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2363 lines
100 KiB
Python
2363 lines
100 KiB
Python
# Copyright 2021 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import dataclasses
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from enum import IntEnum
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import inspect
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import numpy as np
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from collections import OrderedDict, Counter
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from typing import (Callable, Sequence, Tuple, Union, cast, List, Optional,
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Iterable, NamedTuple, Any)
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import itertools as it
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from functools import partial, lru_cache
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import threading
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import warnings
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import jax
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from jax._src import core
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from jax import stages
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from jax.errors import JAXTypeError
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from jax.interpreters import partial_eval as pe
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from jax.interpreters import xla
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from jax._src.interpreters.pxla import PartitionSpec
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from jax.tree_util import (
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tree_map, tree_flatten, tree_unflatten, treedef_is_leaf, tree_structure,
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treedef_tuple)
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from jax._src.sharding import Sharding
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from jax._src.sharding_impls import (
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NamedSharding, XLACompatibleSharding, GSPMDSharding,
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XLADeviceAssignment, SingleDeviceSharding, PmapSharding)
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from jax._src import array
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from jax._src import dispatch
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from jax._src import mesh
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from jax._src import linear_util as lu
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from jax._src import source_info_util
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from jax._src import traceback_util
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from jax._src import util
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from jax._src import xla_bridge as xb
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from jax._src.api_util import (
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argnums_partial_except, flatten_axes, flatten_fun, flatten_fun_nokwargs,
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donation_vector, shaped_abstractify, check_callable,
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argnames_partial_except, resolve_argnums, FLAGS)
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from jax._src.config import config
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from jax._src.interpreters import ad
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from jax._src.interpreters import batching
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from jax._src.interpreters import mlir
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from jax._src.interpreters import pxla
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from jax._src.lib.mlir import ir
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from jax._src.lib.mlir.dialects import func as func_dialect
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from jax._src.lib import xla_client as xc
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from jax._src.traceback_util import api_boundary
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from jax._src.tree_util import (prefix_errors, _generate_key_paths)
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from jax._src.util import (
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HashableFunction, safe_map, safe_zip, wraps,
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distributed_debug_log, split_list, tuple_insert, weakref_lru_cache,
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merge_lists)
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traceback_util.register_exclusion(__file__)
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class _FromGdaSingleton:
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pass
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FROM_GDA = _FromGdaSingleton()
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def _is_from_gda(x):
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# It's occasionally possible to end up with two FROM_GDA singletons (e.g. if
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# pickling in_axis_resources and sending to other processes). Make sure this
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# doesn't cause an error to avoid user confusion.
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return isinstance(x, type(FROM_GDA))
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_AUTOAxisResource = pxla.AUTOAxisResource
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AUTO = pxla.AUTO
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is_auto = pxla.is_auto
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_UnspecifiedValue = pxla.UnspecifiedValue
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_UNSPECIFIED = pxla._UNSPECIFIED
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_is_unspecified = pxla._is_unspecified
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def _is_unspecified_or_from_gda_or_auto(x):
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return _is_from_gda(x) or is_auto(x) or _is_unspecified(x)
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PjitSharding = Union[GSPMDSharding, _UnspecifiedValue, _AUTOAxisResource]
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PjitShardingMinusUnspecified = Union[GSPMDSharding, _AUTOAxisResource]
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MeshSharding = Union[NamedSharding, _UnspecifiedValue, _AUTOAxisResource]
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MeshShardingMinusUnspecified = Union[NamedSharding, _AUTOAxisResource]
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def _check_all_or_none_unspecified(axis_resources, name):
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if not axis_resources:
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return False
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unspecified_count = 0
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unspecified = _is_unspecified(axis_resources[0])
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for resource in axis_resources:
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current_is_unspecified = _is_unspecified(resource)
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if current_is_unspecified:
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unspecified_count += 1
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assert unspecified_count == 1
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if current_is_unspecified != unspecified:
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raise ValueError(f'`pjit._UNSPECIFIED` exists in {name}. '
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f'Make sure that every entry in {name} is '
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'`pjit._UNSPECIFIED`.')
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return unspecified
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def _try_infer_args(f, tree):
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dummy_args = tree_unflatten(tree, [False] * tree.num_leaves)
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try:
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return inspect.signature(f).bind(*dummy_args)
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except (TypeError, ValueError):
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return None
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def _find_arg_mismatch(arg_list, fails, fun_name):
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first_err, second_err = fails
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mismatched_args_msg = []
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for name, inp_da, aval in arg_list:
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if first_err.m_type == pxla.MismatchType.ARG_SHARDING:
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if first_err.da == inp_da:
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mismatched_args_msg.append(
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(f"argument {name} of {fun_name} with shape {aval.str_short()} and "
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f"{first_err._dev_ids_plat_str}"))
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break
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for name, inp_da, aval in arg_list:
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if second_err.m_type == pxla.MismatchType.ARG_SHARDING:
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if second_err.da == inp_da:
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mismatched_args_msg.append(
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(f"argument {name} of {fun_name} with shape {aval.str_short()} and "
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f"{second_err._dev_ids_plat_str}"))
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break
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return mismatched_args_msg
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def _device_assignment_mismatch_error(fun, fails, in_tree, args_flat, api_name):
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sig = _try_infer_args(fun, in_tree)
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args = tree_unflatten(in_tree, args_flat)
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args_aug = _generate_key_paths(args)
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arg_list = []
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for arg_key, val in args_aug:
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ak, *rem_keys = arg_key
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if sig is not None:
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loc = ''.join(str(k) for k in rem_keys)
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arg_name = f'{list(sig.arguments.keys())[ak.idx]}{loc}'
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else:
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arg_name = ''
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da = val.sharding._device_assignment if hasattr(val, 'sharding') else None
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arg_list.append((arg_name, da, shaped_abstractify(val)))
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fun_name = getattr(fun, '__qualname__', getattr(fun, '__name__', str(fun)))
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mismatched_args_msg = _find_arg_mismatch(arg_list, fails, fun_name)
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if len(mismatched_args_msg) == 2:
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first, second = mismatched_args_msg # pylint: disable=unbalanced-tuple-unpacking
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extra_msg = f" Got {first} and {second}"
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elif len(mismatched_args_msg) == 1:
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first, second = fails
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# Choose the failure left which is not already covered by ARG_SHARDING.
