator for test classes or methods to pytest.mark if installed. | pytest_mark_if_available | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def skip_under_pytest(reason: str):
"""A decorator for test methods to skip the test when run under pytest."""
reason = "Running under pytest: " + reason
def skip(test_method):
return unittest.skipIf(is_running_under_pytest(), reason)(test_method)
return skip | A decorator for test methods to skip the test when run under pytest. | skip_under_pytest | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def _rand_dtype(rand, shape, dtype, scale=1., post=lambda x: x):
"""Produce random values given shape, dtype, scale, and post-processor.
Args:
rand: a function for producing random values of a given shape, e.g. a
bound version of either np.RandomState.randn or np.RandomState.rand.
shape: a shape valu... | Produce random values given shape, dtype, scale, and post-processor.
Args:
rand: a function for producing random values of a given shape, e.g. a
bound version of either np.RandomState.randn or np.RandomState.rand.
shape: a shape value as a tuple of positive integers.
dtype: a numpy dtype.
scale... | _rand_dtype | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def rand_fullrange(rng, standardize_nans=False):
"""Random numbers that span the full range of available bits."""
def gen(shape, dtype, post=lambda x: x):
dtype = np.dtype(dtype)
size = dtype.itemsize * math.prod(_dims_of_shape(shape))
vals = rng.randint(0, np.iinfo(np.uint8).max, size=size, dtype=np.ui... | Random numbers that span the full range of available bits. | rand_fullrange | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def rand_indices_unique_along_axis(rng):
"""Sample an array of given shape containing indices up to dim (exclusive),
such that the indices are unique along the given axis.
Optionally, convert some of the resulting indices to negative indices."""
def fn(dim, shape, axis, allow_negative=True):
batch_size = ma... | Sample an array of given shape containing indices up to dim (exclusive),
such that the indices are unique along the given axis.
Optionally, convert some of the resulting indices to negative indices. | rand_indices_unique_along_axis | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def with_config(**kwds):
"""Test case decorator for subclasses of JaxTestCase"""
def decorator(cls):
assert inspect.isclass(cls) and issubclass(cls, JaxTestCase), "@with_config can only wrap JaxTestCase class definitions."
cls._default_thread_local_config = {}
for b in cls.__bases__:
cls._default_... | Test case decorator for subclasses of JaxTestCase | with_config | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def promote_like_jnp(fun, inexact=False):
"""Decorator that promotes the arguments of `fun` to `jnp.result_type(*args)`.
jnp and np have different type promotion semantics; this decorator allows
tests make an np reference implementation act more like a jnp
implementation.
"""
_promote = promote_dtypes_inex... | Decorator that promotes the arguments of `fun` to `jnp.result_type(*args)`.
jnp and np have different type promotion semantics; this decorator allows
tests make an np reference implementation act more like a jnp
implementation.
| promote_like_jnp | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def assertDeprecationWarnsOrRaises(self, deprecation_id: str, message: str):
"""Assert warning or error, depending on deprecation state.
For use with functions that call :func:`jax._src.deprecations.warn`.
"""
if deprecations.is_accelerated(deprecation_id):
return self.assertRaisesRegex(ValueErro... | Assert warning or error, depending on deprecation state.
For use with functions that call :func:`jax._src.deprecations.warn`.
| assertDeprecationWarnsOrRaises | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def assertArraysAllClose(self, actual, desired, *, check_dtypes=True, atol=None,
rtol=None, err_msg=''):
"""Assert that actual and desired are close (up to numerical tolerances)."""
self.assertEqual(actual.shape, desired.shape)
atol = max(tolerance(_dtype(actual), atol), tolerance... | Assert that actual and desired are close (up to numerical tolerances). | assertArraysAllClose | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def assertAllClose(self, actual, desired, *, check_dtypes=True, atol=None, rtol=None,
canonicalize_dtypes=True, err_msg=''):
"""Assert that actual and desired, either arrays or nested tuples/lists, are close."""
