1989 lines
84 KiB
Python
1989 lines
84 KiB
Python
# Copyright 2021 The JAX Authors.
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# https://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
|
||
import dataclasses
|
||
import inspect
|
||
import logging
|
||
import weakref
|
||
import numpy as np
|
||
from typing import (Callable, Sequence, Tuple, Union, cast, List, Optional,
|
||
Iterable, NamedTuple, Any)
|
||
import itertools as it
|
||
from functools import partial, lru_cache
|
||
import threading
|
||
import warnings
|
||
|
||
from jax._src import core
|
||
from jax._src import stages
|
||
from jax._src import dispatch
|
||
from jax._src import mesh as mesh_lib
|
||
from jax._src import linear_util as lu
|
||
from jax._src import op_shardings
|
||
from jax._src import sharding_impls
|
||
from jax._src import source_info_util
|
||
from jax._src import traceback_util
|
||
from jax._src import api
|
||
from jax._src import xla_bridge as xb
|
||
from jax._src.api_util import (
|
||
argnums_partial_except, flatten_axes, flatten_fun, flatten_fun_nokwargs,
|
||
donation_vector, shaped_abstractify, check_callable, resolve_argnums,
|
||
argnames_partial_except, debug_info, result_paths, jaxpr_debug_info)
|
||
from jax._src.errors import JAXTypeError
|
||
from jax._src.interpreters import partial_eval as pe
|
||
from jax._src.partition_spec import PartitionSpec
|
||
from jax._src.interpreters import xla
|
||
|
||
from jax._src.config import config
|
||
from jax._src.interpreters import ad
|
||
from jax._src.interpreters import batching
|
||
from jax._src.interpreters import mlir
|
||
from jax._src.interpreters import pxla
|
||
from jax._src.lib.mlir import ir
|
||
from jax._src.lib.mlir.dialects import func as func_dialect
|
||
from jax._src.lib import xla_client as xc
|
||
from jax._src.sharding_impls import (
|
||
NamedSharding, XLACompatibleSharding, GSPMDSharding,
|
||
XLADeviceAssignment, SingleDeviceSharding, PmapSharding,
|
||
AUTO, UNSPECIFIED, UnspecifiedValue,
|
||
ParsedPartitionSpec, SpecSync, get_single_pspec, is_auto, is_unspecified,
|
||
is_unspecified_or_auto, prepare_axis_resources, parse_flatten_op_sharding)
|
||
from jax._src.traceback_util import api_boundary
|
||
from jax._src.tree_util import (
|
||
tree_map, tree_flatten, tree_unflatten, treedef_is_leaf, tree_structure,
|
||
treedef_tuple, broadcast_prefix, all_leaves,
|
||
prefix_errors, generate_key_paths)
|
||
from jax._src.util import (
|
||
HashableFunction, safe_map, safe_zip, wraps,
|
||
distributed_debug_log, split_list, weakref_lru_cache,
|
||
merge_lists, flatten, unflatten)
|
||
|
||
map, unsafe_map = safe_map, map
|
||
zip, unsafe_zip = safe_zip, zip
|
||
|
||
traceback_util.register_exclusion(__file__)
|
||
|
||
PjitSharding = Union[GSPMDSharding, UnspecifiedValue, AUTO]
|
||
PjitShardingMinusUnspecified = Union[GSPMDSharding, AUTO]
|
||
MeshSharding = Union[NamedSharding, UnspecifiedValue, AUTO]
|
||
MeshShardingMinusUnspecified = Union[NamedSharding, AUTO]
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
def _try_infer_args(f, tree):
|
||
dummy_args = tree_unflatten(tree, [False] * tree.num_leaves)
|
||
try:
|
||
return inspect.signature(f).bind(*dummy_args)
|
||
except (TypeError, ValueError):
|
||
return None
|
||
|
||
|
||
def _find_arg_mismatch(arg_list, fails, fun_name):
|
||
first_err, second_err = fails
|
||
mismatched_args_msg = []
|
||
for name, inp_da, aval in arg_list:
|
||
if first_err.m_type == pxla.MismatchType.ARG_SHARDING:
|
||
if first_err.da == inp_da:
|
||
mismatched_args_msg.append(
|
||
(f"argument {name} of {fun_name} with shape {aval.str_short()} and "
|
||
f"{first_err._dev_ids_plat_str}"))
|
||
break
|
||
|
||
for name, inp_da, aval in arg_list:
|
||
if second_err.m_type == pxla.MismatchType.ARG_SHARDING:
|
||
if second_err.da == inp_da:
|
||
mismatched_args_msg.append(
|
||
(f"argument {name} of {fun_name} with shape {aval.str_short()} and "
|
||
f"{second_err._dev_ids_plat_str}"))
|
||
break
|
||
return mismatched_args_msg
|
||
|
||
# TODO(yashkatariya): Try to use debug_info that is populated in
|
||
# common_infer_params.
|
||
def _get_arg_names(fun, in_tree, args_flat):
|
||
sig = _try_infer_args(fun, in_tree)
|
||
args_aug = generate_key_paths(tree_unflatten(in_tree, args_flat))
|
||
|
||
arg_names = []
|
||
for arg_key, val in args_aug:
|
||
ak, *rem_keys = arg_key
|
||
if sig is not None:
|
||
loc = ''.join(str(k) for k in rem_keys)
|
||
try:
|
||
arg_name = f'{list(sig.arguments.keys())[ak.idx]}{loc}'
|
||
except IndexError:
|
||
arg_name = '' # E.g. variadic positional argument.
|
||
else:
|
||
arg_name = ''
|
||
arg_names.append(arg_name)
|
||
return arg_names
|
||
|
||
|
||
def _device_assignment_mismatch_error(fun_name, fails, args_flat, api_name,
|
||
arg_names):
|
||
arg_list = []
|
||
for a, n in zip(args_flat, arg_names):
|
||
da = a.sharding._device_assignment if hasattr(a, 'sharding') else None
|
||
arg_list.append((n, da, shaped_abstractify(a)))
|
||
|
||
mismatched_args_msg = _find_arg_mismatch(arg_list, fails, fun_name)
|
||
|
||
if len(mismatched_args_msg) == 2:
|
||
first, second = mismatched_args_msg # pylint: disable=unbalanced-tuple-unpacking
|
||
extra_msg = f" Got {first} and {second}"
|
||
elif len(mismatched_args_msg) == 1:
|
||
first, second = fails
|
||
# Choose the failure left which is not already covered by ARG_SHARDING.
|
||
left = second if first.m_type == pxla.MismatchType.ARG_SHARDING else first
|
||
extra_msg = f" Got {mismatched_args_msg[0]} and{left._str(api_name)}"
|
||
else:
|
||
first, second = fails
|
||
extra_msg = f" Got{first._str(api_name)} and{second._str(api_name)}"
|
||
msg = (f"Received incompatible devices for {api_name}ted computation.{extra_msg}")
|
||
return msg
|
||
|
||
|
||
def _python_pjit_helper(fun, infer_params_fn, *args, **kwargs):
|
||
args_flat, _, params, in_tree, out_tree, _, jaxpr = infer_params_fn(
|
||
*args, **kwargs)
|
||
for arg in args_flat:
|
||
dispatch.check_arg(arg)
|
||
try:
|
||
out_flat = pjit_p.bind(*args_flat, **params)
|
||
except pxla.DeviceAssignmentMismatchError as e:
|
||
fails, = e.args
|
||
api_name = 'jit' if params['resource_env'] is None else 'pjit'
|
||
arg_names = _get_arg_names(fun, in_tree, args_flat)
|
||
fun_name = getattr(fun, '__qualname__', getattr(fun, '__name__', str(fun)))
|
||
msg = _device_assignment_mismatch_error(
|
||
fun_name, fails, args_flat, api_name, arg_names)
|
||
raise ValueError(msg) from None
|
||
outs = tree_unflatten(out_tree, out_flat)
|
||
return outs, out_flat, out_tree, args_flat, jaxpr
|
||
|
||
|
||
def _python_pjit(fun: Callable, infer_params_fn):
|
||
|
||
@wraps(fun)
|
||
@api_boundary
|
||
def wrapped(*args, **kwargs):
|
||
if config.jax_disable_jit:
|
||
return fun(*args, **kwargs)
|
||
return _python_pjit_helper(fun, infer_params_fn, *args, **kwargs)[0]
|
||
|
||
def _python_pjit_evict_fn():
|
||
_create_pjit_jaxpr.evict_function(fun) # type: ignore
|
||
wrapped.clear_cache = _python_pjit_evict_fn
|
||
return wrapped
|
||
|
||
|
||
def _get_fastpath_data(executable, out_tree, args_flat, out_flat):
|
||
use_fastpath = (
|
||
executable is not None and
|
||
isinstance(executable, pxla.MeshExecutable) and
|
||
isinstance(executable.unsafe_call, pxla.ExecuteReplicated) and
|
||
# No effects in computation
|
||
not executable.unsafe_call.ordered_effects and
|
||
not executable.unsafe_call.has_unordered_effects and
|
||
not executable.unsafe_call.has_host_callbacks and
|
||
all(isinstance(x, xc.ArrayImpl) for x in out_flat)
|
||
)
|
||
|
||
if use_fastpath:
|
||
out_avals = [o.aval for o in out_flat]
|
||
out_committed = [o._committed for o in out_flat]
|
||
kept_var_bitvec = [i in executable._kept_var_idx
|
||
for i in range(len(args_flat))]
|
||
fastpath_data = pxla.MeshExecutableFastpathData(
|
||
executable.xla_executable, out_tree, executable._in_shardings,
|
||
executable._out_shardings, out_avals, out_committed, kept_var_bitvec)
|
||
else:
|
||
fastpath_data = None
|
||
return fastpath_data
|
||
|
||
|
||
class _MostRecentPjitCallExecutable(threading.local):
|
||
def __init__(self):
|
||
self.weak_key_dict = weakref.WeakKeyDictionary()
|
||
|
||
_most_recent_pjit_call_executable = _MostRecentPjitCallExecutable()
|
||
|
||
|
||
def _read_most_recent_pjit_call_executable(jaxpr):
|
||
return _most_recent_pjit_call_executable.weak_key_dict.get(jaxpr, None)
|
||
|
||
|
||
def _cpp_pjit_evict_fn(self):
|
||
self._clear_cache()
|
||
_create_pjit_jaxpr.evict_function(self._fun) # type: ignore
|
||
|
||
|
||
# The entries are doubled here from the default 4096 because _pjit_call_impl
|
||
# also has a cpp dispatch path and that would double the number of entries in
|
||
# the global shared cache.
|
||
_cpp_pjit_cache = xc._xla.PjitFunctionCache(capacity=8192)
|
||
|
||
|
||
def _get_cpp_global_cache(pjit_has_explicit_sharding):
|
||
if pjit_has_explicit_sharding:
|
||
return xc._xla.PjitFunctionCache()
|
||
else:
|
||
return _cpp_pjit_cache
|
||
|
||
|
||
def _cpp_pjit(fun: Callable, infer_params_fn, static_argnums, static_argnames,
|
||
donate_argnums, pjit_has_explicit_sharding):
|
||
|
||
@api_boundary
|
||
def cache_miss(*args, **kwargs):
|
||
outs, out_flat, out_tree, args_flat, jaxpr = _python_pjit_helper(
|
||
fun, infer_params_fn, *args, **kwargs)
|
||
executable = _read_most_recent_pjit_call_executable(jaxpr)
|
||
fastpath_data = _get_fastpath_data(executable, out_tree, args_flat, out_flat)
|
||
return outs, fastpath_data
|
||
|
||
cpp_pjit_f = xc._xla.pjit( # type: ignore
|
||
getattr(fun, "__name__", "<unnamed function>"), # type: ignore
|
||
fun, cache_miss, static_argnums, static_argnames, # type: ignore
|
||
donate_argnums, _get_cpp_global_cache(pjit_has_explicit_sharding)) # type: ignore
|
||
|
||
cpp_pjitted_f = wraps(fun)(cpp_pjit_f)
|
||
cpp_pjitted_f._fun = fun
|
||
type(cpp_pjitted_f).clear_cache = _cpp_pjit_evict_fn
|
||
return cpp_pjitted_f
|
||
|
||
|
||
def _resolve_axis_resources_and_shardings_arg(
|
||
in_shardings, out_shardings, in_axis_resources, out_axis_resources):
|
||
if not is_unspecified(in_shardings) and not is_unspecified(in_axis_resources):
|
||
raise ValueError(
|
||
'Setting both in_shardings and in_axis_resources is not '
|
||
'allowed. in_axis_resources is deprecated. Please use in_shardings.')
|
||
if not is_unspecified(out_shardings) and not is_unspecified(out_axis_resources):
|
||
raise ValueError(
|
||
'Setting both out_shardings and out_axis_resources is not '
|
||
'allowed. out_axis_resources is deprecated. Please use out_shardings.')