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left = second if first.m_type == pxla.MismatchType.ARG_SHARDING else first
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extra_msg = f" Got {mismatched_args_msg[0]} and{left._str(api_name)}"
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else:
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first, second = fails
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extra_msg = f" Got{first._str(api_name)} and{second._str(api_name)}"
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msg = (f"Received incompatible devices for {api_name}ted computation.{extra_msg}")
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return msg
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def _python_pjit_helper(fun, infer_params_fn, *args, **kwargs):
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args_flat, _, params, in_tree, out_tree, _ = infer_params_fn(
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*args, **kwargs)
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for arg in args_flat:
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dispatch.check_arg(arg)
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try:
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out_flat = pjit_p.bind(*args_flat, **params)
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except pxla.DeviceAssignmentMismatchError as e:
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fails, = e.args
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api_name = 'jit' if params['resource_env'] is None else 'pjit'
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msg = _device_assignment_mismatch_error(
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fun, fails, in_tree, args_flat, api_name)
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raise ValueError(msg) from None
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outs = tree_unflatten(out_tree, out_flat)
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return outs, out_flat, out_tree, args_flat
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def _python_pjit(fun: Callable, infer_params_fn):
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@wraps(fun)
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@api_boundary
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def wrapped(*args, **kwargs):
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if config.jax_disable_jit:
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return fun(*args, **kwargs)
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return _python_pjit_helper(fun, infer_params_fn, *args, **kwargs)[0]
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def _python_pjit_evict_fn():
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_create_pjit_jaxpr.evict_function(fun) # type: ignore
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wrapped.clear_cache = _python_pjit_evict_fn
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return wrapped
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class _MostRecentPjitCallExecutable(threading.local):
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def __init__(self):
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self.value = None
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_most_recent_pjit_call_executable = _MostRecentPjitCallExecutable()
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def _read_most_recent_pjit_call_executable():
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executable = _most_recent_pjit_call_executable.value
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_most_recent_pjit_call_executable.value = None
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return executable
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def _cpp_pjit_evict_fn(self):
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self._clear_cache()
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_create_pjit_jaxpr.evict_function(self._fun) # type: ignore
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_cpp_pjit_cache = xc._xla.PjitFunctionCache()
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def _cpp_pjit(fun: Callable, infer_params_fn, static_argnums, static_argnames,
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donate_argnums, pjit_has_explicit_sharding):
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@api_boundary
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def cache_miss(*args, **kwargs):
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outs, out_flat, out_tree, args_flat = _python_pjit_helper(
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fun, infer_params_fn, *args, **kwargs)
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executable = _read_most_recent_pjit_call_executable()
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use_fastpath = (
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executable is not None and
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isinstance(executable, pxla.MeshExecutable) and
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isinstance(executable.unsafe_call, pxla.ExecuteReplicated) and
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# No effects in computation
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not executable.unsafe_call.ordered_effects and
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not executable.unsafe_call.has_unordered_effects and
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not executable.unsafe_call.has_host_callbacks and
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all(isinstance(x, xc.ArrayImpl) for x in out_flat)
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)
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if use_fastpath:
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out_avals = [o.aval for o in out_flat]
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out_committed = [o._committed for o in out_flat]
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kept_var_bitvec = [i in executable._kept_var_idx
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for i in range(len(args_flat))]
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fastpath_data = pxla.MeshExecutableFastpathData(
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executable.xla_executable, out_tree, executable._in_shardings,
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executable._out_shardings, out_avals, out_committed, kept_var_bitvec)
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else:
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fastpath_data = None
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return outs, fastpath_data
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if pjit_has_explicit_sharding:
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global_cache = xc._xla.PjitFunctionCache()
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else:
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global_cache = _cpp_pjit_cache
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cpp_pjit_f = xc._xla.pjit( # type: ignore
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getattr(fun, "__name__", "<unnamed function>"), # type: ignore
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fun, cache_miss, static_argnums, static_argnames, # type: ignore
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donate_argnums, global_cache) # type: ignore
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cpp_pjitted_f = wraps(fun)(cpp_pjit_f)
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cpp_pjitted_f._fun = fun
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type(cpp_pjitted_f).clear_cache = _cpp_pjit_evict_fn
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return cpp_pjitted_f
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def _resolve_axis_resources_and_shardings_arg(
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in_shardings, out_shardings, in_axis_resources, out_axis_resources):
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if not _is_unspecified(in_shardings) and not _is_unspecified(in_axis_resources):
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raise ValueError(
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'Setting both in_shardings and in_axis_resources is not '
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'allowed. in_axis_resources is deprecated. Please use in_shardings.')
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if not _is_unspecified(out_shardings) and not _is_unspecified(out_axis_resources):
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raise ValueError(
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'Setting both out_shardings and out_axis_resources is not '
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'allowed. out_axis_resources is deprecated. Please use out_shardings.')
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if not _is_unspecified(in_axis_resources):
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final_in_shardings = in_axis_resources
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else:
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final_in_shardings = in_shardings
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if not _is_unspecified(out_axis_resources):
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final_out_shardings = out_axis_resources
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else:
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final_out_shardings = out_shardings
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return final_in_shardings, final_out_shardings
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def pre_infer_params(fun, in_shardings, out_shardings,
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donate_argnums, static_argnums, static_argnames, device,
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backend, abstracted_axes):
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# TODO(yashkatariya, mattjj): Remove when pjit supports dynamic shapes.
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if jax.config.jax_dynamic_shapes:
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raise ValueError("Dynamic shapes is not supported with pjit yet.")
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if abstracted_axes and not jax.config.jax_dynamic_shapes:
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raise ValueError("abstracted_axes must be used with --jax_dynamic_shapes")
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check_callable(fun)
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if (not jax.config.jax_array and
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(_is_unspecified(in_shardings) or _is_unspecified(out_shardings))):
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raise ValueError(
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"in_shardings and out_shardings should not "
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"be the unspecified singleton value. Please enable `jax.Array` to use "
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"this feature. You can use jax.config.update('jax_array', True) or "
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"set the environment variable JAX_ARRAY=1 , or set the `jax_array` "
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"boolean flag to something true-like.")
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if backend is not None or device is not None:
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warnings.warn(
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'backend and device argument on jit is deprecated. You can use a '
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'`jax.sharding.Mesh` context manager or device_put the arguments '
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'before passing them to `jit`. Please see '
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'https://jax.readthedocs.io/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html '
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'for more information.', DeprecationWarning)
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if device is not None and backend is not None:
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raise ValueError("can't specify both a device and a backend for jit, "
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f"got {device=} and {backend=}")
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if not _is_unspecified(in_shardings):
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raise ValueError('If backend or device is specified on jit, then '
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'in_shardings should not be specified.')
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if not _is_unspecified(out_shardings):
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raise ValueError('If backend or device is specified on jit, then '
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'out_shardings should not be specified.')
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if isinstance(in_shardings, list):
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# To be a tree prefix of the positional args tuple, in_axes can never be a
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# list: if in_axes is not a leaf, it must be a tuple of trees. However,
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# in cases like these users expect tuples and lists to be treated
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# essentially interchangeably, so we canonicalize lists to tuples here
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# rather than raising an error. https://github.com/google/jax/issues/2367
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in_shardings = tuple(in_shardings)
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in_shardings, _, _ = _prepare_axis_resources(in_shardings, 'in_shardings')
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out_shardings, _, _ = _prepare_axis_resources(out_shardings, 'out_shardings')
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donate_argnums, static_argnums, static_argnames = resolve_argnums(
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fun, donate_argnums, static_argnums, static_argnames)
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return (in_shardings, out_shardings, donate_argnums, static_argnums,
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static_argnames)
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def post_infer_params(fun, infer_params_fn, static_argnums, static_argnames,
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donate_argnums, abstracted_axes,
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pjit_has_explicit_sharding):
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if FLAGS.experimental_cpp_pjit and abstracted_axes is None:
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wrapped = _cpp_pjit(fun, infer_params_fn, static_argnums, static_argnames,
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donate_argnums, pjit_has_explicit_sharding)
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else:
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wrapped = _python_pjit(fun, infer_params_fn)
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@api_boundary
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def lower(*args, _experimental_lowering_platform: Optional[str] = None,
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**kwargs):
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(args_flat, flat_local_in_avals, params, in_tree, out_tree,
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donate_argnums) = infer_params_fn(*args, **kwargs)
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if jax.config.jax_array:
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resource_env = params['resource_env']
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mesh = None if resource_env is None else resource_env.physical_mesh
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in_shardings = _resolve_in_shardings(
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args_flat, params['in_shardings'], params['out_shardings'], mesh)
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else:
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in_shardings = params['in_shardings']
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in_is_global = _calc_is_global_sequence(
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params['in_positional_semantics'], in_shardings)
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lowering = _pjit_lower(
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params['jaxpr'], in_shardings, params['out_shardings'],
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params['resource_env'], params['donated_invars'], params['name'],
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in_is_global, params['keep_unused'], always_lower=True,
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lowering_platform=_experimental_lowering_platform)
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if kwargs:
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args_kwargs_in_tree = in_tree
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else:
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args_kwargs_in_tree = treedef_tuple([in_tree, tree_flatten({})[1]])
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return stages.Lowered.from_flat_info(
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lowering, args_kwargs_in_tree, flat_local_in_avals, donate_argnums,
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out_tree)
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wrapped.lower = lower
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return wrapped
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def _pjit_explicit_sharding(in_shardings, out_shardings, device,
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backend) -> bool:
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in_shardings_flat, _ = tree_flatten(in_shardings)
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out_shardings_flat, _ = tree_flatten(out_shardings)
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return (device is not None or
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backend is not None or
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any(not _is_unspecified(i) for i in in_shardings_flat) or
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any(not _is_unspecified(i) for i in out_shardings_flat))
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class PjitInfo(NamedTuple):
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||
fun: Callable
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in_shardings: Any
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out_shardings: Any
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static_argnums: Tuple[int, ...]
