2499 lines
106 KiB
Python
2499 lines
106 KiB
Python
# Copyright 2021 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from collections import defaultdict
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from collections.abc import Callable, Sequence, Iterable
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import contextlib
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from dataclasses import dataclass, replace
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from functools import partial
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import inspect
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import itertools as it
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import weakref
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from typing import NamedTuple, Any, Union
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import warnings
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import numpy as np
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from jax._src import api
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from jax._src import api_util
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from jax._src import config
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from jax._src import core
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from jax._src import dispatch
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from jax._src import dtypes
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from jax._src import effects
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from jax._src import linear_util as lu
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from jax._src import mesh as mesh_lib
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from jax._src import op_shardings
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from jax._src import profiler
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from jax._src import sharding_impls
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from jax._src import source_info_util
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from jax._src import stages
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from jax._src import traceback_util
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from jax._src import tree_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.core import typeof, cur_qdd
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from jax._src.api_util import (
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flatten_axes, donation_vector, check_callable, resolve_argnums, debug_info,
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check_no_aliased_ref_args, _check_no_aliased_closed_over_refs,
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flatten_axis_resources)
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from jax._src.interpreters import partial_eval as pe
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from jax._src.partition_spec import PartitionSpec
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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.interpreters import remat
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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.mesh import AbstractMesh
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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, GSPMDSharding,
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make_single_device_sharding, AUTO, UNSPECIFIED, UnspecifiedValue,
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prepare_axis_resources, parse_flatten_op_sharding, canonicalize_sharding,
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_internal_use_concrete_mesh)
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from jax._src.layout import Format, Layout, AutoLayout, get_layout_for_vmap
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from jax._src.state.types import RefEffect
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from jax._src.traceback_util import api_boundary
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from jax._src.tree_util import (
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tree_flatten, tree_unflatten, tree_structure, treedef_children,
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PyTreeDef, none_leaf_registry as none_lr, tree_map, FlatTree)
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from jax._src.typing import Array, ArrayLike
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from jax._src.util import (
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HashableFunction, safe_map, safe_zip, wraps, distributed_debug_log,
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split_list, weakref_lru_cache, merge_lists, subs_list, fun_name)
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from jax._src.lib import jax_jit
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map, unsafe_map = safe_map, map
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zip, unsafe_zip = safe_zip, zip
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traceback_util.register_exclusion(__file__)
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PjitSharding = Union[GSPMDSharding, UnspecifiedValue, AUTO]
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PjitShardingMinusUnspecified = Union[GSPMDSharding, AUTO]
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MeshSharding = Union[NamedSharding, UnspecifiedValue, AUTO]
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MeshShardingMinusUnspecified = Union[NamedSharding, AUTO]
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class PjitInfo(NamedTuple):
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"""Things that we know about a jit instance before it is called.
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In other words, this structure contains arguments to jit()/pjit(),
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preprocessed and validated.
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"""
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fun_sourceinfo: str
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fun_signature: inspect.Signature | None
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# Shardings, as specified by the user. These can either be UNSPECIFIED or they
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# can be a tree (prefix) of shardings or None.
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user_specified_in_shardings: bool
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in_shardings_treedef: PyTreeDef
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in_shardings_leaves: tuple[Any, ...]
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out_shardings_treedef: PyTreeDef
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out_shardings_leaves: tuple[Any, ...]
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in_layouts_treedef: PyTreeDef
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in_layouts_leaves: tuple[Any, ...]
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out_layouts_treedef: PyTreeDef
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out_layouts_leaves: tuple[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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donate_argnames: tuple[str, ...]
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device: xc.Device | None
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backend: str | None
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keep_unused: bool
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inline: bool
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use_resource_env: bool # False for jit, True for pjit
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compiler_options_kvs: tuple[tuple[str, Any], ...]
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# Hash and compare PjitInfo by identity when used as a cache key.
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def __hash__(self):
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return id(self)
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def __eq__(self, other):
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return self is other
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def _run_python_pjit(p, args_flat, fun: Callable, 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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if (core.trace_state_clean() and not config.debug_key_reuse.value
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and not p.params['jaxpr'].jaxpr.is_high):
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args_flat = map(core.full_lower, args_flat)
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core.check_eval_args(args_flat)
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out_flat, compiled, profiler, const_args = _pjit_call_impl_python(
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*args_flat, **p.params)
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else:
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out_flat = jit_p.bind(*args_flat, **p.params)
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compiled = None
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profiler = None
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const_args = []
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except stages.DeviceAssignmentMismatchError as e:
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fails, = e.args
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fun_name = getattr(fun, '__qualname__', getattr(fun, '__name__', str(fun)))
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arg_types = map(convert_to_metaty, args_flat)
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msg = stages._device_assignment_mismatch_error(
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fun_name, fails, arg_types, 'jit', p.arg_names)
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raise ValueError(msg) from None
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except dtypes.InvalidInputException as e:
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arg_names = [''] * len(args_flat) if p.arg_names is None else p.arg_names
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# Run canonicalization again to figure out which arg failed.
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if p.params['jaxpr'].consts:
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raise TypeError(e.args[0]) from e
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else:
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for arg, name, aval in zip(args_flat, arg_names, p.in_avals):
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try:
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dtypes.canonicalize_value(arg)
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except dtypes.InvalidInputException as _:
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# Reraise as TypeError with the new message.
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raise TypeError(
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f"Argument '{name}' of shape {aval.str_short()} of type"
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f' {type(arg)} is not a valid JAX type.') from e
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raise AssertionError("Unreachable") from e
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except api_util.InternalFloatingPointError as e:
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if getattr(fun, '_apply_primitive', False):
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raise FloatingPointError(
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f"invalid value ({e.ty}) encountered in {fun.__qualname__}") from None
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api_util.maybe_recursive_nan_check(e, fun, args, kwargs) # should always raise.
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raise RuntimeError("Internal error") from e # fall-back error to be safe.
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outs = tree_unflatten(p.out_tree, out_flat)
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return (outs, out_flat, p.out_tree, args_flat,
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p.params['jaxpr'], compiled, profiler, const_args)
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def _need_to_rebuild_with_fdo(pgle_profiler):
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return (pgle_profiler is not None and pgle_profiler.is_enabled()
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and not pgle_profiler.is_fdo_consumed())
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def _get_fastpath_data(
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executable, out_tree, args_flat, out_flat, effects, consts_for_constvars,
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pgle_profiler, const_args: Sequence[ArrayLike]
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) -> pxla.MeshExecutableFastpathData | None:
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if (
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executable is None
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or not isinstance(executable, pxla.MeshExecutable)
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or not isinstance(executable.unsafe_call, pxla.ExecuteReplicated)
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# No effects in computation
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or executable.unsafe_call.ordered_effects
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or executable.unsafe_call.has_unordered_effects
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# no ref state effects
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or any(isinstance(e, RefEffect) for e in effects)
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or _need_to_rebuild_with_fdo(pgle_profiler)
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or config.no_execution.value
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):
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return None
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out_reflattened, out_tree = pxla.reflatten_outputs_for_dispatch(out_tree, out_flat)
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if not all(isinstance(x, xc.ArrayImpl) for x in out_reflattened):
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return None
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out_avals = [o.aval for o in out_reflattened]
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out_committed = [o._committed for o in out_reflattened]
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kept_var_bitvec = [i in executable._kept_var_idx
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for i in range(len(const_args) + len(args_flat))]
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in_shardings = [
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sharding_impls.physical_sharding(a, s)
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if a is not core.abstract_token and dtypes.issubdtype(a.dtype, dtypes.extended)
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else s
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for s, a in zip(executable._in_shardings, executable.in_avals)
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]
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return pxla.MeshExecutableFastpathData(
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executable.xla_executable, out_tree, in_shardings,
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executable._out_shardings, out_avals, out_committed, kept_var_bitvec,
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executable._dispatch_in_layouts, const_args)
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# The entries are doubled here from the default 4096 because _pjit_call_impl
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# also has a cpp dispatch path and that would double the number of entries in
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# the global shared cache.
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# This cache is only used for jit's with only fun. For example: jax.jit(f)
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_cpp_pjit_cache_fun_only = xc._xla.PjitFunctionCache(capacity=8192)
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# This cache is used for jit where extra arguments are defined other than the
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# fun. For example: jax.jit(f, donate_argnums=...) OR
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# jax.jit(f, out_shardings=...), etc. We don't use the same cache because the
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# capacity might get full very fast because of all the jitted function in JAX
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# which might evict train_step for example.
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_cpp_pjit_cache_explicit_attributes = xc._xla.PjitFunctionCache(capacity=8192)
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def _get_cpp_global_cache(contains_explicit_attributes: bool):
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if contains_explicit_attributes:
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return _cpp_pjit_cache_explicit_attributes
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else:
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return _cpp_pjit_cache_fun_only
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def _cpp_pjit(fun: Callable, jit_info: PjitInfo):
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@api_boundary
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def cache_miss(*args, **kwargs):
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# args do not include the const args
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# See https://docs.jax.dev/en/latest/internals/constants.html.
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if config.no_tracing.value:
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raise RuntimeError(f"re-tracing function {jit_info.fun_sourceinfo} for "
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"`jit`, but 'no_tracing' is set")
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p, args_flat = _infer_params(fun, jit_info, args, kwargs)
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(outs, out_flat, out_tree, args_flat, jaxpr,
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executable, pgle_profiler, const_args) = _run_python_pjit(
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p, args_flat, fun, args, kwargs)
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maybe_fastpath_data = _get_fastpath_data(
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executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,
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pgle_profiler, const_args)
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return outs, maybe_fastpath_data, _need_to_rebuild_with_fdo(pgle_profiler)
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cache_key = pxla.JitGlobalCppCacheKeys(
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donate_argnums=jit_info.donate_argnums,
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donate_argnames=jit_info.donate_argnames,
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device=jit_info.device, backend=jit_info.backend,
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in_shardings_treedef=jit_info.in_shardings_treedef,
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in_shardings_leaves=jit_info.in_shardings_leaves,
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out_shardings_treedef=jit_info.out_shardings_treedef,
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out_shardings_leaves=jit_info.out_shardings_leaves,
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in_layouts_treedef=jit_info.in_layouts_treedef,
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in_layouts_leaves=jit_info.in_layouts_leaves,
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out_layouts_treedef=jit_info.out_layouts_treedef,
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out_layouts_leaves=jit_info.out_layouts_leaves,
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compiler_options_kvs=jit_info.compiler_options_kvs)
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cpp_pjit_f = xc._xla.pjit(
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fun_name(fun), fun, cache_miss, jit_info.static_argnums,
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jit_info.static_argnames, cache_key, tree_util.dispatch_registry,
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pxla.cc_shard_arg,
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_get_cpp_global_cache(cache_key.contains_explicit_attributes))
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cpp_pjitted_f = wraps(fun)(cpp_pjit_f)
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cpp_pjitted_f._fun = fun # pyrefly: ignore[missing-attribute]
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cpp_pjitted_f._jit_info = jit_info # pyrefly: ignore[missing-attribute]
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cpp_jitted_f_class = type(cpp_pjitted_f)
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cpp_jitted_f_class.clear_cache = jit_evict_fn
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cpp_jitted_f_class.lower = jit_lower
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cpp_jitted_f_class.trace = jit_trace
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cpp_jitted_f_class.eval_shape = jit_eval_shape
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return cpp_pjitted_f
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@api_boundary
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def jit_trace(jit_func, *args, **kwargs) -> stages.Traced:
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p, args_flat = _infer_params(jit_func._fun, jit_func._jit_info, args, kwargs)
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arg_types = map(convert_to_metaty, args_flat)
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return stages.Traced(arg_types, p.params, p.in_tree, p.out_tree, p.consts)
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@api_boundary
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def jit_lower(jit_func, *args, **kwargs):
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return jit_trace(jit_func, *args, **kwargs).lower()
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@api_boundary
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def jit_eval_shape(jit_func, *args, **kwargs):
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return jit_trace(jit_func, *args, **kwargs).out_info
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def jit_evict_fn(self):
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self._clear_cache()
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pe.trace_to_jaxpr.evict_weakref(self._fun)
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_infer_params_cached.cache_clear()
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def _split_layout_and_sharding(entries):
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entries_flat, treedef = tree_flatten(entries, is_leaf=lambda x: x is None)
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layouts, shardings = [], []
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for e in entries_flat:
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if isinstance(e, Format):
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layouts.append(e.layout)
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shardings.append(e.sharding)
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elif isinstance(e, (Layout, AutoLayout)):
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raise ValueError(
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'`jax.jit` does not accept device-local layouts directly. Create '
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'a `Format` instance wrapping this device-local layout and pass '
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f'that to `jit` instead. Got {e}')
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else:
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layouts.append(None)
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shardings.append(e)
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assert len(layouts) == len(shardings)
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return tree_unflatten(treedef, layouts), tree_unflatten(treedef, shardings)
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def _parse_jit_arguments(fun: Callable, *, in_shardings: Any,
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out_shardings: Any,
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static_argnums: int | Sequence[int] | None,
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static_argnames: str | Iterable[str] | None,
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donate_argnums: int | Sequence[int] | None,
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donate_argnames: str | Iterable[str] | None,
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keep_unused: bool, device: xc.Device | None,
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backend: str | None, inline: bool,
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compiler_options: dict[str, Any] | None,
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use_resource_env: bool) -> PjitInfo:
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"""Parses the arguments to jit/pjit.
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Performs any preprocessing and validation of the arguments that we can do
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ahead of time before the jit()-ed function is invoked.