if isinstance(actual, dict):
self.assertIsInstance(desired, dict)
s... | Assert that actual and desired, either arrays or nested tuples/lists, are close. | assertAllClose | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def assertMultiLineStrippedEqual(self, expected, what):
"""Asserts two strings are equal, after dedenting and stripping each line."""
expected = textwrap.dedent(expected)
what = textwrap.dedent(what)
ignore_space_re = re.compile(r'\s*\n\s*')
expected_clean = re.sub(ignore_space_re, '\n', expected.st... | Asserts two strings are equal, after dedenting and stripping each line. | assertMultiLineStrippedEqual | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def _CompileAndCheck(self, fun, args_maker, *, check_dtypes=True, tol=None,
rtol=None, atol=None, check_cache_misses=True):
"""Helper method for running JAX compilation and allclose assertions."""
args = args_maker()
def wrapped_fun(*args):
self.assertTrue(python_should_be_exec... | Helper method for running JAX compilation and allclose assertions. | _CompileAndCheck | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def strict_promotion_if_dtypes_match(dtypes):
"""
Context manager to enable strict promotion if all dtypes match,
and enable standard dtype promotion otherwise.
"""
if all(dtype == dtypes[0] for dtype in dtypes):
return jax.numpy_dtype_promotion('strict')
return jax.numpy_dtype_promotion('standard') |
Context manager to enable strict promotion if all dtypes match,
and enable standard dtype promotion otherwise.
| strict_promotion_if_dtypes_match | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def parameterized_filterable(*,
kwargs: Sequence[dict[str, Any]],
testcase_name: Callable[[dict[str, Any]], str] | None = None,
one_containing: str | None = None,
):
"""Decorator for named parameterized tests, with filtering support.
Works like ``parameterized.named_parameters``, except that it sanitiz... | Decorator for named parameterized tests, with filtering support.
Works like ``parameterized.named_parameters``, except that it sanitizes the test
names so that we can use ``pytest -k`` and ``python test.py -k`` test filtering.
This means, e.g., that many special characters are replaced with `_`.
It also suppor... | parameterized_filterable | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def register_event_duration_listener(callback):
"""Manages registering/unregistering an event duration listener callback."""
try:
monitoring.register_event_duration_secs_listener(callback)
yield
finally:
monitoring._unregister_event_duration_listener_by_callback(callback) | Manages registering/unregistering an event duration listener callback. | register_event_duration_listener | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def set_env(**kwargs):
"""Context manager to temporarily set/unset one or more environment variables.
Caution: setting environment variables is not thread-safe. If you use this
utility, you must annotate your test using, e.g., @thread_unsafe_test() or
@thread_unsafe_test_class().
Examples:
>>> import o... | Context manager to temporarily set/unset one or more environment variables.
Caution: setting environment variables is not thread-safe. If you use this
utility, you must annotate your test using, e.g., @thread_unsafe_test() or
@thread_unsafe_test_class().
Examples:
>>> import os
>>> os.environ['my_var... | set_env | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def numpy_vecdot(x, y, axis):
"""Implementation of numpy.vecdot for testing on numpy < 2.0.0"""
if numpy_version() >= (2, 0, 0):
raise ValueError("should be calling vecdot directly on numpy 2.0.0")
x = np.moveaxis(x, axis, -1)
y = np.moveaxis(y, axis, -1)
x, y = np.broadcast_arrays(x, y)
return np.matmu... | Implementation of numpy.vecdot for testing on numpy < 2.0.0 | numpy_vecdot | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def complex_plane_sample(dtype, size_re=10, size_im=None):
"""Return a 2-D array of complex numbers that covers the complex plane
with a grid of samples.
The size of the grid is (3 + 2 * size_im) x (3 + 2 * size_re)
that includes infinity points, extreme finite points, and the
specified number of... | Return a 2-D array of complex numbers that covers the complex plane
with a grid of samples.
The size of the grid is (3 + 2 * size_im) x (3 + 2 * size_re)
that includes infinity points, extreme finite points, and the
specified number of points from real and imaginary axis.
For example:
>... | complex_plane_sample | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def nptomp(self, x):
"""Convert numpy array/scalar to an array/instance of mpmath number type.