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||
static_argnames: Tuple[str, ...]
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||
donate_argnums: Tuple[int, ...]
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||
device: Optional[xc.Device]
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||
backend: Optional[str]
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||
keep_unused: bool
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inline: bool
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resource_env: Any
|
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|
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|
||
def common_infer_params(pjit_info_args, *args, **kwargs):
|
||
(fun, user_in_shardings, user_out_shardings, static_argnums, static_argnames,
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||
donate_argnums, device, backend, keep_unused, inline,
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||
resource_env) = pjit_info_args
|
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|
||
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
|
||
if pjit_mesh.empty:
|
||
if jax.config.jax_array:
|
||
# Don't enforce requiring a mesh when `jax_array` flag is enabled. But
|
||
# if mesh is not empty then pjit will respect it.
|
||
pass
|
||
else:
|
||
raise RuntimeError("pjit requires a non-empty mesh! Are you sure that "
|
||
"it's defined at the call site?")
|
||
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.")
|
||
|
||
f = lu.wrap_init(fun)
|
||
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)
|
||
args_flat, in_tree = tree_flatten((dyn_args, dyn_kwargs))
|
||
flat_fun, out_tree = flatten_fun(f, in_tree)
|
||
else:
|
||
args_flat, 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 jax.config.jax_debug_nans:
|
||
donated_invars = donation_vector(donate_argnums, dyn_args, dyn_kwargs)
|
||
else:
|
||
donated_invars = (False,) * len(args_flat)
|
||
|
||
if jax.config.jax_array:
|
||
# 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.
|
||
if backend or device:
|
||
in_shardings = out_shardings = _create_sharding_with_device_backend(
|
||
device, backend)
|
||
else:
|
||
in_shardings = tree_map(
|
||
lambda x: _create_sharding_for_array(pjit_mesh, x), user_in_shardings)
|
||
out_shardings = tree_map(
|
||
lambda x: _create_sharding_for_array(pjit_mesh, x), user_out_shardings)
|
||
else:
|
||
in_shardings = tree_map(
|
||
lambda x: _create_mesh_pspec_sharding_from_parsed_pspec(pjit_mesh, x),
|
||
user_in_shardings)
|
||
out_shardings = tree_map(
|
||
lambda x: x if _is_unspecified(x) else
|
||
_create_mesh_pspec_sharding_from_parsed_pspec(pjit_mesh, x), user_out_shardings)
|
||
|
||
del user_in_shardings, user_out_shardings
|
||
|
||
local_in_avals = tuple(shaped_abstractify(a) for a in args_flat)
|
||
|
||
in_positional_semantics = (pxla._PositionalSemantics.GLOBAL,) * len(args_flat)
|
||
out_positional_semantics = pxla._PositionalSemantics.GLOBAL
|
||
|
||
global_in_avals, canonicalized_in_shardings_flat = _process_in_axis_resources(
|
||
hashable_pytree(in_shardings), local_in_avals, in_tree, in_positional_semantics,
|
||
resource_env)
|
||
|
||
jaxpr, consts, canonicalized_out_shardings_flat = _pjit_jaxpr(
|
||
flat_fun, hashable_pytree(out_shardings), global_in_avals,
|
||
HashableFunction(out_tree, closure=()),
|
||
('jit' if resource_env is None else 'pjit'))
|
||
|
||
if (any(_is_from_gda(i) for i in canonicalized_in_shardings_flat) or
|
||
not jax.config.jax_array):
|
||
canonicalized_in_shardings_flat = _maybe_replace_from_gda_with_pspec(
|
||
canonicalized_in_shardings_flat, args_flat)
|
||
|
||
assert len(args_flat) == len(canonicalized_in_shardings_flat)
|
||
|
||
canonicalized_in_shardings_flat = (
|
||
_UNSPECIFIED,) * len(consts) + canonicalized_in_shardings_flat
|
||
donated_invars = (False,) * len(consts) + donated_invars
|
||
in_positional_semantics = (
|
||
pxla._PositionalSemantics.GLOBAL,) * len(consts) + in_positional_semantics
|
||
|
||
# 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>'),
|
||
in_positional_semantics=in_positional_semantics,
|
||
out_positional_semantics=out_positional_semantics,
|
||
keep_unused=keep_unused,
|
||
inline=inline,
|
||
)
|
||
return (consts + args_flat, local_in_avals, params, in_tree, out_tree(),
|
||
donate_argnums)
|
||
|
||
|
||
# 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.
|
||
|
||
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 valid resource assignment specifications are:
|
||
- :py:obj:`None`, in which case the value will be replicated on all devices
|
||
- :py:class:`XLACompatibleSharding`, which will decide how the value
|
||
will be partitioned. With this, using a mesh context manager is not
|
||
required.
|
||
- :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.
|
||
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.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)
|
||
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_from_gda_or_auto(x):
|
||
return x
|
||
return pxla.create_mesh_pspec_sharding(mesh, x.user_spec, x)
|
||
|
||
|
||
def _create_sharding_for_array(mesh, x):
|
||
# TODO(yashkatariya): Only check for auto and unspecified here after
|
||
# FROM_GDA is removed.
|
||
if isinstance(x, XLACompatibleSharding) or _is_unspecified_or_from_gda_or_auto(x):
|
||
return x
|
||
if mesh is None:
|
||
raise RuntimeError(
|
||
"jit does not support using the mesh context manager and passing "
|
||
"PartitionSpecs to in_shardings or out_shardings. Please pass in "
|
||
"the `Sharding` explicitly via in_shardings or out_shardings.")
|
||
if mesh.empty:
|
||
raise RuntimeError("pjit requires a non-empty mesh! Is a mesh defined at "
|
||
"the call site? Alternatively, provide a "
|
||
"XLACompatibleSharding to pjit 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])
|
||
out._device_backend = True
|
||
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)
|
||
|
||
if config.jax_array:
|
||
axis_tree = shardings
|
||
else:
|
||
# Replace axis_resources with unparsed versions to avoid revealing internal details
|
||
axis_tree = tree_map(lambda parsed: parsed.spec, shardings)
|
||
|
||
# Because ecause 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, local_in_avals,
|
||
in_tree, in_positional_semantics,
|
||
resource_env):
|
||
orig_in_shardings = in_shardings_thunk()
|
||
# Only do this if original in_shardings are unspecified. If they are
|
||
# FROM_GDA or AUTO, go via flatten_axis_resources.
|
||
if _is_unspecified(orig_in_shardings):
|
||
in_shardings_flat = (orig_in_shardings,) * len(local_in_avals)
|
||
else:
|
||
in_shardings_flat = flatten_axis_resources(
|
||
"pjit in_shardings", in_tree, orig_in_shardings,
|
||
tupled_args=True)
|
||
|
||
pjit_check_aval_sharding(in_shardings_flat, local_in_avals, "pjit arguments",
|
||
allow_uneven_sharding=False)