|
||
if (not is_unspecified(in_axis_resources) or
|
||
not is_unspecified(out_axis_resources)):
|
||
warnings.warn(
|
||
'in_axis_resources and out_axis_resources are deprecated. Please use '
|
||
'in_shardings and out_shardings as their replacement.',
|
||
DeprecationWarning)
|
||
|
||
if not is_unspecified(in_axis_resources):
|
||
final_in_shardings = in_axis_resources
|
||
else:
|
||
final_in_shardings = in_shardings
|
||
|
||
if not is_unspecified(out_axis_resources):
|
||
final_out_shardings = out_axis_resources
|
||
else:
|
||
final_out_shardings = out_shardings
|
||
return final_in_shardings, final_out_shardings
|
||
|
||
|
||
def pre_infer_params(fun, in_shardings, out_shardings,
|
||
donate_argnums, static_argnums, static_argnames, device,
|
||
backend, abstracted_axes):
|
||
if abstracted_axes and not config.jax_dynamic_shapes:
|
||
raise ValueError("abstracted_axes must be used with --jax_dynamic_shapes")
|
||
|
||
check_callable(fun)
|
||
|
||
if backend is not None or device is not None:
|
||
warnings.warn(
|
||
'backend and device argument on jit is deprecated. You can use a '
|
||
'`jax.sharding.Mesh` context manager or device_put the arguments '
|
||
'before passing them to `jit`. Please see '
|
||
'https://jax.readthedocs.io/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html '
|
||
'for more information.', DeprecationWarning)
|
||
if device is not None and backend is not None:
|
||
raise ValueError("can't specify both a device and a backend for jit, "
|
||
f"got {device=} and {backend=}")
|
||
if not is_unspecified(in_shardings):
|
||
raise ValueError('If backend or device is specified on jit, then '
|
||
'in_shardings should not be specified.')
|
||
if not is_unspecified(out_shardings):
|
||
raise ValueError('If backend or device is specified on jit, then '
|
||
'out_shardings should not be specified.')
|
||
|
||
if isinstance(in_shardings, list):
|
||
# To be a tree prefix of the positional args tuple, in_axes can never be a
|
||
# list: if in_axes is not a leaf, it must be a tuple of trees. However,
|
||
# in cases like these users expect tuples and lists to be treated
|
||
# essentially interchangeably, so we canonicalize lists to tuples here
|
||
# rather than raising an error. https://github.com/google/jax/issues/2367
|
||
in_shardings = tuple(in_shardings)
|
||
|
||
in_shardings, _, _ = prepare_axis_resources(in_shardings, 'in_shardings')
|
||
out_shardings, _, _ = prepare_axis_resources(out_shardings, 'out_shardings')
|
||
|
||
donate_argnums, static_argnums, static_argnames = resolve_argnums(
|
||
fun, donate_argnums, static_argnums, static_argnames)
|
||
|
||
return (in_shardings, out_shardings, donate_argnums, static_argnums,
|
||
static_argnames)
|
||
|
||
|
||
def post_infer_params(fun, infer_params_fn, static_argnums, static_argnames,
|
||
donate_argnums, abstracted_axes,
|
||
pjit_has_explicit_sharding):
|
||
if abstracted_axes is None:
|
||
wrapped = _cpp_pjit(fun, infer_params_fn, static_argnums, static_argnames,
|
||
donate_argnums, pjit_has_explicit_sharding)
|
||
else:
|
||
wrapped = _python_pjit(fun, infer_params_fn)
|
||
|
||
@api_boundary
|
||
def lower(*args, **kwargs):
|
||
_experimental_lowering_platform = kwargs.pop(
|
||
'_experimental_lowering_platform', None)
|
||
(args_flat, flat_global_in_avals, params, in_tree, out_tree,
|
||
donate_argnums, _) = infer_params_fn(*args, **kwargs)
|
||
resource_env = params['resource_env']
|
||
mesh = None if resource_env is None else resource_env.physical_mesh
|
||
try:
|
||
in_shardings = _resolve_in_shardings(
|
||
args_flat, params['in_shardings'], params['out_shardings'], mesh)
|
||
lowering = _pjit_lower(
|
||
params['jaxpr'], in_shardings, params['out_shardings'],
|
||
params['resource_env'], params['donated_invars'], params['name'],
|
||
params['keep_unused'], params['inline'], always_lower=True,
|
||
lowering_platform=_experimental_lowering_platform)
|
||
except pxla.DeviceAssignmentMismatchError as e:
|
||
fails, = e.args
|
||
api_name = 'jit' if params['resource_env'] is None else 'pjit'
|
||
arg_names = _get_arg_names(fun, in_tree, args_flat)
|
||
fun_name = getattr(fun, '__qualname__', getattr(fun, '__name__', str(fun)))
|
||
msg = _device_assignment_mismatch_error(
|
||
fun_name, fails, args_flat, api_name, arg_names)
|
||
raise ValueError(msg) from None
|
||
|
||
if kwargs:
|
||
args_kwargs_in_tree = in_tree
|
||
else:
|
||
args_kwargs_in_tree = treedef_tuple([in_tree, tree_flatten({})[1]])
|
||
|
||
return stages.Lowered.from_flat_info(
|
||
lowering, args_kwargs_in_tree, flat_global_in_avals, donate_argnums,
|
||
out_tree)
|
||
|
||
wrapped.lower = lower
|
||
return wrapped
|
||
|
||
|
||
def _pjit_explicit_sharding(in_shardings, out_shardings, device,
|
||
backend) -> bool:
|
||
in_shardings_flat, _ = tree_flatten(in_shardings)
|
||
out_shardings_flat, _ = tree_flatten(out_shardings)
|
||
return (device is not None or
|
||
backend is not None or
|
||
any(not is_unspecified(i) for i in in_shardings_flat) or
|
||
any(not is_unspecified(i) for i in out_shardings_flat))
|
||
|
||
|
||
class PjitInfo(NamedTuple):
|
||
fun: Callable
|
||
in_shardings: Any
|
||
out_shardings: Any
|
||
static_argnums: Tuple[int, ...]
|
||
static_argnames: Tuple[str, ...]
|
||
donate_argnums: Tuple[int, ...]
|
||
device: Optional[xc.Device]
|
||
backend: Optional[str]
|
||
keep_unused: bool
|
||
inline: bool
|
||
resource_env: Any
|
||
abstracted_axes: Optional[Any]
|
||
|
||
|
||
def common_infer_params(pjit_info_args, *args, **kwargs):
|
||
(fun, user_in_shardings, user_out_shardings, static_argnums, static_argnames,
|
||
donate_argnums, device, backend, keep_unused, inline,
|
||
resource_env, abstracted_axes) = pjit_info_args
|
||
|
||
if kwargs and not is_unspecified(user_in_shardings):
|
||
raise ValueError(
|
||
"pjit does not support kwargs when in_shardings is specified.")
|
||
|
||
if resource_env is not None:
|
||
pjit_mesh = resource_env.physical_mesh
|
||
else:
|
||
pjit_mesh = None
|
||
|
||
if (backend or device) and pjit_mesh is not None and not pjit_mesh.empty:
|
||
raise ValueError(
|
||
"Mesh context manager should not be used with jit when backend or "
|
||
"device is also specified as an argument to jit.")
|
||
|
||
axes_specs = _flat_axes_specs(abstracted_axes, *args, **kwargs)
|
||
|
||
jit_name = 'jit' if resource_env is None else 'pjit'
|
||
dbg = debug_info(jit_name, fun, args, kwargs, static_argnums, static_argnames)
|
||
f = lu.wrap_init(fun)
|
||
f, res_paths = result_paths(f)
|
||
f, dyn_args = argnums_partial_except(f, static_argnums, args,
|
||
allow_invalid=True)
|
||
del args
|
||
|
||
# TODO(yashkatariya): Merge the nokwargs and kwargs path. One blocker is
|
||
# flatten_axes which if kwargs are present in the treedef (even empty {}),
|
||
# leads to wrong expansion.
|
||
if kwargs:
|
||
f, dyn_kwargs = argnames_partial_except(f, static_argnames, kwargs)
|
||
explicit_args, in_tree = tree_flatten((dyn_args, dyn_kwargs))
|
||
flat_fun, out_tree = flatten_fun(f, in_tree)
|
||
else:
|
||
explicit_args, in_tree = tree_flatten(dyn_args)
|
||
flat_fun, out_tree = flatten_fun_nokwargs(f, in_tree)
|
||
dyn_kwargs = ()
|
||
del kwargs
|
||
|
||
if donate_argnums and not config.jax_debug_nans:
|
||
donated_invars = donation_vector(donate_argnums, dyn_args, dyn_kwargs)
|
||
else:
|
||
donated_invars = (False,) * len(explicit_args)
|
||
|
||
# If backend or device is set as an arg on jit, then resolve them to
|
||
# in_shardings and out_shardings as if user passed in in_shardings
|
||
# and out_shardings.
|
||
device_or_backend_set = False
|
||
if backend or device:
|
||
in_shardings = out_shardings = _create_sharding_with_device_backend(
|
||
device, backend)
|
||
device_or_backend_set = True
|
||
else:
|
||
in_shardings = tree_map(
|
||
lambda x: _create_sharding_for_array(pjit_mesh, x, 'in_shardings',
|
||
jit_name),
|
||
user_in_shardings)
|
||
out_shardings = tree_map(
|
||
lambda x: _create_sharding_for_array(pjit_mesh, x, 'out_shardings',
|
||
jit_name),
|
||
user_out_shardings)
|
||
|
||
del user_in_shardings, user_out_shardings
|
||
|
||
if config.jax_dynamic_shapes:
|
||
in_type = pe.infer_lambda_input_type(axes_specs, explicit_args)
|
||
in_avals = tuple(a for a, e in in_type if e)
|
||
else:
|
||
avals = []
|
||
for i, a in enumerate(explicit_args):
|
||
try:
|
||
avals.append(shaped_abstractify(a))
|
||
except OverflowError as e:
|
||
arg_path = (f"argument path is {dbg.arg_names[i]}" if dbg
|
||
else f"flattened argument number is {i}")
|
||
raise OverflowError(
|
||
"An overflow was encountered while parsing an argument to a jitted "
|
||
f"computation, whose {arg_path}."
|
||
) from e
|
||
in_type = in_avals = tuple(avals)
|
||
|
||
canonicalized_in_shardings_flat = _process_in_axis_resources(
|
||
hashable_pytree(in_shardings), in_avals, in_tree, resource_env, dbg,
|
||
device_or_backend_set)
|
||
|
||
jaxpr, consts, canonicalized_out_shardings_flat = _pjit_jaxpr(
|
||
flat_fun, hashable_pytree(out_shardings), in_type, dbg,
|
||
device_or_backend_set, HashableFunction(out_tree, closure=()),
|
||
HashableFunction(res_paths, closure=()))
|
||
|
||
assert len(explicit_args) == len(canonicalized_in_shardings_flat)
|
||
|
||
if config.jax_dynamic_shapes:
|
||
implicit_args = _extract_implicit_args(in_type, explicit_args)
|
||
else:
|
||
implicit_args = []
|
||
args_flat = [*implicit_args, *explicit_args]
|
||
|
||
num_extra_args = len(implicit_args) + len(consts)
|
||
canonicalized_in_shardings_flat = \
|
||
(UNSPECIFIED,) * num_extra_args + canonicalized_in_shardings_flat
|
||
donated_invars = (False,) * num_extra_args + donated_invars
|
||
assert (len(canonicalized_in_shardings_flat) == len(donated_invars) ==
|
||
len(consts) + len(args_flat))
|
||
|
||
# in_shardings and out_shardings here are all GSPMDSharding.
|
||
params = dict(
|
||
jaxpr=jaxpr,
|
||
in_shardings=canonicalized_in_shardings_flat,
|
||
out_shardings=canonicalized_out_shardings_flat,
|
||
resource_env=resource_env,
|
||
donated_invars=donated_invars,
|
||
name=getattr(flat_fun, '__name__', '<unnamed function>'),
|
||
keep_unused=keep_unused,
|
||
inline=inline,
|
||
)
|
||
return (consts + args_flat, in_type, params, in_tree, out_tree(),
|
||
donate_argnums, jaxpr)
|
||
|
||
def _extract_implicit_args(
|
||
in_type: Sequence[Tuple[core.AbstractValue, bool]],
|
||
explicit_args: Sequence[Any]
|
||
) -> Sequence[core.Tracer]:
|
||
"""
|
||
Given an input type and explicitly-passed arguments (per the user-facing API
|
||
calling convention), extract implicit axis size arguments from shapes of
|
||
explicit arguments (for the trace-time / jaxpr-level calling convention).