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"""
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check_callable(fun)
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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'
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' `jax.device_put(..., jax.local_devices(backend="cpu")[0])` on the'
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' inputs to the jitted function to get the same behavior.',
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DeprecationWarning,
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)
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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 in_shardings is not None and not isinstance(in_shardings, UnspecifiedValue):
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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 out_shardings is not None and not isinstance(out_shardings, UnspecifiedValue):
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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/jax-ml/jax/issues/2367
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in_shardings = tuple(in_shardings)
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in_layouts, in_shardings = _split_layout_and_sharding(in_shardings)
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out_layouts, out_shardings = _split_layout_and_sharding(out_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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allow_unconstrained_dims=True)
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user_specified_in_shardings = (in_shardings is not None and
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not isinstance(in_shardings, UnspecifiedValue))
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in_shardings_leaves, in_shardings_treedef = none_lr.flatten(in_shardings)
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out_shardings_leaves, out_shardings_treedef = none_lr.flatten(out_shardings)
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in_layouts_leaves, in_layouts_treedef = none_lr.flatten(in_layouts)
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out_layouts_leaves, out_layouts_treedef = none_lr.flatten(out_layouts)
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fun_sourceinfo = api_util.fun_sourceinfo(fun)
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fun_signature = api_util.fun_signature(fun)
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donate_argnums, donate_argnames, static_argnums, static_argnames = resolve_argnums(
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fun, fun_signature, donate_argnums, donate_argnames, static_argnums,
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static_argnames)
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compiler_options_kvs = (() if compiler_options is None else
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tuple(compiler_options.items()))
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return PjitInfo(
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fun_sourceinfo=fun_sourceinfo,
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fun_signature=fun_signature,
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user_specified_in_shardings=user_specified_in_shardings,
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in_shardings_treedef=in_shardings_treedef,
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in_shardings_leaves=tuple(in_shardings_leaves),
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out_shardings_treedef=out_shardings_treedef,
|
|
out_shardings_leaves=tuple(out_shardings_leaves),
|
|
in_layouts_treedef=in_layouts_treedef,
|
|
in_layouts_leaves=tuple(in_layouts_leaves),
|
|
out_layouts_treedef=out_layouts_treedef,
|
|
out_layouts_leaves=tuple(out_layouts_leaves),
|
|
static_argnums=static_argnums,
|
|
static_argnames=static_argnames, donate_argnums=donate_argnums,
|
|
donate_argnames=donate_argnames, device=device, backend=backend,
|
|
keep_unused=keep_unused, inline=inline,
|
|
use_resource_env=use_resource_env,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
|
|
def make_jit(fun: Callable,
|
|
*,
|
|
in_shardings: Any,
|
|
out_shardings: Any,
|
|
static_argnums: int | Sequence[int] | None,
|
|
static_argnames: str | Iterable[str] | None,
|
|
donate_argnums: int | Sequence[int] | None,
|
|
donate_argnames: str | Iterable[str] | None,
|
|
keep_unused: bool,
|
|
device: xc.Device | None,
|
|
backend: str | None,
|
|
inline: bool,
|
|
compiler_options: dict[str, Any] | None,
|
|
use_resource_env: bool) -> Any:
|
|
"""jit() and pjit() are thin wrappers around this function."""
|
|
jit_info = _parse_jit_arguments(
|
|
fun, in_shardings=in_shardings, out_shardings=out_shardings,
|
|
static_argnums=static_argnums, static_argnames=static_argnames,
|
|
donate_argnums=donate_argnums, donate_argnames=donate_argnames,
|
|
keep_unused=keep_unused, device=device, backend=backend, inline=inline,
|
|
compiler_options=compiler_options,
|
|
use_resource_env=use_resource_env)
|
|
return _cpp_pjit(fun, jit_info)
|
|
|
|
|
|
class PjitParams(NamedTuple):
|
|
# Only jaxpr constants, we can't keep other arguments alive. These go as
|
|
# first arguments for `params['jaxpr']`.
|
|
consts: list[ArrayLike] # Corresponding to jaxpr.constvars
|
|
# Everything we need to trace, lower, and compile the jit function; passed
|
|
# to `pjit_call_impl_python`, along with the `args_flat`
|
|
params: dict[str, Any]
|
|
in_avals: tuple[core.AbstractValue, ...] # Not including the const_args
|
|
in_tree: PyTreeDef # Not including the const_args
|
|
out_tree: PyTreeDef
|
|
arg_names: tuple[str, ...] # Not including the const_args
|
|
|
|
|
|
def _trace_for_jit(
|
|
fun: Callable, ji: PjitInfo, ctx_mesh: mesh_lib.Mesh,
|
|
dbg: core.DebugInfo, avals, args, kwargs) -> PjitParams:
|
|
args_ft = FlatTree.flatten_static_argnums_argnames(
|
|
args, kwargs, ji.static_argnums, ji.static_argnames)
|
|
avals_ft = args_ft.update(avals)
|
|
|
|
has_kwargs = bool(kwargs)
|
|
if has_kwargs and ji.user_specified_in_shardings:
|
|
raise ValueError(
|
|
"pjit does not support kwargs when in_shardings is specified.")
|
|
|
|
if not ctx_mesh.empty and (ji.backend or ji.device):
|
|
raise ValueError(
|
|
"Mesh context manager should not be used with jit when backend or "
|
|
"device is also specified as an argument to jit.")
|
|
|
|
if (ji.donate_argnums or ji.donate_argnames) and not config.debug_nans.value:
|
|
donated_invars = donation_vector(ji.donate_argnums, ji.donate_argnames,
|
|
avals_ft.tree)
|
|
else:
|
|
donated_invars = (False,) * len(avals_ft)
|
|
|
|
# If backend or device is set as an arg on jit, then resolve them to
|
|
# in_shardings and out_shardings as if user passed in in_shardings
|
|
# and out_shardings.
|
|
device_or_backend_set = bool(ji.backend or ji.device)
|
|
if device_or_backend_set:
|
|
sharding = _create_sharding_with_device_backend(ji.device, ji.backend)
|
|
leaves, treedef = tree_flatten(sharding)
|
|
in_shardings_leaves = out_shardings_leaves = tuple(leaves)
|
|
in_shardings_treedef = out_shardings_treedef = treedef
|
|
else:
|
|
api_name = 'pjit' if ji.use_resource_env else 'jit'
|
|
in_shardings_leaves = tuple(
|
|
_create_sharding_for_array(ctx_mesh, x, 'in_shardings', api_name)
|
|
for x in ji.in_shardings_leaves)
|
|
out_shardings_leaves = tuple(
|
|
_create_sharding_for_array(ctx_mesh, x, 'out_shardings', api_name)
|
|
for x in ji.out_shardings_leaves)
|
|
in_shardings_treedef = ji.in_shardings_treedef
|
|
out_shardings_treedef = ji.out_shardings_treedef
|
|
|
|
assert None not in in_shardings_leaves
|
|
assert None not in out_shardings_leaves
|
|
|
|
in_type = avals_ft.map2(
|
|
lambda a, x: core.AvalQDD(a, cur_qdd(x)) if a.has_qdd else a,
|
|
args_ft)
|
|
assert avals_ft is not None
|
|
|
|
in_shardings_flat, in_layouts_flat = _process_in_axis_resources(
|
|
in_shardings_treedef, in_shardings_leaves,
|
|
ji.in_layouts_treedef, ji.in_layouts_leaves,
|
|
avals_ft, dbg, device_or_backend_set, has_kwargs)
|
|
|
|
qdd_token = _qdd_cache_index(fun, in_type.vals) # represents qdd state context
|
|
|
|
elapsed_time_ctx = (
|
|
dispatch.log_elapsed_time(
|
|
"Finished tracing {fun_name} for jit in {elapsed_time:.9f} sec",
|
|
fun_name(fun), event=dispatch.JAXPR_TRACE_EVENT)
|
|
if core.trace_state_clean() else contextlib.nullcontext())
|
|
with elapsed_time_ctx:
|
|
if ji.use_resource_env: # pjit
|
|
with (_internal_use_concrete_mesh(ctx_mesh),
|
|
mesh_lib.use_abstract_mesh(ctx_mesh.abstract_mesh)):
|
|
jaxpr, out_avals = pe.trace_to_jaxpr(fun, in_type, dbg, qdd_token)
|
|
else:
|
|
jaxpr, out_avals = pe.trace_to_jaxpr(fun, in_type, dbg, qdd_token)
|
|
|
|
if config.debug_key_reuse.value:
|
|
# Import here to avoid circular imports
|
|
from jax.experimental.key_reuse._core import check_key_reuse_jaxpr # pytype: disable=import-error
|
|
check_key_reuse_jaxpr(jaxpr.jaxpr)
|
|
|
|
result_paths = tuple(f"result{lu._clean_keystr_arg_names(path)}"
|
|
for path in out_avals.paths)
|
|
jaxpr.jaxpr._debug_info = jaxpr.debug_info._replace(result_paths=result_paths)
|
|
|
|
# TODO(mattjj,yashkatariya): if we take the 'true' path then we *must* fall
|
|
# off the C++ dispatch fast path for correctness. Ensure that happens.
|
|
if any(isinstance(c, core.Tracer) or core.typeof(c).has_qdd for c in jaxpr.consts):
|
|
jaxpr, consts = pe.separate_consts(jaxpr)
|
|
else:
|
|
consts = []
|
|
|
|
if config.mutable_array_checks.value:
|
|
_check_no_aliased_closed_over_refs(dbg, (*jaxpr.consts, *consts), args_ft.vals)
|
|
_qdd_cache_update(fun, in_type.vals, qdd_token, consts,
|
|
jaxpr.in_aval_qdds[:len(consts)])
|
|
|
|
out_shardings_flat, out_layouts_flat = _check_and_canonicalize_out_shardings(
|
|
out_shardings_treedef, out_shardings_leaves, ji.out_layouts_treedef,
|
|
ji.out_layouts_leaves, out_avals.tree,
|
|
tuple(out_avals), jaxpr.jaxpr._debug_info, device_or_backend_set)
|
|
|
|
assert len(args_ft.vals) == len(in_shardings_flat) == len(in_layouts_flat)
|
|
|
|
num_extra_args = len(consts)
|
|
in_shardings_flat = (UNSPECIFIED,) * num_extra_args + in_shardings_flat
|
|
in_layouts_flat = (None,) * num_extra_args + in_layouts_flat
|
|
donated_invars = (False,) * num_extra_args + donated_invars
|
|
assert (len(in_shardings_flat) == len(in_layouts_flat) ==
|
|
len(donated_invars) == len(consts) + len(avals_ft))
|
|
|
|
params = dict(
|
|
jaxpr=jaxpr,
|
|
in_shardings=in_shardings_flat,
|
|
out_shardings=out_shardings_flat,
|
|
in_layouts=in_layouts_flat,
|
|
out_layouts=out_layouts_flat,
|
|
donated_invars=donated_invars,
|
|
ctx_mesh=ctx_mesh,
|
|
name=fun_name(fun),
|
|
keep_unused=ji.keep_unused,
|
|
inline=ji.inline,
|
|
compiler_options_kvs=ji.compiler_options_kvs,
|
|
)
|
|
return PjitParams(consts, params, avals_ft.vals, avals_ft.tree_without_statics,
|
|
out_avals.tree, dbg.safe_arg_names(len(avals_ft)))
|
|
|
|
|
|
@dataclass(slots=True)
|
|
class InferParamsCacheEntry:
|
|
pjit_params: PjitParams | None = None
|
|
|
|
@weakref_lru_cache
|
|
def _infer_params_cached(
|
|
fun: Callable, jit_info: PjitInfo, signature: jax_jit.ArgumentSignature,
|
|
in_avals: tuple[core.AbstractValue, ...], ctx_mesh: mesh_lib.Mesh
|
|
) -> InferParamsCacheEntry:
|
|
return InferParamsCacheEntry()
|
|
|
|
def get_ctx_mesh(use_resource_env):
|
|
if use_resource_env:
|
|
return mesh_lib.thread_resources.env.physical_mesh
|
|
else:
|
|
conc_mesh = mesh_lib.get_concrete_mesh()
|
|
if not conc_mesh.empty:
|
|
return conc_mesh
|
|
else:
|
|
abs_mesh = mesh_lib.get_abstract_mesh()
|
|
# TODO(yashkatariya): Make top-level use_abstract_mesh work with Auto mode
|
|
# too. But there are failures in user code so restricting it to Explicit
|
|
# mode for now.
|
|
if not abs_mesh.empty and abs_mesh._any_axis_explicit:
|
|
return abs_mesh
|
|
return conc_mesh
|
|
|
|
def _infer_params(
|
|
fun: Callable, ji: PjitInfo, args: tuple[Any, ...], kwargs: dict[str, Any]
|
|
) -> tuple[PjitParams, list[core.Value]]:
|
|
ctx_mesh = get_ctx_mesh(ji.use_resource_env)
|
|
dbg_fn = lambda: debug_info(
|
|
'jit', fun, args, kwargs, static_argnums=ji.static_argnums,
|
|
static_argnames=ji.static_argnames, sourceinfo=ji.fun_sourceinfo,
|
|
signature=ji.fun_signature)
|
|
arg_signature, dynargs = jax_jit.parse_arguments(
|
|
args, tuple(kwargs.values()), tuple(kwargs.keys()), ji.static_argnums,
|
|
ji.static_argnames, tree_util.tracing_registry)
|
|
avals = _infer_input_type(fun, dbg_fn, dynargs)
|
|
entry = _infer_params_cached(fun, ji, arg_signature, avals, ctx_mesh)
|
|
|
|
if entry.pjit_params is not None:
|
|
return entry.pjit_params, entry.pjit_params.consts + dynargs
|
|
|
|
p = _trace_for_jit(fun, ji, ctx_mesh, dbg_fn(), avals, args, kwargs)
|
|
if p.params['jaxpr'].jaxpr.is_high:
|
|
return p, p.consts + dynargs
|
|
entry.pjit_params = p
|
|
return p, p.consts + dynargs
|
|
|
|
|
|
def _infer_input_type(fun: Callable, dbg_fn: Callable[[], core.DebugInfo],
|
|
explicit_args) -> tuple[core.AbstractValue, ...]:
|
|
avals = []
|
|
i = -1
|
|
x = None
|
|
try:
|
|
for i, x in enumerate(explicit_args):
|
|
avals.append(core.shaped_abstractify(x))
|
|
except OverflowError:
|
|
dbg = dbg_fn()
|
|
arg_path = f"argument path is {dbg.arg_names[i] if dbg.arg_names is not None else 'unknown'}"
|
|
raise OverflowError(
|
|
"An overflow was encountered while parsing an argument to a jitted "
|
|
f"computation, whose {arg_path}. Got {type(x)} with value {x}"
|
|
) from None
|
|
except TypeError:
|
|
dbg = dbg_fn()
|
|
arg_description = f"path {dbg.arg_names[i] if dbg.arg_names is not None else 'unknown'}"
|
|
raise TypeError(
|
|
f"Error interpreting argument to {fun} as an abstract array."
|
|
f" The problematic value is of type {type(x)} and was passed to"
|
|
f" the function at {arg_description}.\n"
|
|
"This typically means that a jit-wrapped function was called with a non-array"
|
|
" argument, and this argument was not marked as static using the"
|
|
" static_argnums or static_argnames parameters of jax.jit."
|
|
) from None
|
|
if config.mutable_array_checks.value:
|
|
check_no_aliased_ref_args(dbg_fn, avals, explicit_args)
|
|
return tuple(avals)
|
|
|
|
|
|
class JitWrapped(stages.Wrapped):
|
|
|
|
def eval_shape(self, *args, **kwargs):
|
|
"""See ``jax.eval_shape``."""
|
|
raise NotImplementedError
|
|
|
|
def trace(self, *args, **kwargs) -> stages.Traced:
|
|
raise NotImplementedError
|
|
|
|
|
|
# in_shardings and out_shardings can't be None as the default value
|
|
# because `None` means that the input is fully replicated.
|
|
@partial(api_boundary, repro_api_name="pjit.pjit")
|
|
def pjit(
|
|
fun: Callable,
|
|
in_shardings: Any = UNSPECIFIED,
|
|
out_shardings: Any = UNSPECIFIED,
|
|
static_argnums: int | Sequence[int] | None = None,
|
|
static_argnames: str | Iterable[str] | None = None,
|
|
donate_argnums: int | Sequence[int] | None = None,
|
|
donate_argnames: str | Iterable[str] | None = None,
|
|
keep_unused: bool = False,
|
|
device: xc.Device | None = None,
|
|
backend: str | None = None,
|
|
inline: bool = False,
|
|
compiler_options: dict[str, Any] | None = None,
|
|
) -> JitWrapped:
|
|
"""`jax.experimental.pjit.pjit` has been deprecated. Please use `jax.jit`."""
