# Copyright 2023 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

from collections.abc import Callable, Hashable, Sequence, Set
import enum
from functools import partial
import inspect
from math import prod
import operator as op
from typing import Any, TypeVar, Union

import numpy as np

import jax
import jax.numpy as jnp
from jax.sharding import NamedSharding, PartitionSpec
from jax._src import ad_util
from jax._src import api_util
from jax._src import config
from jax._src import core
from jax._src import debugging
from jax._src import dispatch
from jax._src import dtypes
from jax._src import linear_util as lu
from jax._src import sharding_impls
from jax._src import source_info_util
from jax._src import traceback_util
from jax._src import util
from jax._src.core import pvary
from jax._src.core import Tracer, typeof
from jax._src.mesh import (AbstractMesh, Mesh, AxisType, use_abstract_mesh,
                           get_abstract_mesh, get_concrete_mesh)
from jax._src.api import _shared_code_pmap, _prepare_pmap
from jax._src.lib.mlir import ir
from jax._src.lib.mlir.dialects import hlo, sdy
from jax._src.util import (HashableFunction, HashablePartial, unzip2,
                           as_hashable_function, memoize, partition_list,
                           merge_lists, split_list, subs_list2,
                           fun_name as util_fun_name)
from jax._src.interpreters import batching
from jax._src.interpreters import mlir
from jax._src.interpreters import partial_eval as pe
from jax._src.interpreters import pxla
from jax._src.interpreters import ad
from jax.tree_util import (tree_map, tree_flatten, tree_unflatten,
                           tree_structure, tree_leaves, keystr)
from jax._src.tree_util import (broadcast_prefix, prefix_errors, PyTreeDef,
                                generate_key_paths, KeyPath)
from jax.experimental.multihost_utils import (host_local_array_to_global_array,
                                              global_array_to_host_local_array)

P = PartitionSpec

map, unsafe_map = util.safe_map, map
zip, unsafe_zip = util.safe_zip, zip
traceback_util.register_exclusion(__file__)

# API

Specs = Any  # PyTree[PartitionSpec]
AxisName = Hashable


def shard_map(f=None, /, *, out_specs: Specs, axis_names: Set[AxisName] = set(),
              in_specs: Specs | None = None,
              mesh: Mesh | AbstractMesh | None = None, check_vma: bool = True):
  """Map a function over shards of data using a mesh of devices.

  See the docs at https://docs.jax.dev/en/latest/notebooks/shard_map.html.

  Args:
    f: callable to be mapped. Each application of ``f``, or "instance" of ``f``,
      takes as input a shard of the mapped-over arguments and produces a shard
      of the output.
    mesh: (optional, default None) a ``jax.sharding.Mesh`` representing the
      array of devices over which to shard the data and on which to execute
      instances of ``f``. The names of the ``Mesh`` can be used in collective
      communication operations in ``f``. If mesh is None, it will be inferred
      from the context which can be set via `jax.sharding.use_mesh` context
      manager.
    in_specs: (optional, default None) a pytree with
      ``jax.sharding.PartitionSpec`` instances as leaves, with a tree structure
      that is a tree prefix of the args tuple to be mapped over. Similar to
      ``jax.sharding.NamedSharding``, each ``PartitionSpec`` represents how the
      corresponding argument (or subtree of arguments) should be sharded along
      the named axes of ``mesh``. In each ``PartitionSpec``, mentioning a
      ``mesh`` axis name at a position expresses sharding the corresponding
      argument array axis along that positional axis; not mentioning an axis
      name expresses replication. If ``None``, all mesh axes must be of type
      `Explicit`, in which case the in_specs are inferred from the argument types.
    out_specs: a pytree with ``PartitionSpec`` instances as leaves, with a tree
      structure that is a tree prefix of the output of ``f``. Each
      ``PartitionSpec`` represents how the corresponding output shards should be
      concatenated. In each ``PartitionSpec``, mentioning a ``mesh`` axis name
      at a position expresses concatenation of that mesh axis's shards along the
      corresponding positional axis; not mentioning a ``mesh`` axis name
      expresses a promise that the output values are equal along that mesh axis,
      and that rather than concatenating only a single value should be produced.
    axis_names: (optional, default set()) set of axis names from ``mesh`` over
      which the function ``f`` is manual. If empty, ``f``, is manual
      over all mesh axes.
    check_vma: (optional) boolean (default True) representing whether to enable
      additional validity checks and automatic differentiation optimizations.
      The validity checks concern whether any mesh axis names not mentioned in
      ``out_specs`` are consistent with how the outputs of ``f`` are replicated.

  Returns:
    A callable representing a mapped version of ``f``, which accepts positional
    arguments corresponding to those of ``f`` and produces output corresponding
    to that of ``f``.
  """
  kwargs = dict(mesh=mesh, in_specs=in_specs, out_specs=out_specs,
                axis_names=axis_names, check_vma=check_vma)
  if f is None:
    return lambda g: _shard_map(g, **kwargs)
  return _shard_map(f, **kwargs)

def _axes_to_pspec(axis_name, axis):
  if axis is None:
    return P()
  return P(*[None] * axis + [axis_name])

class InferFromArgs:

  def __repr__(self):
    return "jax.sharding.Infer"

  def __reduce__(self):
    return (_get_default_infer, ())

Infer = InferFromArgs()

def _get_default_infer():
  return Infer

# TODO(yashkatariya): We need a singleton which users can provide to `in_axes`
# to tell smap to infer in_specs from args when mesh is fully explicit.
def smap(f, /, *, in_axes=Infer, out_axes, axis_name: AxisName):
  if isinstance(axis_name, (list, tuple)):
    raise TypeError(
        f"smap axis_name should be a `str` or a `Hashable`, but got {axis_name}")
  if (in_axes is not None and in_axes is not Infer and
      not isinstance(in_axes, (int, tuple))):
    raise TypeError(
        "smap in_axes must be an int, None, jax.sharding.Infer, or a tuple of"
        " entries corresponding to the positional arguments passed to the"
        f" function, but got {in_axes}.")
  if (in_axes is not Infer and
      not all(isinstance(l, int) for l in tree_leaves(in_axes))):
    raise TypeError(
        "smap in_axes must be an int, None, jax.sharding.Infer, or (nested)"
        f" container with those types as leaves, but got {in_axes}.")
  if not all(isinstance(l, int) for l in tree_leaves(out_axes)):
    raise TypeError("smap out_axes must be an int, None, or (nested) container "
                    f"with those types as leaves, but got {out_axes}.")

  in_specs = (None if in_axes is Infer else
              tree_map(partial(_axes_to_pspec, axis_name), in_axes,
                       is_leaf=lambda x: x is None))
  out_specs = tree_map(partial(_axes_to_pspec, axis_name), out_axes,
                       is_leaf=lambda x: x is None)
  return _shard_map(f, mesh=None, in_specs=in_specs, out_specs=out_specs,
                    axis_names={axis_name}, check_vma=True, _smap=True)


def _shard_map(f: Callable, *, mesh: Mesh | AbstractMesh | None,
               in_specs: Specs, out_specs: Specs | Callable[[], Specs],
               axis_names: Set[AxisName], check_vma: bool,
               _skip_mesh_check: bool = False, _smap: bool = False) -> Callable:
  if not callable(f):
    raise TypeError("shard_map requires a callable for its first argument, "
                    f"but got {f} of type {type(f)}.")