|
||
"""
|
||
# First, using `in_type` construct a list to represent the full argument list,
|
||
# leaving the implicit arguments as None placeholders for now.
|
||
explicit_args_ = iter(explicit_args)
|
||
args = [next(explicit_args_) if expl else None for _, expl in in_type]
|
||
assert next(explicit_args_, None) is None
|
||
del explicit_args, explicit_args_
|
||
|
||
# Next, populate the implicit arguments using the DBIdxs in `in_type`.
|
||
for i, (aval, explicit) in enumerate(in_type):
|
||
if not explicit or not isinstance(aval, core.DShapedArray):
|
||
continue # can't populate an implicit argument
|
||
arg = args[i]
|
||
assert arg is not None
|
||
for d1, d2 in zip(aval.shape, arg.aval.shape):
|
||
if isinstance(d1, core.DBIdx):
|
||
if args[d1.val] is None:
|
||
args[d1.val] = d2
|
||
assert core.same_referent(args[d1.val], d2)
|
||
assert all(x is not None for x in args)
|
||
return [x for x, (_, e) in zip(args, in_type) if not e] # type: ignore
|
||
|
||
def _flat_axes_specs(abstracted_axes, *args, **kwargs
|
||
) -> Optional[List[pe.AbstractedAxesSpec]]:
|
||
if abstracted_axes is None: return None
|
||
if kwargs: raise NotImplementedError
|
||
def ax_leaf(l):
|
||
return (isinstance(l, dict) and all_leaves(l.values()) or
|
||
isinstance(l, tuple) and all_leaves(l, lambda x: x is None))
|
||
return broadcast_prefix(abstracted_axes, args, ax_leaf)
|
||
|
||
|
||
# in_shardings and out_shardings can't be None as the default value
|
||
# because `None` means that the input is fully replicated.
|
||
def pjit(
|
||
fun: Callable,
|
||
in_shardings=UNSPECIFIED,
|
||
out_shardings=UNSPECIFIED,
|
||
in_axis_resources=UNSPECIFIED,
|
||
out_axis_resources=UNSPECIFIED,
|
||
static_argnums: Union[int, Sequence[int], None] = None,
|
||
static_argnames: Union[str, Iterable[str], None] = None,
|
||
donate_argnums: Union[int, Sequence[int]] = (),
|
||
keep_unused: bool = False,
|
||
device: Optional[xc.Device] = None,
|
||
backend: Optional[str] = None,
|
||
inline: bool = False,
|
||
abstracted_axes: Optional[Any] = None,
|
||
) -> stages.Wrapped:
|
||
"""Makes ``fun`` compiled and automatically partitioned across multiple devices.
|
||
|
||
NOTE: This function is now equivalent to jax.jit please use that instead.
|
||
The returned function has semantics equivalent to those of ``fun``, but is
|
||
compiled to an XLA computation that runs across multiple devices
|
||
(e.g. multiple GPUs or multiple TPU cores). This can be useful if the jitted
|
||
version of ``fun`` would not fit in a single device's memory, or to speed up
|
||
``fun`` by running each operation in parallel across multiple devices.
|
||
|
||
The partitioning over devices happens automatically based on the
|
||
propagation of the input partitioning specified in ``in_shardings`` and
|
||
the output partitioning specified in ``out_shardings``. The resources
|
||
specified in those two arguments must refer to mesh axes, as defined by
|
||
the :py:func:`jax.sharding.Mesh` context manager. Note that the mesh
|
||
definition at :func:`~pjit` application time is ignored, and the returned function
|
||
will use the mesh definition available at each call site.
|
||
|
||
Inputs to a :func:`~pjit`'d function will be automatically partitioned across devices
|
||
if they're not already correctly partitioned based on ``in_shardings``.
|
||
In some scenarios, ensuring that the inputs are already correctly pre-partitioned
|
||
can increase performance. For example, if passing the output of one
|
||
:func:`~pjit`'d function to another :func:`~pjit`’d function (or the same
|
||
:func:`~pjit`’d function in a loop), make sure the relevant
|
||
``out_shardings`` match the corresponding ``in_shardings``.
|
||
|
||
.. note::
|
||
**Multi-process platforms:** On multi-process platforms such as TPU pods,
|
||
:func:`~pjit` can be used to run computations across all available devices across
|
||
processes. To achieve this, :func:`~pjit` is designed to be used in SPMD Python
|
||
programs, where every process is running the same Python code such that all
|
||
processes run the same :func:`~pjit`'d function in the same order.
|
||
|
||
When running in this configuration, the mesh should contain devices across
|
||
all processes. However, any input argument dimensions partitioned over
|
||
multi-process mesh axes should be of size equal to the corresponding *local*
|
||
mesh axis size, and outputs will be similarly sized according to the local
|
||
mesh. ``fun`` will still be executed across *all* devices in the mesh,
|
||
including those from other processes, and will be given a global view of the
|
||
data spread across multiple processes as a single array. However, outside
|
||
of :func:`~pjit` every process only "sees" its local piece of the input and output,
|
||
corresponding to its local sub-mesh.
|
||
|
||
This means that each process's participating local devices must form a
|
||
_contiguous_ local sub-mesh within the full global mesh. A contiguous
|
||
sub-mesh is one where all of its devices are adjacent within the global
|
||
mesh, and form a rectangular prism.
|
||
|
||
The SPMD model also requires that the same multi-process :func:`~pjit`'d
|
||
functions must be run in the same order on all processes, but they can be
|
||
interspersed with arbitrary operations running in a single process.
|
||
|
||
Args:
|
||
fun: Function to be compiled. Should be a pure function, as side-effects may
|
||
only be executed once. Its arguments and return value should be arrays,
|
||
scalars, or (nested) standard Python containers (tuple/list/dict) thereof.
|
||
Positional arguments indicated by ``static_argnums`` can be anything at
|
||
all, provided they are hashable and have an equality operation defined.
|
||
Static arguments are included as part of a compilation cache key, which is
|
||
why hash and equality operators must be defined.
|
||
in_shardings: Pytree of structure matching that of arguments to ``fun``,
|
||
with all actual arguments replaced by resource assignment specifications.
|
||
It is also valid to specify a pytree prefix (e.g. one value in place of a
|
||
whole subtree), in which case the leaves get broadcast to all values in
|
||
that subtree.
|
||
|
||
The ``in_shardings`` argument is optional. JAX will infer the shardings
|
||
from the input :py:class:`jax.Array`'s, and defaults to replicating the input
|
||
if the sharding cannot be inferred.
|
||
|
||
The valid resource assignment specifications are:
|
||
|
||
- :py:class:`XLACompatibleSharding`, which will decide how the value
|
||
will be partitioned. With this, using a mesh context manager is not
|
||
required.
|
||
- For backwards compatibility, in_shardings still supports ingesting
|
||
:py:class:`PartitionSpec` and :py:obj:`None`. These 2 options can
|
||
*only* be used with the mesh context manager.
|
||
|
||
- :py:obj:`None`, in which case the value will be replicated on all devices
|
||
- :py:class:`PartitionSpec`, a tuple of length at most equal to the rank
|
||
of the partitioned value. Each element can be a :py:obj:`None`, a mesh
|
||
axis or a tuple of mesh axes, and specifies the set of resources assigned
|
||
to partition the value's dimension matching its position in the spec.
|
||
|
||
The size of every dimension has to be a multiple of the total number of
|
||
resources assigned to it.
|
||
out_shardings: Like ``in_shardings``, but specifies resource
|
||
assignment for function outputs.
|
||
The ``out_shardings`` argument is optional. If not specified, :py:func:`jax.jit`
|
||
will use GSPMD's sharding propagation to determine how to shard the outputs.
|
||
in_axis_resources: (Deprecated) Please use in_shardings.
|
||
out_axis_resources: (Deprecated) Please use out_shardings.
|
||
static_argnums: An optional int or collection of ints that specify which
|
||
positional arguments to treat as static (compile-time constant).
|
||
Operations that only depend on static arguments will be constant-folded in
|
||
Python (during tracing), and so the corresponding argument values can be
|
||
any Python object.
|
||
|
||
Static arguments should be hashable, meaning both ``__hash__`` and
|
||
``__eq__`` are implemented, and immutable. Calling the jitted function
|
||
with different values for these constants will trigger recompilation.
|
||
Arguments that are not arrays or containers thereof must be marked as
|
||
static.
|
||
|
||
If ``static_argnums`` is not provided, no arguments are treated as static.
|
||
static_argnames: An optional string or collection of strings specifying
|
||
which named arguments to treat as static (compile-time constant). See the
|
||
comment on ``static_argnums`` for details. If not
|
||
provided but ``static_argnums`` is set, the default is based on calling
|
||
``inspect.signature(fun)`` to find corresponding named arguments.
|
||
donate_argnums: Specify which argument buffers are "donated" to the computation.
|
||
It is safe to donate argument buffers if you no longer need them once the
|
||
computation has finished. In some cases XLA can make use of donated
|
||
buffers to reduce the amount of memory needed to perform a computation,
|
||
for example recycling one of your input buffers to store a result. You
|
||
should not reuse buffers that you donate to a computation, JAX will raise
|
||
an error if you try to.
|
||
For more details on buffer donation see the `FAQ <https://jax.readthedocs.io/en/latest/faq.html#buffer-donation>`_.
|
||
keep_unused: If `False` (the default), arguments that JAX determines to be
|
||
unused by `fun` *may* be dropped from resulting compiled XLA executables.
|
||
Such arguments will not be transferred to the device nor provided to the
|
||
underlying executable. If `True`, unused arguments will not be pruned.
|
||
device: This argument is deprecated. Please put your arguments on the
|
||
device you want before passing them to jit.
|
||
Optional, the Device the jitted function will run on. (Available devices
|
||
can be retrieved via :py:func:`jax.devices`.) The default is inherited
|
||
from XLA's DeviceAssignment logic and is usually to use
|
||
``jax.devices()[0]``.
|
||
backend: This argument is deprecated. Please put your arguments on the
|
||
backend you want before passing them to jit.
|
||
Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or
|
||
``'tpu'``.
|
||
|
||
Returns:
|
||
A wrapped version of ``fun``, set up for just-in-time compilation and
|
||
automatically partitioned by the mesh available at each call site.
|
||
|
||
For example, a convolution operator can be automatically partitioned over
|
||
an arbitrary set of devices by a single :func:`~pjit` application:
|
||
|
||
>>> import jax
|
||
>>> import jax.numpy as jnp
|
||
>>> import numpy as np
|
||
>>> from jax.sharding import Mesh, PartitionSpec
|
||
>>> from jax.experimental.pjit import pjit
|
||
>>>
|
||
>>> x = jnp.arange(8, dtype=jnp.float32)
|
||
>>> f = pjit(lambda x: jax.numpy.convolve(x, jnp.asarray([0.5, 1.0, 0.5]), 'same'),
|
||
... in_shardings=None, out_shardings=PartitionSpec('devices'))
|
||
>>> with Mesh(np.array(jax.devices()), ('devices',)):
|
||
... print(f(x)) # doctest: +SKIP
|
||
[ 0.5 2. 4. 6. 8. 10. 12. 10. ]
|
||
"""
|
||
in_shardings, out_shardings = _resolve_axis_resources_and_shardings_arg(
|
||
in_shardings, out_shardings, in_axis_resources, out_axis_resources)
|
||
|
||
(in_shardings, out_shardings, donate_argnums, static_argnums,
|
||
static_argnames) = pre_infer_params(
|
||
fun, in_shardings, out_shardings, donate_argnums,
|
||
static_argnums, static_argnames, device, backend, abstracted_axes)
|
||
|
||
def infer_params(*args, **kwargs):
|
||
# Putting this outside of wrapped would make resources lexically scoped
|
||
resource_env = mesh_lib.thread_resources.env
|
||
pjit_info_args = PjitInfo(
|
||
fun=fun, in_shardings=in_shardings,
|
||
out_shardings=out_shardings, static_argnums=static_argnums,
|
||
static_argnames=static_argnames, donate_argnums=donate_argnums,
|
||
device=device, backend=backend, keep_unused=keep_unused,
|
||
inline=inline, resource_env=resource_env,
|
||
abstracted_axes=abstracted_axes)
|
||
return common_infer_params(pjit_info_args, *args, **kwargs)
|
||
|
||
has_explicit_sharding = _pjit_explicit_sharding(
|
||
in_shardings, out_shardings, device, backend)
|
||
return post_infer_params(fun, infer_params, static_argnums, static_argnames,
|
||
donate_argnums, abstracted_axes,
|
||
has_explicit_sharding)
|
||
|
||
|
||
def hashable_pytree(pytree):
|
||
vals, treedef = tree_flatten(pytree)
|
||
vals = tuple(vals)
|
||
return HashableFunction(lambda: tree_unflatten(treedef, vals),
|
||
closure=(treedef, vals))
|
||
|
||
|
||
@lru_cache(maxsize=4096)
|
||
def _create_mesh_pspec_sharding_from_parsed_pspec(mesh, x):
|
||
if is_unspecified_or_auto(x):
|
||
return x
|
||
return pxla.create_mesh_pspec_sharding(mesh, x.user_spec, x)
|
||
|
||
|
||
def _create_sharding_for_array(mesh, x, name, api_name):
|
||
if isinstance(x, XLACompatibleSharding) or is_unspecified_or_auto(x):
|
||
return x
|
||
if mesh is None:
|
||
msg = ('jax.jit only supports `XLACompatibleSharding`s being passed to'
|
||
f' {name}. Looks like you are passing either `PartitionSpec` or `None`'
|
||
f' which is not allowed in jax.jit.\n')
|
||
if name == 'in_shardings':
|
||
msg += (f'Note that {name} argument is optional. JAX will infer the shardings'
|
||
" from the input jax.Array's and will default to replicating the"
|
||
' input if the sharding cannot be inferred.')