"""
if isinstance(x, np.ndarray):
return np.fromiter(map(self.nptomp, x.flatten()), dtype=object).reshape(x.shape)
elif isinstance(x, np.floating):
mpmath = self.mpmath
ctx = self.get_context... | Convert numpy array/scalar to an array/instance of mpmath number type.
| nptomp | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def mptonp(self, x):
"""Convert mpmath instance to numpy array/scalar type.
"""
if isinstance(x, np.ndarray) and x.dtype.kind == 'O':
x_flat = x.flatten()
item = x_flat[0]
ctx = item.context
fp_format = self.contexts_inv[ctx]
if isinstance(item, ctx.mpc):
dtype = getatt... | Convert mpmath instance to numpy array/scalar type.
| mptonp | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def normalize(self, exact, reference, value):
"""Normalize reference and value using precision defined by the
difference of exact and reference.
"""
def worker(ctx, s, e, r, v):
ss, sm, se, sbc = s._mpf_
es, em, ee, ebc = e._mpf_
rs, rm, re, rbc = r._mpf_
vs, vm, ve, vbc = v._mpf... | Normalize reference and value using precision defined by the
difference of exact and reference.
| normalize | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def setup_hypothesis(max_examples=30) -> None:
"""Sets up the hypothesis profiles.
Sets up the hypothesis testing profiles, and selects the one specified by
the ``JAX_HYPOTHESIS_PROFILE`` environment variable (or the
``--jax_hypothesis_profile`` configuration.
Args:
max_examples: the maximum number of h... | Sets up the hypothesis profiles.
Sets up the hypothesis testing profiles, and selects the one specified by
the ``JAX_HYPOTHESIS_PROFILE`` environment variable (or the
``--jax_hypothesis_profile`` configuration.
Args:
max_examples: the maximum number of hypothesis examples to try, when using
the defa... | setup_hypothesis | python | jax-ml/jax | jax/_src/test_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_util.py | Apache-2.0 |
def raise_on_warnings():
"Context manager that raises an exception if a warning is raised."
if warnings.showwarning is not _showwarning:
with warnings.catch_warnings():
warnings.simplefilter("error")
yield
return
def handler(message, category, filename, lineno, file=None, line=None):
rais... | Context manager that raises an exception if a warning is raised. | raise_on_warnings | python | jax-ml/jax | jax/_src/test_warning_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_warning_util.py | Apache-2.0 |
def record_warnings():
"Context manager that yields a list of warnings that are raised."
if warnings.showwarning is not _showwarning:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
yield w
return
log = []
def handler(message, category, filename, lineno, fil... | Context manager that yields a list of warnings that are raised. | record_warnings | python | jax-ml/jax | jax/_src/test_warning_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_warning_util.py | Apache-2.0 |
def ignore_warning(*, message: str | None = None, category: type = Warning):
"Context manager that ignores any matching warnings."
if warnings.showwarning is not _showwarning:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="" if message is None else message, category=ca... | Context manager that ignores any matching warnings. | ignore_warning | python | jax-ml/jax | jax/_src/test_warning_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/test_warning_util.py | Apache-2.0 |
def to_json(self) -> bytes:
"""Serializes the backend config into JSON."""
# We format the JSON ourselves, because json.dumps seems to be overly slow.
config = io.BytesIO()
config.write(b'{"custom_call_config": {"body": "')
config.write(base64.b64encode(self.lowered_module_asm))
config.write(b'"... | Serializes the backend config into JSON. | to_json | python | jax-ml/jax | jax/_src/tpu_custom_call.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tpu_custom_call.py | Apache-2.0 |
def _get_device_type(module: ir.Module) -> str | None:
"""Determines the device type based on the core_type annotations."""