|
|
return make_jit(
|
|
fun, in_shardings=in_shardings, out_shardings=out_shardings,
|
|
static_argnums=static_argnums, static_argnames=static_argnames,
|
|
donate_argnums=donate_argnums, donate_argnames=donate_argnames,
|
|
keep_unused=keep_unused, device=device, backend=backend, inline=inline,
|
|
compiler_options=compiler_options, use_resource_env=True)
|
|
|
|
|
|
def hashable_pytree(pytree):
|
|
vals, treedef = tree_flatten(pytree)
|
|
vals = tuple(vals)
|
|
return HashableFunction(lambda: tree_unflatten(treedef, vals),
|
|
closure=(treedef, vals))
|
|
|
|
|
|
def _create_sharding_for_array(mesh, x, name, api_name):
|
|
if x is None:
|
|
if api_name == 'jit' or mesh.empty:
|
|
return UNSPECIFIED
|
|
return sharding_impls.cached_named_sharding(mesh, PartitionSpec())
|
|
if isinstance(x, (AUTO, UnspecifiedValue, Sharding)):
|
|
return x
|
|
if mesh.empty:
|
|
raise RuntimeError(
|
|
f'{api_name} requires a non-empty mesh in context if you are passing'
|
|
f' `PartitionSpec`s to {name}. You can define a context mesh via'
|
|
' `jax.set_mesh(mesh)`. Alternatively, provide `Sharding`s to'
|
|
f' {name} and then the mesh context manager is not required.')
|
|
assert isinstance(x, PartitionSpec), x
|
|
return sharding_impls.cached_named_sharding(mesh, x)
|
|
|
|
|
|
def _create_sharding_with_device_backend(device, backend):
|
|
if device is not None:
|
|
assert backend is None
|
|
out = make_single_device_sharding(device)
|
|
elif backend is not None:
|
|
assert device is None
|
|
out = make_single_device_sharding(
|
|
xb.get_backend(backend).local_devices()[0])
|
|
else:
|
|
raise AssertionError('Unreachable!')
|
|
out._device_backend = True
|
|
return out
|
|
|
|
|
|
@util.cache(max_size=4096, trace_context_in_key=False)
|
|
def _process_in_axis_resources(in_shardings_treedef, in_shardings_leaves,
|
|
in_layouts_treedef, in_layouts_leaves,
|
|
in_avals, dbg: core.DebugInfo,
|
|
device_or_backend_set, kws):
|
|
if kws:
|
|
in_tree = in_avals.tree_without_statics
|
|
else:
|
|
in_tree, _ = treedef_children(in_avals.tree_without_statics)
|
|
|
|
orig_in_shardings = tree_unflatten(in_shardings_treedef, in_shardings_leaves)
|
|
# Only do this if original in_shardings are unspecified. If it is AUTO, go
|
|
# via flatten_axis_resources.
|
|
if isinstance(orig_in_shardings, UnspecifiedValue):
|
|
in_shardings_flat = (orig_in_shardings,) * len(in_avals)
|
|
else:
|
|
in_shardings_flat = flatten_axis_resources(
|
|
"pjit in_shardings", in_tree, orig_in_shardings, tupled_args=True)
|
|
|
|
in_layouts = tree_unflatten(in_layouts_treedef, in_layouts_leaves)
|
|
if in_layouts is None:
|
|
in_layouts_flat = (in_layouts,) * len(in_avals)
|
|
else:
|
|
in_layouts_flat = flatten_axis_resources(
|
|
"pjit in_layouts", in_tree, in_layouts, tupled_args=True)
|
|
|
|
pjit_check_aval_sharding(in_shardings_flat, in_avals,
|
|
dbg.safe_arg_names(len(in_avals)),
|
|
"pjit arguments", allow_uneven_sharding=False)
|
|
check_aval_layout_compatibility(
|
|
in_layouts_flat, in_avals,
|
|
dbg.safe_arg_names(len(in_avals)), "jit arguments")
|
|
return in_shardings_flat, in_layouts_flat
|
|
|
|
@util.cache(max_size=4096, trace_context_in_key=False)
|
|
def _check_and_canonicalize_out_shardings(
|
|
out_shardings_treedef, out_shardings_leaves, out_layouts_treedef,
|
|
out_layouts_leaves, out_tree, out_avals,
|
|
debug_info: core.DebugInfo,
|
|
device_or_backend_set):
|
|
orig_out_shardings = tree_unflatten(out_shardings_treedef, out_shardings_leaves)
|
|
if isinstance(orig_out_shardings, (UnspecifiedValue, Sharding)):
|
|
out_shardings_flat = (orig_out_shardings,) * len(out_avals)
|
|
else:
|
|
out_shardings_flat = flatten_axis_resources(
|
|
"pjit out_shardings", out_tree, orig_out_shardings,
|
|
tupled_args=False)
|
|
|
|
out_layouts = tree_unflatten(out_layouts_treedef, out_layouts_leaves)
|
|
if out_layouts is None:
|
|
out_layouts_flat = (out_layouts,) * len(out_avals)
|
|
else:
|
|
out_layouts_flat = flatten_axis_resources(
|
|
"pjit out_layouts", out_tree, out_layouts, tupled_args=False)
|
|
|
|
pjit_check_aval_sharding(
|
|
out_shardings_flat, out_avals,
|
|
debug_info.safe_result_paths(len(out_avals)),
|
|
"pjit outputs", allow_uneven_sharding=False)
|
|
check_aval_layout_compatibility(
|
|
out_layouts_flat, out_avals,
|
|
debug_info.safe_result_paths(len(out_avals)),
|
|
"jit outputs")
|
|
return out_shardings_flat, out_layouts_flat
|
|
|
|
_seen_qdds = weakref.WeakKeyDictionary()
|
|
|
|
def _seen_qdds_get(fun, in_type) -> list:
|
|
cache = _seen_qdds.setdefault(fun, defaultdict(list))
|
|
assert cache is not None # pyrefly#2407
|
|
return cache[in_type]
|
|
|
|
def _qdd_cache_index(fun, in_type) -> int:
|
|
cases = _seen_qdds_get(fun, in_type)
|
|
for i, records in enumerate(cases):
|
|
for obj, qdd in records:
|
|
if core.cur_qdd(obj) != qdd: break
|
|
else:
|
|
return i
|
|
return len(cases)
|
|
|
|
def _qdd_cache_update(fun, in_type, i, consts, aval_qdds):
|
|
cases = _seen_qdds_get(fun, in_type)
|
|
if i == len(cases):
|
|
cases.append([(c, aval_qdd.qdd) for c, aval_qdd in zip(consts, aval_qdds)
|
|
if aval_qdd.has_qdd])
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class IgnoreKey:
|
|
val: Any
|
|
def __hash__(self):
|
|
return hash(self.__class__)
|
|
def __eq__(self, other):
|
|
return isinstance(other, IgnoreKey) # ignore self.val!
|
|
|
|
|
|
def pjit_check_aval_sharding(
|
|
shardings, flat_avals, names: Sequence[str],
|
|
what_aval: str, allow_uneven_sharding: bool):
|
|
for aval, s, name in zip(flat_avals, shardings, names):
|
|
if isinstance(s, (UnspecifiedValue, AUTO)):
|
|
continue
|
|
name_str = f' with pytree key path {name}' if name else ''
|
|
shape = aval.shape
|
|
try:
|
|
s.check_compatible_aval(shape)
|
|
except ValueError as e:
|
|
raise ValueError(
|
|
f'One of {what_aval}{name_str} is incompatible with its sharding '
|
|
f'annotation {s}: {e}')
|
|
|
|
if not allow_uneven_sharding:
|
|
s.shard_shape(aval.shape) # will check for divisibility
|
|
|
|
def check_aval_layout_compatibility(
|
|
layouts, flat_avals, names: Sequence[str], what_aval: str):
|
|
for aval, l, name in zip(flat_avals, layouts, names):
|
|
if l is None or isinstance(l, AutoLayout):
|
|
continue
|
|
name_str = f' with pytree key path {name}' if name else ''
|
|
try:
|
|
l.check_compatible_aval(aval.shape)
|
|
except ValueError as e:
|
|
raise ValueError(
|
|
f'One of {what_aval}{name_str} is incompatible with its layout '
|
|
f'annotation {l}: {e}')
|
|
|
|
|
|
# -------------------- pjit rules --------------------
|
|
|
|
jit_p = core.Primitive("jit")
|
|
jit_p.is_effectful = lambda params: bool(params['jaxpr'].effects)
|
|
jit_p.multiple_results = True
|
|
jit_p.skip_canonicalization = True
|
|
|
|
def _is_high(*_, jaxpr, **__) -> bool:
|
|
return jaxpr.jaxpr.is_high
|
|
jit_p.is_high = _is_high
|
|
|
|
def _to_lojax(*hi_args, jaxpr, **params):
|
|
# convert closed-over boxes to explicit args
|
|
jaxpr, closed_over_himutables = pe.convert_const_himutables(jaxpr)
|
|
hi_args = [*closed_over_himutables, *hi_args]
|
|
params = _converted_mutables_add_params(len(closed_over_himutables), **params)
|
|
|
|
lo_args_lol = [aval.read_loval_in(x) if aval.has_qdd else aval.lower_val(x)
|
|
for aval, x in zip(jaxpr.in_aval_qdds, hi_args)]
|
|
lo_args = [x for xs in lo_args_lol for x in xs]
|
|
|
|
in_avals = FlatTree.flatten(([[typeof(x) for x in xs] for xs in lo_args_lol], {}))
|
|
lo_jaxpr, out_avals = pe.lower_jaxpr(jaxpr, in_avals)
|
|
params = _lojax_expand_params(in_avals, out_avals, **params)
|
|
|
|
all_outs = jit_p.bind(*lo_args, jaxpr=lo_jaxpr, **params)
|
|
out_mut, lo_outs = out_avals.update(all_outs).unpack()
|
|
for a, x, u in zip(jaxpr.final_aval_qdds, hi_args, out_mut.unpack()):
|
|
if a.has_qdd:
|
|
a.aval.update_from_loval2(a.qdd, x, u)
|
|
return [a.raise_val2(y) for a, y in zip(jaxpr.out_avals, lo_outs.unpack())]
|
|
jit_p.to_lojax = _to_lojax
|
|
|
|
def _converted_mutables_add_params(
|
|
n, *, donated_invars, in_shardings, in_layouts, **params):
|
|
donated_invars = (False,) * n + donated_invars
|
|
in_shardings = (UNSPECIFIED,) * n + in_shardings
|
|
in_layouts = (None,) * n + in_layouts
|
|
return dict(params, donated_invars=donated_invars, in_shardings=in_shardings,
|
|
in_layouts=in_layouts)
|
|
|
|
|
|
def _lojax_expand_params(
|
|
in_avals_, out_avals, donated_invars, in_shardings, in_layouts,
|
|
out_shardings, out_layouts, **params):
|
|
in_avals, () = in_avals_.unpack()
|
|
in_lol = in_avals.unpack()
|
|
mut_out_lol, out_lol_ = out_avals.unpack()
|
|
out_lol = out_lol_.unpack()
|
|
|
|
# some pjit params match the length of hi_jaxpr.invars/outvars, so when
|
|
# lowering we must expand them to match their number of lojax types
|
|
def expand(lol, stuff):
|
|
return tuple(x for l, x in zip(lol, stuff) for _ in l)
|
|
donated_invars = expand(in_lol , donated_invars)
|
|
in_shardings = expand(in_lol , in_shardings )
|
|
in_layouts = expand(in_lol , in_layouts )
|
|
out_shardings = expand(out_lol, out_shardings )
|
|
out_layouts = expand(out_lol, out_layouts )
|
|
|
|
# also, the lo_jaxpr has pure outputs corresponding to mutable hi_jaxpr types
|
|
num_muts_out = len(mut_out_lol) # it's a flat tree
|
|
out_shardings = (UNSPECIFIED,) * num_muts_out + out_shardings
|
|
out_layouts = (None,) * num_muts_out + out_layouts
|
|
|
|
new_params = dict(params, donated_invars=donated_invars,
|
|
in_shardings=in_shardings, in_layouts=in_layouts,
|
|
out_shardings=out_shardings, out_layouts=out_layouts)
|
|
return new_params
|
|
|
|
|
|
def _resolve_in_layouts(args, jit_in_layouts, resolved_in_shardings,
|
|
in_avals) -> Sequence[Layout | AutoLayout | None]:
|
|
# If device or backend is set, return the default layout. This is because you
|
|
# can pass arrays on cpu (with untiled layouts) to jit with backend='tpu'
|
|
# which causes error checks to fail. Returning the default layout allows
|
|
# this to exist. It's the same for handling shardings.
|
|
if pxla.check_device_backend_on_shardings(resolved_in_shardings):
|
|
return (None,) * len(jit_in_layouts)
|
|
|
|
resolved_in_layouts: list[Layout | AutoLayout | None] = []
|
|
for arg, jit_in_l, rs, aval in safe_zip(
|
|
args, jit_in_layouts, resolved_in_shardings, in_avals):
|
|
committed = arg.committed
|
|
# `arg_layout` is only used for checking purposes in the `else` branch
|
|
# below. We cannot replace default layout with None to raise nicer errors.
|
|
# `dispatch_arg_layout` replaces default layouts with `None` to simplify
|
|
# dispatch and lowering logic downstream.
|
|
if arg.format is not None:
|
|
arg_layout = arg.format.layout
|
|
dispatch_arg_layout = (None if pxla.is_default_layout(arg_layout, rs, aval)
|
|
else arg_layout)
|
|
else:
|
|
arg_layout, dispatch_arg_layout = None, None
|
|
if jit_in_l is None:
|
|
if committed:
|
|
if isinstance(rs, UnspecifiedValue):
|
|
resolved_in_layouts.append(None)
|
|
else:
|
|
resolved_in_layouts.append(dispatch_arg_layout)
|
|
else:
|
|
resolved_in_layouts.append(None)
|
|
else:
|
|
# arg_layout can be None because some backends don't implement the
|
|
# required layout methods. Hence `arr.format` can return
|
|
# `Format(None, sharding)`
|
|
if (committed
|
|
and not isinstance(rs, UnspecifiedValue)
|
|
and arg_layout is not None
|
|
and not pxla.is_user_xla_layout_equal(jit_in_l, arg_layout)):
|
|
extra_msg = ''
|
|
if isinstance(jit_in_l, AutoLayout):
|
|
extra_msg = (
|
|
' The layout given to `jax.jit` is `Layout.AUTO` but'
|
|
' the corresponding argument passed is a `jax.Array` with a'
|
|
' concrete layout. Consider passing a `jax.ShapeDtypeStruct`'
|
|
' instead of `jax.Array` as an argument to the jitted function '
|
|
' when using `Layout.AUTO`.'
|
|
)
|
|
raise ValueError('Layout passed to jit does not match the layout '
|
|
'on the respective arg. '
|
|
f'Got jit layout: {jit_in_l},\n'
|
|
f'arg layout: {arg_layout} for arg type: {arg.aval}.'
|
|
f'{extra_msg}')
|
|
jit_in_l = (None if isinstance(jit_in_l, Layout) and
|
|
pxla.is_default_layout(jit_in_l, rs, aval) else jit_in_l)
|
|
resolved_in_layouts.append(jit_in_l)
|
|
return tuple(resolved_in_layouts)
|
|
|
|
def _resolve_out_layouts(out_layouts, out_shardings, out_avals):
|
|
new_out_layouts = []
|
|
for out_l, out_s, out_aval in safe_zip(out_layouts, out_shardings, out_avals):
|
|
if out_l is None:
|
|
new_out_layouts.append(None)
|
|
elif (isinstance(out_l, Layout) and
|
|
pxla.is_default_layout(out_l, out_s, out_aval)):
|
|
new_out_layouts.append(None)
|
|
else:
|
|
new_out_layouts.append(out_l)
|
|
return tuple(new_out_layouts)
|
|
|
|
def finalize_arg_sharding(arg_s, committed):
|
|
if isinstance(arg_s, UnspecifiedValue):
|
|
return arg_s
|
|
else:
|
|
if committed:
|
|
return arg_s
|
|
else:
|
|
assert isinstance(arg_s, Sharding)
|
|
if arg_s.num_devices == 1:
|
|
return UNSPECIFIED
|
|
raise NotImplementedError('Having uncommitted Array sharded on '
|
|
'multiple devices is not supported.')