|
||
elif name == 'out_shardings':
|
||
msg += (f'Note that {name} is optional. If not specified, jax.jit will'
|
||
" use GSPMD's sharding propagation to figure out what the sharding"
|
||
' of the output(s) should be.')
|
||
raise RuntimeError(msg)
|
||
if mesh.empty:
|
||
raise RuntimeError(
|
||
f'{api_name} requires a non-empty mesh if you are passing'
|
||
f' `PartitionSpec`s or `None` to {name}! Is a mesh defined at the call'
|
||
f' site? Alternatively, provide `XLACompatibleSharding`s to {name} and'
|
||
' then the mesh context manager is not required.')
|
||
# A nice user error is raised in prepare_axis_resources.
|
||
assert isinstance(x, ParsedPartitionSpec), x
|
||
return _create_mesh_pspec_sharding_from_parsed_pspec(mesh, x)
|
||
|
||
|
||
def _create_sharding_with_device_backend(device, backend):
|
||
if device is not None:
|
||
assert backend is None
|
||
out = SingleDeviceSharding(device)
|
||
elif backend is not None:
|
||
assert device is None
|
||
out = SingleDeviceSharding(
|
||
xb.get_backend(backend).get_default_device_assignment(1)[0])
|
||
return out
|
||
|
||
|
||
def flatten_axis_resources(what, tree, shardings, tupled_args):
|
||
try:
|
||
return tuple(flatten_axes(what, tree, shardings, tupled_args=tupled_args))
|
||
except ValueError:
|
||
pass # Raise a tree prefix error below
|
||
|
||
# Tree leaves are always valid prefixes, so if there was a prefix error as
|
||
# assumed here, axis_resources must not be a leaf.
|
||
assert not treedef_is_leaf(tree_structure(shardings))
|
||
|
||
# Check the type directly rather than using isinstance because of namedtuples.
|
||
if tupled_args and (type(shardings) is not tuple or
|
||
len(shardings) != len(tree.children())):
|
||
# We know axis_resources is meant to be a tuple corresponding to the args
|
||
# tuple, but while it is a non-leaf pytree, either it wasn't a tuple or it
|
||
# wasn't the right length.
|
||
msg = (f"{what} specification must be a tree prefix of the positional "
|
||
f"arguments tuple passed to the `pjit`-decorated function. In "
|
||
f"particular, {what} must either be a None, a PartitionSpec, or "
|
||
f"a tuple of length equal to the number of positional arguments.")
|
||
# If `tree` represents an args tuple, then `axis_resources` must be a tuple.
|
||
# TODO(mattjj,apaszke): disable implicit list casts, remove 'or list' below
|
||
if type(shardings) is not tuple:
|
||
msg += f" But {what} is not a tuple: got {type(shardings)} instead."
|
||
elif len(shardings) != len(tree.children()):
|
||
msg += (f" But {what} is the wrong length: got a tuple or list of length "
|
||
f"{len(shardings)} for an args tuple of length "
|
||
f"{len(tree.children())}.")
|
||
|
||
# As an extra hint, let's check if the user just forgot to wrap
|
||
# shardings in a singleton tuple.
|
||
if len(tree.children()) == 1:
|
||
try: flatten_axes(what, tree, (shardings,))
|
||
except ValueError: pass # That's not the issue.
|
||
else:
|
||
msg += (f" Given the corresponding argument being "
|
||
f"passed, it looks like {what} might need to be wrapped in "
|
||
f"a singleton tuple.")
|
||
|
||
raise ValueError(msg)
|
||
|
||
axis_tree = shardings
|
||
|
||
# Because we only have the `tree` treedef and not the full pytree here,
|
||
# we construct a dummy tree to compare against. Revise this in callers?
|
||
dummy_tree = tree_unflatten(tree, [PytreeLeaf()] * tree.num_leaves)
|
||
errors = prefix_errors(axis_tree, dummy_tree)
|
||
if errors:
|
||
e = errors[0] # Only show information about the first disagreement found.
|
||
raise e(what)
|
||
|
||
# At this point we've failed to find a tree prefix error.
|
||
assert False, "Please open a bug report!" # This should be unreachable.
|
||
|
||
class PytreeLeaf:
|
||
def __repr__(self): return "pytree leaf"
|
||
|
||
|
||
@lru_cache(maxsize=4096)
|
||
def _process_in_axis_resources(in_shardings_thunk, in_avals, in_tree,
|
||
resource_env, debug_info, device_or_backend_set):
|
||
orig_in_shardings = in_shardings_thunk()
|
||
# Only do this if original in_shardings are unspecified. If it is AUTO, go
|
||
# via flatten_axis_resources.
|
||
if is_unspecified(orig_in_shardings):
|
||
in_shardings_flat = (orig_in_shardings,) * len(in_avals)
|
||
else:
|
||
in_shardings_flat = flatten_axis_resources(
|
||
"pjit in_shardings", in_tree, orig_in_shardings,
|
||
tupled_args=True)
|
||
|
||
if not config.jax_dynamic_shapes:
|
||
pjit_check_aval_sharding(in_shardings_flat, in_avals,
|
||
None if debug_info is None else debug_info.arg_names,
|
||
"pjit arguments", allow_uneven_sharding=False)
|
||
canonicalized_shardings = tuple(
|
||
i if is_unspecified_or_auto(i) else
|
||
to_gspmd_sharding(i, aval.ndim, device_or_backend_set)
|
||
for i, aval in zip(in_shardings_flat, in_avals))
|
||
return canonicalized_shardings
|
||
|
||
|
||
@lu.cache
|
||
def _create_pjit_jaxpr(fun, in_type, debug_info, out_paths):
|
||
with dispatch.log_elapsed_time(
|
||
"Finished tracing + transforming {fun_name} for pjit in {elapsed_time} sec",
|
||
fun_name=fun.__name__, event=dispatch.JAXPR_TRACE_EVENT):
|
||
pe_debug = debug_info and pe.debug_info_final(fun, debug_info.traced_for)
|
||
if config.jax_dynamic_shapes:
|
||
jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic2(
|
||
lu.annotate(fun, in_type), debug_info=pe_debug)
|
||
else:
|
||
jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic(
|
||
fun, in_type, debug_info=pe_debug)
|
||
|
||
if not config.jax_dynamic_shapes:
|
||
jaxpr = jaxpr_debug_info(jaxpr, debug_info, out_paths())
|
||
|
||
if any(isinstance(c, core.Tracer) for c in consts):
|
||
closed_jaxpr = pe.close_jaxpr(pe.convert_constvars_jaxpr(jaxpr))
|
||
final_consts = consts
|
||
else:
|
||
closed_jaxpr = core.ClosedJaxpr(jaxpr, consts)
|
||
final_consts = []
|
||
return closed_jaxpr, final_consts, global_out_avals
|
||
|
||
|
||
@lru_cache(maxsize=4096)
|
||
def _check_and_canonicalize_out_shardings(
|
||
out_shardings_thunk, out_tree, out_type, debug_info, device_or_backend_set):
|
||
orig_out_shardings = out_shardings_thunk()
|
||
# TODO(yashkatariya): Remove the if branch and fix flatten_axis_resources
|
||
# instead. This condition exists because flatten_axis_resources passes in an
|
||
# `object()` while unflattening which breaks assertion is user defined
|
||
# pytrees (which shouldn't exist but they do).
|
||
if (is_unspecified(orig_out_shardings) or
|
||
isinstance(orig_out_shardings, XLACompatibleSharding)):
|
||
out_shardings_flat = (orig_out_shardings,) * len(out_type)
|
||
else:
|
||
out_shardings_flat = flatten_axis_resources(
|
||
"pjit out_shardings", out_tree(), orig_out_shardings,
|
||
tupled_args=False)
|
||
|
||
if not config.jax_dynamic_shapes:
|
||
pjit_check_aval_sharding(
|
||
out_shardings_flat, out_type,
|
||
None if debug_info is None else debug_info.result_paths,
|
||
"pjit outputs", allow_uneven_sharding=False)
|
||
|
||
canonicalized_out_shardings_flat = tuple(
|
||
o if is_unspecified(o) or is_auto(o) else
|
||
to_gspmd_sharding(o, aval.ndim, device_or_backend_set)
|
||
for o, aval in zip(out_shardings_flat, out_type)
|
||
)
|
||
return canonicalized_out_shardings_flat
|
||
|
||
|
||
def _pjit_jaxpr(fun, out_shardings_thunk, in_type, debug_info,
|
||
device_or_backend_set, out_tree, result_paths):
|
||
jaxpr, final_consts, out_type = _create_pjit_jaxpr(
|
||
fun, in_type, debug_info, result_paths)
|
||
canonicalized_out_shardings_flat = _check_and_canonicalize_out_shardings(
|
||
out_shardings_thunk, out_tree, tuple(out_type), jaxpr.jaxpr.debug_info,
|
||
device_or_backend_set)
|
||
# lu.cache needs to be able to create weakrefs to outputs, so we can't return a plain tuple
|
||
return jaxpr, final_consts, canonicalized_out_shardings_flat
|
||
|
||
|
||
def pjit_check_aval_sharding(
|
||
shardings, flat_avals, names: Optional[Tuple[str, ...]],
|
||
what_aval: str, allow_uneven_sharding: bool):
|
||
new_names = [''] * len(shardings) if names is None else names
|
||
for aval, s, name in zip(flat_avals, shardings, new_names):
|
||
if is_unspecified_or_auto(s):
|
||
continue
|
||
name_str = f' with pytree key path {name}' if name else ''
|
||
shape = aval.shape
|
||
try:
|
||
# Sharding interfaces can implement `is_compatible_aval` as an optional
|
||
# method to raise a more meaningful error.
|
||
if hasattr(s, 'is_compatible_aval'):
|
||
s.is_compatible_aval(shape)
|
||
else:
|
||
s._to_xla_hlo_sharding(len(shape))
|
||
except ValueError as e:
|
||
raise ValueError(
|
||
f'One of {what_aval}{name_str} is incompatible with its sharding '
|
||
f'annotation {s}: {str(e)}')
|
||
# Use the `OpSharding` proto to find out how many ways each dimension of
|
||
# the aval is sharded. This approach will work across all
|
||
# XLACompatibleSharding.
|
||
hlo_sharding = s._to_xla_hlo_sharding(len(shape))
|
||
assert hlo_sharding is not None
|
||
num_ways_dim_sharded, _ = op_shardings.get_num_ways_dim_sharded(hlo_sharding)
|
||
for i, size in enumerate(num_ways_dim_sharded):
|
||
if not allow_uneven_sharding and shape[i] % size != 0:
|
||
raise ValueError(f"One of {what_aval}{name_str} was given the sharding "
|
||
f"of {s}, which implies that "
|
||
f"the global size of its dimension {i} should be "
|
||
f"divisible by {size}, but it is equal to {shape[i]} "
|
||
f"(full shape: {shape})")
|
||
|
||
|
||
# -------------------- pjit rules --------------------
|
||
|
||
pjit_p = core.AxisPrimitive("pjit")
|
||
pjit_p.multiple_results = True
|
||
|
||
|
||
def _resolve_in_shardings(
|
||
args, pjit_in_shardings: Sequence[PjitSharding],
|
||
out_shardings: Sequence[PjitSharding],
|
||
pjit_mesh: Optional[pxla.Mesh]) -> Sequence[PjitSharding]:
|
||
# If True, means that device or backend is set by the user on pjit and it
|
||
# has the same semantics as device_put i.e. doesn't matter which device the
|
||
# arg is on, reshard it to the device mentioned. So don't do any of the
|
||
# checks and just return the pjit_in_shardings directly. `shard_args` will
|
||
# handle the resharding.
|
||
if pxla.check_device_backend_on_shardings(pjit_in_shardings):
|
||
return pjit_in_shardings
|
||
|
||
committed_arg_shardings = []
|
||
for a in args:
|
||
if hasattr(a, 'sharding'):
|
||
arg_s = a.sharding
|
||
if not isinstance(arg_s, XLACompatibleSharding):
|
||
raise ValueError(f'One of the argument to pjit got sharding {arg_s} '
|
||
'which is not a subclass of XLACompatibleSharding.')