sparsecore_func_found = False
tensorcore_func_found = False
def assign_device_type_based_on_core_type(op: ir.Operation) -> ir.WalkResult:
nonlocal sparsecore_func_found
nonlocal t... | Determines the device type based on the core_type annotations. | _get_device_type | python | jax-ml/jax | jax/_src/tpu_custom_call.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tpu_custom_call.py | Apache-2.0 |
def as_tpu_kernel(
module: ir.Module,
out_type: Any,
*,
cost_estimate: CostEstimate | None = None,
backend: str | xla_client.Client = "tpu",
kernel_name: str | None = None,
vmem_limit_bytes: int | None = None,
flags: dict[str, bool | int | float] | None = None,
allow_input_fusion: Se... | Turns an MLIR Mosaic kernel into a JAX-compatible function. | as_tpu_kernel | python | jax-ml/jax | jax/_src/tpu_custom_call.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tpu_custom_call.py | Apache-2.0 |
def _running_under_ipython() -> bool:
"""Returns true if we appear to be in an IPython session."""
try:
get_ipython() # type: ignore
return True
except NameError:
return False | Returns true if we appear to be in an IPython session. | _running_under_ipython | python | jax-ml/jax | jax/_src/traceback_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/traceback_util.py | Apache-2.0 |
def _ipython_supports_tracebackhide() -> bool:
"""Returns true if the IPython version supports __tracebackhide__."""
import IPython # pytype: disable=import-error
return IPython.version_info[:2] >= (7, 17) | Returns true if the IPython version supports __tracebackhide__. | _ipython_supports_tracebackhide | python | jax-ml/jax | jax/_src/traceback_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/traceback_util.py | Apache-2.0 |
def flatten(tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> tuple[list[tree_util.Leaf], tree_util.PyTreeDef]:
"""Flattens a pytree.
The flattening order (i.e. the order of elements in the output list)
is deterministic, corresponding to a left-to-right depth-first tree
trave... | Flattens a pytree.
The flattening order (i.e. the order of elements in the output list)
is deterministic, corresponding to a left-to-right depth-first tree
traversal.
Args:
tree: a pytree to flatten.
is_leaf: an optionally specified function that will be called at each
flattening step. It should... | flatten | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def leaves(tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> list[tree_util.Leaf]:
"""Gets the leaves of a pytree.
Args:
tree: the pytree for which to get the leaves
is_leaf : an optionally specified function that will be called at each
flattening step. It should retu... | Gets the leaves of a pytree.
Args:
tree: the pytree for which to get the leaves
is_leaf : an optionally specified function that will be called at each
flattening step. It should return a boolean, which indicates whether the
flattening should traverse the current object, or if it should be stopped... | leaves | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def map(f: Callable[..., Any],
tree: Any,
*rest: Any,
is_leaf: Callable[[Any], bool] | None = None) -> Any:
"""Maps a multi-input function over pytree args to produce a new pytree.
Args:
f: function that takes ``1 + len(rest)`` arguments, to be applied at the
corresponding leaves ... | Maps a multi-input function over pytree args to produce a new pytree.
Args:
f: function that takes ``1 + len(rest)`` arguments, to be applied at the
corresponding leaves of the pytrees.
tree: a pytree to be mapped over, with each leaf providing the first
positional argument to ``f``.
rest: a ... | map | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def reduce(function: Callable[[T, Any], T],
tree: Any,
initializer: Any = tree_util.no_initializer,
is_leaf: Callable[[Any], bool] | None = None) -> T:
"""Call reduce() over the leaves of a tree.
Args:
function: the reduction function
tree: the pytree to reduce over
ini... | Call reduce() over the leaves of a tree.
Args:
function: the reduction function
tree: the pytree to reduce over
initializer: the optional initial value
is_leaf : an optionally specified function that will be called at each
flattening step. It should return a boolean, which indicates whether the... | reduce | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def structure(tree: Any,
is_leaf: None | (Callable[[Any], bool]) = None) -> tree_util.PyTreeDef:
"""Gets the treedef for a pytree.
Args:
tree: the pytree for which to get the leaves
is_leaf : an optionally specified function that will be called at each
flattening step. It should return ... | Gets the treedef for a pytree.