  @util.wraps(f)
  @traceback_util.api_boundary
  def wrapped(*args):
    nonlocal mesh, axis_names
    mesh, axis_names = _shmap_checks(mesh, axis_names, in_specs, out_specs,
                                     _skip_mesh_check, _smap)
    fun = lu.wrap_init(
        f, debug_info=api_util.debug_info("shard_map", f, args, {}))
    args_flat, in_tree = tree_flatten(args)
    fun, out_tree = api_util.flatten_fun_nokwargs(fun, in_tree)

    try:
      in_specs_flat = broadcast_prefix(
          in_specs, args, is_leaf=lambda x: x is None)
    except ValueError:
      e, *_ = prefix_errors(in_specs, args)
      raise e('shard_map in_specs') from None

    if (in_specs is None and
        all(mesh._name_to_type[a] == AxisType.Explicit for a in axis_names)):
      arg_s = [typeof(a).sharding for a in args_flat]
      assert all(i is None for i in in_specs_flat), in_specs_flat
      in_specs_flat = [_manual_spec(axis_names, s.spec) for s in arg_s]

    dyn_argnums, in_specs_flat = unzip2((i, s) for i, s in enumerate(in_specs_flat)
                                        if s is not None)
    fun, args_flat = api_util.argnums_partial(fun, dyn_argnums, args_flat, False)
    _check_specs_vs_args(f, mesh, in_tree, in_specs, dyn_argnums, in_specs_flat,
                         args_flat)

    @memoize
    def out_specs_thunk():
      if callable(out_specs):
        out_specs_ = out_specs()
        _check_specs(SpecErrorType.out, out_specs_, axis_names)
      else:
        out_specs_ = out_specs
      dummy = tree_unflatten(out_tree(), [object()] * out_tree().num_leaves)
      try:
        out_specs_flat = broadcast_prefix(out_specs_, dummy)
      except ValueError:
        e, *_ = prefix_errors(out_specs_, dummy)
        raise e('shard_map out_specs') from None
      return tuple(out_specs_flat)

    if check_vma:
      fun = _implicit_pvary_on_output(fun, out_specs_thunk)

    try:
      out_flat = shard_map_p.bind(
          fun, *args_flat, mesh=mesh, in_specs=in_specs_flat,
          out_specs_thunk=out_specs_thunk, check_vma=check_vma,
          manual_axes=axis_names)
    except _SpecError as e:
      fails, = e.args
      if not callable(out_specs):
        msg = _spec_rank_error(SpecErrorType.out, f, out_tree(), out_specs, fails)
        if any(fail is not no_fail and not fail.shape for fail in fails):
          msg += (" In particular, for rank 0 outputs which are not constant "
                  "over the mesh, add at least one (singleton) axis to them so "
                  "that they can be concatenated using out_specs.")
        raise ValueError(msg) from None
    except _RepError as e:
      fails, = e.args
      if not callable(out_specs):
        msg = _inout_vma_error(f, mesh, out_tree(), out_specs, fails)
        raise ValueError(msg) from None
    return tree_unflatten(out_tree(), out_flat)
  return wrapped


def _shmap_checks(mesh, axis_names, in_specs, out_specs, _skip_mesh_check,
                  _smap):
  if mesh is None:
    mesh = get_abstract_mesh()
    if mesh.empty:
      raise ValueError(
          "The context mesh cannot be empty. Use"
          " `jax.sharding.use_mesh(mesh)` to enter into a mesh context")
  else:
    ctx_mesh = get_abstract_mesh()
    if (not _skip_mesh_check and not ctx_mesh.empty and
        mesh.abstract_mesh != ctx_mesh):
      raise ValueError(
          f"The context mesh {ctx_mesh} should match the mesh passed to"
          f" shard_map {mesh}")

  if not isinstance(mesh, (Mesh, AbstractMesh)):
    raise TypeError("shard_map requires a `jax.sharding.Mesh` or a "
                    "`jax.sharding.AbstractMesh` instance for its "
                    f"second argument, but got {mesh} of type {type(mesh)}.")

  if not isinstance(axis_names, (frozenset, set)):
    raise TypeError(
        "`axis_names` argument of shard_map should be of type `frozenset` or"
        f" `set`. Got type: {type(axis_names)}")
  if isinstance(axis_names, set):
    axis_names = frozenset(axis_names)
  if not axis_names:
    axis_names = frozenset(mesh.axis_names)
  if not axis_names.issubset(mesh.axis_names):
    raise ValueError(
        f"jax.shard_map requires axis_names={axis_names} to be a subset of "
        f"mesh.axis_names={mesh.axis_names}")

  if (in_specs is None and
      not all(mesh._name_to_type[a] == AxisType.Explicit for a in axis_names)):
    axis_types = ', '.join(str(mesh._name_to_type[a]) for a in axis_names)
    if _smap:
      msg = (f"in_axes was not specified when axis_name={axis_names} was of"
             f" type {axis_types}")
    else:
      msg = ("shard_map in_specs argument must be a pytree of"
             " `jax.sharding.PartitionSpec` instances, but it was `None` when"
             f" {axis_names=} are of type {axis_types}")
    raise TypeError(msg)

  if in_specs is not None:
    _check_specs(SpecErrorType.input, in_specs, axis_names)
  if not callable(out_specs):
    _check_specs(SpecErrorType.out, out_specs, axis_names)
  return mesh, axis_names

def _manual_spec(manual_axes, spec: P) -> P:
  out = []  # type: ignore
  for s in spec:
    if s is None:
      out.append(s)
    elif isinstance(s, tuple):
      temp = [p if p in manual_axes else None for p in s]
      while temp and temp[-1] is None:
        temp.pop()
      if None in temp:
        raise ValueError(f"Invalid spec: {spec}")
      out.append(None if len(temp) == 0 else tuple(temp))
    else:
      out.append(s if s in manual_axes else None)
  return P(*out)


# Error checking and messages

SpecErrorType = enum.Enum('SpecErrorType', ['input', 'out'])

def _check_specs(error_type: SpecErrorType, specs: Any, manual_axes) -> None:
  if error_type == SpecErrorType.input and specs is None:
    raise TypeError(
        "shard_map in_specs argument must be a pytree of "
        "`jax.sharding.PartitionSpec` instances, but it was None.\n"
        "Instead of `in_specs=None`, did you mean `in_specs=P()`, "
        "where `P = jax.sharding.PartitionSpec`?")

  def check_spec(p):
    if not isinstance(p, PartitionSpec):
      return False
    for names in p:
      names = (names,) if not isinstance(names, tuple) else names
      for name in names:
        if name is not None and name not in manual_axes:
          return False
    return True

  if all(check_spec(p) for p in tree_leaves(specs)):
    return
  prefix = 'in' if error_type == SpecErrorType.input else 'out'
  msgs = [f"  {prefix}_specs{keystr(key)} is {x} of type {type(x).__name__}, "
          for key, x in generate_key_paths(specs) if not isinstance(x, P)]
  if not msgs:
    for key, p in generate_key_paths(specs):
      for names in p:
        names = (names,) if not isinstance(names, tuple) else names
        for name in names:
          if name is not None and name not in manual_axes:
            msgs.append(f"  {prefix}_specs{keystr(key)} refers to {repr(name)}")
    raise ValueError(
        f"shard_map {prefix}_specs argument must refer to an axis "
        f"marked as manual ({manual_axes}), but:\n\n"
        + '\n\n'.join(msgs) + '\n\n'
        f"Check the {prefix}_specs values passed to shard_map.")
  raise TypeError(
      f"shard_map {prefix}_specs argument must be a pytree of "
      f"`jax.sharding.PartitionSpec` instances, but:\n\n"
      + '\n\n'.join(msgs) + '\n\n'
      f"Check the {prefix}_specs values passed to shard_map.")