|
||
# Don't consider PmapSharding inputs as committed. They will get resharded
|
||
# unconditionally.
|
||
if isinstance(arg_s, PmapSharding):
|
||
continue
|
||
if getattr(a, '_committed', True):
|
||
committed_arg_shardings.append((arg_s, pxla.MismatchType.ARG_SHARDING, None))
|
||
|
||
# Check if the device_assignment across inputs, outputs and arguments is the
|
||
# same.
|
||
pxla._get_and_check_device_assignment(
|
||
it.chain(
|
||
committed_arg_shardings,
|
||
[(i, pxla.MismatchType.IN_SHARDING, None) for i in pjit_in_shardings],
|
||
[(o, pxla.MismatchType.OUT_SHARDING, None) for o in out_shardings]),
|
||
(None if pjit_mesh is None or pjit_mesh.empty else list(pjit_mesh.devices.flat)))
|
||
|
||
resolved_in_shardings = []
|
||
for arg, pjit_in_s in zip(args, pjit_in_shardings):
|
||
arg_s, committed = ((arg.sharding, getattr(arg, '_committed', True))
|
||
if hasattr(arg, 'sharding') else (UNSPECIFIED, False))
|
||
if is_unspecified(pjit_in_s):
|
||
if is_unspecified(arg_s):
|
||
resolved_in_shardings.append(arg_s)
|
||
else:
|
||
if committed:
|
||
# If the arg has a PmapSharding, then reshard it unconditionally.
|
||
if isinstance(arg_s, PmapSharding):
|
||
resolved_in_shardings.append(UNSPECIFIED)
|
||
else:
|
||
resolved_in_shardings.append(to_gspmd_sharding(
|
||
cast(XLACompatibleSharding, arg_s), arg.ndim))
|
||
else:
|
||
if dispatch.is_single_device_sharding(arg_s):
|
||
resolved_in_shardings.append(UNSPECIFIED)
|
||
else:
|
||
raise NotImplementedError('Having uncommitted Array sharded on '
|
||
'multiple devices is not supported.')
|
||
else:
|
||
if (isinstance(arg, np.ndarray) and
|
||
not pjit_in_s.is_fully_replicated and # type: ignore
|
||
xb.process_count() > 1):
|
||
raise ValueError(
|
||
'Passing non-trivial shardings for numpy '
|
||
'inputs is not allowed. To fix this error, either specify a '
|
||
'replicated sharding explicitly or use '
|
||
'`jax.experimental.multihost_utils.host_local_array_to_global_array(...)` '
|
||
'to convert your host local numpy inputs to a jax.Array which you '
|
||
'can pass to pjit. '
|
||
'If the numpy input is the same on each process, then you can use '
|
||
'`jax.make_array_from_callback(...) to create a `jax.Array` which '
|
||
'you can pass to pjit. '
|
||
'Please see the jax.Array migration guide for more information '
|
||
'https://jax.readthedocs.io/en/latest/jax_array_migration.html#handling-of-host-local-inputs-to-pjit-like-batch-etc. '
|
||
f'Got arg shape: {arg.shape}, arg value: {arg}')
|
||
if not is_unspecified(arg_s):
|
||
if (committed and
|
||
not isinstance(arg_s, PmapSharding) and
|
||
not op_shardings.are_op_shardings_equal(
|
||
pjit_in_s._to_xla_hlo_sharding(arg.ndim), # type: ignore
|
||
arg_s._to_xla_hlo_sharding(arg.ndim))):
|
||
op = getattr(pjit_in_s, '_original_sharding', pjit_in_s)
|
||
raise ValueError('Sharding passed to pjit does not match the sharding '
|
||
'on the respective arg. '
|
||
f'Got pjit sharding: {op},\n'
|
||
f'arg sharding: {arg_s} for arg shape: {arg.shape}, '
|
||
f'arg value: {arg}')
|
||
resolved_in_shardings.append(pjit_in_s)
|
||
|
||
return tuple(resolved_in_shardings)
|
||
|
||
|
||
def _pjit_call_impl_python(
|
||
*args, jaxpr, in_shardings, out_shardings, resource_env, donated_invars,
|
||
name, keep_unused, inline):
|
||
global _most_recent_pjit_call_executable
|
||
|
||
in_shardings = _resolve_in_shardings(
|
||
args, in_shardings, out_shardings,
|
||
resource_env.physical_mesh if resource_env is not None else None)
|
||
|
||
compiled = _pjit_lower(
|
||
jaxpr, in_shardings, out_shardings, resource_env,
|
||
donated_invars, name, keep_unused, inline,
|
||
always_lower=False, lowering_platform=None).compile()
|
||
_most_recent_pjit_call_executable.weak_key_dict[jaxpr] = compiled
|
||
# This check is expensive so only do it if enable_checks is on.
|
||
if compiled._auto_spmd_lowering and config.jax_enable_checks:
|
||
pxla.check_gda_or_array_xla_sharding_match(args, compiled._in_shardings,
|
||
jaxpr.jaxpr.debug_info)
|
||
if config.jax_distributed_debug:
|
||
# Defensively only perform fingerprint logic if debug logging is enabled
|
||
# NOTE(skyewm): I didn't benchmark this
|
||
fingerprint = None
|
||
if hasattr(compiled.runtime_executable(), "fingerprint"):
|
||
fingerprint = compiled.runtime_executable().fingerprint
|
||
if fingerprint is not None:
|
||
fingerprint = fingerprint.hex()
|
||
distributed_debug_log(("Running pjit'd function", name),
|
||
("in_shardings", in_shardings),
|
||
("out_shardings", out_shardings),
|
||
("abstract args", map(xla.abstractify, args)),
|
||
("fingerprint", fingerprint))
|
||
try:
|
||
return compiled.unsafe_call(*args), compiled
|
||
except FloatingPointError:
|
||
assert config.jax_debug_nans or config.jax_debug_infs # compiled_fun can only raise in this case
|
||
|
||
_ = core.jaxpr_as_fun(jaxpr)(*args) # may raise, not return
|
||
|
||
# If control reaches this line, we got a NaN on the output of `compiled`
|
||
# but not `fun.call_wrapped` on the same arguments. Let's tell the user.
|
||
msg = ("An invalid value was encountered in the output of the "
|
||
f"`jit`-decorated function {name}. Because "
|
||
"config.jax_debug_nans and/or config.jax_debug_infs is set, the "
|
||
"de-optimized function (i.e., the function as if the `jit` "
|
||
"decorator were removed) was called in an attempt to get a more "
|
||
"precise error message. However, the de-optimized function did not "
|
||
"produce invalid values during its execution. This behavior can "
|
||
"result from `jit` optimizations causing the invalid value to be "
|
||
"produced. It may also arise from having nan/inf constants as "
|
||
"outputs, like `jax.jit(lambda ...: jax.numpy.nan)(...)`. "
|
||
"\n\n"
|
||
"It may be possible to avoid the invalid value by removing the "
|
||
"`jit` decorator, at the cost of losing optimizations. "
|
||
"\n\n"
|
||
"If you see this error, consider opening a bug report at "
|
||
"https://github.com/google/jax.")
|
||
raise FloatingPointError(msg)
|
||
|
||
|
||
@weakref_lru_cache
|
||
def _get_jaxpr_as_fun(jaxpr, in_shardings, out_shardings, resource_env,
|
||
donated_invars, name, keep_unused, inline):
|
||
# The input jaxpr to `_get_jaxpr_as_fun` is under a weakref_lru_cache so
|
||
# returning `core.jaxpr_as_fun(jaxpr)` directly creates a strong reference to
|
||
# the jaxpr defeating the purpose of weakref_lru_cache. So return a function
|
||
# that closes over a weakrefed jaxpr and gets called inside that function.
|
||
# This way there won't be a strong reference to the jaxpr from the output
|
||
# function.
|
||
jaxpr = weakref.ref(jaxpr)
|
||
return lambda *args: core.jaxpr_as_fun(jaxpr())(*args) # pylint: disable=unnecessary-lambda
|
||
|
||
|
||
def _pjit_call_impl(*args, jaxpr,
|
||
in_shardings, out_shardings, resource_env,
|
||
donated_invars, name, keep_unused, inline):
|
||
def call_impl_cache_miss(*args_, **kwargs_):
|
||
out_flat, compiled = _pjit_call_impl_python(
|
||
*args, jaxpr=jaxpr, in_shardings=in_shardings,
|
||
out_shardings=out_shardings, resource_env=resource_env,
|
||
donated_invars=donated_invars, name=name, keep_unused=keep_unused,
|
||
inline=inline)
|
||
fastpath_data = _get_fastpath_data(
|
||
compiled, tree_structure(out_flat), args, out_flat)
|
||
return out_flat, fastpath_data
|
||
|
||
f = _get_jaxpr_as_fun(
|
||
jaxpr, tuple(getattr(i, '_original_sharding', i) for i in in_shardings),
|
||
tuple(getattr(o, '_original_sharding', o) for o in out_shardings),
|
||
resource_env, donated_invars, name, keep_unused, inline)
|
||
donated_argnums = [i for i, d in enumerate(donated_invars) if d]
|
||
has_explicit_sharding = _pjit_explicit_sharding(
|
||
in_shardings, out_shardings, None, None)
|
||
return xc._xla.pjit(name, f, call_impl_cache_miss, [], [], donated_argnums,
|
||
_get_cpp_global_cache(has_explicit_sharding))(*args)
|
||
|
||
pjit_p.def_impl(_pjit_call_impl)
|
||
|
||
|
||
@dataclasses.dataclass(frozen=True)
|
||
class SameDeviceAssignmentTuple:
|
||
shardings: Tuple[PjitSharding, ...]
|
||
# device_assignment is Optional because shardings can contain `AUTO` and in
|
||
# that case `mesh` is compulsory to be used. So in that case
|
||
# `_pjit_lower_cached` cache, resource_env will check against the devices.
|
||
device_assignment: Optional[XLADeviceAssignment]
|
||
|
||
def __hash__(self):
|
||
shardings_hash = tuple(
|
||
s._hlo_sharding_hash if isinstance(s, GSPMDSharding) else s # type: ignore
|
||
for s in self.shardings)
|
||
if self.device_assignment is None:
|
||
return hash(shardings_hash)
|
||
else:
|
||
return hash((shardings_hash, *self.device_assignment))
|
||
|
||
def __eq__(self, other):
|
||
if not isinstance(other, SameDeviceAssignmentTuple):
|
||
return False
|
||
eq = []
|
||
for s, o in zip(self.shardings, other.shardings):
|
||
s = getattr(s, "_original_sharding", s)
|
||
o = getattr(o, "_original_sharding", o)
|
||
if isinstance(s, GSPMDSharding) and isinstance(o, GSPMDSharding):
|
||
eq.append(op_shardings.are_op_shardings_equal(
|
||
s._hlo_sharding, o._hlo_sharding))
|
||
else:
|
||
eq.append(s == o)
|
||
return all(eq) and self.device_assignment == other.device_assignment
|
||
|
||
|
||
def _pjit_lower(
|
||
jaxpr: core.ClosedJaxpr,
|
||
in_shardings,
|
||
out_shardings,
|
||
*args, **kwargs):
|
||
da = _fast_path_get_device_assignment(it.chain(in_shardings, out_shardings))
|
||
in_shardings = SameDeviceAssignmentTuple(tuple(in_shardings), da)
|
||
out_shardings = SameDeviceAssignmentTuple(tuple(out_shardings), da)
|
||
return _pjit_lower_cached(jaxpr, in_shardings, out_shardings, *args, **kwargs)
|
||
|
||
|
||
@weakref_lru_cache
|
||
def _pjit_lower_cached(
|
||
jaxpr: core.ClosedJaxpr,
|
||
sdat_in_shardings: SameDeviceAssignmentTuple,
|
||
sdat_out_shardings: SameDeviceAssignmentTuple,
|
||
resource_env,
|
||
donated_invars,
|
||
name: str,
|
||
keep_unused: bool,
|
||
inline: bool,
|
||
always_lower: bool,
|
||
*,
|
||
lowering_platform: Optional[str]):
|
||
in_shardings: Tuple[PjitShardingMinusUnspecified, ...] = cast(
|
||
Tuple[PjitShardingMinusUnspecified, ...], sdat_in_shardings.shardings)
|
||
out_shardings: Tuple[PjitSharding, ...] = sdat_out_shardings.shardings
|
||
|
||
if resource_env is not None:
|
||
pxla.resource_typecheck(jaxpr, resource_env, {}, lambda: "pjit")
|
||
|
||
if resource_env is not None:
|
||
mesh = resource_env.physical_mesh
|
||
api_name = 'pjit'
|
||
else:
|
||
# resource_env is `None` in the jit wrapper around pjit.
|
||
mesh = None
|
||
api_name = 'jit'