|
||
global_in_avals = local_in_avals
|
||
# TODO(yashkatariya): Only check for is_auto or _is_unspecified when
|
||
# FROM_GDA is removed.
|
||
canonicalized_shardings = tuple(
|
||
i if _is_unspecified_or_from_gda_or_auto(i) else to_gspmd_sharding(i, aval.ndim)
|
||
for i, aval in safe_zip(in_shardings_flat, global_in_avals))
|
||
return tuple(global_in_avals), canonicalized_shardings
|
||
|
||
|
||
@lu.cache
|
||
def _create_pjit_jaxpr(fun, global_in_avals, api_name):
|
||
prev_positional_val = pxla.positional_semantics.val
|
||
try:
|
||
pxla.positional_semantics.val = pxla._PositionalSemantics.GLOBAL
|
||
with dispatch.log_elapsed_time(f"Finished tracing + transforming {fun.__name__} "
|
||
"for pjit in {elapsed_time} sec",
|
||
event=dispatch.JAXPR_TRACE_EVENT):
|
||
jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic(
|
||
fun, global_in_avals, debug_info=pe.debug_info_final(fun, api_name))
|
||
finally:
|
||
pxla.positional_semantics.val = prev_positional_val
|
||
|
||
if any(isinstance(c, core.Tracer) for c in consts):
|
||
jaxpr = pe.convert_constvars_jaxpr(jaxpr)
|
||
jaxpr = pe.close_jaxpr(jaxpr)
|
||
final_consts = consts
|
||
else:
|
||
jaxpr = core.ClosedJaxpr(jaxpr, consts)
|
||
final_consts = []
|
||
return jaxpr, final_consts, global_out_avals
|
||
|
||
|
||
@lru_cache(maxsize=4096)
|
||
def _check_and_canonicalize_out_shardings(
|
||
out_shardings_thunk, out_tree, global_out_avals):
|
||
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(global_out_avals)
|
||
else:
|
||
out_shardings_flat = flatten_axis_resources(
|
||
"pjit out_shardings", out_tree(), orig_out_shardings,
|
||
tupled_args=False)
|
||
|
||
pjit_check_aval_sharding(out_shardings_flat, global_out_avals, "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)
|
||
for o, aval in safe_zip(out_shardings_flat, global_out_avals)
|
||
)
|
||
return canonicalized_out_shardings_flat
|
||
|
||
|
||
def _pjit_jaxpr(fun, out_shardings_thunk, global_in_avals, out_tree, api_name):
|
||
jaxpr, final_consts, global_out_avals = _create_pjit_jaxpr(
|
||
fun, global_in_avals, api_name)
|
||
canonicalized_out_shardings_flat = _check_and_canonicalize_out_shardings(
|
||
out_shardings_thunk, out_tree, tuple(global_out_avals))
|
||
# 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, what_aval: str, allow_uneven_sharding: bool):
|
||
for aval, s in zip(flat_avals, shardings):
|
||
if _is_unspecified_or_from_gda_or_auto(s):
|
||
continue
|
||
global_str = "" if s.is_fully_addressable else " global"
|
||
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_op_sharding(len(shape))
|
||
except ValueError as e:
|
||
raise ValueError(f'One of {what_aval} 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.
|
||
op_sharding = s._to_xla_op_sharding(len(shape))
|
||
assert op_sharding is not None
|
||
num_ways_dim_sharded, _ = pxla.get_num_ways_dim_sharded(
|
||
cast(xc.OpSharding, op_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} was given the sharding "
|
||
f"of {s}, which implies that "
|
||
f"the{global_str} size of its dimension {i} should be "
|
||
f"divisible by {size}, but it is equal to {shape[i]} "
|
||
f"(full shape: {shape}) ")
|
||
|
||
|
||
class SpecSync(IntEnum):
|
||
"""Encodes how much out of sync the real value of partitions is compared to the user specified one.
|
||
|
||
We use this to make sure we don't show garbage modified values while claiming
|
||
that the users have specified them like that.
|
||
"""
|
||
OUT_OF_SYNC = 0 # Arbitrary changes, including new axes inserted
|
||
DIM_PERMUTE = 1 # Dimensions permuted, but no new sharding axes
|
||
IN_SYNC = 2 # Entirely in sync
|
||
|
||
class ParsedPartitionSpec:
|
||
__slots__ = ('unsafe_user_spec', 'partitions', 'sync')
|
||
|
||
def __init__(self, user_spec, partitions, sync=SpecSync.IN_SYNC):
|
||
self.unsafe_user_spec = user_spec
|
||
# None in partitions represents unconstrained dim.
|
||
# TODO(yashkatariya): May use a sentinel value.
|
||
self.partitions = tuple(partitions)
|
||
self.sync = sync
|
||
|
||
@property
|
||
def user_spec(self):
|
||
return self.unsynced_user_spec(SpecSync.IN_SYNC)
|
||
|
||
def get_partition_spec(self) -> PartitionSpec:
|
||
if self.sync < SpecSync.IN_SYNC:
|
||
return _get_single_pspec(self)
|
||
else:
|
||
if isinstance(self.unsafe_user_spec, PartitionSpec):
|
||
return self.unsafe_user_spec
|
||
else:
|
||
return _get_single_pspec(self)
|
||
|
||
def unsynced_user_spec(self, min_sync):
|
||
if self.sync < min_sync:
|
||
raise AssertionError(f"Please open a bug report! ({self.sync} >= {min_sync})")
|
||
return self.unsafe_user_spec
|
||
|
||
def insert_axis_partitions(self, dim, val):
|
||
parts = self.partitions
|
||
too_short = dim - len(parts)
|
||
if too_short > 0:
|
||
parts += ((),) * too_short
|
||
new_partitions = tuple_insert(parts, dim, val)
|
||
new_sync = SpecSync.DIM_PERMUTE if (val == () or val is None) else SpecSync.OUT_OF_SYNC
|
||
return ParsedPartitionSpec(self.unsafe_user_spec, new_partitions, sync=new_sync)
|
||
|
||
@classmethod
|
||
def from_user_input(cls, entry, arg_name, allow_unconstrained_dims=False):
|
||
if entry is None:
|
||
return cls(entry, ())
|
||
if not isinstance(entry, PartitionSpec):
|
||
raise TypeError(f"{arg_name} are expected to be "
|
||
f"PartitionSpec instances or None, but got {entry}")
|
||
axis_specs = []
|
||
for axis_spec in entry:
|
||
if axis_spec is None:
|
||
axis_spec = ()
|
||
elif isinstance(axis_spec, (list, tuple)):
|
||
axis_spec = tuple(axis_spec)
|
||
elif axis_spec == PartitionSpec.UNCONSTRAINED:
|
||
if not allow_unconstrained_dims:
|
||
raise ValueError(f"Unconstrained dims are not allowed: {entry}")
|
||
axis_spec = None
|
||
else:
|
||
axis_spec = (axis_spec,)
|
||
axis_specs.append(axis_spec)
|
||
return cls(entry, axis_specs)
|
||
|
||
def __hash__(self):
|
||
return hash((self.partitions, self.sync))
|
||
|
||
def __eq__(self, other):
|
||
return (self.partitions == other.partitions and
|
||
self.sync == other.sync)
|
||
|
||
def __len__(self):
|
||
return len(self.partitions)
|
||
|
||
def __getitem__(self, i):
|
||
return self.partitions[i]
|
||
|
||
def __iter__(self):
|
||
return iter(self.partitions)
|
||
|
||
def __repr__(self):
|
||
return (f"ParsedPartitionSpec(partitions={self.partitions}, "
|
||
f"unsafe_user_spec={self.unsafe_user_spec}, "
|
||
f"sync={self.sync})")
|
||
|
||
class CanonicalizedParsedPartitionSpec(ParsedPartitionSpec):
|
||
"""ParsedPartitionSpecs that are canonicalized.
|
||
|
||
ParsedPartitionSpecs may contain trailing empty tuples, that make them
|
||
semantically different in general, and yet in some situations we prefer
|
||
to regard them as equivalent. For example, partitions of () and ((),)
|
||
cannot be always considered equivalent, since the first one is a valid
|
||
spec for a scalar value, while the second is not! However, when either of
|
||
those are applied to a 2D array, they both mean that the array is fully
|
||
replicated.