class NoFail: pass
no_fail = NoFail()

def _check_specs_vs_args(
    f: Callable, mesh: Mesh | AbstractMesh, in_tree: PyTreeDef, in_specs: Specs,
    dyn_argnums: Sequence[int], in_specs_flat: Sequence[P],
    xs: Sequence) -> None:
  in_avals = map(core.shaped_abstractify, xs)
  fail = [a if not len(p) <= a.ndim else no_fail
          for p, a in zip(in_specs_flat, in_avals)]
  if any(f is not no_fail for f in fail):
    fail = _expand_fail(in_tree, dyn_argnums, fail)
    msg = _spec_rank_error(SpecErrorType.input, f, in_tree, in_specs, fail)
    raise ValueError(msg)
  in_names_flat = tuple(map(_spec_to_names, in_specs_flat))
  fail = [a if any(a.shape[d] % prod(mesh.shape[n] for n in ns)
                   for d, ns in names.items()) else no_fail
          for a, names in zip(in_avals, in_names_flat)]
  if any(f is not no_fail for f in fail):
    fail = _expand_fail(in_tree, dyn_argnums, fail)
    msg = _spec_divisibility_error(f, mesh, in_tree, in_specs, fail)
    raise ValueError(msg)

def _expand_fail(in_tree: PyTreeDef, dyn_argnums: Sequence[int],
                 fail: Sequence[core.ShapedArray | NoFail]
                 ) -> list[core.ShapedArray | NoFail]:
  fail_: list[core.ShapedArray | NoFail] = [no_fail] * in_tree.num_leaves
  for i, f in zip(dyn_argnums, fail):
    fail_[i] = f
  return fail_

def _spec_rank_error(
    error_type: SpecErrorType, f: Callable, tree: PyTreeDef, specs: Specs,
    fails: list[core.ShapedArray | NoFail]) -> str:
  fun_name = util_fun_name(f)
  if error_type == SpecErrorType.input:
    prefix, base = 'in', 'args'
    ba = _try_infer_args(f, tree)
  else:
    prefix, base = 'out', f'{fun_name}(*args)'
  msgs = []
  for (spec_key, spec), (fail_key, aval) in _iter_paths(tree, specs, fails):
    extra = ""
    if error_type == SpecErrorType.input and ba is not None:
      arg_key, *_ = fail_key
      if arg_key.idx < len(ba.arguments):
        param_name = list(ba.arguments.keys())[arg_key.idx]
        extra = (f", where {base}{arg_key} is bound to {fun_name}'s "
                 f"parameter '{param_name}',")
      else:
        param = list(ba.signature.parameters.values())[-1]
        assert param.kind == inspect.Parameter.VAR_POSITIONAL
        extra = (f", where {base}{arg_key} is the index "
                 f"{arg_key.idx - len(ba.signature.parameters) + 1} component "
                 f"of {fun_name}'s varargs parameter '{param.name}',")
    msgs.append(
        f"* {prefix}_specs{keystr(spec_key)} is {spec} which has length "
        f"{len(spec)}, but "
        f"{base}{keystr(fail_key)}{extra} has shape {aval.str_short()}, "
        f"which has rank {aval.ndim} (and {aval.ndim} < {len(spec)})")
  assert msgs
  if len(msgs) == 1: msgs = [msgs[0][2:]]  # remove the bullet point
  msg = (f"shard_map applied to the function '{fun_name}' was given an "
         f"{prefix}_specs entry which is too long to be compatible with the "
         f"corresponding {prefix}put value from the function:\n\n"
         + '\n\n'.join(msgs) + '\n\n' +
         f"Entries in {prefix}_specs must be of length no greater than the "
         f"number of axes in the corresponding {prefix}put value.\n\n"
         f"Either revise the spec to be shorter, or modify '{fun_name}' so "
         f"that its {prefix}puts have sufficient rank.")
  if any(not aval.ndim for _, (_, aval) in _iter_paths(tree, specs, fails)):
    msg += (f"\n\nFor scalar values (rank 0), consider using an {prefix}_specs "
            "entry of `P()`, where `P = jax.sharding.PartitionSpec`.")
  return msg

def _spec_divisibility_error(
    f: Callable, mesh: Mesh | AbstractMesh, tree: PyTreeDef, specs: Specs,
    fails: list[core.ShapedArray | NoFail]) -> str:
  ba = _try_infer_args(f, tree)
  fun_name = getattr(f, '__name__', str(f))
  msgs = []
  for (spec_key, spec), (fail_key, aval) in _iter_paths(tree, specs, fails):
    extra = ""
    if ba is not None:
      arg_key, *_ = fail_key
      if arg_key.idx < len(ba.arguments):
        param_name = list(ba.arguments.keys())[arg_key.idx]
        extra = (f", where args{arg_key} is bound to {fun_name}'s "
                 f"parameter '{param_name}',")
      else:
        param = list(ba.signature.parameters.values())[-1]
        assert param.kind == inspect.Parameter.VAR_POSITIONAL
        extra = (f", where args{arg_key} is the index "
                 f"{arg_key.idx - len(ba.signature.parameters) + 1} component "
                 f"of {fun_name}'s varargs parameter '{param.name}',")
    names = _spec_to_names(spec)
    for d, ns in names.items():
      if aval.shape[d] % prod(mesh.shape[n] for n in ns):
        axis = f"axes {ns}" if len(ns) > 1 else f"axis '{ns[0]}'"
        total = 'total ' if len(ns) > 1 else ''
        sz = prod(mesh.shape[n] for n in ns)
        msgs.append(
            f"* args{keystr(fail_key)} of shape {aval.str_short()}{extra} "
            f"corresponds to in_specs{keystr(spec_key)} of value {spec}, "
            f"which maps array axis {d} (of size {aval.shape[d]}) to mesh "
            f"{axis} (of {total}size {sz}), but {sz} does not evenly divide "
            f"{aval.shape[d]}")
  assert msgs
  if len(msgs) == 1: msgs = [msgs[0][2:]]  # remove the bullet point
  msg = (f"shard_map applied to the function '{fun_name}' was given argument "
         f"arrays with axis sizes that are not evenly divisible by the "
         f"corresponding mesh axis sizes:\n\n"
         f"The mesh given has shape {tuple(mesh.shape.values())} with "
         f"corresponding axis names {mesh.axis_names}.\n\n"
         + '\n\n'.join(msgs) + '\n\n' +
         f"Array arguments' axis sizes must be evenly divisible by the mesh "
         f"axis or axes indicated by the corresponding elements of the "
         f"argument's in_specs entry. Consider checking that in_specs are "
         f"correct, and if so consider changing the mesh axis sizes or else "
         f"padding the input and adapting '{fun_name}' appropriately.")
  return msg

def _inout_vma_error(f: Callable, mesh: Mesh | AbstractMesh, tree: PyTreeDef,
                     specs: Specs, fails: list[set | NoFail]) -> str:
  fun_name = getattr(f, '__name__', str(f))
  msgs = []
  for (spec_key, spec), (fail_key, vma) in _iter_paths(tree, specs, fails):
    unmentioned = _unmentioned(mesh, spec)
    if len(unmentioned) > 1:
      need_vma = ','.join(map(str, order_wrt_mesh(mesh, _spec_to_vma(spec))))
      got_vma = ','.join(map(str, order_wrt_mesh(mesh, vma)))
      diff = ','.join(map(str, order_wrt_mesh(
          mesh, [n for n in unmentioned if n in vma])))
      msgs.append(
          f"* out_specs{keystr(spec_key)} is {spec} which implies that the "
          f"corresponding output value is only varying across mesh axes "
          f"{{{need_vma}}} and not {{{diff}}}, but it was inferred to be "
          f"possibly varying over {{{got_vma}}}")
    else:
      need_rep_, = unmentioned
      msgs.append(
          f"* out_specs{keystr(spec_key)} is {spec} which implies that the "
          f"corresponding output value is replicated across mesh axis "
          f"'{need_rep_}', but could not infer replication over any axes")
  assert msgs
  if len(msgs) == 1: msgs = [msgs[0][2:]]  # remove the bullet point
  msg = (f"shard_map applied to the function '{fun_name}' was given "
         f"out_specs which require replication which can't be statically "
         f"inferred given the mesh:\n\n"
         f"The mesh given has shape {tuple(mesh.shape.values())} with "
         f"corresponding axis names {mesh.axis_names}.\n\n"
         + '\n\n'.join(msgs) + '\n\n' +
         "Check if these output values are meant to be replicated over those "
         "mesh axes. If not, consider revising the corresponding out_specs "
         "entries. If so, consider disabling the check by passing the "
         "check_vma=False argument to `jax.shard_map`.")
  return msg

def _unmentioned(mesh: Mesh | AbstractMesh, spec) -> list[AxisName]:
  vma_set = _spec_to_vma(spec)
  return [n for n in mesh.axis_names if n not in vma_set]


def _try_infer_args(f, tree):
  dummy_args = tree_unflatten(tree, [False] * tree.num_leaves)
  try:
    return inspect.signature(f).bind(*dummy_args)
  except (TypeError, ValueError):
    return None