|
||
|
||
# For `pjit(xmap)` cases, it needs to take the `lower_mesh_computation` path
|
||
# because `xmap` only supports SPMDAxisContext right now.
|
||
if dispatch.jaxpr_has_primitive(jaxpr.jaxpr, 'xmap'):
|
||
return pxla.lower_mesh_computation(
|
||
jaxpr, api_name, name, mesh,
|
||
in_shardings, out_shardings, donated_invars,
|
||
True, jaxpr.in_avals, tiling_method=None,
|
||
lowering_platform=lowering_platform)
|
||
else:
|
||
return pxla.lower_sharding_computation(
|
||
jaxpr, api_name, name, in_shardings, out_shardings,
|
||
tuple(donated_invars), tuple(jaxpr.in_avals),
|
||
keep_unused=keep_unused, inline=inline, always_lower=always_lower,
|
||
devices_from_context=(
|
||
None if mesh is None or mesh.empty else list(mesh.devices.flat)),
|
||
lowering_platform=lowering_platform)
|
||
|
||
|
||
def pjit_staging_rule(trace, *args, **params):
|
||
if (params["inline"] and
|
||
all(is_unspecified(i) for i in params["in_shardings"]) and
|
||
all(is_unspecified(o) for o in params["out_shardings"])):
|
||
jaxpr = params['jaxpr']
|
||
return core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, *args,
|
||
propagate_source_info=False)
|
||
elif config.jax_dynamic_shapes:
|
||
source_info = source_info_util.current()
|
||
out_tracers = []
|
||
for aval in _out_type(params['jaxpr']):
|
||
if type(aval) is core.DShapedArray:
|
||
shape = [args[d.val] if type(d) is core.InDBIdx else
|
||
out_tracers[d.val] if type(d) is core.OutDBIdx else
|
||
d for d in aval.shape]
|
||
aval = aval.update(shape=tuple(core.get_referent(d) for d in shape))
|
||
out_tracers.append(pe.DynamicJaxprTracer(trace, aval, source_info))
|
||
eqn = core.new_jaxpr_eqn(
|
||
map(trace.getvar, args), map(trace.makevar, out_tracers), pjit_p, params,
|
||
params['jaxpr'].effects, source_info)
|
||
trace.frame.add_eqn(eqn)
|
||
return out_tracers
|
||
else:
|
||
return trace.default_process_primitive(pjit_p, args, params)
|
||
pe.custom_staging_rules[pjit_p] = pjit_staging_rule
|
||
|
||
# TODO(mattjj): remove/trivialize this when jaxprs have type annotation on them,
|
||
# since it's actually not possible in general to infer the type from the term
|
||
def _out_type(jaxpr: core.ClosedJaxpr) -> List[core.AbstractValue]:
|
||
out = []
|
||
in_idx = {v: i for i, v in enumerate(jaxpr.jaxpr.invars)}
|
||
out_idx = {x: i for i, x in enumerate(jaxpr.jaxpr.invars)
|
||
if type(x) is core.Var}
|
||
for x in jaxpr.jaxpr.outvars:
|
||
aval = x.aval
|
||
if type(aval) is core.DShapedArray:
|
||
shape = [core.InDBIdx(in_idx[d]) if d in in_idx else
|
||
core.OutDBIdx(out_idx[d]) if d in out_idx else
|
||
d for d in x.aval.shape]
|
||
aval = aval.update(shape=tuple(shape))
|
||
out.append(aval)
|
||
return out
|
||
|
||
|
||
def _pjit_typecheck(ctx_factory, *in_atoms, jaxpr, **params):
|
||
return core._check_call(ctx_factory, pjit_p, in_atoms,
|
||
dict(params, call_jaxpr=jaxpr.jaxpr))
|
||
core.custom_typechecks[pjit_p] = _pjit_typecheck
|
||
|
||
|
||
def _pjit_abstract_eval(*args, jaxpr, out_shardings, resource_env, **_):
|
||
return jaxpr.out_avals, jaxpr.effects
|
||
pjit_p.def_effectful_abstract_eval(_pjit_abstract_eval)
|
||
|
||
|
||
def _pjit_lowering(ctx, *args, name, jaxpr, in_shardings,
|
||
out_shardings, resource_env, donated_invars,
|
||
keep_unused, inline):
|
||
effects = list(ctx.tokens_in.effects())
|
||
output_types = map(mlir.aval_to_ir_types, ctx.avals_out)
|
||
output_types = [mlir.token_type()] * len(effects) + output_types
|
||
flat_output_types = flatten(output_types)
|
||
|
||
arg_shardings = [None if is_unspecified(i) else
|
||
i._to_xla_hlo_sharding(aval.ndim)
|
||
for aval, i in zip(ctx.avals_in, in_shardings)]
|
||
result_shardings = [None if is_unspecified(o) else
|
||
o._to_xla_hlo_sharding(aval.ndim)
|
||
for aval, o in zip(ctx.avals_out, out_shardings)]
|
||
|
||
# TODO(b/228598865): inlined calls cannot have shardings set directly on the
|
||
# inputs or outputs because they are lost during MLIR->HLO conversion.
|
||
# using_sharding_annotation=False means we add an identity operation instead.
|
||
func = mlir.lower_jaxpr_to_fun(
|
||
ctx.module_context, name, jaxpr, effects, arg_shardings=arg_shardings,
|
||
result_shardings=result_shardings, use_sharding_annotations=False,
|
||
api_name=('jit' if resource_env is None else 'pjit'))
|
||
tokens_in = [ctx.tokens_in.get(eff) for eff in effects]
|
||
args = (*ctx.dim_var_values, *tokens_in, *args)
|
||
call = func_dialect.CallOp(flat_output_types,
|
||
ir.FlatSymbolRefAttr.get(func.name.value),
|
||
mlir.flatten_lowering_ir_args(args))
|
||
out_nodes = unflatten(call.results, map(len, output_types))
|
||
tokens, out_nodes = split_list(out_nodes, [len(effects)])
|
||
tokens_out = ctx.tokens_in.update_tokens(mlir.TokenSet(zip(effects, tokens)))
|
||
ctx.set_tokens_out(tokens_out)
|
||
return out_nodes
|
||
|
||
mlir.register_lowering(pjit_p, _pjit_lowering)
|
||
|
||
|
||
def _pjit_batcher(insert_axis, spmd_axis_name,
|
||
axis_size, axis_name, main_type,
|
||
vals_in, dims_in,
|
||
jaxpr, in_shardings, out_shardings,
|
||
resource_env, donated_invars, name, keep_unused, inline):
|
||
new_jaxpr, axes_out = batching.batch_jaxpr2(
|
||
jaxpr, axis_size, dims_in, axis_name=axis_name,
|
||
spmd_axis_name=spmd_axis_name, main_type=main_type)
|
||
|
||
# `insert_axis` is set to True only for some `xmap` uses.
|
||
new_parts = (axis_name,) if insert_axis else (
|
||
() if spmd_axis_name is None else spmd_axis_name)
|
||
|
||
if resource_env is not None:
|
||
mesh = resource_env.physical_mesh
|
||
else:
|
||
mesh = None
|
||
|
||
in_shardings = tuple(
|
||
_pjit_batcher_for_sharding(i, axis_in, new_parts, mesh, aval.ndim)
|
||
if axis_in is not None else i
|
||
for axis_in, i, aval in zip(dims_in, in_shardings, new_jaxpr.in_avals))
|
||
out_shardings = tuple(
|
||
_pjit_batcher_for_sharding(o, axis_out, new_parts, mesh, aval.ndim)
|
||
if axis_out is not None else o
|
||
for axis_out, o, aval in zip(axes_out, out_shardings, new_jaxpr.out_avals))
|
||
vals_out = pjit_p.bind(
|
||
*vals_in,
|
||
jaxpr=new_jaxpr,
|
||
in_shardings=in_shardings,
|
||
out_shardings=out_shardings,
|
||
resource_env=resource_env,
|
||
donated_invars=donated_invars,
|
||
name=name,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
return vals_out, axes_out
|
||
|
||
batching.spmd_axis_primitive_batchers[pjit_p] = partial(_pjit_batcher, False)
|
||
batching.axis_primitive_batchers[pjit_p] = partial(_pjit_batcher, False, None)
|
||
pxla.spmd_primitive_batchers[pjit_p] = partial(_pjit_batcher, True, None)
|
||
|
||
def _pjit_batcher_for_sharding(
|
||
s: Union[GSPMDSharding, UnspecifiedValue],
|
||
dim: int, val: Tuple[str, ...], mesh, ndim: int):
|
||
if is_unspecified(s):
|
||
return s
|
||
if not val:
|
||
if sharding_impls.is_op_sharding_replicated(s._hlo_sharding): # type: ignore
|
||
return s
|
||
old_op = s._hlo_sharding.to_proto() # type: ignore
|
||
new_op = old_op.clone() # type: ignore
|
||
tad = list(new_op.tile_assignment_dimensions)
|
||
tad.insert(dim, 1)
|
||
new_op.tile_assignment_dimensions = tad
|
||
new_gs = GSPMDSharding(s._device_assignment, new_op) # type: ignore
|
||
if hasattr(s, '_original_sharding'):
|
||
vmapped_s, _ = pxla._get_out_sharding_from_orig_sharding(
|
||
[new_gs], [None], s._original_sharding, None, [False])[0] # type: ignore
|
||
new_gs = to_gspmd_sharding(vmapped_s, ndim)
|
||
return new_gs
|
||
else:
|
||
assert isinstance(s, GSPMDSharding)
|
||
if isinstance(getattr(s, '_original_sharding', None), NamedSharding):
|
||
mesh = s._original_sharding.mesh # type: ignore
|
||
assert mesh is not None and not mesh.empty
|
||
parsed_pspec = parse_flatten_op_sharding(s._hlo_sharding, mesh)[0] # type: ignore
|
||
parsed_pspec = parsed_pspec.insert_axis_partitions(dim, val)
|
||
mps = NamedSharding._from_parsed_pspec(mesh, parsed_pspec)
|
||
return GSPMDSharding(mps._device_assignment, mps._to_xla_hlo_sharding(ndim))
|
||
|
||
|
||
def _pjit_jvp(primals_in, tangents_in,
|
||
jaxpr, in_shardings, out_shardings,
|
||
resource_env, donated_invars, name, keep_unused, inline):
|
||
is_nz_tangents_in = [type(t) is not ad.Zero for t in tangents_in]
|
||
jaxpr_jvp, is_nz_tangents_out = ad.jvp_jaxpr(
|
||
jaxpr, is_nz_tangents_in, instantiate=False)
|
||
|
||
def _filter_zeros(is_nz_l, l):
|
||
return (x for nz, x in zip(is_nz_l, l) if nz)
|
||
_filter_zeros_in = partial(_filter_zeros, is_nz_tangents_in)
|
||
_filter_zeros_out = partial(_filter_zeros, is_nz_tangents_out)
|
||
outputs = pjit_p.bind(
|
||
*primals_in, *_filter_zeros_in(tangents_in),
|
||
jaxpr=jaxpr_jvp,
|
||
in_shardings=(*in_shardings, *_filter_zeros_in(in_shardings)),
|
||
out_shardings=(*out_shardings, *_filter_zeros_out(out_shardings)),
|
||
resource_env=resource_env,
|
||
donated_invars=(*donated_invars, *_filter_zeros_in(donated_invars)),
|
||
name=name,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
|
||
primals_out, tangents_out = split_list(outputs, [len(jaxpr.jaxpr.outvars)])
|
||
assert len(primals_out) == len(jaxpr.jaxpr.outvars)
|
||
tangents_out_it = iter(tangents_out)
|
||
return primals_out, [next(tangents_out_it) if nz else ad.Zero(aval)
|
||
for nz, aval in zip(is_nz_tangents_out, jaxpr.out_avals)]
|
||
ad.primitive_jvps[pjit_p] = _pjit_jvp
|
||
|
||
|
||
@weakref_lru_cache
|
||
def _known_jaxpr_fwd(known_jaxpr: core.ClosedJaxpr,
|
||
fwds_known: Tuple[Optional[int]]) -> core.ClosedJaxpr:
|
||
updated_jaxpr = known_jaxpr.jaxpr.replace(
|
||
outvars=[x for x, i in zip(known_jaxpr.jaxpr.outvars, fwds_known)
|
||
if i is None])
|
||
return known_jaxpr.replace(jaxpr=updated_jaxpr)
|
||
|
||
|
||
def _pjit_partial_eval(trace, *in_tracers,
|
||
jaxpr, in_shardings, out_shardings,
|
||
resource_env, donated_invars, name, keep_unused, inline):
|
||
in_pvals = [t.pval for t in in_tracers]
|
||
|
||
known_ins = tuple(pv.is_known() for pv in in_pvals)
|
||
unknown_ins = tuple(not k for k in known_ins)
|
||
known_jaxpr, unknown_jaxpr, unknown_outs, res_avals = pe.partial_eval_jaxpr_nounits(
|
||
jaxpr, unknown_ins, instantiate=False)
|
||
unknown_outs = tuple(unknown_outs)
|
||
known_outs = tuple(not uk for uk in unknown_outs)
|
||
num_residuals = len(res_avals)
|
||
|
||
def keep_where(l, should_keep):
|
||
return tuple(x for x, keep in unsafe_zip(l, should_keep) if keep)
|
||
|
||
residual_shardings = (UNSPECIFIED,) * num_residuals
|
||
# Compute the known outputs
|
||
known_params = dict(
|
||
jaxpr=known_jaxpr,
|
||
in_shardings=keep_where(in_shardings, known_ins),
|
||
out_shardings=(
|
||
keep_where(out_shardings, known_outs) + residual_shardings),
|
||
resource_env=resource_env,
|
||
donated_invars=keep_where(donated_invars, known_ins),
|
||
name=name,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
|
||
fwds_known = pe._jaxpr_forwarding(known_params['jaxpr'].jaxpr)