|
|
|
|
def _resolve_in_shardings(args, pjit_in_shardings: Sequence[PjitSharding]
|
|
) -> 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
|
|
|
|
resolved_in_shardings: list[PjitSharding] = []
|
|
for arg, pjit_in_s in zip(args, pjit_in_shardings):
|
|
# arg sharding can be None in case of ShapeDtypeStruct. jax.Array does
|
|
# not allow None as the sharding.
|
|
arg_s, committed = ((arg.sharding, arg.committed) if arg.sharding is not None
|
|
else (UNSPECIFIED, False))
|
|
if isinstance(arg_s, NamedSharding) and arg_s.mesh.empty:
|
|
arg_s, committed = UNSPECIFIED, False
|
|
if isinstance(pjit_in_s, UnspecifiedValue):
|
|
resolved_in_shardings.append(finalize_arg_sharding(arg_s, committed))
|
|
else:
|
|
if (arg.is_np_array and not pjit_in_s.is_fully_replicated and # pyrefly: ignore[missing-attribute]
|
|
xb.process_count() > 1):
|
|
raise ValueError(
|
|
'Passing non-trivial shardings for numpy '
|
|
'inputs is not allowed. To fix this error, either specify a '
|
|
'replicated sharding explicitly or use '
|
|
'`jax.make_array_from_process_local_data(...)` '
|
|
'to convert your host local numpy inputs to a jax.Array which you '
|
|
'can pass to jit. '
|
|
'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 '
|
|
f'you can pass to jit. Got arg type: {arg.aval}')
|
|
if not isinstance(arg_s, UnspecifiedValue) and arg_s._is_concrete:
|
|
# jax.jit does not allow resharding across different memory kinds even
|
|
# if the argument is uncommitted. Use jax.device_put for those cases,
|
|
# either outside or inside jax.jit.
|
|
if pjit_in_s.memory_kind != arg_s.memory_kind: # pyrefly: ignore[missing-attribute]
|
|
raise ValueError(
|
|
'Memory kinds passed to jax.jit does not match memory kind on the'
|
|
f' respective arg. Got jit memory kind: {pjit_in_s.memory_kind}, '
|
|
f'arg memory kind: {arg_s.memory_kind} for arg type: {arg.aval}')
|
|
if (committed and
|
|
not op_shardings.are_hlo_shardings_equal(
|
|
pjit_in_s._to_xla_hlo_sharding(arg.ndim), # pyrefly: ignore[missing-attribute]
|
|
arg_s._to_xla_hlo_sharding(arg.ndim))):
|
|
raise ValueError('Sharding passed to jit does not match the sharding '
|
|
'on the respective arg. '
|
|
f'Got jit sharding: {pjit_in_s},\n'
|
|
f'arg sharding: {arg_s} for arg type: {arg.aval}')
|
|
resolved_in_shardings.append(pjit_in_s)
|
|
|
|
return tuple(resolved_in_shardings)
|
|
|
|
|
|
def _resolve_and_lower(
|
|
args, jaxpr: core.ClosedJaxpr, in_shardings, out_shardings, in_layouts,
|
|
out_layouts, donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
lowering_platforms, lowering_parameters, pgle_profiler,
|
|
compiler_options_kvs) -> pxla.MeshComputation:
|
|
in_shardings = _resolve_in_shardings(args, in_shardings)
|
|
in_layouts = _resolve_in_layouts(args, in_layouts, in_shardings,
|
|
jaxpr.in_avals)
|
|
out_layouts = _resolve_out_layouts(out_layouts, out_shardings, jaxpr.out_avals)
|
|
return _pjit_lower(
|
|
jaxpr, in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline, compiler_options_kvs,
|
|
lowering_platforms=lowering_platforms,
|
|
lowering_parameters=lowering_parameters,
|
|
pgle_profiler=pgle_profiler)
|
|
|
|
_pgle_profiler_dict = weakref.WeakKeyDictionary()
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class MetaTy:
|
|
aval: Any
|
|
sharding: Any
|
|
format: Any
|
|
committed: bool
|
|
is_np_array: bool
|
|
|
|
replace = replace
|
|
|
|
@property
|
|
def shape(self):
|
|
return self.aval.shape
|
|
|
|
@property
|
|
def ndim(self):
|
|
return self.aval.ndim
|
|
|
|
@util.cache(max_size=4096, trace_context_in_key=False)
|
|
def create_meta_ty(aval, arg_sharding, arg_format, arg_committed, is_np_array):
|
|
return MetaTy(aval, arg_sharding, arg_format, arg_committed, is_np_array)
|
|
|
|
def convert_to_metaty(arg):
|
|
# TODO(yashkatariya): Remove this Tracer special case after
|
|
# getattr(Tracer, 'sharding') is fast.
|
|
if isinstance(arg, core.Tracer):
|
|
return create_meta_ty(arg.aval, None, None, True, False)
|
|
aval = core.shaped_abstractify(arg)
|
|
arg_sharding = getattr(arg, 'sharding', None)
|
|
arg_format = getattr(arg, 'format', None)
|
|
arg_committed = getattr(arg, '_committed', True)
|
|
is_np_array = isinstance(arg, np.ndarray)
|
|
return create_meta_ty(aval, arg_sharding, arg_format, arg_committed,
|
|
is_np_array)
|
|
|
|
|
|
def _pjit_call_impl_python(
|
|
*args,
|
|
jaxpr: core.ClosedJaxpr,
|
|
in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs):
|
|
util.test_event("jit_cpp_cache_miss")
|
|
pgle_compile_options, pgle_profiler = {}, None
|
|
if config.enable_pgle.value and config.pgle_profiling_runs.value > 0:
|
|
compilation_target_key = jaxpr
|
|
pgle_profiler = _pgle_profiler_dict.get(compilation_target_key)
|
|
if pgle_profiler is None:
|
|
pgle_profiler = profiler.PGLEProfiler(
|
|
config.pgle_profiling_runs.value,
|
|
config.pgle_aggregation_percentile.value)
|
|
_pgle_profiler_dict[compilation_target_key] = pgle_profiler
|
|
|
|
# The method below will return FDO profile when module was profiled
|
|
# config.jax_pgle_profiling_runs amount of times, otherwise the result will
|
|
# be None.
|
|
fdo_profile = pgle_profiler.consume_fdo_profile()
|
|
if fdo_profile is not None:
|
|
pgle_compile_options['fdo_profile'] = fdo_profile
|
|
|
|
compiler_options_kvs = compiler_options_kvs + tuple(pgle_compile_options.items())
|
|
# Passing mutable PGLE profile here since it should be extracted by JAXPR to
|
|
# initialize the fdo_profile compile option.
|
|
arg_types = map(convert_to_metaty, args)
|
|
computation = _resolve_and_lower(
|
|
arg_types, jaxpr=jaxpr, in_shardings=in_shardings,
|
|
out_shardings=out_shardings, in_layouts=in_layouts,
|
|
out_layouts=out_layouts, donated_invars=donated_invars,
|
|
ctx_mesh=ctx_mesh, name=name, keep_unused=keep_unused,
|
|
inline=inline, lowering_platforms=None,
|
|
lowering_parameters=mlir.LoweringParameters(),
|
|
pgle_profiler=pgle_profiler,
|
|
compiler_options_kvs=compiler_options_kvs,
|
|
)
|
|
compiled = computation.compile()
|
|
|
|
# This check is expensive so only do it if enable_checks is on.
|
|
if compiled._auto_spmd_lowering and config.enable_checks.value:
|
|
pxla.check_array_xla_sharding_layout_match(
|
|
args, compiled._in_shardings, compiled._in_layouts, # pyrefly: ignore[missing-attribute]
|
|
jaxpr.jaxpr.debug_info.safe_arg_names(len(args)))
|
|
if config.distributed_debug.value:
|
|
# 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),
|
|
("in_layouts", in_layouts),
|
|
("out_layouts", out_layouts),
|
|
("abstract args", map(core.typeof, args)),
|
|
("fingerprint", fingerprint))
|
|
return (compiled.unsafe_call(*computation.const_args, *args),
|
|
compiled, pgle_profiler, computation.const_args)
|
|
|
|
@weakref_lru_cache
|
|
def _get_jaxpr_as_fun(jaxpr, in_shardings, out_shardings, in_layouts,
|
|
out_layouts, donated_invars, ctx_mesh, name,
|
|
keep_unused, inline, compiler_options_kvs):
|
|
# The input jaxpr to `_get_jaxpr_as_fun` is under a weakref_lru_cache so
|
|
# returning `core.jaxpr_as_fun(jaxpr)` directly creates a strong reference to
|
|
# the jaxpr defeating the purpose of weakref_lru_cache. So return a function
|
|
# that closes over a weakrefed jaxpr and gets called inside that function.
|
|
# This way there won't be a strong reference to the jaxpr from the output
|
|
# function.
|
|
jaxpr = weakref.ref(jaxpr)
|
|
return lambda *args: core.jaxpr_as_fun(jaxpr())(*args)
|
|
|
|
|
|
def _pjit_call_impl(*args, jaxpr: core.ClosedJaxpr,
|
|
in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs):
|
|
def call_impl_cache_miss(*args_, **kwargs_):
|
|
# args_ do not include the const args
|
|
# See https://docs.jax.dev/en/latest/internals/constants.html.
|
|
# TODO(necula): remove num_const_args when fixing the C++ path
|
|
out_flat, compiled, pgle_profiler, const_args = _pjit_call_impl_python(
|
|
*args, jaxpr=jaxpr, in_shardings=in_shardings,
|
|
out_shardings=out_shardings, in_layouts=in_layouts,
|
|
out_layouts=out_layouts, donated_invars=donated_invars,
|
|
ctx_mesh=ctx_mesh, name=name, keep_unused=keep_unused,
|
|
inline=inline, compiler_options_kvs=compiler_options_kvs)
|
|
fastpath_data = _get_fastpath_data(
|
|
compiled, tree_structure(out_flat), args, out_flat,
|
|
jaxpr.effects, jaxpr.consts, pgle_profiler,
|
|
const_args)
|
|
return out_flat, fastpath_data, _need_to_rebuild_with_fdo(pgle_profiler)
|
|
|
|
f = _get_jaxpr_as_fun(
|
|
jaxpr, in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs)
|
|
donated_argnums = tuple(i for i, d in enumerate(donated_invars) if d)
|
|
cache_key = pxla.JitGlobalCppCacheKeys(
|
|
donate_argnums=donated_argnums, donate_argnames=None,
|
|
device=None, backend=None,
|
|
in_shardings_treedef=None, in_shardings_leaves=in_shardings,
|
|
out_shardings_treedef=None, out_shardings_leaves=out_shardings,
|
|
in_layouts_treedef=None, in_layouts_leaves=in_layouts,
|
|
out_layouts_treedef=None, out_layouts_leaves=out_layouts)
|
|
return xc._xla.pjit(
|
|
name, f, call_impl_cache_miss, [], [], cache_key,
|
|
tree_util.dispatch_registry, pxla.cc_shard_arg,
|
|
_get_cpp_global_cache(cache_key.contains_explicit_attributes))(*args)
|
|
|
|
jit_p.def_impl(_pjit_call_impl)
|
|
|
|
# This cache is important for python dispatch performance.
|
|
@weakref_lru_cache
|
|
def _pjit_lower(
|
|
jaxpr: core.ClosedJaxpr,
|
|
in_shardings,
|
|
out_shardings,
|
|
in_layouts: pxla.MaybeLayout,
|
|
out_layouts: pxla.MaybeLayout,
|
|
donated_invars,
|
|
ctx_mesh,
|
|
name: str,
|
|
keep_unused: bool,
|
|
inline: bool,
|
|
compiler_options_kvs: tuple[tuple[str, Any], ...],
|
|
*,
|
|
lowering_platforms: tuple[str, ...] | None,
|
|
lowering_parameters: mlir.LoweringParameters,
|
|
pgle_profiler: profiler.PGLEProfiler | None) -> pxla.MeshComputation:
|
|
return pxla.lower_sharding_computation(
|
|
jaxpr, 'jit', name, in_shardings, out_shardings,
|
|
in_layouts, out_layouts, tuple(donated_invars),
|
|
keep_unused=keep_unused, context_mesh=ctx_mesh,
|
|
compiler_options_kvs=compiler_options_kvs,
|
|
lowering_platforms=lowering_platforms,
|
|
lowering_parameters=lowering_parameters,
|
|
pgle_profiler=pgle_profiler)
|
|
|
|
|
|
def pjit_staging_rule(trace, source_info, *args, **params):
|
|
if params["compiler_options_kvs"]:
|
|
raise ValueError(
|
|
'`compiler_options` can only be passed to top-level `jax.jit`. Got'
|
|
f' compiler_options={dict(params["compiler_options_kvs"])} specified on'
|
|
f' a nested jit with name: {params["name"]} and source info:'
|
|
f' {source_info_util.summarize(source_info)}')
|
|
# If we're inlining, no need to compute forwarding information; the inlined
|
|
# computation will in effect forward things.
|
|
if (params["inline"] and
|
|
all(isinstance(i, UnspecifiedValue) for i in params["in_shardings"]) and
|
|
all(isinstance(o, UnspecifiedValue) for o in params["out_shardings"]) and
|
|
all(i is None for i in params["in_layouts"]) and
|
|
all(o is None for o in params["out_layouts"])):
|
|
jaxpr = params["jaxpr"]
|
|
out = pe.inline_jaxpr_into_trace(
|
|
trace, source_info, jaxpr.jaxpr, jaxpr.consts, *args)
|
|
return [trace.to_jaxpr_tracer(x, source_info) for x in out]
|
|
|
|
jaxpr = params['jaxpr']
|
|
if any(isinstance(c, core.Ref) for c in jaxpr.consts):
|
|
jaxpr, consts = pxla._move_mutable_consts(jaxpr)
|
|
consts = [trace.new_const(c, source_info) for c in consts]
|
|
in_shardings = (*params['in_shardings'],) + (UNSPECIFIED,) * len(consts)
|
|
in_layouts = (*params['in_layouts'],) + (None,) * len(consts)
|
|
donated_invars = (*params['donated_invars'],) + (False,) * len(consts)
|
|
new_params = dict(params, jaxpr=jaxpr, in_shardings=in_shardings,
|
|
in_layouts=in_layouts, donated_invars=donated_invars)
|
|
out_tracers = trace.default_process_primitive(
|
|
jit_p, (*args, *consts), new_params, source_info=source_info)
|
|
else:
|
|
out_tracers = trace.default_process_primitive(
|
|
jit_p, args, params, source_info=source_info)
|
|
# TODO(mattjj): handle qdd in the presence of refs
|
|
for v, x in zip(it.chain(jaxpr.constvars, jaxpr.invars), it.chain(jaxpr.consts, args)):
|
|
if v.initial_qdd:
|
|
assert core.cur_qdd(x) == v.initial_qdd
|
|
x.aval_mutable_qdd.mutable_qdd.update(v.final_qdd)
|
|
return out_tracers
|
|
pe.custom_staging_rules[jit_p] = pjit_staging_rule
|
|
|
|
|
|
def pjit_forwarding_rule(eqn):