T = TypeVar('T')
def _iter_paths(tree: PyTreeDef, specs: Specs, fails: list[T | NoFail]
                ) -> list[tuple[tuple[KeyPath, P], tuple[KeyPath, T]]]:
  failures = tree_unflatten(tree, fails)
  failures_aug = generate_key_paths(failures)
  specs_ = tree_unflatten(tree_structure(specs), generate_key_paths(specs))
  leaf = lambda x: x is None or type(x) is tuple and len(x) == 2 and type(x[1]) is P
  specs_aug = broadcast_prefix(specs_, failures, is_leaf=leaf)
  return [(s, (fail_key, fail_data)) for s, (fail_key, fail_data)
          in zip(specs_aug, failures_aug)
          if s is not None and fail_data is not no_fail]

# Primitive

@lu.transformation2
def _implicit_pvary_on_output(f, out_specs_thunk, *args, **kwargs):
  out_flat = f(*args, **kwargs)
  return [pvary(o, tuple(_spec_to_vma(sp) - typeof(o).vma))
          for o, sp in zip(out_flat, out_specs_thunk())]

JaxType = Any
MaybeTracer = Union[JaxType, Tracer]

class ShardMapPrimitive(core.Primitive):
  multiple_results = True

  def bind(self, *args, **params):
    return self._true_bind(*args, **params)

  def bind_with_trace(self, trace, fun_and_args, params):
    fun: lu.WrappedFun
    fun, *args = fun_and_args
    return trace.process_shard_map(shard_map_p, fun, args, **params)

  def get_bind_params(self, params):
    new_params = dict(params)
    jaxpr: core.Jaxpr = new_params.pop('jaxpr')
    subfun = lu.hashable_partial(lu.wrap_init(core.eval_jaxpr,
                                              debug_info=jaxpr.debug_info),
                                 jaxpr, ())
    axes = new_params.pop('out_specs')
    new_params['out_specs_thunk'] = HashableFunction(lambda: axes, closure=axes)
    return [subfun], new_params

shard_map_p = ShardMapPrimitive('shard_map')

# Staging

@util.cache(max_size=256, trace_context_in_key=True)
def _as_manual_mesh(mesh, manual_axes: frozenset):
  not_manual = set(mesh.axis_names) - manual_axes
  cur_mesh = get_abstract_mesh()
  if cur_mesh.empty:
    cur_mesh = mesh
  explicit_axes, auto_axes = set(), set()  # type: ignore
  for a in not_manual:
    if cur_mesh._name_to_type[a] == AxisType.Auto:
      auto_axes.add(a)
    else:
      assert cur_mesh._name_to_type[a] == AxisType.Explicit, (
          a, cur_mesh._name_to_type[a])
      explicit_axes.add(a)

  new_axis_types = []
  for n in mesh.axis_names:
    if n in manual_axes:
      new_axis_types.append(AxisType.Manual)
    elif n in auto_axes:
      new_axis_types.append(AxisType.Auto)
    else:
      assert n in explicit_axes
      new_axis_types.append(AxisType.Explicit)
  return AbstractMesh(mesh.axis_sizes, mesh.axis_names,
                      axis_types=tuple(new_axis_types))


def _extend_axis_env(mesh, manual_axes):
  return core.extend_axis_env_nd([(k, v) for k, v in mesh.shape.items()
                                  if k in manual_axes])

def _shard_map_staging(
    trace: pe.DynamicJaxprTrace, prim: core.Primitive, f: lu.WrappedFun,
    in_tracers: Sequence[Any], *, mesh: Mesh,
    in_specs, out_specs_thunk, check_vma: bool, manual_axes: frozenset,
  ) -> Sequence[pe.DynamicJaxprTracer]:
  source_info = source_info_util.current()
  to_jaxpr_tracer = partial(trace.to_jaxpr_tracer, source_info=source_info)
  in_tracers = map(to_jaxpr_tracer, in_tracers)
  inner_mesh = _as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes))
  in_avals = [t.aval for t in in_tracers]
  in_avals_ = map(partial(_shard_aval, mesh, manual_axes, check_vma), in_specs,
                  in_avals)
  with (_extend_axis_env(mesh, manual_axes), use_abstract_mesh(inner_mesh),
        config._check_vma(check_vma)):
    jaxpr, out_avals_, consts, () = pe.trace_to_jaxpr_dynamic(f, in_avals_)
  _check_names(out_specs_thunk(), out_avals_)
  if check_vma:
    out_vma = [v.aval.vma for v in jaxpr.outvars]
    _check_vmas(mesh, out_specs_thunk(), out_vma)
  out_avals = map(_check_shapedarray, out_avals_)
  out_avals = [_check_shapedarray(_unshard_aval(mesh, check_vma, spec, aval))
               for spec, aval in zip(out_specs_thunk(), out_avals)]
  out_tracers = [pe.DynamicJaxprTracer(trace, a, source_info) for a in out_avals]
  invars = map(trace.getvar, in_tracers)
  constvars = map(trace.getvar, map(to_jaxpr_tracer, consts))
  outvars = map(trace.makevar, out_tracers)
  in_specs_staged = (P(),) * len(consts) + tuple(in_specs)  # type: ignore
  with (_extend_axis_env(mesh, manual_axes), use_abstract_mesh(inner_mesh),
        config._check_vma(check_vma)):
    jaxpr = pe.convert_constvars_jaxpr(jaxpr)
  params = dict(mesh=mesh, in_specs=in_specs_staged,
                out_specs=tuple(out_specs_thunk()), jaxpr=jaxpr,
                check_vma=check_vma, manual_axes=manual_axes)
  effs = core.filter_named_axis_effects(jaxpr.effects, mesh.axis_names)
  eqn = pe.new_jaxpr_eqn([*constvars, *invars], outvars, prim, params,
                         effs, source_info)
  trace.frame.add_eqn(eqn)
  return out_tracers
pe.DynamicJaxprTrace.process_shard_map = _shard_map_staging

# TODO add underscore version, for direct-linearize to consume

def _spec_to_names(spec: PartitionSpec):
  return {i: names if isinstance(names, tuple) else (names,)
          for i, names in enumerate(spec) if names is not None}

def _check_shapedarray(aval: core.AbstractValue) -> core.ShapedArray:
  assert isinstance(aval, core.ShapedArray)
  return aval

def _shard_aval(mesh: Mesh, manual_axes, check_vma, spec,
                aval: core.AbstractValue) -> core.AbstractValue:
  if type(aval) in core.shard_aval_handlers:
    return core.shard_aval_handlers[type(aval)](mesh, manual_axes, check_vma,
                                                spec, aval)
  raise NotImplementedError(f"Unsupported aval type: {type(aval)}")

def _unshard_aval(mesh: Mesh, check_vma, spec,
                  aval: core.AbstractValue) -> core.AbstractValue:
  if type(aval) in core.unshard_aval_handlers:
    return core.unshard_aval_handlers[type(aval)](mesh, check_vma, spec, aval)
  else:
    raise NotImplementedError(f"Unsupported aval type: {type(aval)}")

def _shard_shaped_array(mesh: Mesh, manual_axes: frozenset, check_vma,
                        spec, aval: core.AbstractValue) -> core.AbstractValue:
  assert isinstance(aval, core.ShapedArray)
  names = _spec_to_names(spec)
  new_shape = tuple(sz // prod(mesh.shape[n] for n in names.get(i, ()))
                    for i, sz in enumerate(aval.shape))
  manual_mesh = _as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes))
  new_sharding = NamedSharding(manual_mesh, aval.sharding.spec)
  vma = _spec_to_vma(spec) if check_vma else frozenset()
  vma = vma | aval.vma
  return aval.update(shape=new_shape, sharding=new_sharding, vma=vma)
core.shard_aval_handlers[core.ShapedArray] = _shard_shaped_array