|
||
|
||
So CanonicalizedParsedPartitionSpecs removes the trailing empty tuples from
|
||
partitions.
|
||
"""
|
||
|
||
def __init__(self, parsed_pspec: ParsedPartitionSpec):
|
||
partitions = list(parsed_pspec.partitions)
|
||
while partitions and partitions[-1] == ():
|
||
partitions.pop()
|
||
|
||
super().__init__(parsed_pspec.unsafe_user_spec, partitions,
|
||
parsed_pspec.sync)
|
||
|
||
def __repr__(self):
|
||
return (f"CanonicalizedParsedPartitionSpec(partitions={self.partitions}, "
|
||
f"unsafe_user_spec={self.unsafe_user_spec}, "
|
||
f"sync={self.sync})")
|
||
|
||
|
||
def _prepare_axis_resources(axis_resources,
|
||
arg_name,
|
||
allow_unconstrained_dims=False):
|
||
# PyTrees don't treat None values as leaves, so we use an is_leaf function.
|
||
entries, treedef = tree_flatten(axis_resources, is_leaf=lambda x: x is None)
|
||
what = f"{arg_name} leaf specifications"
|
||
# All entries should be specified or if unspecified then there should only
|
||
# be 1 entry for that since _UNSPECIFIED is a private API.
|
||
_check_all_or_none_unspecified(entries, arg_name)
|
||
|
||
new_entries = []
|
||
for entry in entries:
|
||
if _is_unspecified_or_from_gda_or_auto(entry):
|
||
new_entries.append(entry)
|
||
elif isinstance(entry, Sharding):
|
||
if isinstance(entry, PmapSharding):
|
||
raise ValueError(f'One of {what} got sharding {entry} which is not '
|
||
'allowed.')
|
||
if not isinstance(entry, XLACompatibleSharding):
|
||
raise ValueError(f'One of {what} got sharding {entry} which is not a '
|
||
'subclass of XLACompatibleSharding.')
|
||
new_entries.append(entry)
|
||
else:
|
||
new_entries.append(ParsedPartitionSpec.from_user_input(
|
||
entry, what, allow_unconstrained_dims=allow_unconstrained_dims))
|
||
|
||
_check_unique_resources(new_entries, arg_name)
|
||
return tree_unflatten(treedef, new_entries), new_entries, treedef
|
||
|
||
|
||
def _check_unique_resources(axis_resources, arg_name):
|
||
for arg_axis_resources in axis_resources:
|
||
if not arg_axis_resources: continue
|
||
if (_is_unspecified_or_from_gda_or_auto(arg_axis_resources) or
|
||
isinstance(arg_axis_resources, XLACompatibleSharding)):
|
||
continue
|
||
constrained_dims = [d for d in arg_axis_resources if d is not None]
|
||
resource_counts = Counter(it.chain.from_iterable(constrained_dims))
|
||
if not resource_counts: continue
|
||
if resource_counts.most_common(1)[0][1] > 1:
|
||
multiple_uses = [r for r, c in resource_counts.items() if c > 1]
|
||
if multiple_uses:
|
||
raise ValueError(f"A single {arg_name} specification can map every mesh axis "
|
||
f"to at most one positional dimension, but {arg_axis_resources.user_spec} "
|
||
f"has duplicate entries for {mesh.show_axes(multiple_uses)}")
|
||
|
||
# -------------------- 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 safe_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 pxla.is_op_sharding_replicated(
|
||
pjit_in_s._to_xla_op_sharding(arg.ndim)) and xb.process_count() > 1: # type: ignore
|
||
raise ValueError(
|
||
'When jax.Array is enabled, 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 pxla.are_op_shardings_equal(
|
||
pjit_in_s._to_xla_op_sharding(arg.ndim), # type: ignore
|
||
arg_s._to_xla_op_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(*args, jaxpr,
|
||
in_shardings, out_shardings, resource_env,
|
||
donated_invars, name,
|
||
in_positional_semantics, out_positional_semantics,
|
||
keep_unused, inline):
|
||
|
||
global _most_recent_pjit_call_executable
|
||
|
||
if config.jax_array:
|
||
in_shardings = _resolve_in_shardings(
|
||
args, in_shardings, out_shardings,
|
||
resource_env.physical_mesh if resource_env is not None else None)
|
||
|
||
in_is_global = _calc_is_global_sequence(in_positional_semantics, in_shardings)
|
||
if config.jax_array:
|
||
_allow_propagation_to_outputs = [_is_unspecified(o) for o in out_shardings]
|
||
else:
|
||
_allow_propagation_to_outputs = [False] * len(out_shardings)
|
||
compiled = _pjit_lower(
|
||
jaxpr, in_shardings, out_shardings, resource_env,
|
||
donated_invars, name, in_is_global, keep_unused,
|
||
always_lower=False, lowering_platform=None).compile(
|
||
_allow_propagation_to_outputs=_allow_propagation_to_outputs)
|
||
_most_recent_pjit_call_executable.value = 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)
|
||
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", list(map(xla.abstractify, args))),
|
||
("fingerprint", fingerprint))
|
||
try:
|
||
return compiled.unsafe_call(*args)
|
||
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)
|
||
|
||
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._op_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
|
||
return (all(pxla.are_op_shardings_equal(s._op_sharding, o._op_sharding) # pytype: disable=attribute-error
|
||
if isinstance(s, GSPMDSharding) and isinstance(o, GSPMDSharding)
|
||
else s == o
|
||
for s, o in safe_zip(self.shardings, other.shardings)) 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(in_shardings, da)
|
||
out_shardings = SameDeviceAssignmentTuple(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,
|
||
in_is_global: Sequence[bool],
|
||
keep_unused: 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'
|
||
|
||
# Convert to `NamedSharding` when `jax_array` is not enabled. This is
|
||
# because GDA/SDA/DA are dependent on mesh for generating outputs.
|
||
# NamedSharding is required for host-local inputs too.
|
||
any_auto = pxla.check_if_any_auto(it.chain(in_shardings, out_shardings))
|
||
if not config.jax_array or any_auto:
|
||
in_shardings: Tuple[MeshShardingMinusUnspecified, ...] = cast( # type:ignore[no-redef]
|
||
Tuple[MeshShardingMinusUnspecified, ...], tuple(
|
||
NamedSharding._from_parsed_pspec(
|
||
mesh, parse_flatten_op_sharding(i._op_sharding, mesh)[0]) # type: ignore
|
||
if isinstance(i, GSPMDSharding) else i
|
||
for i in in_shardings
|
||
))
|
||
out_shardings: Tuple[MeshSharding, ...] = cast( # type: ignore[no-redef]
|
||
Tuple[MeshSharding, ...], tuple(
|
||
NamedSharding._from_parsed_pspec(
|
||
mesh, parse_flatten_op_sharding(o._op_sharding, mesh)[0]) # type: ignore
|
||
if isinstance(o, GSPMDSharding) else o
|
||
for o in out_shardings
|
||
))
|
||
|
||
# For `pjit(xmap)` cases, it needs to take the `lower_mesh_computation` path
|
||
# because `xmap` only supports SPMDAxisContext right now.
|
||
if any_auto or 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, in_is_global=in_is_global,
|
||
lowering_platform=lowering_platform)
|
||
else:
|
||
return pxla.lower_sharding_computation(
|
||
jaxpr, api_name, name, in_shardings, out_shardings, donated_invars,
|
||
jaxpr.in_avals, in_is_global=in_is_global, keep_unused=keep_unused,
|
||
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)
|
||
else:
|
||
return trace.default_process_primitive(pjit_p, args, params)
|
||
|
||
pe.custom_staging_rules[pjit_p] = pjit_staging_rule
|
||
|
||
|
||
def _pjit_abstract_eval(*args, jaxpr, out_shardings, resource_env,
|
||
out_positional_semantics, **_):
|
||
if config.jax_array:
|
||
return jaxpr.out_avals, jaxpr.effects
|
||
return global_to_local(out_positional_semantics, jaxpr.out_avals,
|
||
out_shardings, resource_env.physical_mesh), 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,
|
||
in_positional_semantics, out_positional_semantics,
|
||
keep_unused, inline):
|
||
if not config.jax_jit_pjit_api_merge:
|
||
if not isinstance(ctx.module_context.axis_context,
|
||
(mlir.SPMDAxisContext, mlir.ShardingContext)):
|
||
raise RuntimeError("Nesting pjit() inside jit() is not allowed.")