|
|
return [None] * len(eqn.outvars), eqn
|
|
# TODO(mattjj): Remove pjit_forwarding_rule and also in staging rule.
|
|
pe.forwarding_rules[jit_p] = pjit_forwarding_rule
|
|
|
|
|
|
def _pjit_typecheck(ctx_factory, *in_atoms, jaxpr, **params):
|
|
return core._check_call(ctx_factory, jit_p, in_atoms,
|
|
dict(params, call_jaxpr=jaxpr.jaxpr))
|
|
core.custom_typechecks[jit_p] = _pjit_typecheck
|
|
|
|
|
|
def _pjit_abstract_eval(*args, jaxpr, out_shardings, **_):
|
|
effs = core.eqn_effects(jaxpr) if jaxpr.constvars else jaxpr.effects
|
|
return jaxpr.out_avals, effs
|
|
jit_p.def_effectful_abstract_eval(_pjit_abstract_eval)
|
|
|
|
|
|
def _pjit_cached_lower_jaxpr_to_fun(
|
|
ctx: mlir.LoweringRuleContext, name: str, jaxpr: core.ClosedJaxpr,
|
|
num_const_args: int, in_avals, effects, in_shardings, out_shardings,
|
|
in_layouts, out_layouts, api_name):
|
|
assert len(in_avals) == num_const_args + len(jaxpr.in_avals)
|
|
assert len(in_avals) == len(in_shardings)
|
|
assert len(in_avals) == len(in_layouts)
|
|
mod_ctx = ctx.module_context
|
|
axis_ctx = ctx.module_context.axis_context
|
|
num_devices = None
|
|
if isinstance(axis_ctx, sharding_impls.ShardingContext):
|
|
num_devices = axis_ctx.num_devices
|
|
elif isinstance(axis_ctx, sharding_impls.SPMDAxisContext):
|
|
num_devices = axis_ctx.mesh.size
|
|
key = (jit_p, name, jaxpr, effects, num_devices,
|
|
pxla.SemanticallyEqualShardings(in_shardings, in_avals),
|
|
pxla.SemanticallyEqualShardings(out_shardings, jaxpr.out_avals),
|
|
in_layouts, out_layouts, api_name)
|
|
|
|
func = mod_ctx.cached_primitive_lowerings.get(key, None)
|
|
if func is None:
|
|
arg_shardings = [None if isinstance(i, UnspecifiedValue) else i
|
|
for i in in_shardings]
|
|
result_shardings = [None if isinstance(o, UnspecifiedValue) else o
|
|
for o in out_shardings]
|
|
# TODO(b/228598865): non-top-level functions 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(
|
|
mod_ctx, name, jaxpr, effects,
|
|
num_const_args=num_const_args, in_avals=in_avals,
|
|
arg_shardings=arg_shardings, result_shardings=result_shardings,
|
|
use_sharding_annotations=False,
|
|
arg_layouts=in_layouts, result_layouts=out_layouts)
|
|
|
|
mod_ctx.cached_primitive_lowerings[key] = func
|
|
return func
|
|
|
|
|
|
def _pjit_lowering(ctx: mlir.LoweringRuleContext, *args, name: str,
|
|
jaxpr: core.ClosedJaxpr, in_shardings,
|
|
out_shardings, in_layouts, out_layouts, donated_invars,
|
|
ctx_mesh, keep_unused, inline, compiler_options_kvs):
|
|
effects = list(ctx.tokens_in.effects())
|
|
output_types = map(mlir._aval_to_ir_types, ctx.avals_out)
|
|
output_types = [mlir.token_type()] * len(effects) + output_types
|
|
flat_output_types = mlir.flatten_ir_types(output_types)
|
|
|
|
const_args_and_avals = core.jaxpr_const_args(jaxpr.jaxpr)
|
|
const_args, const_arg_avals = util.unzip2(const_args_and_avals)
|
|
in_avals = (*const_arg_avals, *jaxpr.in_avals)
|
|
ca_shardings = const_args_shardings(const_args)
|
|
in_shardings = ca_shardings + in_shardings
|
|
ca_layouts = const_args_layouts(const_args, const_arg_avals, ca_shardings)
|
|
in_layouts = ca_layouts + in_layouts
|
|
|
|
func = _pjit_cached_lower_jaxpr_to_fun(
|
|
ctx, name, jaxpr, len(const_args), in_avals, tuple(effects), in_shardings,
|
|
out_shardings, in_layouts, out_layouts, api_name='jit')
|
|
|
|
tokens_in = [ctx.tokens_in.get(eff) for eff in effects]
|
|
hoisted_const_values = mlir.flatten_ir_values(
|
|
mlir.ir_constants(c, const_lowering=ctx.const_lowering, aval=aval)
|
|
for c, aval in const_args_and_avals
|
|
)
|
|
args = (*ctx.dim_var_values, *tokens_in, *hoisted_const_values, *args)
|
|
with mlir.source_info_to_location(
|
|
ctx.module_context, None,
|
|
ctx.name_stack.extend(util.wrap_name('jit', name)), ctx.traceback):
|
|
call = func_dialect.CallOp(
|
|
flat_output_types, ir.FlatSymbolRefAttr.get(func.name.value),
|
|
mlir.flatten_ir_values(args))
|
|
mlir.wrap_compute_type_in_place(ctx, call) # pyrefly: ignore[bad-argument-type]
|
|
out_nodes = mlir.unflatten_ir_values_like_types(call.results, 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
|
|
|
|
# TODO(phawkins): this is marked uncacheable because it has its own cache and
|
|
# because the cache breaks jaxpr metadata like source locations. We should fix
|
|
# the metadata problem and consolidate the caches.
|
|
mlir.register_lowering(jit_p, _pjit_lowering, cacheable=False)
|
|
|
|
def const_args_shardings(const_args: Sequence[Array | np.ndarray]) -> Sequence[PjitSharding]:
|
|
const_args_types = map(convert_to_metaty, const_args)
|
|
return _resolve_in_shardings(
|
|
const_args_types, (sharding_impls.UNSPECIFIED,) * len(const_args))
|
|
|
|
def const_args_layouts(
|
|
const_args: Sequence[ArrayLike],
|
|
avals: Sequence[core.AbstractValue],
|
|
shardings: Sequence[PjitSharding]
|
|
) -> Sequence[Layout | AutoLayout | None]:
|
|
const_args_types = map(convert_to_metaty, const_args)
|
|
return _resolve_in_layouts(
|
|
const_args_types, (None,) * len(const_args), shardings, avals)
|
|
|
|
def _pjit_batcher(axis_data, vals_in,
|
|
dims_in: tuple[int, ...],
|
|
jaxpr: core.ClosedJaxpr,
|
|
in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs):
|
|
new_jaxpr, axes_out = batching.batch_jaxpr2(jaxpr, axis_data, dims_in)
|
|
in_shardings = tuple(
|
|
_pjit_batcher_for_sharding(i, axis_in, axis_data.spmd_name, ctx_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, axis_data.spmd_name, ctx_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))
|
|
# TODO(yashkatariya): Figure out layouts should change under vmap.
|
|
if not (all(l is None for l in in_layouts) and
|
|
all(l is None for l in out_layouts)):
|
|
raise NotImplementedError(
|
|
'Concrete layouts are not supported for vmap(jit).')
|
|
|
|
vals_out = jit_p.bind(
|
|
*vals_in,
|
|
jaxpr=new_jaxpr,
|
|
in_shardings=in_shardings,
|
|
out_shardings=out_shardings,
|
|
in_layouts=in_layouts,
|
|
out_layouts=out_layouts,
|
|
donated_invars=donated_invars,
|
|
ctx_mesh=ctx_mesh,
|
|
name=name,
|
|
keep_unused=keep_unused,
|
|
inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
|
|
return vals_out, axes_out
|
|
|
|
batching.fancy_primitive_batchers[jit_p] = _pjit_batcher
|
|
|
|
|
|
def _pjit_batcher_for_sharding(
|
|
s, dim: int, spmd_axis_name: tuple[str, ...] | None,
|
|
mesh, ndim: int):
|
|
if isinstance(s, UnspecifiedValue):
|
|
return s
|
|
hlo_s = s._to_xla_hlo_sharding(ndim)
|
|
if spmd_axis_name is None:
|
|
if sharding_impls.is_hlo_sharding_replicated(hlo_s):
|
|
return s
|
|
if isinstance(s, NamedSharding) and isinstance(s.mesh, AbstractMesh):
|
|
return NamedSharding(
|
|
s.mesh, pxla.batch_spec(s.spec, dim, PartitionSpec.UNCONSTRAINED))
|
|
new_op = hlo_s.to_proto().clone()
|
|
tad = list(new_op.tile_assignment_dimensions)
|
|
tad.insert(dim, 1)
|
|
new_op.tile_assignment_dimensions = tad
|
|
new_gs = GSPMDSharding(s._internal_device_list, new_op)
|
|
return pxla._get_out_sharding_from_orig_sharding([new_gs], [None], s, None)[0]
|
|
else:
|
|
if isinstance(s, NamedSharding) and isinstance(s.mesh, AbstractMesh):
|
|
return NamedSharding(
|
|
s.mesh, pxla.batch_spec(s.spec, dim, spmd_axis_name))
|
|
if isinstance(s, NamedSharding):
|
|
mesh = s.mesh
|
|
if mesh.empty or mesh.is_scalar:
|
|
raise ValueError(
|
|
'If you are using spmd_axis_name parameter of jax.vmap,'
|
|
' please make sure to run your jitted function inside the mesh'
|
|
' context manager. Only `jax.lax.with_sharding_constraint` with'
|
|
' `jax.sharding.NamedSharding` as an input can be transformed with'
|
|
' spmd_axis_name batching rules outside of an explicit mesh context'
|
|
f' manager scope {s!r}')
|
|
spec = parse_flatten_op_sharding(hlo_s, mesh)[0]
|
|
return NamedSharding(
|
|
mesh, pxla.batch_spec(spec, dim, spmd_axis_name))
|
|
|
|
|
|
def _pjit_jvp(primals_in, tangents_in,
|
|
jaxpr, in_shardings, out_shardings, in_layouts, out_layouts,
|
|
donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs):
|
|
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 = jit_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)),
|
|
in_layouts=(*in_layouts, *_filter_zeros_in(in_layouts)),
|
|
out_layouts=(*out_layouts, *_filter_zeros_out(out_layouts)),
|
|
donated_invars=(*donated_invars, *_filter_zeros_in(donated_invars)),
|
|
ctx_mesh=ctx_mesh,
|
|
name=name,
|
|
keep_unused=keep_unused,
|
|
inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
|
|
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[jit_p] = _pjit_jvp
|
|
|
|
|
|
def _pjit_linearize(is_vjp, nzs, *primals_in, jaxpr, in_shardings, out_shardings,
|
|
in_layouts, out_layouts, donated_invars, ctx_mesh, name,
|
|
keep_unused, inline, compiler_options_kvs):
|
|
primal_jaxpr, num_residuals_out, nzs_out, in_fwd_res, tangent_jaxpr = \
|
|
ad.linearize_jaxpr(jaxpr, nzs, is_vjp=is_vjp)
|
|
num_residuals_in = len(in_fwd_res)
|
|
num_primals_out = len(primal_jaxpr.out_avals) - num_residuals_out
|
|
|
|
res_shardings_in = (UNSPECIFIED,) * num_residuals_in
|
|
res_layouts_in = (None,) * num_residuals_in
|
|
res_donated = (False,) * num_residuals_in
|
|
primal_out_shardings = tuple(out_shardings) + (UNSPECIFIED,) * num_residuals_out
|
|
primal_out_layouts = tuple(out_layouts) + (None,) * num_residuals_out
|
|
|
|
config.enable_checks.value and core.check_jaxpr(primal_jaxpr.jaxpr)
|
|
config.enable_checks.value and core.check_jaxpr(tangent_jaxpr.jaxpr)
|
|
|
|
def keep_where(l, should_keep):
|
|
return tuple(x for x, keep in zip(l, should_keep) if keep)
|
|
|
|
# Input-to-output forwarding.
|
|
in_fwd = pe._jaxpr_forwarding(primal_jaxpr.jaxpr)
|
|
in_fwd_primal, in_fwd_res_ = split_list(in_fwd, [num_primals_out])
|
|
assert all(f is None for f in in_fwd_res_)
|
|
in_fwd = [
|
|
fwd if isinstance(os, UnspecifiedValue) and ol is None else None
|
|
for os, ol, fwd in zip(out_shardings, out_layouts, in_fwd_primal)
|
|
] + in_fwd_res_
|
|
del in_fwd_res_, in_fwd_primal
|
|
keep = [f is None for f in in_fwd]
|
|
primal_jaxpr = pe.prune_closed_jaxpr_outputs(primal_jaxpr, keep)
|
|
primal_out_shardings = keep_where(primal_out_shardings, keep)
|
|
primal_out_layouts = keep_where(primal_out_layouts, keep)
|
|
_, kept_res = split_list(keep, [num_primals_out])
|
|
num_kept_residuals = sum(kept_res)