def _unshard_shaped_array(mesh: Mesh, check_vma, spec, aval: core.AbstractValue
                          ) -> core.AbstractValue:
  assert isinstance(aval, core.ShapedArray)
  names = _spec_to_names(spec)
  new_shape = tuple(sz * prod(mesh.shape[n] for n in names.get(i, ()))
                    for i, sz in enumerate(aval.shape))
  names_spec = spec._normalized_spec_for_aval(aval.ndim)
  if aval.ndim == 0:
    out_spec = P()
  else:
    out_spec = []  # type: ignore
    for name_s, aval_s in zip(names_spec, aval.sharding.spec):
      if name_s and not aval_s:
        out_spec.append(name_s)
      elif aval_s and not name_s:
        out_spec.append(aval_s)
      elif not name_s and not aval_s:
        out_spec.append(None)
      else:
        assert name_s and aval_s
        name_s = name_s if isinstance(name_s, tuple) else (name_s,)
        aval_s = aval_s if isinstance(aval_s, tuple) else (aval_s,)
        out_spec.append(name_s + aval_s)
    out_spec = PartitionSpec(*out_spec)
  new_mesh = (mesh.abstract_mesh if get_abstract_mesh().empty else
              get_abstract_mesh())
  new_sharding = NamedSharding(new_mesh, out_spec)
  manual_axes = set(new_mesh.manual_axes)
  vma = (frozenset(v for v in aval.vma if v in manual_axes)
         if check_vma else frozenset())
  return aval.update(shape=new_shape, sharding=new_sharding, vma=vma)
core.unshard_aval_handlers[core.ShapedArray] = _unshard_shaped_array

# Type-checking

def _shard_map_typecheck(_, *in_atoms, jaxpr, mesh, in_specs, out_specs,
                         check_vma, manual_axes):
  # TODO(mattjj,parkers): check auto
  for v, x, in_spec in zip(jaxpr.invars, in_atoms, in_specs):
    if not core.typecompat(v.aval, _shard_aval(
        mesh, manual_axes, check_vma, in_spec, x.aval)):
      raise core.JaxprTypeError("shard_map argument avals not compatible with "
                                "jaxpr binder avals and in_specs")
  with _extend_axis_env(mesh, manual_axes), config._check_vma(check_vma):
    core.check_jaxpr(jaxpr)
  if check_vma:
    out_vma = [v.aval.vma for v in jaxpr.outvars]
    for vma, out_spec in zip(out_vma, out_specs):
      if not _valid_repeats(mesh, vma, out_spec):
        raise core.JaxprTypeError(
            "shard_map can't prove output is sufficiently replicated")
  out_avals_sharded = [x.aval for x in jaxpr.outvars]
  out_avals = map(partial(_unshard_aval, mesh, check_vma), out_specs,
                  out_avals_sharded)
  effs = core.filter_named_axis_effects(jaxpr.effects, mesh.axis_names)
  return out_avals, effs
core.custom_typechecks[shard_map_p] = _shard_map_typecheck


def _valid_repeats(mesh: Mesh, vma: Set[AxisName], spec) -> bool:
  um = set(_unmentioned(mesh, spec)) - set(mesh.manual_axes)
  if any(u in vma for u in um):
    return False
  return True

# Lowering

def _shardy_shard_map_sharding(
    ctx: mlir.LoweringRuleContext, mesh, manual_axes, spec, aval_in
) -> sharding_impls.SdyArray:
  ns = _make_scoped_manual_sharding(ctx, mesh, spec)
  if dtypes.issubdtype(aval_in.dtype, dtypes.extended):
    ns = sharding_impls.physical_sharding(aval_in, ns)
    aval_in = core.physical_aval(aval_in)
  sdy_sharding = ns._to_sdy_sharding(aval_in.ndim)
  if len(manual_axes) < len(mesh.axis_names):
    for dim_sharding in sdy_sharding.dim_shardings:
      dim_sharding.is_open = True
  return sdy_sharding


def _shardy_shard_map_token_sharding(
    ctx: mlir.LoweringRuleContext, mesh
  ) -> ir.Attribute:
  ns = _make_scoped_manual_sharding(ctx, mesh, P())
  return ns._to_sdy_sharding(0)


def _get_spmdaxis_ctx_mesh(mesh):
  if isinstance(mesh, AbstractMesh):
    concrete_mesh = get_concrete_mesh()
    return concrete_mesh if concrete_mesh is not None else mesh
  return mesh


def _shard_map_lowering_shardy(
    ctx, in_nodes, jaxpr, mesh, in_specs, out_specs, manual_axes, check_vma):
  axis_ctx = ctx.module_context.axis_context
  in_avals_ = [v.aval for v in jaxpr.invars]
  if isinstance(axis_ctx, sharding_impls.SPMDAxisContext):
    # Nested `ManualComputationOp`s cannot refer to axes that are already
    # manual. So figure out what axes are free thus far.
    shardy_manual_axes = frozenset(mesh.axis_names) - axis_ctx.manual_axes
  else:
    shardy_manual_axes = manual_axes
  new_axis_context = sharding_impls.SPMDAxisContext(
      _get_spmdaxis_ctx_mesh(mesh), manual_axes)
  sub_ctx = ctx.module_context.replace(axis_context=new_axis_context)

  tokens = [ctx.tokens_in.get(eff) for eff in ctx.tokens_in.effects()]
  num_tokens = len(tokens)
  manual_axes = order_wrt_mesh(mesh, shardy_manual_axes)
  if np.prod([mesh.shape[a] for a in manual_axes]) == 1:
    # No need for a `ManualComputationOp` if all manual axes are size 1.
    with _extend_axis_env(mesh, manual_axes), config._check_vma(check_vma):
      out_nodes, tokens_out = mlir.jaxpr_subcomp(
          sub_ctx, jaxpr, ctx.name_stack,
          mlir.TokenSet(zip(ctx.tokens_in.effects(), tokens)),
          (), *in_nodes,
          dim_var_values=ctx.dim_var_values)
      ctx.set_tokens_out(tokens_out)
    return out_nodes

  in_shardings = list(
      map(partial(_shardy_shard_map_sharding, ctx, mesh, manual_axes),
          in_specs, ctx.avals_in))
  num_dim_vars = len(ctx.dim_var_values)
  in_shardings = ([_shardy_shard_map_token_sharding(ctx, mesh)]
                  * (num_tokens + num_dim_vars) + in_shardings)
  in_shardings = sharding_impls.SdyArrayList(in_shardings).build()

  out_shardings = list(
      map(partial(_shardy_shard_map_sharding, ctx, mesh, manual_axes),
          out_specs, ctx.avals_out))
  out_shardings = [
      _shardy_shard_map_token_sharding(ctx, mesh)] * num_tokens + out_shardings
  out_shardings = sharding_impls.SdyArrayList(out_shardings).build()

  output_types = ([hlo.TokenType.get()] * num_tokens +
                  list(map(mlir.aval_to_ir_type, ctx.avals_out)))