|
||
|
||
# Only forward the outvars where the out_sharding is UNSPECIFIED.
|
||
known_user_out_shardings = keep_where(known_params['out_shardings'], known_outs)
|
||
fwds_known_user = [
|
||
fwd if is_unspecified(os) else None
|
||
for os, fwd in zip(known_user_out_shardings,
|
||
fwds_known[:len(known_user_out_shardings)])]
|
||
fwds_known = fwds_known_user + fwds_known[len(known_user_out_shardings):]
|
||
del fwds_known_user
|
||
|
||
# Remove forwarded outvars and out_shardings
|
||
known_params['jaxpr'] = _known_jaxpr_fwd(known_params['jaxpr'], tuple(fwds_known))
|
||
known_out_shardings = tuple(
|
||
s for s, i in zip(known_params['out_shardings'], fwds_known) if i is None)
|
||
known_params['out_shardings'] = known_out_shardings
|
||
del known_out_shardings
|
||
|
||
assert len(known_params['out_shardings']) == len(known_params['jaxpr'].out_avals)
|
||
|
||
# Bind known things to pjit_p.
|
||
known_inputs = [pv.get_known() for pv in in_pvals if pv.is_known()]
|
||
all_known_outs = pjit_p.bind(*known_inputs, **known_params)
|
||
|
||
known_outs_iter = iter(all_known_outs)
|
||
all_known_outs = [next(known_outs_iter)
|
||
if fwd_idx is None else known_inputs[fwd_idx]
|
||
for fwd_idx in fwds_known]
|
||
assert next(known_outs_iter, None) is None
|
||
del known_outs_iter, known_inputs
|
||
|
||
if num_residuals:
|
||
known_out_vals, residual_vals = \
|
||
split_list(all_known_outs, [len(all_known_outs) - num_residuals])
|
||
else:
|
||
known_out_vals, residual_vals = all_known_outs, ()
|
||
residual_tracers = [trace.new_instantiated_const(residual) for residual in residual_vals]
|
||
|
||
# The convention of partial_eval_jaxpr_nounits is to place residual binders
|
||
# at the front of the jaxpr produced, so we move them to the back since both
|
||
# the jaxpr equation built below and the pjit transpose rule assume a
|
||
# residual-inputs-last convention.
|
||
unknown_jaxpr = pe.move_binders_to_back(
|
||
unknown_jaxpr, [True] * num_residuals + [False] * sum(unknown_ins))
|
||
# Prepare unknown tracers
|
||
unknown_params = dict(
|
||
jaxpr=unknown_jaxpr,
|
||
in_shardings=(keep_where(in_shardings, unknown_ins) + residual_shardings),
|
||
out_shardings=keep_where(out_shardings, unknown_outs),
|
||
resource_env=resource_env,
|
||
donated_invars=(keep_where(donated_invars, unknown_ins) +
|
||
(False,) * num_residuals),
|
||
name=name,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
unknown_tracers_in = [t for t in in_tracers if not t.pval.is_known()]
|
||
unknown_out_avals = unknown_jaxpr.out_avals
|
||
unknown_tracers_out = [
|
||
pe.JaxprTracer(trace, pe.PartialVal.unknown(aval), None)
|
||
for aval in unknown_out_avals
|
||
]
|
||
eqn = pe.new_eqn_recipe((*unknown_tracers_in, *residual_tracers),
|
||
unknown_tracers_out,
|
||
pjit_p,
|
||
unknown_params,
|
||
unknown_jaxpr.effects,
|
||
source_info_util.current())
|
||
for t in unknown_tracers_out: t.recipe = eqn
|
||
return merge_lists(unknown_outs, known_out_vals, unknown_tracers_out)
|
||
|
||
pe.custom_partial_eval_rules[pjit_p] = _pjit_partial_eval
|
||
|
||
|
||
def _pjit_partial_eval_custom_params_updater(
|
||
unks_in: Sequence[bool], inst_in: Sequence[bool],
|
||
kept_outs_known: Sequence[bool], kept_outs_staged: Sequence[bool],
|
||
num_res: int, params_known: dict, params_staged: dict
|
||
) -> Tuple[dict, dict]:
|
||
# prune inputs to jaxpr_known according to unks_in
|
||
donated_invars_known, _ = pe.partition_list(unks_in, params_known['donated_invars'])
|
||
in_shardings_known, _ = pe.partition_list(unks_in, params_known['in_shardings'])
|
||
if num_res == 0:
|
||
residual_shardings = []
|
||
else:
|
||
residual_shardings = [UNSPECIFIED] * num_res
|
||
_, out_shardings_known = pe.partition_list(kept_outs_known, params_known['out_shardings'])
|
||
new_params_known = dict(params_known,
|
||
in_shardings=tuple(in_shardings_known),
|
||
out_shardings=(*out_shardings_known, *residual_shardings),
|
||
donated_invars=tuple(donated_invars_known))
|
||
assert len(new_params_known['in_shardings']) == len(params_known['jaxpr'].in_avals)
|
||
assert len(new_params_known['out_shardings']) == len(params_known['jaxpr'].out_avals)
|
||
|
||
# added num_res new inputs to jaxpr_staged, and pruning according to inst_in
|
||
_, donated_invars_staged = pe.partition_list(inst_in, params_staged['donated_invars'])
|
||
donated_invars_staged = [False] * num_res + donated_invars_staged
|
||
_, in_shardings_staged = pe.partition_list(inst_in, params_staged['in_shardings'])
|
||
in_shardings_staged = [*residual_shardings, *in_shardings_staged]
|
||
|
||
_, out_shardings_staged = pe.partition_list(kept_outs_staged, params_staged['out_shardings'])
|
||
|
||
new_params_staged = dict(params_staged,
|
||
in_shardings=tuple(in_shardings_staged),
|
||
out_shardings=tuple(out_shardings_staged),
|
||
donated_invars=tuple(donated_invars_staged))
|
||
assert len(new_params_staged['in_shardings']) == len(params_staged['jaxpr'].in_avals)
|
||
assert len(new_params_staged['out_shardings']) == len(params_staged['jaxpr'].out_avals)
|
||
return new_params_known, new_params_staged
|
||
|
||
pe.partial_eval_jaxpr_custom_rules[pjit_p] = \
|
||
partial(pe.closed_call_partial_eval_custom_rule, 'jaxpr',
|
||
_pjit_partial_eval_custom_params_updater)
|
||
|
||
|
||
@lu.cache
|
||
def _pjit_transpose_trace(fun, in_avals):
|
||
transpose_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(fun, in_avals)
|
||
transpose_jaxpr = core.ClosedJaxpr(transpose_jaxpr, consts)
|
||
return transpose_jaxpr
|
||
|
||
|
||
def _pjit_transpose(reduce_axes, cts_in, *primals_in,
|
||
jaxpr, in_shardings, out_shardings,
|
||
resource_env, donated_invars, name, keep_unused, inline):
|
||
def prune_type(ty, xs, maybe_zeros):
|
||
return tuple(x for x, mz in zip(xs, maybe_zeros) if type(mz) is not ty)
|
||
|
||
body = lu.wrap_init(ad.closed_backward_pass)
|
||
body = lu.hashable_partial(body, jaxpr, reduce_axes, False)
|
||
primals_and_nz_cts_in, in_treedef = tree_flatten((primals_in, cts_in))
|
||
body, cts_out_treedef_thunk = flatten_fun_nokwargs(body, in_treedef)
|
||
|
||
transpose_in_shardings = (
|
||
*prune_type(ad.UndefinedPrimal, in_shardings, primals_in),
|
||
*prune_type(ad.Zero, out_shardings, cts_in)
|
||
)
|
||
global_cts_in_avals = tuple(core.raise_to_shaped(core.get_aval(ct))
|
||
for ct in primals_and_nz_cts_in)
|
||
|
||
transpose_jaxpr = _pjit_transpose_trace(body, global_cts_in_avals)
|
||
cts_out_treedef = cts_out_treedef_thunk()
|
||
transpose_out_shardings = prune_type(
|
||
ad.Zero,
|
||
in_shardings,
|
||
tree_unflatten(cts_out_treedef, [object()] * cts_out_treedef.num_leaves))
|
||
|
||
nz_cts_out = pjit_p.bind(
|
||
*primals_and_nz_cts_in,
|
||
jaxpr=transpose_jaxpr,
|
||
in_shardings=transpose_in_shardings,
|
||
out_shardings=transpose_out_shardings,
|
||
resource_env=resource_env,
|
||
donated_invars=(False,) * len(primals_and_nz_cts_in),
|
||
name=name,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
return tree_unflatten(cts_out_treedef, nz_cts_out)
|
||
ad.reducing_transposes[pjit_p] = _pjit_transpose
|
||
|
||
|
||
@weakref_lru_cache
|
||
def _dce_jaxpr_pjit(
|
||
jaxpr: core.ClosedJaxpr, used_outputs: Tuple[bool]
|
||
) -> Tuple[core.ClosedJaxpr, List[bool]]:
|
||
new_jaxpr, used_inputs = pe.dce_jaxpr(jaxpr.jaxpr, used_outputs)
|
||
return core.ClosedJaxpr(new_jaxpr, jaxpr.consts), used_inputs
|
||
|
||
|
||
def dce_jaxpr_pjit_rule(used_outputs: List[bool], eqn: core.JaxprEqn
|
||
) -> Tuple[List[bool], Optional[core.JaxprEqn]]:
|
||
dced_jaxpr, used_inputs = _dce_jaxpr_pjit(
|
||
eqn.params['jaxpr'], tuple(used_outputs))
|
||
|
||
def keep_where(xs, keeps):
|
||
return tuple(x for x, keep in zip(xs, keeps) if keep)
|
||
|
||
eqn_params = eqn.params
|
||
new_params = dict(
|
||
eqn_params,
|
||
jaxpr=dced_jaxpr,
|
||
in_shardings=keep_where(eqn_params["in_shardings"], used_inputs),
|
||
out_shardings=keep_where(eqn_params["out_shardings"], used_outputs),
|
||
donated_invars=keep_where(eqn_params["donated_invars"], used_inputs),
|
||
)
|
||
if not any(used_inputs) and not any(used_outputs) and not dced_jaxpr.effects:
|
||
return used_inputs, None
|
||
else:
|
||
new_eqn = core.new_jaxpr_eqn(
|
||
[v for v, used in zip(eqn.invars, used_inputs) if used],
|
||
[v for v, used in zip(eqn.outvars, used_outputs) if used],
|
||
eqn.primitive, new_params, dced_jaxpr.effects, eqn.source_info)
|
||
return used_inputs, new_eqn
|
||
|
||
pe.dce_rules[pjit_p] = dce_jaxpr_pjit_rule
|
||
|
||
|
||
def _check_resources_against_named_axes(what, aval, pos_axis_resources, named_axis_resources):
|
||
pjit_resources = set(
|
||
it.chain.from_iterable([d for d in pos_axis_resources if d is not None]))
|
||
aval_resources = set(it.chain.from_iterable(
|
||
named_axis_resources[a] for a in aval.named_shape))
|
||
overlap = pjit_resources & aval_resources
|
||
if overlap:
|
||
raise JAXTypeError(
|
||
f"{what} has an axis resources specification of "
|
||
f"{pos_axis_resources.unsynced_user_spec(SpecSync.DIM_PERMUTE)} "
|
||
f"that uses one or more mesh axes already used by xmap to partition "
|
||
f"a named axis appearing in its named_shape (both use mesh axes "
|
||
f"{mesh_lib.show_axes(overlap)})")
|
||
|
||
def _resource_typing_pjit(avals, params, source_info, resource_env, named_axis_resources):
|
||
jaxpr = params["jaxpr"]
|
||
what = "pjit input"
|
||
if (resource_env is not None and params['resource_env'] is not None and
|
||
resource_env.physical_mesh != params['resource_env'].physical_mesh):
|
||
raise RuntimeError("Changing the physical mesh is not allowed inside pjit.")