Args:
tree: the pytree for which to get the leaves
is_leaf : an optionally specified function that will be called at each
flattening step. It should return a boolean, which indicates whether the
flattening should traverse the current object, or if it should be stopp... | structure | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def transpose(outer_treedef: tree_util.PyTreeDef,
inner_treedef: tree_util.PyTreeDef | None,
pytree_to_transpose: Any) -> Any:
"""Transform a tree having tree structure (outer, inner) into one having structure (inner, outer).
Args:
outer_treedef: PyTreeDef representing the outer tre... | Transform a tree having tree structure (outer, inner) into one having structure (inner, outer).
Args:
outer_treedef: PyTreeDef representing the outer tree.
inner_treedef: PyTreeDef representing the inner tree.
If None, then it will be inferred from outer_treedef and the structure of
pytree_to_tra... | transpose | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def unflatten(treedef: tree_util.PyTreeDef,
leaves: Iterable[tree_util.Leaf]) -> Any:
"""Reconstructs a pytree from the treedef and the leaves.
The inverse of :func:`tree_flatten`.
Args:
treedef: the treedef to reconstruct
leaves: the iterable of leaves to use for reconstruction. The itera... | Reconstructs a pytree from the treedef and the leaves.
The inverse of :func:`tree_flatten`.
Args:
treedef: the treedef to reconstruct
leaves: the iterable of leaves to use for reconstruction. The iterable must
match the leaves of the treedef.
Returns:
The reconstructed pytree, containing the ... | unflatten | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def flatten_with_path(
tree: Any, is_leaf: Callable[..., bool] | None = None,
is_leaf_takes_path: bool = False,
) -> tuple[list[tuple[tree_util.KeyPath, Any]], tree_util.PyTreeDef]:
"""Flattens a pytree like ``tree_flatten``, but also returns each leaf's key path.
Args:
tree: a pytree to flatten. If it... | Flattens a pytree like ``tree_flatten``, but also returns each leaf's key path.
Args:
tree: a pytree to flatten. If it contains a custom type, it is recommended
to be registered with ``register_pytree_with_keys``.
Returns:
A pair which the first element is a list of key-leaf pairs, each of
which... | flatten_with_path | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def leaves_with_path(
tree: Any, is_leaf: Callable[..., bool] | None = None,
is_leaf_takes_path: bool = False,
) -> list[tuple[tree_util.KeyPath, Any]]:
"""Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
Args:
tree: a pytree. If it contains a custom type, it is recomm... | Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path.
Args:
tree: a pytree. If it contains a custom type, it is recommended to be
registered with ``register_pytree_with_keys``.
Returns:
A list of key-leaf pairs, each of which contains a leaf and its key path.
Examples... | leaves_with_path | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def map_with_path(
f: Callable[..., Any],
tree: Any,
*rest: Any,
is_leaf: Callable[..., bool] | None = None,
is_leaf_takes_path: bool = False,
) -> Any:
"""Maps a multi-input function over pytree key path and args to produce a new pytree.
This is a more powerful alternative of ``tree_map`` that... | Maps a multi-input function over pytree key path and args to produce a new pytree.
This is a more powerful alternative of ``tree_map`` that can take the key path
of each leaf as input argument as well.
Args:
f: function that takes ``2 + len(rest)`` arguments, aka. the key path and
each corresponding l... | map_with_path | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def broadcast(prefix_tree: Any, full_tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> list[Any]:
"""Broadcasts a tree prefix into the full structure of a given tree.
Args:
prefix_tree: a pytree that is a tree prefix of full_tree.
full_tree: a pytree with the st... | Broadcasts a tree prefix into the full structure of a given tree.