  args = (*ctx.dim_var_values, *tokens, *in_nodes)
  manual_computation_op = sdy.ManualComputationOp(
      output_types,
      mlir.flatten_ir_values(args),
      in_shardings, out_shardings,
      sdy.ManualAxesAttr.get(
          ir.ArrayAttr.get([ir.StringAttr.get(i) for i in manual_axes])))
  block = ir.Block.create_at_start(
      manual_computation_op.body,
      (*(i if isinstance(i, ir.Type) else i.type for i in ctx.dim_var_values),
       *([hlo.TokenType.get()] * num_tokens),
       *map(mlir.aval_to_ir_type, in_avals_)))
  with (ir.InsertionPoint(block), _extend_axis_env(mesh, manual_axes),
        config._check_vma(check_vma)):
    out_nodes_, tokens_out = mlir.jaxpr_subcomp(
        sub_ctx, jaxpr, ctx.name_stack,
        mlir.TokenSet(zip(
            ctx.tokens_in.effects(), block.arguments[:num_tokens])),
        (), *block.arguments[num_tokens+num_dim_vars:],
        dim_var_values=ctx.dim_var_values)
    sdy.ReturnOp([ir.Value(x) for x in (*[v for _, v in tokens_out.items()],
                                        *out_nodes_)])
    num_tokens = len(tokens_out.effects())
    tokens_out = tokens_out.update_tokens(mlir.TokenSet(zip(
        ctx.tokens_in.effects(), manual_computation_op.results[:num_tokens])))
    ctx.set_tokens_out(tokens_out)

  return manual_computation_op.results[num_tokens:]


def _shard_map_lowering(ctx, *in_nodes, jaxpr, mesh, in_specs, out_specs,
                        check_vma, manual_axes):
  if config.use_shardy_partitioner.value:
    return _shard_map_lowering_shardy(
        ctx, in_nodes, jaxpr, mesh, in_specs, out_specs, manual_axes, check_vma)

  in_avals_ = [v.aval for v in jaxpr.invars]
  out_avals_ = [x.aval for x in jaxpr.outvars]
  in_nodes_ = map(partial(_xla_shard, ctx, mesh, manual_axes), in_specs,
                  ctx.avals_in, in_avals_, in_nodes)
  new_axis_context = sharding_impls.SPMDAxisContext(
      _get_spmdaxis_ctx_mesh(mesh), manual_axes)
  sub_ctx = ctx.module_context.replace(axis_context=new_axis_context)
  with _extend_axis_env(mesh, manual_axes), config._check_vma(check_vma):
    out_nodes_, tokens_out = mlir.call_lowering(
        "shmap_body", ctx.name_stack, jaxpr, None, sub_ctx, in_avals_,
        out_avals_, ctx.tokens_in, *in_nodes_,
        dim_var_values=ctx.dim_var_values,
        arg_names=map(_pspec_mhlo_attrs, in_specs, in_avals_),
        result_names=map(_pspec_mhlo_attrs, out_specs, out_avals_))
  ctx.set_tokens_out(tokens_out)
  return map(partial(_xla_unshard, ctx, mesh, manual_axes), out_specs,
             out_avals_, ctx.avals_out, out_nodes_)
mlir.register_lowering(shard_map_p, _shard_map_lowering)

def _make_scoped_manual_sharding(ctx, mesh, spec):
  axis_ctx = ctx.module_context.axis_context
  mesh = mesh.abstract_mesh
  if isinstance(axis_ctx, sharding_impls.SPMDAxisContext):
    mesh = mesh.update_axis_types(
        {a: AxisType.Manual for a in axis_ctx.manual_axes})
  return NamedSharding(mesh, spec)

def _xla_shard(ctx: mlir.LoweringRuleContext, mesh, manual_axes, spec,
               aval_in, aval_out, x):
  if prod([size for n, size in mesh.shape.items() if n in manual_axes]) == 1:
    return x
  ns = _make_scoped_manual_sharding(ctx, mesh, spec)
  if dtypes.issubdtype(aval_in.dtype, dtypes.extended):
    ns = sharding_impls.physical_sharding(aval_in, ns)
    aval_in = core.physical_aval(aval_in)
  shard_proto = ns._to_xla_hlo_sharding(aval_in.ndim).to_proto()
  unspecified = (set(range(aval_in.ndim))
                 if len(manual_axes) < len(mesh.axis_names) else set())
  sx = mlir.wrap_with_sharding_op(ctx, x, aval_in, shard_proto,
                                  unspecified_dims=unspecified)
  manual_proto = pxla.manual_proto(
      aval_in, manual_axes | set(mesh.manual_axes), mesh)
  return mlir.wrap_with_full_to_shard_op(ctx, sx, aval_out, manual_proto,
                                         unspecified)

def _xla_unshard(ctx: mlir.LoweringRuleContext, mesh, manual_axes, spec,
                 aval_in, aval_out, x):
  if prod([size for n, size in mesh.shape.items() if n in manual_axes]) == 1:
    return x
  ns = _make_scoped_manual_sharding(ctx, mesh, spec)
  if dtypes.issubdtype(aval_out.dtype, dtypes.extended):
    ns = sharding_impls.physical_sharding(aval_out, ns)
    aval_out = core.physical_aval(aval_out)
  unspecified = (set(range(aval_in.ndim))
                 if len(manual_axes) < len(mesh.axis_names) else set())
  if dtypes.issubdtype(aval_in.dtype, dtypes.extended):
    aval_in = core.physical_aval(aval_in)
  manual_proto = pxla.manual_proto(
      aval_in, manual_axes | set(mesh.manual_axes), mesh)
  sx = mlir.wrap_with_sharding_op(ctx, x, aval_in, manual_proto,
                                  unspecified_dims=unspecified)
  shard_proto = ns._to_xla_hlo_sharding(aval_out.ndim).to_proto()
  return mlir.wrap_with_shard_to_full_op(ctx, sx, aval_out, shard_proto,
                                         unspecified)

def _pspec_mhlo_attrs(spec, aval: core.AbstractValue) -> str:
  if isinstance(aval, core.ShapedArray):
    names = _spec_to_names(spec)
    return str(map(names.get, range(aval.ndim)))
  return ''

# Eager evaluation

def get_mesh_from_args(args_flat, mesh):
  for a in args_flat:
    if hasattr(a, 'sharding') and isinstance(a.sharding, NamedSharding):
      if a.sharding.mesh.shape_tuple != mesh.shape_tuple:
        aval = core.shaped_abstractify(a)
        raise ValueError(
            f"Mesh shape of the input {a.sharding.mesh.shape_tuple} does not"
            " match the mesh shape passed to shard_map "
            f" {mesh.shape_tuple} for shape {aval.str_short()}")
      mesh = a.sharding.mesh
  if isinstance(mesh, AbstractMesh):
    raise ValueError(
        "Please pass `jax.Array`s with a `NamedSharding` as input to"
        " `shard_map` when passing `AbstractMesh` to the mesh argument.")
  assert isinstance(mesh, Mesh)
  return mesh

def _vma_to_spec(mesh, vma):
  return P(order_wrt_mesh(mesh, vma))

def _spec_to_vma(spec):
  return frozenset(p for s in spec if s is not None
                   for p in (s if isinstance(s, tuple) else (s,)))

def order_wrt_mesh(mesh, x):
  return tuple(a for a in mesh.axis_names if a in x)

def _shard_map_impl(trace, prim, fun, args, *, mesh, in_specs, out_specs_thunk,
                    check_vma, manual_axes):
  if len(manual_axes) < len(mesh.axis_names):
    raise NotImplementedError
  del prim
  if isinstance(mesh, AbstractMesh):
    concrete_mesh = get_concrete_mesh()
    mesh = concrete_mesh if concrete_mesh is not None else mesh
    mesh = get_mesh_from_args(args, mesh)
  cur_mesh = get_abstract_mesh()
  args = map(partial(_unmatch_spec, mesh, check_vma, context_mesh=cur_mesh),
             in_specs, args)
  in_vma = map(_spec_to_vma, in_specs)
  outs, out_vma = _run_shmap(fun, mesh, manual_axes, args, in_vma, check_vma,
                             cur_mesh)
  out_avals = [core.mapped_aval(x.shape[0], 0, core.get_aval(x)) for x in outs]
  _check_names(out_specs_thunk(), out_avals)  # pytype: disable=wrong-arg-types
  if check_vma:
    _check_vmas(mesh, out_specs_thunk(), out_vma)
    src_pspecs = tuple(_vma_to_spec(mesh, r) for r in out_vma)
  else:
    src_pspecs = tuple(P(mesh.axis_names) for _ in out_vma)
  dst_pspecs = out_specs_thunk()
  return map(partial(_match_spec, mesh, check_vma), src_pspecs, dst_pspecs,
             outs)
core.EvalTrace.process_shard_map = _shard_map_impl