|
||
|
||
effects = list(ctx.tokens_in.effects())
|
||
output_types = safe_map(mlir.aval_to_ir_types, ctx.avals_out)
|
||
output_types = [mlir.token_type()] * len(effects) + output_types
|
||
flat_output_types = util.flatten(output_types)
|
||
|
||
arg_shardings = [None if _is_unspecified(i) else i._to_xla_op_sharding(aval.ndim)
|
||
for aval, i in safe_zip(ctx.avals_in, in_shardings)]
|
||
result_shardings = [None if _is_unspecified(o) else o._to_xla_op_sharding(aval.ndim)
|
||
for aval, o in safe_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 = util.unflatten(call.results, safe_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, in_positional_semantics,
|
||
out_positional_semantics, 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,
|
||
in_positional_semantics=in_positional_semantics,
|
||
out_positional_semantics=out_positional_semantics,
|
||
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:
|
||
new_op = s._op_sharding.clone() # type: ignore
|
||
tad = list(new_op.tile_assignment_dimensions)
|
||
tad.insert(dim, 1)
|
||
new_op.tile_assignment_dimensions = tad
|
||
return GSPMDSharding(s._device_assignment, new_op) # type: ignore
|
||
else:
|
||
assert isinstance(s, GSPMDSharding)
|
||
assert mesh is not None and not mesh.empty
|
||
parsed_pspec = parse_flatten_op_sharding(s._op_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_op_sharding(ndim))
|
||
|
||
|
||
def _pjit_jvp(primals_in, tangents_in,
|
||
jaxpr, in_shardings, out_shardings,
|
||
resource_env, donated_invars, name, in_positional_semantics,
|
||
out_positional_semantics, 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,
|
||
in_positional_semantics=(*in_positional_semantics, *_filter_zeros_in(in_positional_semantics)),
|
||
out_positional_semantics=out_positional_semantics,
|
||
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 safe_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, in_positional_semantics,
|
||
out_positional_semantics, 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 zip(l, should_keep) if keep)
|
||
|
||
if config.jax_array:
|
||
residual_shardings = (_UNSPECIFIED,) * num_residuals
|
||
else:
|
||
da = list(resource_env.physical_mesh.devices.flat)
|
||
residual_shardings = (GSPMDSharding.get_replicated(da),) * 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,
|
||
in_positional_semantics=keep_where(in_positional_semantics, known_ins),
|
||
out_positional_semantics=out_positional_semantics,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
|
||
if not config.jax_array:
|
||
if num_residuals:
|
||
in_is_global = _calc_is_global_sequence(
|
||
known_params['in_positional_semantics'], known_params['in_shardings'])
|
||
compiled = _pjit_lower(
|
||
known_params["jaxpr"], known_params["in_shardings"],
|
||
known_params["out_shardings"], known_params["resource_env"],
|
||
known_params["donated_invars"], known_params["name"],
|
||
in_is_global, known_params['keep_unused'], always_lower=False,
|
||
lowering_platform=None).compile(
|
||
_allow_propagation_to_outputs=[True] * len(known_params['out_shardings']),
|
||
_allow_compile_replicated=False)
|
||
da = compiled._device_assignment
|
||
_, out_gspmd_shardings = pxla.get_gspmd_shardings_from_executable(
|
||
compiled.xla_executable, da, len(known_jaxpr.in_avals),
|
||
len(known_jaxpr.out_avals))
|
||
assert len(out_gspmd_shardings) == len(known_jaxpr.out_avals), (
|
||
len(out_gspmd_shardings), len(known_jaxpr.out_avals))
|
||
out_op_shardings = [o._to_xla_op_sharding(a.ndim) for o, a in
|
||
safe_zip(out_gspmd_shardings, known_jaxpr.out_avals)]
|
||
residual_op_shardings = tuple(out_op_shardings[-num_residuals:])
|
||
else:
|
||
residual_op_shardings = ()
|
||
assert len(residual_shardings) == len(residual_op_shardings), (
|
||
len(residual_shardings), len(residual_op_shardings))
|
||
residual_shardings = tuple(GSPMDSharding(da, op) for op in residual_op_shardings)
|
||
known_params['out_shardings'] = (
|
||
keep_where(out_shardings, known_outs) + residual_shardings)
|
||
|
||
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 safe_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 safe_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,
|
||
in_positional_semantics=(keep_where(
|
||
in_positional_semantics, unknown_ins) + (out_positional_semantics,) * num_residuals),
|
||
out_positional_semantics=out_positional_semantics,
|
||
keep_unused=keep_unused,
|
||
inline=inline)
|
||
unknown_tracers_in = [t for t in in_tracers if not t.pval.is_known()]
|
||
if config.jax_array:
|
||
unknown_out_avals = unknown_jaxpr.out_avals
|
||
else:
|
||
unknown_out_avals = global_to_local(
|
||
unknown_params["out_positional_semantics"], unknown_jaxpr.out_avals,
|
||
unknown_params["out_shardings"],
|
||
unknown_params["resource_env"].physical_mesh)
|
||
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'])
|
||
in_positional_semantics_known, _ = pe.partition_list(
|
||
unks_in, params_known['in_positional_semantics'])
|
||
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),
|
||
in_positional_semantics=tuple(in_positional_semantics_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]
|
||
_, in_positional_semantics_staged = pe.partition_list(
|
||
inst_in, params_staged['in_positional_semantics'])
|
||
in_positional_semantics_staged = [
|
||
pxla._PositionalSemantics.GLOBAL] * num_res + in_positional_semantics_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),
|
||
in_positional_semantics=tuple(in_positional_semantics_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, api_name):
|
||
transpose_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(
|
||
fun, in_avals, debug_info=pe.debug_info_final(fun, api_name))
|
||
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, in_positional_semantics,
|
||
out_positional_semantics, 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)
|
||
)
|
||
transpose_in_positional_semantics = (
|
||
*prune_type(ad.UndefinedPrimal, in_positional_semantics, primals_in),
|
||
*prune_type(ad.Zero, (out_positional_semantics,) * len(cts_in), cts_in)
|
||
)
|
||
global_cts_in_avals = tuple(core.raise_to_shaped(core.get_aval(ct))
|
||
for ct in primals_and_nz_cts_in)
|
||
if not config.jax_array:
|
||
global_cts_in_avals = tuple(local_to_global(
|
||
transpose_in_positional_semantics, global_cts_in_avals,
|
||
transpose_in_shardings, resource_env.physical_mesh))
|
||
|
||
api_name = 'jit' if resource_env is None else 'pjit'
|
||
transpose_jaxpr = _pjit_transpose_trace(body, global_cts_in_avals, api_name)
|
||
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,
|
||
in_positional_semantics=transpose_in_positional_semantics,
|
||
out_positional_semantics=out_positional_semantics,
|
||
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 safe_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),
|
||
in_positional_semantics=keep_where(eqn_params["in_positional_semantics"],
|
||
used_inputs),
|
||
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.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._op_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._op_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(p == pxla._PositionalSemantics.GLOBAL
|
||
for p in params['in_positional_semantics']):
|
||
del params['in_positional_semantics']
|
||
if params['out_positional_semantics'] == pxla._PositionalSemantics.GLOBAL:
|
||
del params['out_positional_semantics']
|
||
if all(pxla._is_unspecified(s) for s in params['in_shardings']):
|
||
del params['in_shardings']
|
||
if all(pxla._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 _resolve_wsc_args(axis_resources, shardings):
|
||
if not _is_unspecified(axis_resources) and not _is_unspecified(shardings):
|
||
raise ValueError(
|
||
'Setting both axis_resources and shardings is not '
|
||
'allowed. axis_resources is deprecated. Please use shardings.')