|
||
|
||
for aval, s in zip(jaxpr.in_avals, params['in_shardings']):
|
||
if is_unspecified(s) or is_auto(s):
|
||
continue
|
||
elif hasattr(s, '_original_sharding') and hasattr(
|
||
s._original_sharding, '_parsed_pspec'):
|
||
parsed_pspec = s._original_sharding._parsed_pspec
|
||
else:
|
||
if resource_env is not None:
|
||
parsed_pspec = parse_flatten_op_sharding(
|
||
s._hlo_sharding, resource_env.physical_mesh)[0]
|
||
else:
|
||
parsed_pspec = None
|
||
if parsed_pspec is not None:
|
||
_check_resources_against_named_axes(what, aval, parsed_pspec,
|
||
named_axis_resources)
|
||
|
||
pxla.resource_typecheck(
|
||
jaxpr.jaxpr, resource_env, named_axis_resources,
|
||
lambda: (f"a pjit'ed function {params['name']} "
|
||
f"(pjit called at {source_info_util.summarize(source_info)})"))
|
||
|
||
what = "pjit output"
|
||
for aval, s in zip(jaxpr.out_avals, params['out_shardings']):
|
||
if is_unspecified(s) or is_auto(s):
|
||
continue
|
||
elif hasattr(s, '_original_sharding') and hasattr(
|
||
s._original_sharding, '_parsed_pspec'):
|
||
parsed_pspec = s._original_sharding._parsed_pspec
|
||
else:
|
||
if resource_env is not None:
|
||
parsed_pspec = parse_flatten_op_sharding(
|
||
s._hlo_sharding, resource_env.physical_mesh)[0]
|
||
else:
|
||
parsed_pspec = None
|
||
if parsed_pspec is not None:
|
||
_check_resources_against_named_axes(what, aval, parsed_pspec,
|
||
named_axis_resources)
|
||
|
||
pxla.custom_resource_typing_rules[pjit_p] = _resource_typing_pjit
|
||
|
||
|
||
def _pjit_pp_rule(eqn, context, settings):
|
||
params = dict(eqn.params)
|
||
del params['inline']
|
||
if not any(params['donated_invars']):
|
||
del params['donated_invars']
|
||
if all(is_unspecified(s) for s in params['in_shardings']):
|
||
del params['in_shardings']
|
||
if all(is_unspecified(s) for s in params['out_shardings']):
|
||
del params['out_shardings']
|
||
if not params['keep_unused']:
|
||
del params['keep_unused']
|
||
if (params['resource_env'] is None or
|
||
params['resource_env'].physical_mesh.empty):
|
||
del params['resource_env']
|
||
return core._pp_eqn(eqn.replace(params=params), context, settings)
|
||
core.pp_eqn_rules[pjit_p] = _pjit_pp_rule
|
||
|
||
|
||
# -------------------- with_sharding_constraint --------------------
|
||
|
||
def with_sharding_constraint(x, shardings):
|
||
"""Mechanism to constrain the sharding of an Array inside a jitted computation
|
||
|
||
This is a strict constraint for the GSPMD partitioner and not a hint. For examples
|
||
of how to use this function, see `Distributed arrays and automatic parallelization`_.
|
||
|
||
Args:
|
||
x: PyTree of jax.Arrays which will have their shardings constrainted
|
||
shardings: PyTree of sharding specifications. Valid values are the same as for
|
||
the ``in_shardings`` argument of :func:`jax.experimental.pjit`.
|
||
Returns:
|
||
x_with_shardings: PyTree of jax.Arrays with specified sharding constraints.
|
||
|
||
.. _Distributed arrays and automatic parallelization: https://jax.readthedocs.io/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html
|
||
"""
|
||
x_flat, tree = tree_flatten(x)
|
||
user_shardings, _, _ = prepare_axis_resources(
|
||
shardings, "shardings", allow_unconstrained_dims=True)
|
||
del shardings
|
||
|
||
user_shardings_flat = tuple(
|
||
flatten_axes("with_sharding_constraint shardings", tree, user_shardings))
|
||
del user_shardings
|
||
|
||
resource_env = mesh_lib.thread_resources.env
|
||
mesh = resource_env.physical_mesh
|
||
|
||
shardings_flat = [_create_sharding_for_array(mesh, a, 'shardings',
|
||
'with_sharding_constraint')
|
||
for a in user_shardings_flat]
|
||
unconstrained_dims = [get_unconstrained_dims(s)
|
||
if isinstance(s, NamedSharding) else {}
|
||
for s in shardings_flat]
|
||
del user_shardings_flat
|
||
|
||
pjit_check_aval_sharding(
|
||
shardings_flat, x_flat, None, "with_sharding_constraint arguments",
|
||
allow_uneven_sharding=True)
|
||
|
||
outs = [sharding_constraint_p.bind(xf, sharding=to_gspmd_sharding(i, xf.ndim),
|
||
resource_env=resource_env,
|
||
unconstrained_dims=ud)
|
||
for xf, i, ud in zip(x_flat, shardings_flat, unconstrained_dims)]
|
||
return tree_unflatten(tree, outs)
|
||
|
||
def _identity_fn(x): return x
|
||
|
||
def _sharding_constraint_impl(x, sharding, resource_env, unconstrained_dims):
|
||
if hasattr(x, 'sharding') and x.sharding.is_equivalent_to(sharding, x.ndim):
|
||
return x
|
||
# Run a jit here to raise good errors when device assignment don't match.
|
||
return api.jit(_identity_fn, out_shardings=sharding)(x)
|
||
|
||
|
||
sharding_constraint_p = core.Primitive("sharding_constraint")
|
||
sharding_constraint_p.def_impl(_sharding_constraint_impl)
|
||
sharding_constraint_p.def_abstract_eval(lambda x, **_: x)
|
||
ad.deflinear2(sharding_constraint_p,
|
||
lambda ct, _, **params: (sharding_constraint_p.bind(ct, **params),))
|
||
|
||
def _sharding_constraint_hlo_lowering(ctx, x_node, *, sharding,
|
||
resource_env, unconstrained_dims):
|
||
aval, = ctx.avals_in
|
||
out_aval, = ctx.avals_out
|
||
axis_ctx = ctx.module_context.axis_context
|
||
# axis_ctx and manual_axes is *only used with xmap* and xmap only works with
|
||
# NamedSharding. So convert the GSPMDSharding to NamedSharding
|
||
# and then convert it back with the added special axes.
|
||
if isinstance(axis_ctx, sharding_impls.SPMDAxisContext):
|
||
mesh = resource_env.physical_mesh
|
||
parsed_pspec = parse_flatten_op_sharding(sharding._hlo_sharding, mesh)[0]
|
||
mps = NamedSharding._from_parsed_pspec(mesh, parsed_pspec)
|
||
sharding = GSPMDSharding(
|
||
mps._device_assignment, mps._to_xla_hlo_sharding(aval.ndim, axis_ctx=axis_ctx))
|
||
return [
|
||
mlir.wrap_with_sharding_op(ctx,
|
||
x_node, out_aval,
|
||
sharding._to_xla_hlo_sharding(aval.ndim).to_proto(),
|
||
unspecified_dims=unconstrained_dims)
|
||
]
|
||
mlir.register_lowering(sharding_constraint_p,
|
||
_sharding_constraint_hlo_lowering)
|
||
|
||
|
||
def _sharding_constraint_batcher(insert_axis, spmd_axis_name, axis_size,
|
||
axis_name, main_type, vals_in, dims_in,
|
||
sharding, resource_env, unconstrained_dims):
|
||
x, = vals_in
|
||
d, = dims_in
|
||
# None means unconstrained in ParsedPartitionSpec
|
||
new_parts = (axis_name,) if insert_axis else (
|
||
None if spmd_axis_name is None else spmd_axis_name)
|
||
unconstrained_dims = {ud + (d <= ud) for ud in unconstrained_dims}
|
||
if new_parts is None:
|
||
unconstrained_dims.add(d)
|
||
y = sharding_constraint_p.bind(
|
||
x,
|
||
sharding=_pjit_batcher_for_sharding(
|
||
sharding, d, new_parts, resource_env.physical_mesh, x.ndim),
|
||
resource_env=resource_env,
|
||
unconstrained_dims=unconstrained_dims)
|
||
return y, d
|
||
batching.spmd_axis_primitive_batchers[sharding_constraint_p] = partial(
|
||
_sharding_constraint_batcher, False)
|
||
batching.axis_primitive_batchers[sharding_constraint_p] = partial(
|
||
_sharding_constraint_batcher, False, None)
|
||
pxla.spmd_primitive_batchers[sharding_constraint_p] = partial(
|
||
_sharding_constraint_batcher, True, None)
|
||
|
||
|
||
def _resource_typing_sharding_constraint(avals, params, source_info,
|
||
resource_env, named_axis_resources):
|
||
aval, = avals
|
||
if hasattr(params['sharding'], '_original_sharding'):
|
||
parsed_pspec = params['sharding']._original_sharding._parsed_pspec
|
||
else:
|
||
parsed_pspec = parse_flatten_op_sharding(
|
||
params['sharding']._hlo_sharding, resource_env.physical_mesh)[0]
|
||
_check_resources_against_named_axes(
|
||
"with_sharding_constraint input", aval, parsed_pspec, named_axis_resources)
|
||
|
||
pxla.custom_resource_typing_rules[sharding_constraint_p] = \
|
||
_resource_typing_sharding_constraint
|
||
|
||
# -------------------- helpers --------------------
|
||
|
||
@lru_cache(maxsize=2048)
|
||
def to_gspmd_sharding(s: XLACompatibleSharding, ndim: int,
|
||
device_or_backend_set: bool = False) -> GSPMDSharding:
|
||
if isinstance(s, GSPMDSharding):
|
||
return s
|
||
gs = GSPMDSharding(s._device_assignment, s._to_xla_hlo_sharding(ndim))
|
||
gs._original_sharding = s
|
||
if device_or_backend_set:
|
||
gs._original_sharding._device_backend = device_or_backend_set
|
||
return gs
|
||
|
||
|
||
def get_unconstrained_dims(sharding: NamedSharding):
|
||
assert sharding._parsed_pspec is not None
|
||
return {i for i, axes in enumerate(sharding._parsed_pspec)
|
||
if axes is None}
|
||
|
||
|
||
def _fast_path_get_device_assignment(
|
||
shardings: Iterable[PjitSharding]) -> Optional[XLADeviceAssignment]:
|
||
da = None
|
||
for i in shardings:
|
||
if is_unspecified(i):
|
||
continue
|
||
if is_auto(i):
|
||
return i.mesh._flat_devices_tuple # type: ignore
|
||
return i._device_assignment # type: ignore
|
||
return da
|
||
|
||
|
||
def _get_partition_spec(ppspec: Sequence[ParsedPartitionSpec]) -> Sequence[PartitionSpec]:
|
||
return [get_single_pspec(p) for p in ppspec]
|
||
|
||
|
||
def _get_op_sharding_from_executable(
|
||
executable) -> Tuple[Sequence[xc.OpSharding], Sequence[xc.OpSharding]]:
|
||
in_op_shardings: List[xc.OpSharding] = []
|
||
parameter_shardings_from_xla = executable.get_parameter_shardings()
|
||
if parameter_shardings_from_xla is not None:
|
||
in_op_shardings = parameter_shardings_from_xla
|
||
|
||
out_op_shardings: List[xc.OpSharding] = []
|
||
output_shardings_from_xla = executable.get_output_shardings()
|
||
if output_shardings_from_xla is not None:
|
||
out_op_shardings = output_shardings_from_xla
|
||
|
||
return in_op_shardings, out_op_shardings
|
||
|
||
|
||
def _get_ppspec_from_executable(executable, mesh) -> Tuple[Sequence[ParsedPartitionSpec], Sequence[ParsedPartitionSpec]]:
|
||
input_op_shardings: Sequence[xc.OpSharding] = executable.hlo_modules()[0].spmd_parameters_shardings
|
||
output_op_sharding: xc.OpSharding = executable.hlo_modules()[0].spmd_output_sharding
|
||
in_ppspec: List[ParsedPartitionSpec] = []
|
||
for s in input_op_shardings:
|
||
in_ppspec.extend(parse_flatten_op_sharding(s, mesh))
|
||
out_ppspec = parse_flatten_op_sharding(output_op_sharding, mesh)
|
||
return in_ppspec, out_ppspec
|
||
|
||
|
||
def _get_pspec_from_executable(
|
||
executable, mesh: pxla.Mesh
|
||
) -> Tuple[Tuple[PartitionSpec, ...], Tuple[PartitionSpec, ...]]:
|
||
in_ppspec, out_ppspec = _get_ppspec_from_executable(executable, mesh)
|
||
out_partition_spec = _get_partition_spec(out_ppspec)
|
||
in_partition_spec = _get_partition_spec(in_ppspec)
|
||
return tuple(in_partition_spec), tuple(out_partition_spec)
|