def _run_shmap(f, mesh, manual_axes, args, vmas, check_vma, context_mesh):
  trace = ShardMapTrace(mesh, manual_axes, check_vma, context_mesh)
  in_tracers = map(partial(ShardMapTracer, trace), vmas, args)
  inner_mesh = _as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes))
  with (core.set_current_trace(trace), _extend_axis_env(mesh, manual_axes),
        use_abstract_mesh(inner_mesh), config._check_vma(check_vma)):
    ans = f.call_wrapped(*in_tracers)
    outs, out_vma = unzip2(map(trace.to_val_vma_pair, ans))
  return outs, out_vma


def _unmatch_spec(mesh: Mesh, check_vma, in_spec, x: JaxType, context_mesh
                  ) -> JaxType:
  with (core.eval_context(), jax.disable_jit(False),
        use_abstract_mesh(context_mesh)):
    return jax.jit(HashablePartial(_unmatch, mesh, check_vma, in_spec))(x)

def _unmatch(mesh, check_vma, in_spec, x):
  if check_vma:
    used_axes = _spec_to_vma(in_spec)
    dst = P(order_wrt_mesh(mesh, used_axes))
  else:
    dst = P(mesh.axis_names)
    check_vma = False
  return shard_map(_add_singleton, mesh=mesh, in_specs=(in_spec,),
                   out_specs=dst, check_vma=check_vma)(x)

def _check_names(specs, avals: Sequence[core.ShapedArray]) -> None:
  fail = [a if sp and len(sp) > a.ndim else no_fail
          for sp, a in zip(specs, avals)]
  if any(f is not no_fail for f in fail):
    raise _SpecError(fail)

class _SpecError(Exception):
  pass

def _check_vmas(mesh, specs, vmas):
  fail = [vma if not _valid_repeats(mesh, vma, sp) else no_fail
          for sp, vma in zip(specs, vmas)]
  if any(f is not no_fail for f in fail):
    raise _RepError(fail)

class _RepError(Exception):
  pass

def _match_spec(mesh: Mesh, check_vma, src_pspec: PartitionSpec,
                dst_pspec: PartitionSpec, x: JaxType) -> JaxType:
  fn = HashablePartial(_match, mesh, check_vma, src_pspec, dst_pspec)
  with core.eval_context(), jax.disable_jit(False):
    return jax.jit(fn, out_shardings=NamedSharding(mesh, dst_pspec))(x)

def _match(mesh, check_vma, src_pspec, dst_pspec, x):
  return shard_map(_rem_singleton, mesh=mesh, in_specs=src_pspec,
                   out_specs=dst_pspec, check_vma=check_vma)(x)

def _rem_singleton(x): return jnp.squeeze(x, axis=0)
def _add_singleton(x): return jnp.expand_dims(x, axis=0)

def _maybe_check_special(outs):
  if not config.debug_nans.value and not config.debug_infs.value: return
  bufs = [s.data for leaf in tree_leaves(outs)
          for s in getattr(leaf, 'addressable_shards', [])]
  try:
    dispatch.check_special('shard_map', bufs)
  except api_util.InternalFloatingPointError as e:
    raise FloatingPointError(f'Invalid value ({e.ty}) encountered in sharded computation.') from None

class ShardMapTrace(core.Trace):
  __slots__ = ("mesh", "manual_axes", "check", "context_mesh")

  mesh: Mesh
  manual_axes: frozenset[AxisName]
  check: bool
  context_mesh: AbstractMesh

  def __init__(self, mesh, manual_axes, check, context_mesh):
    super().__init__()
    self.mesh = mesh
    self.manual_axes = manual_axes
    self.check = check
    self.context_mesh = context_mesh

  def to_val_vma_pair(self, val):
    if isinstance(val, ShardMapTracer):
      return val.val, val.vma
    elif isinstance(val, Tracer):
      raise Exception(f"Shouldn't have any non-shard_map tracers: {val}")
    else:
      val_ = _unmatch_spec(self.mesh, self.check, P(), val, self.context_mesh)
      return val_, frozenset()

  def process_primitive(self, prim, tracers, params):
    in_vals, in_vma = unzip2(map(self.to_val_vma_pair, tracers))
    if self.check:
      out_avals, _ = prim.abstract_eval(*(typeof(t) for t in tracers), **params)
      out_avals = tuple(out_avals) if type(out_avals) is list else out_avals
      out_vma = tree_map(lambda a: a.vma, out_avals)
      in_specs  = tuple(map(partial(_vma_to_spec, self.mesh), in_vma))
      out_specs = tree_map(partial(_vma_to_spec, self.mesh), out_vma)
    else:
      out_vma = frozenset()
      in_specs = out_specs = P(self.mesh.axis_names)

    eager_rule = eager_rules.get(prim)
    if eager_rule:
      out_vals = eager_rule(self.mesh, *in_vals, **params)
    else:
      f = HashablePartial(
          _prim_applier, prim, self.check, tuple(params.items()), self.mesh,
          in_specs, out_specs)
      with (core.eval_context(), jax.disable_jit(False), jax.debug_nans(False),
            jax.debug_infs(False), use_abstract_mesh(self.context_mesh)):
        out_vals = jax.jit(f)(*in_vals)
      _maybe_check_special(out_vals)
    if prim.multiple_results:
      out_vma = (out_vma if isinstance(out_vma, (list, tuple))
                 else [out_vma] * len(out_vals))
      return map(partial(ShardMapTracer, self), out_vma, out_vals)
    return ShardMapTracer(self, out_vma, out_vals)

  def process_call(self, call_primitive, fun, tracers, params):
    raise NotImplementedError(
        f"Eager evaluation of `{call_primitive}` inside a `shard_map` isn't "
        "yet supported. Put a `jax.jit` around the `shard_map`-decorated "
        "function, and open a feature request at "
        "https://github.com/jax-ml/jax/issues !")

  def process_map(self, map_primitive, fun, tracers, params):
    raise NotImplementedError(
        "Eager evaluation of `pmap` inside a `shard_map` isn't yet supported."
        "Put a `jax.jit` around the `shard_map`-decorated function, and open "
        "a feature request at https://github.com/jax-ml/jax/issues !")

  def process_custom_jvp_call(self, prim, fun, jvp, tracers, *, symbolic_zeros):
    # Since ShardMapTrace is only used as a base main, we can drop the jvp.
    del prim, jvp, symbolic_zeros
    in_vals, in_vma = unzip2(map(self.to_val_vma_pair, tracers))
    out_vals, out_vma = _run_shmap(fun, self.mesh, self.manual_axes, in_vals,
                                   in_vma, self.check, self.context_mesh)
    return map(partial(ShardMapTracer, self), out_vma, out_vals)

  def process_custom_vjp_call(self, prim, fun, fwd, bwd, tracers, out_trees,
                              symbolic_zeros):
    if symbolic_zeros:
      msg = ("custom_vjp symbolic_zeros support with shard_map is not "
             "implemented; please open an issue at "
             "https://github.com/jax-ml/jax/issues")
      raise NotImplementedError(msg)
    del prim, fwd, bwd, out_trees, symbolic_zeros
    in_vals, in_vma = unzip2(map(self.to_val_vma_pair, tracers))
    out_vals, out_vma = _run_shmap(fun, self.mesh, self.manual_axes, in_vals,
                                   in_vma, self.check, self.context_mesh)
    return map(partial(ShardMapTracer, self), out_vma, out_vals)


class ShardMapTracer(core.Tracer):
  vma: frozenset[AxisName]
  val: JaxType

  def __init__(self, trace, vma, val):
    self._trace = trace
    if isinstance(vma, set):
      vma = frozenset(vma)
    assert isinstance(vma, frozenset)
    self.vma = vma
    self.val = val