|
|
del keep, kept_res, num_primals_out
|
|
|
|
# Output-to-output forwarding.
|
|
num_primals_out = len(primal_jaxpr.out_avals) - num_kept_residuals
|
|
out_vars, res_vars = split_list(primal_jaxpr.jaxpr.outvars, [num_primals_out])
|
|
idx_map = {id(v): i for i, v in enumerate(out_vars)}
|
|
out_fwd = [None] * num_primals_out + [idx_map.get(id(v)) for v in res_vars]
|
|
keep = [f is None for f in out_fwd]
|
|
primal_jaxpr = pe.prune_closed_jaxpr_outputs(primal_jaxpr, keep)
|
|
primal_out_shardings = keep_where(primal_out_shardings, keep)
|
|
primal_out_layouts = keep_where(primal_out_layouts, keep)
|
|
del keep
|
|
|
|
tangent_avals_out = [a.to_tangent_aval() for a in jaxpr.out_avals]
|
|
|
|
def tangent_fun(residuals, *tangents):
|
|
tangents_nz = _filter_zeros(nzs, tangents)
|
|
nz_tangents_out = jit_p.bind(
|
|
*residuals, *tangents_nz, jaxpr=tangent_jaxpr,
|
|
in_shardings=res_shardings_in + _filter_zeros(nzs, in_shardings),
|
|
out_shardings=_filter_zeros(nzs_out, out_shardings),
|
|
in_layouts=res_layouts_in + _filter_zeros(nzs, in_layouts),
|
|
out_layouts=_filter_zeros(nzs_out, out_layouts),
|
|
donated_invars=res_donated + _filter_zeros(nzs, donated_invars),
|
|
ctx_mesh=ctx_mesh,
|
|
name=name,
|
|
keep_unused=keep_unused,
|
|
inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
nz_tangents_out_ = iter(nz_tangents_out)
|
|
tangents_out = [next(nz_tangents_out_) if nz else ad.Zero(aval)
|
|
for (aval, nz) in zip(tangent_avals_out, nzs_out)]
|
|
return tangents_out
|
|
|
|
def _filter_zeros(is_nz_l, l):
|
|
return tuple(x for nz, x in zip(is_nz_l, l) if nz)
|
|
|
|
assert len(in_shardings) == len(primal_jaxpr.in_avals)
|
|
ans = jit_p.bind(*primals_in, jaxpr=primal_jaxpr,
|
|
in_shardings=in_shardings,
|
|
out_shardings=primal_out_shardings,
|
|
in_layouts=in_layouts,
|
|
out_layouts=primal_out_layouts,
|
|
donated_invars=donated_invars,
|
|
ctx_mesh=ctx_mesh,
|
|
name=name,
|
|
keep_unused=keep_unused,
|
|
inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
ans = subs_list(out_fwd, ans, ans)
|
|
ans = subs_list(in_fwd, primals_in, ans)
|
|
primal_ans, residuals_ans = split_list(ans, [len(ans) - num_residuals_out])
|
|
residuals_ans = subs_list(in_fwd_res, [*jaxpr.consts, *primals_in], residuals_ans)
|
|
|
|
return primal_ans, nzs_out, residuals_ans, tangent_fun
|
|
ad.primitive_linearizations[jit_p] = _pjit_linearize
|
|
|
|
|
|
def _pjit_remat(policy, *args, jaxpr, **params):
|
|
jaxpr_fwd, jaxpr_rem, num_res = remat.remat_jaxpr(jaxpr, policy)
|
|
params_fwd, params_rem = _add_res_to_params(num_res, **params)
|
|
primals_res_out = jit_p.bind(*args, jaxpr=jaxpr_fwd, **params_fwd)
|
|
primals_out, res = split_list(primals_res_out, [len(jaxpr.outvars)])
|
|
return primals_out, partial(jit_p.bind, *res, jaxpr=jaxpr_rem, **params_rem)
|
|
remat.rules[jit_p] = _pjit_remat
|
|
|
|
def _add_res_to_params(num_res, in_shardings, out_shardings, in_layouts,
|
|
out_layouts, donated_invars, **params):
|
|
params_fwd = dict(params,
|
|
in_shardings=in_shardings,
|
|
out_shardings=out_shardings + (UNSPECIFIED,) * num_res,
|
|
in_layouts=in_layouts,
|
|
out_layouts=out_layouts + (None,) * num_res,
|
|
donated_invars=donated_invars)
|
|
params_rem = dict(params,
|
|
in_shardings=(UNSPECIFIED,) * num_res + in_shardings,
|
|
out_shardings=out_shardings,
|
|
in_layouts=(None,) * num_res + in_layouts,
|
|
out_layouts=out_layouts,
|
|
donated_invars=(False,) * num_res + donated_invars)
|
|
return params_fwd, params_rem
|
|
|
|
|
|
def _pjit_partial_eval(trace: pe.JaxprTrace,
|
|
*in_tracers,
|
|
jaxpr: core.ClosedJaxpr, in_shardings, out_shardings,
|
|
in_layouts, out_layouts, donated_invars, ctx_mesh,
|
|
name, keep_unused, inline, compiler_options_kvs):
|
|
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_out_avals, in_fwd_res = \
|
|
pe.partial_eval_jaxpr_nounits_fwd(jaxpr, unknown_ins, instantiate=False)
|
|
unknown_outs = tuple(unknown_outs)
|
|
known_outs = tuple(not uk for uk in unknown_outs)
|
|
|
|
# out_shardings and out_layouts for residual values output by known_jaxpr
|
|
def keep_where(l, should_keep):
|
|
return tuple(x for x, keep in zip(l, should_keep) if keep)
|
|
|
|
known_out_shardings = (keep_where(out_shardings, known_outs)
|
|
+ (UNSPECIFIED,) * len(res_out_avals))
|
|
known_out_layouts = (keep_where(out_layouts, known_outs)
|
|
+ (None,) * len(res_out_avals))
|
|
|
|
# Input-to-output forwarding: compute which outputs are just forwarded inputs.
|
|
num_out_primals = len(known_jaxpr.out_avals) - len(res_out_avals)
|
|
in_fwd: list[int | None] = pe._jaxpr_forwarding(known_jaxpr.jaxpr)
|
|
in_fwd_primal, in_fwd_res_ = split_list(in_fwd, [num_out_primals])
|
|
assert all(f is None for f in in_fwd_res_)
|
|
in_fwd = [
|
|
fwd if isinstance(os, UnspecifiedValue) and ol is None else None
|
|
for os, ol, fwd in zip(
|
|
keep_where(out_shardings, known_outs),
|
|
keep_where(out_layouts, known_outs), in_fwd_primal)
|
|
] + in_fwd_res_
|
|
del in_fwd_primal, in_fwd_res_
|
|
# Prune jaxpr outputs and out_shardings by removing the input-forwards.
|
|
keep = [f is None for f in in_fwd]
|
|
known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
|
|
known_out_shardings = keep_where(known_out_shardings, keep)
|
|
known_out_layouts = keep_where(known_out_layouts, keep)
|
|
# Update num_out_primals to reflect pruning.
|
|
kept_primals, kept_res = split_list(keep, [num_out_primals])
|
|
num_out_primals = sum(kept_primals)
|
|
del keep, kept_primals, kept_res
|
|
|
|
# Output-to-output forwarding: compute which residuals are just primal outputs
|
|
out_vars, res_vars = split_list(known_jaxpr.jaxpr.outvars, [num_out_primals])
|
|
idx_map = {id(v): i for i, v in enumerate(out_vars)}
|
|
out_fwd = [None] * num_out_primals + [idx_map.get(id(v)) for v in res_vars]
|
|
# Prune jaxpr outputs and out_shardings by removing forwarded residuals.
|
|
keep = [f is None for f in out_fwd]
|
|
known_jaxpr = pe.prune_closed_jaxpr_outputs(known_jaxpr, keep)
|
|
known_out_shardings = keep_where(known_out_shardings, keep)
|
|
known_out_layouts = keep_where(known_out_layouts, keep)
|
|
del keep
|
|
|
|
known_params = dict(
|
|
jaxpr=known_jaxpr, in_shardings=keep_where(in_shardings, known_ins),
|
|
out_shardings=known_out_shardings,
|
|
in_layouts=keep_where(in_layouts, known_ins),
|
|
out_layouts=known_out_layouts,
|
|
donated_invars=keep_where(donated_invars, known_ins),
|
|
ctx_mesh=ctx_mesh,
|
|
name=name, keep_unused=keep_unused, inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
assert len(known_params['out_shardings']) == len(known_params['jaxpr'].out_avals)
|
|
assert len(known_params['out_layouts']) == 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 = jit_p.bind(*known_inputs, **known_params)
|
|
# Add back in the output fwds.
|
|
all_known_outs = subs_list(out_fwd, all_known_outs, all_known_outs)
|
|
# Add back in the input fwds.
|
|
all_known_outs = subs_list(in_fwd, known_inputs, all_known_outs)
|
|
|
|
known_out_vals, residual_vals = \
|
|
split_list(all_known_outs, [len(all_known_outs) - len(res_out_avals)])
|
|
residual_vals_ = iter(residual_vals)
|
|
residual_vals = [next(residual_vals_) if f is None
|
|
else [*jaxpr.consts, *known_inputs][f] for f in in_fwd_res]
|
|
assert next(residual_vals_, None) is None
|
|
residual_tracers = map(trace.new_instantiated_const, 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] * len(residual_vals) + [False] * sum(unknown_ins))
|
|
|
|
# Set up staged-out 'unknown' eqn
|
|
unknown_in_shardings = (keep_where(in_shardings, unknown_ins)
|
|
+ (UNSPECIFIED,) * len(residual_tracers))
|
|
unknown_in_layouts = (keep_where(in_layouts, unknown_ins)
|
|
+ (None,) * len(residual_tracers))
|
|
unknown_donated_invars = (keep_where(donated_invars, unknown_ins)
|
|
+ (False,) * len(residual_tracers))
|
|
unknown_params = dict(
|
|
jaxpr=unknown_jaxpr,
|
|
in_shardings=unknown_in_shardings,
|
|
in_layouts=unknown_in_layouts,
|
|
out_shardings=keep_where(out_shardings, unknown_outs),
|
|
out_layouts=keep_where(out_layouts, unknown_outs),
|
|
donated_invars=unknown_donated_invars,
|
|
ctx_mesh=ctx_mesh,
|
|
name=name,
|
|
keep_unused=keep_unused,
|
|
inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
unknown_tracers_in = [t for t in in_tracers if not t.pval.is_known()]
|
|
unknown_out_avals = unknown_jaxpr.out_avals
|
|
unknown_tracers_out = [
|
|
pe.JaxprTracer(trace, pe.PartialVal.unknown(aval), None)
|
|
for aval in unknown_out_avals
|
|
]
|
|
unknown_tracers_in = [*unknown_tracers_in, *residual_tracers]
|
|
eqn = pe.new_eqn_recipe(trace, unknown_tracers_in,
|
|
unknown_tracers_out,
|
|
jit_p,
|
|
unknown_params,
|
|
unknown_jaxpr.effects,
|
|
source_info_util.current())
|
|
for t in unknown_tracers_out: t.recipe = eqn
|
|
if effects.partial_eval_kept_effects.filter_in(unknown_jaxpr.effects):
|
|
trace.effect_handles.append(pe.EffectHandle(unknown_tracers_in, eqn))
|
|
return merge_lists(unknown_outs, known_out_vals, unknown_tracers_out)
|
|
|
|
pe.custom_partial_eval_rules[jit_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_out: int, num_res_in: 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'])
|
|
_, out_shardings_known = pe.partition_list(kept_outs_known, params_known['out_shardings'])
|
|
in_layouts_known, _ = pe.partition_list(unks_in, params_known['in_layouts'])
|
|
_, out_layouts_known = pe.partition_list(kept_outs_known, params_known['out_layouts'])
|
|
|
|
new_params_known = dict(params_known,
|
|
in_shardings=tuple(in_shardings_known),
|
|
out_shardings=(*out_shardings_known,
|
|
*[UNSPECIFIED] * num_res_out),
|
|
in_layouts=tuple(in_layouts_known),
|
|
out_layouts=(*out_layouts_known, *[None] * num_res_out),
|
|
donated_invars=tuple(donated_invars_known))
|
|
assert len(new_params_known['in_shardings']) == len(params_known['jaxpr'].in_avals)
|
|
assert len(new_params_known['out_shardings']) == len(params_known['jaxpr'].out_avals)
|
|
assert len(new_params_known['in_layouts']) == len(params_known['jaxpr'].in_avals)
|
|
assert len(new_params_known['out_layouts']) == 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_in + donated_invars_staged
|
|
_, in_shardings_staged = pe.partition_list(inst_in, params_staged['in_shardings'])
|
|
in_shardings_staged = [*[UNSPECIFIED] * num_res_in, *in_shardings_staged]
|
|
_, out_shardings_staged = pe.partition_list(kept_outs_staged, params_staged['out_shardings'])
|
|
_, in_layouts_staged = pe.partition_list(inst_in, params_staged['in_layouts'])
|
|
in_layouts_staged = [*[None] * num_res_in, *in_layouts_staged]
|
|
_, out_layouts_staged = pe.partition_list(kept_outs_staged, params_staged['out_layouts'])
|
|
|
|
new_params_staged = dict(params_staged,
|
|
in_shardings=tuple(in_shardings_staged),
|
|
out_shardings=tuple(out_shardings_staged),
|
|
in_layouts=tuple(in_layouts_staged),
|
|
out_layouts=tuple(out_layouts_staged),
|
|
donated_invars=tuple(donated_invars_staged))
|
|
assert len(new_params_staged['in_shardings']) == len(params_staged['jaxpr'].in_avals)
|
|
assert len(new_params_staged['out_shardings']) == len(params_staged['jaxpr'].out_avals)
|
|
assert len(new_params_staged['in_layouts']) == len(params_staged['jaxpr'].in_avals)
|
|
assert len(new_params_staged['out_layouts']) == len(params_staged['jaxpr'].out_avals)
|
|
return new_params_known, new_params_staged
|
|
|
|
pe.partial_eval_jaxpr_custom_rules[jit_p] = \
|
|
partial(pe.closed_call_partial_eval_custom_rule, 'jaxpr',
|
|
_pjit_partial_eval_custom_params_updater)
|
|
|
|
|
|
def _pjit_transpose_fancy(
|
|
cts_in, *args, jaxpr, in_shardings, out_shardings, in_layouts,
|
|
out_layouts, donated_invars, ctx_mesh, name, keep_unused, inline,
|
|
compiler_options_kvs):
|
|
primals_ctrefs, specs = ad.project_accums(args)
|
|
in_flat, in_tree = tree_flatten((primals_ctrefs, cts_in))
|
|
in_avals = [core.AvalQDD(a, cur_qdd(x)) if (a := typeof(x)).has_qdd
|
|
else a for x in in_flat]
|
|
trans_jaxpr, out_tree = _transpose_jaxpr_fancy(jaxpr, in_tree, (*in_avals,), specs)
|
|
|
|
trans_in_shardings = (
|
|
[s for x, s in zip(args, in_shardings)
|
|
if not isinstance(x, (ad.ValAccum, ad.NullAccum))] +
|
|
[s for x, s in zip(cts_in, out_shardings) if not isinstance(x, ad.Zero)])
|
|
trans_in_layouts = (
|
|
[l for x, l in zip(args, in_layouts)
|
|
if not isinstance(x, (ad.ValAccum, ad.NullAccum))] +
|
|
[l for x, l in zip(cts_in, out_layouts) if not isinstance(x, ad.Zero)])
|
|
cts_out_ = tree_unflatten(out_tree, trans_jaxpr.out_avals)
|
|
trans_out_shardings = tuple(s for x, s in zip(cts_out_, in_shardings)
|
|
if isinstance(x, core.AbstractValue))
|
|
trans_out_layouts = tuple(l for x, l in zip(cts_out_, in_layouts )
|
|
if isinstance(x, core.AbstractValue))
|
|
|
|
try:
|
|
cts_out = jit_p.bind(
|
|
*in_flat, jaxpr=trans_jaxpr, in_shardings=tuple(trans_in_shardings),
|
|
in_layouts=tuple(trans_in_layouts), out_shardings=trans_out_shardings,
|
|
out_layouts=trans_out_layouts, donated_invars=(False,) * len(in_flat),
|
|
ctx_mesh=ctx_mesh, name=name, keep_unused=keep_unused, inline=inline,
|
|
compiler_options_kvs=compiler_options_kvs)
|
|
except api_util.InternalFloatingPointError as e:
|
|
print("Invalid nan value encountered in the backward pass of a jax.jit "
|
|
"function. Calling the de-optimized backward pass.")