|
||
if _is_unspecified(axis_resources) and _is_unspecified(shardings):
|
||
raise ValueError(
|
||
'Not specifying shardings to `with_sharding_constraint` is not allowed. '
|
||
'Please specify the shardings argument with a concrete sharding. Note '
|
||
'that axis_resources is deprecated, so use the shardings argument.')
|
||
|
||
if not _is_unspecified(axis_resources):
|
||
final_shardings = axis_resources
|
||
else:
|
||
final_shardings = shardings
|
||
return final_shardings
|
||
|
||
|
||
# TODO(yashkatariya): Remove the axis_resources argument and make the signature
|
||
# `with_sharding_constraint(x, shardings)` with no defaults after deprecation
|
||
# period is finished. The deprecation period expires 3 months from Feb 13, 2023.
|
||
def with_sharding_constraint(x, axis_resources=_UNSPECIFIED,
|
||
shardings=_UNSPECIFIED):
|
||
final_shardings = _resolve_wsc_args(axis_resources, shardings)
|
||
x_flat, tree = tree_flatten(x)
|
||
user_shardings, _, _ = _prepare_axis_resources(
|
||
final_shardings, "shardings", allow_unconstrained_dims=True)
|
||
del final_shardings
|
||
|
||
user_shardings_flat = tuple(
|
||
flatten_axes("with_sharding_constraint shardings", tree, user_shardings))
|
||
del user_shardings
|
||
|
||
resource_env = jax._src.mesh.thread_resources.env
|
||
mesh = resource_env.physical_mesh
|
||
|
||
if config.jax_array:
|
||
shardings_flat = [_create_sharding_for_array(mesh, a)
|
||
for a in user_shardings_flat]
|
||
unconstrained_dims = [get_unconstrained_dims(s)
|
||
if isinstance(s, NamedSharding) else {}
|
||
for s in shardings_flat]
|
||
else:
|
||
shardings_flat = [pxla.create_mesh_pspec_sharding(mesh, a.user_spec, a)
|
||
for a in user_shardings_flat]
|
||
# Calculate unconstrained_dims from NamedSharding because that information
|
||
# is lost when converted to OpSharding. Bind unconstrained_dims to
|
||
# with_sharding_constraint primitive.
|
||
unconstrained_dims = [get_unconstrained_dims(s) for s in shardings_flat]
|
||
|
||
del user_shardings_flat
|
||
|
||
pjit_check_aval_sharding(shardings_flat, x_flat, "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 safe_zip(x_flat, shardings_flat, unconstrained_dims)]
|
||
return tree_unflatten(tree, outs)
|
||
|
||
def _sharding_constraint_impl(x, sharding, resource_env, unconstrained_dims):
|
||
# TODO(skye): can we also prevent this from being called in other
|
||
# non-pjit contexts? (e.g. pmap, control flow)
|
||
raise NotImplementedError(
|
||
"with_sharding_constraint() should only be called inside pjit()")
|
||
|
||
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
|
||
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, mlir.SPMDAxisContext):
|
||
mesh = resource_env.physical_mesh
|
||
parsed_pspec = parse_flatten_op_sharding(sharding._op_sharding, mesh)[0]
|
||
mps = NamedSharding._from_parsed_pspec(mesh, parsed_pspec)
|
||
sharding = GSPMDSharding(
|
||
mps._device_assignment, mps._to_xla_op_sharding(aval.ndim, axis_ctx=axis_ctx))
|
||
return [
|
||
mlir.wrap_with_sharding_op(
|
||
x_node,
|
||
sharding._to_xla_op_sharding(aval.ndim),
|
||
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']._op_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 --------------------
|
||
|
||
def get_array_mapping(
|
||
axis_resources: Union[ParsedPartitionSpec, _AUTOAxisResource, _UnspecifiedValue]
|
||
) -> pxla.ArrayMappingOrAutoOrUnspecified:
|
||
# TODO(yashkatariya): Use `TypeGuard` on `is_auto` when it is supported.
|
||
# Don't use `is_auto` here to satisfy pytype and mypy.
|
||
if isinstance(axis_resources, (_AUTOAxisResource, _UnspecifiedValue)):
|
||
return axis_resources
|
||
return OrderedDict((axis, i)
|
||
for i, axes in enumerate(axis_resources)
|
||
if axes is not None for axis in axes)
|
||
|
||
|
||
def to_gspmd_sharding(s: XLACompatibleSharding, ndim: int) -> GSPMDSharding:
|
||
if isinstance(s, GSPMDSharding):
|
||
return s
|
||
gspmd_sharding = GSPMDSharding(
|
||
s._device_assignment, s._to_xla_op_sharding(ndim))
|
||
gspmd_sharding._original_sharding = s
|
||
return gspmd_sharding
|
||
|
||
|
||
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 global_to_local(positional_semantics, avals, shardings, mesh):
|
||
if config.jax_array:
|
||
return avals
|
||
if isinstance(positional_semantics, pxla._PositionalSemantics):
|
||
positional_semantics = [positional_semantics] * len(shardings)
|
||
|
||
out = []
|
||
for aval, s, ps in safe_zip(avals, shardings, positional_semantics):
|
||
if (ps == pxla._PositionalSemantics.GLOBAL or
|
||
pxla.is_op_sharding_replicated(s._op_sharding)):
|
||
out.append(aval)
|
||
else:
|
||
# This path is only taken by host-local values. GDA, Array and fully
|
||
# replicated avals don't go through this code path. To convert global
|
||
# avals to host local avals, round trip it via NamedSharding.
|
||
parsed_pspec = parse_flatten_op_sharding(s._op_sharding, mesh)[0]
|
||
out.append(pxla.mesh_global_to_local(
|
||
mesh, get_array_mapping(parsed_pspec), aval))
|
||
return out
|
||
|
||
|
||
def local_to_global(positional_semantics, avals, shardings, mesh):
|
||
if config.jax_array:
|
||
return avals
|
||
out = []
|
||
for aval, s, ps in safe_zip(avals, shardings, positional_semantics):
|
||
if (ps == pxla._PositionalSemantics.GLOBAL or
|
||
pxla.is_op_sharding_replicated(s._op_sharding)):
|
||
out.append(aval)
|
||
else:
|
||
# This path is only taken by host-local values. GDA, Array and fully
|
||
# replicated avals don't go through this code path. To convert host local
|
||
# avals to global avals, round trip it via NamedSharding.
|
||
parsed_pspec = parse_flatten_op_sharding(s._op_sharding, mesh)[0]
|
||
out.append(pxla.mesh_local_to_global(
|
||
mesh, get_array_mapping(parsed_pspec), aval))
|
||
return out
|
||
|
||
|
||
def _calc_is_global_sequence(in_positional_semantics, in_shardings):
|
||
if config.jax_array:
|
||
return (True,) * len(in_positional_semantics)
|
||
return tuple((ips == pxla._PositionalSemantics.GLOBAL or
|
||
pxla.is_op_sharding_replicated(i._op_sharding))
|
||
for ips, i in safe_zip(in_positional_semantics, in_shardings))
|
||
|
||
|
||
def _fast_path_get_device_assignment(
|
||
shardings: Iterable[PjitSharding]) -> Optional[XLADeviceAssignment]:
|
||
da = None
|
||
for i in shardings:
|
||
if is_auto(i) or _is_unspecified(i):
|
||
continue
|
||
da = i._device_assignment # type: ignore
|
||
break
|
||
return da
|
||
|
||
|
||
def _maybe_replace_from_gda_with_pspec(
|
||
in_shardings_flat, args_flat) -> Sequence[XLACompatibleSharding]:
|
||
|
||
@lru_cache()
|
||
def _gda_check_and_get_sharding(
|
||
gda_sharding: NamedSharding, in_sharding: GSPMDSharding, ndim: int):
|
||
if not _is_from_gda(in_sharding) and not pxla.are_op_shardings_equal(
|
||
gda_sharding._to_xla_op_sharding(ndim),
|
||
in_sharding._to_xla_op_sharding(ndim)):
|
||
raise ValueError(
|
||
f"Got an input GDA to pjit with different partitioning than specified in "
|
||
"the in_axis_resources argument to pjit. The partitioning must match, or "
|
||
"use `jax.experimental.pjit.FROM_GDA` in `in_axis_resources` for GDA. "
|
||
f"Got GDA sharding: {gda_sharding} and "
|
||
f"pjit sharding: {in_sharding._original_sharding}") # type: ignore
|
||
return to_gspmd_sharding(gda_sharding, ndim)
|
||
|
||
out = []
|
||
for in_sharding_flat, arg in safe_zip(in_shardings_flat, args_flat):
|
||
if is_auto(in_sharding_flat):
|
||
out.append(in_sharding_flat)
|
||
elif isinstance(arg, array.ArrayImpl):
|
||
out.append(to_gspmd_sharding(arg.sharding, arg.ndim))
|
||
else:
|
||
out.append(in_sharding_flat)
|
||
return tuple(out)
|
||
|
||
|
||
# -------------------- XLA OpSharding to PartitionSpec --------------------
|
||
# Note that OpSharding is more expressive than PartitionSpecs, so it's not
|
||
# always possible to convert them, but the code below should at least
|
||
# support handle all cases when this is possible.
|
||
|
||
def strides_for_sizes(sizes):
|
||
"""Returns an array of strides for major-to-minor sizes."""
|
||
return np.cumprod(sizes[::-1])[::-1] // np.asarray(sizes)
|
||
|
||
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 ``...``. This is satisfied by all OpSharding assignments
|
||
generated from partition specs.
|
||
|
||
Arguments:
|
||
named_sizes: A dictionary mapping axis names to their sizes.
|
||
assignment: A permutation of integers between 0 and the product of all
|
||
named sizes.
|
||
|
||
Returns:
|
||
A major-to-minor list of axis names that corresponds to the given assignment.
|
||
"""
|
||
named_sizes = {name: size for name, size in named_sizes.items() if size != 1}
|
||
sizes = np.fromiter(named_sizes.values(), dtype=np.int64)
|
||
strides = strides_for_sizes(sizes)
|
||
dims = explode_superdims(sizes, unflatten_superdims(assignment))
|
||
dim_to_name = {(size, stride): name for size, stride, name in zip(sizes, strides, named_sizes)}
|
||
return [dim_to_name[d] for d in dims]
|
||
|
||
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(fst, sorted(dims_with_strides, key=snd, reverse=True)))
|
||
assignment == base_array.transpose(argsort(dims_with_strides, key=snd, reverse=True)).flatten()
|
||
|
||
That is, the returned dimensions list all sizes of the base array (with strides
|
||
indicating their initial order). The order of dimensions in the list corresponds
|
||
to the permutation that applied to the base array generates the assignment.
|
||
"""
|
||
def check(cond):
|
||
if cond: return
|
||
raise NotImplementedError("Failed to convert OpSharding into a ShardingSpec. "
|
||
"Please open a bug report!")
|
||
flat_assignment = np.asarray(assignment, dtype=np.int64)
|
||
check(flat_assignment[0] == 0)
|
||
dims = []
|
||
while flat_assignment.size > 1:
|
||
stride = flat_assignment[1]
|
||
for i in range(len(flat_assignment)):
|
||
if flat_assignment[i] != i * stride: break
|
||
else:
|
||
# After this loop i should point to an "element after the sequence", so
|
||
# we have to increment it if the whole array is a strided sequence.
|
||
i += 1
|
||
size = i
|
||
dims.append((size, stride))
|
||
assert size > 1 # Ensure progress
|
||
flat_assignment = flat_assignment[::size]
|
||
return dims
|
||
|
||
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 splits them
|
||
into smaller dimensions such that::
|
||
|
||
set(map(fst, explode_superdims(sizes, dims))) == set(sizes)
|
||
"""
|
||
strides_to_sizes = {stride: size for size, stride in zip(sizes, strides_for_sizes(sizes))}
|
||
dims = list(reversed(dims))
|
||
final_dims = []
|
||
for size, stride in dims:
|
||
target_size = strides_to_sizes[stride]
|
||
new_dims = []
|
||
while size > target_size:
|
||
assert target_size > 1 # Ensure progress
|
||
assert size % target_size == 0
|
||
new_dims.append((target_size, stride))
|
||
size //= target_size
|
||
stride *= target_size
|
||
target_size = strides_to_sizes[stride]
|
||
assert size == target_size
|
||
new_dims.append((size, stride))
|
||
final_dims += reversed(new_dims)
|
||
return final_dims
|
||
|
||
def parse_flatten_op_sharding(op_sharding: xc.OpSharding,
|
||
mesh: pxla.Mesh) -> Sequence[ParsedPartitionSpec]:
|
||
if op_sharding.type == xc.OpSharding.Type.TUPLE:
|
||
out: List[ParsedPartitionSpec] = []
|
||
for s in op_sharding.tuple_shardings:
|
||
out.extend(parse_flatten_op_sharding(s, mesh))
|
||
return out
|
||
elif op_sharding.type == xc.OpSharding.Type.REPLICATED:
|
||
return [CanonicalizedParsedPartitionSpec(ParsedPartitionSpec(None, ()))]
|
||
elif op_sharding.type == xc.OpSharding.Type.OTHER:
|
||
mesh_shape = mesh.shape
|
||
mesh_axis_order = unflatten_array(mesh.shape, op_sharding.tile_assignment_devices)
|
||
mesh_axis = iter(mesh_axis_order)
|
||
shape = op_sharding.tile_assignment_dimensions
|
||
partitions = []
|
||
for dim_size in shape:
|
||
dim_partitions = []
|
||
while dim_size > 1:
|
||
axis = next(mesh_axis)
|
||
axis_size = mesh_shape[axis]
|
||
assert dim_size % axis_size == 0
|
||
dim_size //= axis_size
|
||
dim_partitions.append(axis)
|
||
partitions.append(tuple(dim_partitions))
|
||
if op_sharding.last_tile_dims == [xc.OpSharding.Type.REPLICATED]:
|
||
replicate_on_last_tile_dim = True
|
||
else:
|
||
replicate_on_last_tile_dim = op_sharding.replicate_on_last_tile_dim
|
||
if op_sharding.last_tile_dims:
|
||
raise NotImplementedError("Unhandled OpSharding type. Please open a bug report!")
|
||
if replicate_on_last_tile_dim:
|
||
partitions = partitions[:-1]
|
||
return [ParsedPartitionSpec('<internally generated spec>', partitions)]
|
||
else:
|
||
raise AssertionError("Unhandled OpSharding type. Please open a bug report!")
|
||
|
||
|
||
def _get_op_sharding(op_sharding) -> Sequence[xc.OpSharding]:
|
||
if op_sharding.type == xc.OpSharding.Type.TUPLE:
|
||
out: List[xc.OpSharding] = []
|
||
for s in op_sharding.tuple_shardings:
|
||
out.extend(_get_op_sharding(s))
|
||
return out
|
||
else:
|
||
return [op_sharding]
|
||
|
||
|
||
_get_single_pspec = lambda p: pxla.array_mapping_to_axis_resources(
|
||
cast(pxla.ArrayMapping, get_array_mapping(p)))
|
||
|
||
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)
|