Args:
prefix_tree: a pytree that is a tree prefix of full_tree.
full_tree: a pytree with the structure to broadcast the prefix leaves into.
is_leaf: an optionally specified function that will be called at each
flattening st... | broadcast | python | jax-ml/jax | jax/_src/tree.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree.py | Apache-2.0 |
def tree_flatten(tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> tuple[list[Leaf], PyTreeDef]:
"""Alias of :func:`jax.tree.flatten`."""
return default_registry.flatten(tree, is_leaf) | Alias of :func:`jax.tree.flatten`. | tree_flatten | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_leaves(tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> list[Leaf]:
"""Alias of :func:`jax.tree.leaves`."""
return default_registry.flatten(tree, is_leaf)[0] | Alias of :func:`jax.tree.leaves`. | tree_leaves | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_structure(tree: Any,
is_leaf: None | (Callable[[Any],
bool]) = None) -> PyTreeDef:
"""Alias of :func:`jax.tree.structure`."""
return default_registry.flatten(tree, is_leaf)[1] | Alias of :func:`jax.tree.structure`. | tree_structure | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def all_leaves(iterable: Iterable[Any],
is_leaf: Callable[[Any], bool] | None = None) -> bool:
"""Tests whether all elements in the given iterable are all leaves.
This function is useful in advanced cases, for example if a library allows
arbitrary map operations on a flat iterable of leaves it may... | Tests whether all elements in the given iterable are all leaves.
This function is useful in advanced cases, for example if a library allows
arbitrary map operations on a flat iterable of leaves it may want to check
if the result is still a flat iterable of leaves.
Args:
iterable: Iterable of leaves.
Re... | all_leaves | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def register_pytree_node(
nodetype: type[T],
flatten_func: Callable[[T], tuple[_Children, _AuxData]],
unflatten_func: Callable[[_AuxData, _Children], T],
flatten_with_keys_func: (
Callable[[T], tuple[KeyLeafPairs, _AuxData]] | None
) = None,
) -> None:
"""Extends the set of types that are ... | Extends the set of types that are considered internal nodes in pytrees.
See :ref:`example usage <pytrees>`.
Args:
nodetype: a Python type to register as a pytree.
flatten_func: a function to be used during flattening, taking a value of
type ``nodetype`` and returning a pair, with (1) an iterable for... | register_pytree_node | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_map(f: Callable[..., Any],
tree: Any,
*rest: Any,
is_leaf: Callable[[Any], bool] | None = None) -> Any:
"""Alias of :func:`jax.tree.map`."""
leaves, treedef = tree_flatten(tree, is_leaf)
all_leaves = [leaves] + [treedef.flatten_up_to(r) for r in rest]
return treed... | Alias of :func:`jax.tree.map`. | tree_map | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_transpose(outer_treedef: PyTreeDef, inner_treedef: PyTreeDef | None,
pytree_to_transpose: Any) -> Any:
"""Alias of :func:`jax.tree.transpose`."""
flat, treedef = tree_flatten(pytree_to_transpose)
if inner_treedef is None:
inner_treedef = tree_structure(outer_treedef.flatten_up_to(p... | Alias of :func:`jax.tree.transpose`. | tree_transpose | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def _replace_nones(sentinel, tree):
"""Replaces ``None`` in ``tree`` with ``sentinel``."""
leaves, treedef = none_leaf_registry.flatten(tree)
leaves = map(lambda x: sentinel if x is None else x, leaves)
return treedef.unflatten(leaves) | Replaces ``None`` in ``tree`` with ``sentinel``. | _replace_nones | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_reduce(function: Callable[[T, Any], T],
tree: Any,
initializer: Any = no_initializer,
is_leaf: Callable[[Any], bool] | None = None) -> T:
"""Alias of :func:`jax.tree.reduce`."""
if initializer is no_initializer:
return functools.reduce(function, tree_leav... | Alias of :func:`jax.tree.reduce`. | tree_reduce | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
def tree_broadcast(prefix_tree: Any, full_tree: Any,
is_leaf: Callable[[Any], bool] | None = None
) -> list[Any]:
"""Alias of :func:`jax.tree.broadcast`."""
broadcast_leaves = broadcast_prefix(prefix_tree, full_tree, is_leaf=is_leaf)
return tree_structure(full_tree).unflatten(... | Alias of :func:`jax.tree.broadcast`. | tree_broadcast | python | jax-ml/jax | jax/_src/tree_util.py | https://github.com/jax-ml/jax/blob/master/jax/_src/tree_util.py | Apache-2.0 |
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