|
|
try:
|
|
ad.backward_pass3(jaxpr.jaxpr, False, jaxpr.consts, args, cts_in)
|
|
except (FloatingPointError, ZeroDivisionError) as e2:
|
|
raise e2 from None # great
|
|
else:
|
|
# 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.
|
|
api_util._raise_no_nan_in_deoptimized(e)
|
|
|
|
# pyrefly: ignore[unbound-name] # pyrefly#2219
|
|
for x, ct in zip(args, tree_unflatten(out_tree, cts_out)):
|
|
if isinstance(x, ad.ValAccum): x.accum(ct)
|
|
|
|
@weakref_lru_cache
|
|
def _transpose_jaxpr_fancy(jaxpr, in_tree, in_avals, specs):
|
|
cell = lambda: None
|
|
def transposed(*in_flat):
|
|
primals_ctrefs, cts_in = tree_unflatten(in_tree, in_flat)
|
|
args = ad.unproject_accums(specs, primals_ctrefs)
|
|
ad.backward_pass3(jaxpr.jaxpr, False, jaxpr.consts, args, cts_in)
|
|
cts_out = [x.freeze() if isinstance(x, ad.ValAccum) else None for x in args]
|
|
cts_out, cell.out_tree = tree_flatten(cts_out) # pyrefly: ignore[missing-attribute]
|
|
return cts_out
|
|
dbg = jaxpr.jaxpr.debug_info.with_unknown_names()
|
|
trans_jaxpr, _, consts = pe.trace_to_jaxpr_dynamic(
|
|
lu.wrap_init(transposed, debug_info=dbg), in_avals)
|
|
return core.ClosedJaxpr(trans_jaxpr, consts), cell.out_tree # pyrefly: ignore[missing-attribute]
|
|
ad.fancy_transposes[jit_p] = _pjit_transpose_fancy
|
|
|
|
@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], core.JaxprEqn | None]:
|
|
if not any(used_outputs) and not pe.has_effects(eqn):
|
|
return [False] * len(eqn.invars), None
|
|
|
|
dced_jaxpr, used_inputs = _dce_jaxpr_pjit(
|
|
eqn.params['jaxpr'], tuple(used_outputs))
|
|
|
|
def keep_where(xs, keeps):
|
|
return tuple(x for x, keep in zip(xs, keeps) if keep)
|
|
|
|
eqn_params = eqn.params
|
|
new_params = dict(
|
|
eqn_params,
|
|
jaxpr=dced_jaxpr,
|
|
in_shardings=keep_where(eqn_params["in_shardings"], used_inputs),
|
|
out_shardings=keep_where(eqn_params["out_shardings"], used_outputs),
|
|
in_layouts=keep_where(eqn_params["in_layouts"], used_inputs),
|
|
out_layouts=keep_where(eqn_params["out_layouts"], used_outputs),
|
|
donated_invars=keep_where(eqn_params["donated_invars"], used_inputs),
|
|
)
|
|
if not any(used_inputs) and not any(used_outputs) and not dced_jaxpr.effects:
|
|
return used_inputs, None
|
|
else:
|
|
new_effs = core.eqn_effects(dced_jaxpr)
|
|
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, new_effs, eqn.source_info, eqn.ctx)
|
|
return used_inputs, new_eqn
|
|
|
|
pe.dce_rules[jit_p] = dce_jaxpr_pjit_rule
|
|
|
|
|
|
def _pjit_pp_rule(eqn: core.JaxprEqn,
|
|
context: core.JaxprPpContext,
|
|
settings: core.JaxprPpSettings) -> core.pp.Doc:
|
|
params = dict(eqn.params)
|
|
del params['inline']
|
|
if not any(params['donated_invars']):
|
|
del params['donated_invars']
|
|
if all(isinstance(s, UnspecifiedValue) for s in params['in_shardings']):
|
|
del params['in_shardings']
|
|
if all(isinstance(s, UnspecifiedValue) for s in params['out_shardings']):
|
|
del params['out_shardings']
|
|
if all(l is None for l in params['in_layouts']):
|
|
del params['in_layouts']
|
|
if all(l is None for l in params['out_layouts']):
|
|
del params['out_layouts']
|
|
if not params['keep_unused']:
|
|
del params['keep_unused']
|
|
if params['ctx_mesh'].empty:
|
|
del params['ctx_mesh']
|
|
if not params['compiler_options_kvs']:
|
|
del params['compiler_options_kvs']
|
|
|
|
if params['jaxpr'].jaxpr not in context.shared_jaxprs:
|
|
context.suggest_same_var_names(params['jaxpr'].jaxpr.invars, eqn.invars)
|
|
context.suggest_same_var_names(params['jaxpr'].jaxpr.outvars, eqn.outvars)
|
|
|
|
# Move name= to the front to make the resulting equation easier to scan.
|
|
del params["name"]
|
|
return core._pp_eqn(eqn, context, settings, params=["name"] + sorted(params))
|
|
|
|
core.pp_eqn_rules[jit_p] = _pjit_pp_rule
|
|
|
|
|
|
# -------------------- with_sharding_constraint --------------------
|
|
|
|
def check_shardings_are_auto(s: Sharding) -> None:
|
|
if not isinstance(s, NamedSharding):
|
|
return
|
|
mesh = s.mesh.abstract_mesh
|
|
if not all(mesh._name_to_type[i] == mesh_lib.AxisType.Auto
|
|
for axes in s.spec
|
|
if axes is not PartitionSpec.UNCONSTRAINED and axes is not None
|
|
for i in (axes if isinstance(axes, tuple) else (axes,))):
|
|
raise ValueError(
|
|
'The spec of NamedSharding passed to with_sharding_constraint can'
|
|
f' only refer to Auto axes of the mesh. Got spec={s.spec} and'
|
|
f' mesh={mesh}. You probably meant to use `reshard` API?')
|
|
|
|
def assert_shardings_equal(x_aval, user_sharding: NamedSharding):
|
|
x_spec = x_aval.sharding.spec
|
|
user_spec = user_sharding.spec._normalized_spec_for_aval(x_aval.ndim)
|
|
if config.remove_size_one_mesh_axis_from_type.value:
|
|
user_spec = core.remove_size_one_mesh_axis(user_spec, user_sharding.mesh)
|
|
for x, s in zip(x_spec, user_spec):
|
|
if s is PartitionSpec.UNCONSTRAINED:
|
|
continue
|
|
else:
|
|
if x != s:
|
|
raise AssertionError(
|
|
'`with_sharding_constraint` acts as an assert when all axes of'
|
|
f' mesh are of type `Explicit`. The array sharding: {x_spec} did'
|
|
f' not match the sharding provided: {user_spec}. Please use'
|
|
' `jax.sharding.reshard` to shard your input to the sharding you'
|
|
' want.')
|
|
|
|
|
|
def with_sharding_constraint(x, shardings):
|
|
"""Mechanism to constrain the sharding of an Array inside a jitted computation
|
|
|
|
This is a strict constraint for the GSPMD partitioner and not a hint. For examples
|
|
of how to use this function, see `Distributed arrays and automatic parallelization`_.
|
|
|
|
Inside of a jitted computation, with_sharding_constraint makes it possible to
|
|
constrain intermediate values to an uneven sharding. However, if such an
|
|
unevenly sharded value is output by the jitted computation, it will come out
|
|
as fully replicated, no matter the sharding annotation given.
|
|
|
|
Args:
|
|
x: PyTree of jax.Arrays which will have their shardings constrained
|
|
shardings: PyTree of sharding specifications. Valid values are the same as for
|
|
the ``in_shardings`` argument of :func:`jax.experimental.pjit`.
|
|
Returns:
|
|
x_with_shardings: PyTree of jax.Arrays with specified sharding constraints.
|
|
|
|
.. _Distributed arrays and automatic parallelization: https://docs.jax.dev/en/latest/parallel.html
|
|
"""
|
|
x_flat, tree = tree_flatten(x)
|
|
x_avals_flat = [core.shaped_abstractify(x) for x in x_flat]
|
|
layouts, shardings = _split_layout_and_sharding(shardings)
|
|
|
|
user_shardings = prepare_axis_resources(
|
|
shardings, "shardings", allow_unconstrained_dims=True)
|
|
del shardings
|
|
|
|
user_shardings_flat = tuple(
|
|
flatten_axes("with_sharding_constraint shardings", tree, user_shardings))
|
|
del user_shardings
|
|
|
|
user_layouts_flat = tuple(
|
|
flatten_axes("with_sharding_constraint layouts", tree, layouts))
|
|
del layouts
|
|
|
|
if not mesh_lib.get_concrete_mesh().empty:
|
|
context_mesh = mesh_lib.get_abstract_mesh()
|
|
elif not mesh_lib.get_abstract_mesh().empty:
|
|
context_mesh = mesh_lib.get_abstract_mesh()
|
|
else:
|
|
context_mesh = mesh_lib.thread_resources.env.physical_mesh
|
|
|
|
shardings_flat = [_create_sharding_for_array(context_mesh, a, 'shardings',
|
|
'with_sharding_constraint')
|
|
for a in user_shardings_flat]
|
|
for s, u in zip(shardings_flat, user_shardings_flat):
|
|
if isinstance(s, (UnspecifiedValue, AUTO)):
|
|
raise ValueError(
|
|
f'One of with_sharding_constraint arguments got sharding {u} which is'
|
|
' not allowed. Please only pass `jax.sharding.Sharding` instances.')
|
|
del user_shardings_flat
|
|
|
|
# TODO(bartchr): remove `unconstrained_dims` after migrating to Shardy. It's
|
|
# already part of the shardings.
|
|
unconstrained_dims = [get_unconstrained_dims(s)
|
|
if isinstance(s, NamedSharding) else frozenset()
|
|
for s in shardings_flat]
|
|
|
|
pjit_check_aval_sharding(
|
|
shardings_flat, x_avals_flat, ("",) * len(shardings_flat),
|
|
"with_sharding_constraint arguments",
|
|
allow_uneven_sharding=True)
|
|
check_aval_layout_compatibility(user_layouts_flat, x_avals_flat,
|
|
("",) * len(user_layouts_flat),
|
|
"with_sharding_constraint arguments")
|
|
|
|
outs = []
|
|
for xf, x_aval, s, l, ud in zip(x_flat, x_avals_flat, shardings_flat,
|
|
user_layouts_flat, unconstrained_dims):
|
|
if (mesh_lib.get_abstract_mesh().are_all_axes_explicit and l is None and
|
|
isinstance(s, NamedSharding)):
|
|
assert_shardings_equal(x_aval, s)
|
|
outs.append(xf)
|
|
else:
|
|
check_shardings_are_auto(s)
|
|
outs.append(sharding_constraint_p.bind(
|
|
xf, sharding=s, layout=l, context_mesh=context_mesh,
|
|
unconstrained_dims=ud))
|
|
return tree_unflatten(tree, outs)
|
|
|
|
def _identity_fn(x): return x
|
|
|
|
def _sharding_constraint_impl(x, sharding, layout, context_mesh,
|
|
unconstrained_dims):
|
|
if (isinstance(sharding, NamedSharding) and
|
|
isinstance(sharding.mesh, AbstractMesh)):
|
|
if (not context_mesh.empty and isinstance(context_mesh, AbstractMesh) and
|
|
not hasattr(x, 'sharding')):
|
|
concrete_mesh = mesh_lib.get_concrete_mesh()
|
|
assert not concrete_mesh.empty
|
|
sharding = NamedSharding(concrete_mesh, sharding.spec)
|
|
else:
|
|
aval = core.shaped_abstractify(x)
|
|
if not hasattr(x, 'sharding'):
|
|
raise ValueError(
|
|
'Target sharding contains a `jax.sharding.AbstractMesh` which'
|
|
' requires the input passed should be a `jax.Array`. Got'
|
|
f' {type(x)} with shape {aval.str_short()}')
|
|
if not isinstance(x.sharding, NamedSharding) or x.sharding.mesh.is_scalar: # pyrefly: ignore[missing-attribute]
|
|
raise TypeError(
|
|
'The sharding on the input must be a `NamedSharding` since the'
|
|
' target sharding has an `AbstractMesh` in it. Got sharding type'
|
|
f' {type(x.sharding)} for shape {aval.str_short()}')
|
|
if x.sharding.mesh.shape_tuple != sharding.mesh.shape_tuple:
|
|
raise ValueError(
|
|
f'Mesh shape of the input {x.sharding.mesh.shape_tuple} does not'
|
|
' match the mesh shape of the target sharding'
|
|
f' {sharding.mesh.shape_tuple} for shape {aval.str_short()}')
|
|
sharding = NamedSharding(x.sharding.mesh, sharding.spec)
|
|
|
|
if layout is None:
|
|
# Run a jit here to raise good errors when device assignment don't match.
|
|
return api.jit(_identity_fn, out_shardings=sharding)(x)
|
|
else:
|
|
return api.jit(_identity_fn, out_shardings=Format(layout, sharding))(x)
|
|
|
|
|
|
sharding_constraint_p = core.Primitive("sharding_constraint")
|
|
sharding_constraint_p.def_impl(_sharding_constraint_impl)
|
|
ad.deflinear2(sharding_constraint_p,
|
|
lambda ct, _, **params: (sharding_constraint_p.bind(ct, **params),))
|
|
|
|
def _sharding_constraint_abstract_eval(
|
|
x_aval, *, sharding, layout, context_mesh, unconstrained_dims):
|
|
if isinstance(sharding, NamedSharding):
|
|
return x_aval.update(
|
|
sharding=x_aval.sharding.update(mesh=sharding.mesh.abstract_mesh))
|
|
return x_aval.update(sharding=None)
|
|
sharding_constraint_p.def_abstract_eval(_sharding_constraint_abstract_eval)
|
|
|
|
def _sharding_constraint_hlo_lowering(ctx, x_node, *, sharding, layout,
|
|
context_mesh, unconstrained_dims):
|
|
in_aval, = ctx.avals_in
|
|
out_aval, = ctx.avals_out
|
|
axis_ctx = ctx.module_context.axis_context
|
|
|
|
if (isinstance(sharding, NamedSharding) and
|
|
any(o is not None for o in out_aval.sharding.spec)):
|
|
spec = sharding.spec._normalized_spec_for_aval(in_aval.ndim)
|
|
new_spec = []
|
|
for user_spec, aval_spec in zip(spec, out_aval.sharding.spec):
|
|
if aval_spec is None:
|
|
new_spec.append(user_spec)
|
|
else:
|
|
aval_spec = aval_spec if isinstance(aval_spec, tuple) else (aval_spec,)
|
|
if user_spec is PartitionSpec.UNCONSTRAINED:
|
|
raise NotImplementedError
|
|
if user_spec is None:
|
|
new_spec.append(aval_spec)
|
|
elif isinstance(user_spec, tuple):
|
|
new_spec.append(aval_spec + user_spec)
|
|
else:
|
|
new_spec.append(aval_spec + (user_spec,))
|
|
sharding = sharding.update(spec=new_spec)
|
|
|
|
if dtypes.issubdtype(in_aval.dtype, dtypes.extended):
|
|
in_aval = core.physical_aval(in_aval)
|
|
if (isinstance(axis_ctx, sharding_impls.SPMDAxisContext) and
|
|
axis_ctx.manual_axes):
|
|
sharding = mlir.add_manual_axes(axis_ctx, sharding, in_aval.ndim)
|
|
if config.use_shardy_partitioner.value:
|
|
sharding = sharding._to_sdy_sharding(in_aval.ndim)
|
|
else:
|
|
sharding = sharding._to_xla_hlo_sharding(in_aval.ndim).to_proto()
|
|
out = mlir.wrap_with_sharding_op(
|
|
ctx, x_node, out_aval, sharding, unspecified_dims=unconstrained_dims)
|
|
if layout is not None:
|
|
out = mlir.wrap_with_layout_op(ctx, out, out_aval, layout, in_aval)
|
|
return [out]
|
|
mlir.register_lowering(sharding_constraint_p,
|
|
_sharding_constraint_hlo_lowering)
|
|
|
|
|
|
def _sharding_constraint_batcher(
|
|
axis_data, vals_in, dims_in, sharding, layout, context_mesh,
|
|
unconstrained_dims):
|
|
x, = vals_in
|
|
d, = dims_in
|
|
if d is None:
|
|
out = sharding_constraint_p.bind(
|
|
x, sharding=sharding, layout=layout, context_mesh=context_mesh,
|
|
unconstrained_dims=unconstrained_dims)
|
|
return out, None
|
|
|
|
if axis_data.spmd_name is not None and isinstance(sharding, NamedSharding):
|
|
used = {n for ns in sharding.spec
|
|
for n in (ns if isinstance(ns, tuple) else (ns,))}
|
|
if set(axis_data.spmd_name) & used:
|
|
raise ValueError(f"vmap spmd_axis_name {axis_data.spmd_name} cannot appear in "
|
|
"with_sharding_constraint spec, but got spec "
|
|
f"{sharding.spec}")
|
|
unconstrained_dims = {ud + (d <= ud) for ud in unconstrained_dims}
|
|
if axis_data.spmd_name is None:
|
|
unconstrained_dims.add(d)
|
|
|
|
vmapped_sharding = _pjit_batcher_for_sharding(
|
|
sharding, d, axis_data.spmd_name, context_mesh, x.ndim)
|
|
if unconstrained_dims and isinstance(vmapped_sharding, NamedSharding):
|
|
new_spec = list(vmapped_sharding.spec) + [None] * (x.ndim - len(vmapped_sharding.spec))
|
|
for u in unconstrained_dims:
|
|
new_spec[u] = PartitionSpec.UNCONSTRAINED
|
|
vmapped_sharding = NamedSharding(
|
|
vmapped_sharding.mesh, PartitionSpec(*new_spec))
|
|
|
|
vmapped_layout = (get_layout_for_vmap(d, layout) if layout is not None else
|
|
layout)
|
|
|
|
y = sharding_constraint_p.bind(
|
|
x,
|
|
sharding=vmapped_sharding,
|
|
layout=vmapped_layout,
|
|
context_mesh=context_mesh,
|
|
unconstrained_dims=frozenset(unconstrained_dims))
|
|
return y, d
|
|
batching.fancy_primitive_batchers[sharding_constraint_p] = _sharding_constraint_batcher
|
|
|
|
# -------------------- reshard ------------------------------------
|
|
|
|
def reshard(xs, out_shardings):
|
|
x_flat, treedef = tree_flatten(xs)
|
|
shardings_flat = flatten_axis_resources(
|
|
"reshard out_shardings", treedef, out_shardings, tupled_args=True)
|
|
x_avals_flat = [core.shaped_abstractify(x) for x in x_flat]
|
|
out_flat = []
|
|
for x, x_aval, s in safe_zip(x_flat, x_avals_flat, shardings_flat):
|
|
ds = canonicalize_sharding(s, 'reshard', check_mesh_consistency=False)
|
|
if ds is None:
|
|
raise ValueError(
|
|
'Reshard should only be used with out_shardings which are non-None '
|
|
f'and have a non-empty mesh. Got sharding {s}.'
|
|
)
|
|
ds = ds.update(spec=ds.spec._normalized_spec_for_aval(x_aval.ndim))
|
|
cmesh = (s.mesh if (isinstance(s, NamedSharding) and
|
|
isinstance(s.mesh, mesh_lib.Mesh))
|
|
else None)
|
|
out_flat.append(reshard_p.bind(x, dst_sharding=ds, concrete_mesh=cmesh))
|
|
return tree_unflatten(treedef, out_flat)
|
|
|
|
reshard_p = core.Primitive('reshard')
|
|
reshard_p.skip_canonicalization = True
|
|
|
|
def _reshard_abstract_eval(aval, *, dst_sharding, concrete_mesh):
|
|
assert isinstance(aval, core.ShapedArray)
|
|
if aval.sharding == dst_sharding:
|
|
return aval
|
|
return aval.update(sharding=dst_sharding)
|
|
reshard_p.def_abstract_eval(_reshard_abstract_eval)
|
|
|
|
def _reshard_impl(x, *, dst_sharding, concrete_mesh):
|
|
thunk = lambda: dispatch.apply_primitive(
|
|
reshard_p, x, dst_sharding=dst_sharding, concrete_mesh=concrete_mesh)
|
|
if concrete_mesh is None:
|
|
return thunk()
|
|
else:
|
|
with sharding_impls.set_mesh(concrete_mesh):
|
|
return thunk()
|
|
reshard_p.def_impl(_reshard_impl)
|
|
|
|
def _reshard_jvp_rule(primals, tangents, *, dst_sharding, concrete_mesh):
|
|
(p,), (t,) = primals, tangents
|
|
primal_out = reshard_p.bind(p, dst_sharding=dst_sharding,
|
|
concrete_mesh=concrete_mesh)
|
|
if type(t) is ad.Zero:
|
|
return primal_out, ad.p2tz(primal_out)
|
|
else:
|
|
tangent_out = reshard_p.bind(t, dst_sharding=dst_sharding,
|
|
concrete_mesh=concrete_mesh)
|
|
return primal_out, tangent_out
|
|
|
|
ad.primitive_jvps[reshard_p] = _reshard_jvp_rule
|
|
|
|
def _reshard_transpose_fancy(ct, x, *, dst_sharding, concrete_mesh):
|
|
assert isinstance(x, ad.GradAccum)
|
|
if type(ct) is ad.Zero or isinstance(x, ad.NullAccum):
|
|
return
|
|
out_sharding = x.aval.to_ct_aval().sharding # pyrefly: ignore[missing-attribute]
|
|
with mesh_lib.use_abstract_mesh(out_sharding.mesh):
|
|
x_bar = reshard_p.bind(ct, dst_sharding=out_sharding,
|
|
concrete_mesh=concrete_mesh)
|
|
x.accum(x_bar)
|
|
ad.fancy_transposes[reshard_p] = _reshard_transpose_fancy
|
|
|
|
def _reshard_hlo_lowering(ctx, x_node, *, dst_sharding, concrete_mesh):
|
|
aval_in, = ctx.avals_in
|
|
aval_out, = ctx.avals_out
|
|
if dtypes.issubdtype(aval_in.dtype, dtypes.extended):
|
|
aval_in = core.physical_aval(aval_in)
|
|
proto = (dst_sharding._to_sdy_sharding(aval_in.ndim)
|
|
if config.use_shardy_partitioner.value else
|
|
dst_sharding._to_xla_hlo_sharding(aval_in.ndim).to_proto())
|
|
return [mlir.lower_with_sharding_in_types(ctx, x_node, aval_out, proto)]
|
|
mlir.register_lowering(reshard_p, _reshard_hlo_lowering)
|
|
|
|
def _reshard_batcher(axis_data, vals_in, dims_in, dst_sharding, concrete_mesh):
|
|
x, = vals_in
|
|
d, = dims_in
|
|
if d is None:
|
|
out = reshard_p.bind(x, dst_sharding=dst_sharding,
|
|
concrete_mesh=concrete_mesh)
|
|
return out, None
|
|
vmapped_dst_sharding = batching.get_sharding_for_vmap(
|
|
axis_data, dst_sharding, d)
|
|
y = reshard_p.bind(x, dst_sharding=vmapped_dst_sharding,
|
|
concrete_mesh=concrete_mesh)
|
|
return y, d
|
|
batching.fancy_primitive_batchers[reshard_p] = _reshard_batcher
|
|
|
|
def _pp_reshard(eqn, ctx, settings):
|
|
return core._pp_eqn(eqn.replace(params={}), ctx, settings)
|
|
core.pp_eqn_rules[reshard_p] = _pp_reshard
|
|
|
|
# -------------------- Auto and Explicit mode -------------------------
|
|
|
|
@dataclass(frozen=True, kw_only=True)
|
|
class MeshInfo:
|
|
prev: AbstractMesh
|
|
new: AbstractMesh
|
|
axes: Any
|
|
|
|
def _get_new_mesh(axes: str | tuple[str, ...] | None,
|
|
axis_type: mesh_lib.AxisType, name: str, shardings=None
|
|
) -> MeshInfo | None:
|
|
cur_mesh = mesh_lib.get_abstract_mesh()
|
|
flat_shardings, _ = tree_flatten(shardings)
|
|
sharding_mesh = mesh_lib.empty_abstract_mesh
|
|
for i in flat_shardings:
|
|
if isinstance(i, NamedSharding):
|
|
if not sharding_mesh.empty and sharding_mesh != i.mesh.abstract_mesh:
|
|
raise ValueError(
|
|
f'Shardings passed to {name} should have the same mesh. Got one'
|
|
f' mesh {sharding_mesh} and another {i.mesh}')
|
|
sharding_mesh = i.mesh.abstract_mesh
|
|
|
|
if sharding_mesh.empty and cur_mesh.empty:
|
|
return None
|
|
if not sharding_mesh.empty and not cur_mesh.empty:
|
|
if sharding_mesh != cur_mesh:
|
|
raise ValueError(
|
|
f'Context mesh {cur_mesh} must match the mesh passed to shardings'
|
|
f' {sharding_mesh}. Recommended approach is to use'
|
|
' `jax.set_mesh` context manager.')
|
|
mesh_to_use = cur_mesh
|
|
elif sharding_mesh.empty and not cur_mesh.empty:
|
|
mesh_to_use = cur_mesh
|
|
else:
|
|
assert not sharding_mesh.empty and cur_mesh.empty
|
|
mesh_to_use = sharding_mesh
|
|
|
|
if axes is None:
|
|
axes = mesh_to_use.axis_names
|
|
if not isinstance(axes, tuple):
|
|
axes = (axes,)
|
|
for a in axes:
|
|
if (mesh_to_use._name_to_type[a] == mesh_lib.AxisType.Manual and
|
|
axis_type in {mesh_lib.AxisType.Auto, mesh_lib.AxisType.Explicit}):
|
|
raise NotImplementedError(
|
|
'Going from `Manual` AxisType to `Auto` or `Explicit` AxisType is not'
|
|
' allowed. Please file a bug at https://github.com/jax-ml/jax/issues'
|
|
' with your use case')
|
|
new_mesh = mesh_to_use.update_axis_types({a: axis_type for a in axes})
|
|
return MeshInfo(prev=mesh_to_use, new=new_mesh, axes=axes)
|
|
|
|
|
|
def auto_axes(f=None, /, *, axes: str | tuple[str, ...] | None = None,
|
|
out_sharding=None):
|
|
kwargs = dict(axes_=axes, out_sharding=out_sharding)
|
|
if f is None:
|
|
return lambda g: _auto_axes(g, **kwargs)
|
|
return _auto_axes(f, **kwargs)
|
|
|
|
def _auto_axes(fun, *, axes_, out_sharding):
|
|
@wraps(fun)
|
|
def decorator(*args, **kwargs):
|
|
if out_sharding is None:
|
|
if "out_sharding" in kwargs:
|
|
_out_sharding = kwargs.pop("out_sharding")
|
|
else:
|
|
raise TypeError("Missing required keyword argument: 'out_sharding'")
|
|
else:
|
|
_out_sharding = out_sharding
|
|
mesh_info = _get_new_mesh(
|
|
axes_, mesh_lib.AxisType.Auto, 'auto_axes', shardings=_out_sharding)
|
|
if mesh_info is None:
|
|
return fun(*args, **kwargs)
|
|
if set(mesh_info.prev.auto_axes) == set(mesh_info.axes):
|
|
return fun(*args, **kwargs)
|
|
with mesh_lib.use_abstract_mesh(mesh_info.new):
|
|
in_specs = tree_map(lambda a: core.modify_spec_for_auto_manual(
|
|
core.typeof(a).sharding.spec, mesh_info.new), args)
|
|
args = reshard(args, in_specs)
|
|
out = fun(*args, **kwargs)
|
|
return reshard(out, _out_sharding)
|
|
return decorator
|
|
|
|
|
|
def explicit_axes(f=None, /, *, axes: str | tuple[str, ...] | None = None,
|
|
in_sharding=None):
|
|
kwargs = dict(axes=axes, in_sharding=in_sharding)
|
|
if f is None:
|
|
return lambda g: _explicit_axes(g, **kwargs)
|
|
return _explicit_axes(f, **kwargs)
|
|
|
|
def _explicit_axes(fun, *, axes, in_sharding):
|
|
@wraps(fun)
|
|
def decorator(*args, **kwargs):
|
|
if in_sharding is None:
|
|
if "in_sharding" in kwargs:
|
|
_in_sharding = kwargs.pop("in_sharding")
|
|
else:
|
|
raise TypeError("Missing required keyword argument: 'in_sharding'")
|
|
else:
|
|
_in_sharding = in_sharding
|
|
mesh_info = _get_new_mesh(axes, mesh_lib.AxisType.Explicit, 'explicit_axes')
|
|
if mesh_info is None:
|
|
raise ValueError(
|
|
'Context mesh cannot be empty. Please use `jax.set_mesh` API to enter'
|
|
' into a mesh context when using `explicit_axes` API.')
|
|
with mesh_lib.use_abstract_mesh(mesh_info.new):
|
|
args = reshard(args, _in_sharding)
|
|
out = fun(*args, **kwargs)
|
|
out_specs = tree_map(lambda o: core.modify_spec_for_auto_manual(
|
|
core.typeof(o).sharding.spec, mesh_lib.get_abstract_mesh()), out)
|
|
return reshard(out, out_specs)
|
|
return decorator
|
|
|
|
# -------------------- with_layout_constraint --------------------
|
|
|
|
def with_layout_constraint(x, layouts):
|
|
x_flat, tree = tree_flatten(x)
|
|
x_avals_flat = [core.shaped_abstractify(x) for x in x_flat]
|
|
layouts_flat = tuple(flatten_axes("with_layout_constraint layouts", tree,
|
|
layouts))
|
|
if any(not isinstance(l, Layout) for l in layouts_flat):
|
|
raise ValueError(
|
|
'layouts passed to `with_layout_constraint` must be of type'
|
|
f' `Layout`. Got {[type(l) for l in layouts_flat]}')
|
|
check_aval_layout_compatibility(
|
|
layouts_flat, x_avals_flat, ("",) * len(layouts_flat),
|
|
"with_layout_constraint arguments")
|
|
outs = [layout_constraint_p.bind(xf, layout=l)
|
|
for xf, l in zip(x_flat, layouts_flat)]
|
|
return tree_unflatten(tree, outs)
|
|
|
|
layout_constraint_p = core.Primitive('layout_constraint')
|
|
layout_constraint_p.def_abstract_eval(lambda x, **_: x)
|
|
ad.deflinear2(layout_constraint_p,
|
|
lambda ct, _, **params: (layout_constraint_p.bind(ct, **params),))
|
|
|
|
def _layout_constraint_impl(x, *, layout):
|
|
if not isinstance(x, xc.ArrayImpl):
|
|
raise ValueError(
|
|
'with_layout_constraint in eager mode can only be applied to'
|
|
f' jax.Arrays. Got {type(x)}')
|
|
if x.format.layout == layout:
|
|
return x
|
|
return api.jit(_identity_fn, out_shardings=Format(layout, x.sharding))(x)
|
|
layout_constraint_p.def_impl(_layout_constraint_impl)
|
|
|
|
def _layout_constraint_hlo_lowering(ctx, x_node, *, layout):
|
|
aval, = ctx.avals_in
|
|
out_aval, = ctx.avals_out
|
|
return [mlir.wrap_with_layout_op(ctx, x_node, out_aval, layout, aval)]
|
|
mlir.register_lowering(layout_constraint_p,
|
|
_layout_constraint_hlo_lowering)
|
|
|
|
def _layout_constraint_batcher(axis_data, vals_in, dims_in, layout):
|
|
x, = vals_in
|
|
d, = dims_in
|
|
if d is None:
|
|
return layout_constraint_p.bind(x, layout=layout), None
|
|
vmapped_layout = get_layout_for_vmap(d, layout)
|
|
y = layout_constraint_p.bind(x, layout=vmapped_layout)
|
|
return y, d
|
|
batching.fancy_primitive_batchers[layout_constraint_p] = _layout_constraint_batcher
|
|
|
|
# -------------------- helpers --------------------
|
|
|
|
def get_unconstrained_dims(sharding: NamedSharding):
|
|
assert sharding.spec is not None
|
|
return frozenset(i for i, axes in enumerate(sharding.spec)
|
|
if axes is PartitionSpec.UNCONSTRAINED)
|