  @property
  def aval(self):
    aval = core.get_aval(self.val)
    out = core.mapped_aval(self._trace.mesh.size, 0, aval)
    new_sharding = NamedSharding(
        _as_manual_mesh(self._trace.mesh, self._trace.manual_axes),
        out.sharding.spec)  # pytype: disable=attribute-error
    vma = self.vma if config._check_vma.value else frozenset()
    return out.update(sharding=new_sharding, vma=vma)

  def to_concrete_value(self):
    if self.vma == frozenset():
      with core.eval_context(), use_abstract_mesh(self._trace.context_mesh):
        return core.to_concrete_value(self.val[0])
    else:
      return None

  def __str__(self) -> str:
    pb_names = set(self._trace.mesh.axis_names) - self.vma
    self = pvary(self, tuple(pb_names))
    with core.eval_context(), use_abstract_mesh(self._trace.context_mesh):
      blocks = list(self.val)
    mesh = self._trace.mesh
    axis_names = f"({', '.join(map(str, mesh.axis_names))},)"
    return '\n'.join(
        f"On {device} at mesh coordinates {axis_names} = {idx}:\n{block}\n"
        for (idx, device), block in zip(np.ndenumerate(mesh.devices), blocks))

  __repr__ = __str__  # for debuggers, like `p x`

def _prim_applier(prim, check_vma, params_tup, mesh, in_specs, out_specs, *args):
  def apply(*args):
    outs = prim.bind(*map(_rem_singleton, args), **dict(params_tup))
    return tree_map(_add_singleton, outs)
  out_specs = list(out_specs) if type(out_specs) is tuple else out_specs
  return shard_map(apply, mesh=mesh, in_specs=in_specs, out_specs=out_specs,
                   check_vma=check_vma)(*args)

eager_rules: dict[core.Primitive, Callable] = {}


# TODO(mattjj): working around an apparent XLA or PjRt bug, remove eventually
def _debug_callback_eager_rule(
    mesh,
    *args,
    callback: Callable[..., Any],
    effect: debugging.DebugEffect,
    partitioned: bool,
):
  del effect
  with core.eval_context():
    all_blocks = zip(*map(list, args))
  for (idx, device), blocks in zip(np.ndenumerate(mesh.devices), all_blocks):
    callback(*blocks)
  return []


eager_rules[debugging.debug_callback_p] = _debug_callback_eager_rule

def _device_put_eager_rule(mesh, *xs, srcs, devices, copy_semantics):
  del mesh, srcs, copy_semantics
  for device in devices:
    if device is not None:
      raise ValueError("device_put with explicit device not allowed within "
                       f"shard_map-decorated functions, but got device {device}")
  return xs
eager_rules[dispatch.device_put_p] = _device_put_eager_rule


# Batching

def _modify_specs_axis_data(trace, name, mesh, in_specs, in_dims):
  new_in_specs = [sp if d is batching.not_mapped else pxla.batch_spec(sp, d, name)
                  for sp, d in zip(in_specs, in_dims)]
  new_size = trace.axis_data.size // prod(mesh.shape[n] for n in name)
  new_axis_data = batching.AxisData(
      trace.axis_data.name, new_size, trace.axis_data.spmd_name,
      trace.axis_data.explicit_mesh_axis)
  return new_in_specs, new_axis_data

def _shard_map_batch(
    trace: batching.BatchTrace, prim: core.Primitive, fun: lu.WrappedFun,
    in_tracers: Sequence[batching.BatchTracer], mesh: Mesh,
    in_specs, out_specs_thunk, check_vma: bool, manual_axes: frozenset
    ) -> Sequence[batching.BatchTracer]:
  in_vals, in_dims = unzip2(map(trace.to_batch_info, in_tracers))
  if any(isinstance(d, batching.RaggedAxis) for d in in_dims):
    raise NotImplementedError
  spmd_axis_name = trace.axis_data.spmd_name
  explicit_mesh_axis = trace.axis_data.explicit_mesh_axis
  if spmd_axis_name is not None:
    used = {n for spec in in_specs for n in _spec_to_vma(spec)}
    if not config.disable_vmap_shmap_error.value and set(spmd_axis_name) & used:
      raise ValueError("vmap spmd_axis_name cannot appear in shard_map in_specs")
    new_in_specs, new_axis_data = _modify_specs_axis_data(
        trace, spmd_axis_name, mesh, in_specs, in_dims)
  elif explicit_mesh_axis is not None:
    used = {n for spec in in_specs for n in _spec_to_vma(spec)}
    if set(explicit_mesh_axis) & used:
      raise ValueError("vmapped away explicit mesh axis cannot appear in "
                       "shard_map in_specs")
    new_in_specs, new_axis_data = _modify_specs_axis_data(
        trace, explicit_mesh_axis, mesh, in_specs, in_dims)
  else:
    new_in_specs = [sp if d is batching.not_mapped else pxla.batch_spec(sp, d, None)
                    for sp, d in zip(in_specs, in_dims)]
    new_axis_data = trace.axis_data
  fun, out_dims = batching.batch_subtrace(fun, trace.tag, new_axis_data, tuple(in_dims))

  @as_hashable_function(closure=out_specs_thunk)
  def new_out_specs_thunk():
    return _batch_out_specs(spmd_axis_name, explicit_mesh_axis, out_dims(),
                            out_specs_thunk())

  new_params = dict(mesh=mesh, in_specs=new_in_specs,
                    out_specs_thunk=new_out_specs_thunk, check_vma=check_vma,
                    manual_axes=manual_axes)
  with core.set_current_trace(trace.parent_trace):
    out_vals = prim.bind(fun, *in_vals, **new_params)
  make_tracer = partial(batching.BatchTracer, trace,
                        source_info=source_info_util.current())
  return map(make_tracer, out_vals, out_dims())
batching.BatchTrace.process_shard_map = _shard_map_batch

def _batch_out_specs(spmd_name, explicit_mesh_axis, dims, out_specs):
  if spmd_name is not None:
    used = {n for spec in out_specs for n in _spec_to_vma(spec)}
    if not config.disable_vmap_shmap_error.value and set(spmd_name) & used:
      raise ValueError("vmap spmd_axis_name cannot appear in shard_map out_specs")
    return [sp if d is batching.not_mapped else pxla.batch_spec(sp, d, spmd_name)
            for sp, d in zip(out_specs, dims)]
  elif explicit_mesh_axis is not None:
    used = {n for spec in out_specs for n in _spec_to_vma(spec)}
    if set(explicit_mesh_axis) & used:
      raise ValueError("vmapped away explicit mesh axis cannot appear in "
                       "shard_map out_specs")
    return [sp if d is batching.not_mapped else
            pxla.batch_spec(sp, d, explicit_mesh_axis)
            for sp, d in zip(out_specs, dims)]
  else:
    return [sp if d is batching.not_mapped else pxla.batch_spec(sp, d, None)
            for sp, d in zip(out_specs, dims)]


# Autodiff

def _shard_map_jvp(trace, shard_map_p, f, tracers, mesh, in_specs,
                   out_specs_thunk, check_vma, manual_axes):
  primals, tangents = unzip2(map(trace.to_primal_tangent_pair, tracers))
  which_nz = [     type(t) is not ad.Zero           for t in tangents]
  tangents = [t if type(t) is not ad.Zero else None for t in tangents]
  args, in_tree = tree_flatten((primals, tangents))
  f_jvp = ad.jvp_subtrace(f, trace.tag)
  f_jvp, which_nz_out = ad.nonzero_tangent_outputs(f_jvp)
  tangent_in_specs = [sp for sp, nz in zip(in_specs, which_nz) if nz]

  @as_hashable_function(closure=out_specs_thunk)
  def new_out_specs_thunk():
    out_ax = out_specs_thunk()
    return (*out_ax, *(ax for ax, nz in zip(out_ax, which_nz_out()) if nz))
  params = dict(mesh=mesh, in_specs=(*in_specs, *tangent_in_specs),
                out_specs_thunk=new_out_specs_thunk, check_vma=check_vma,
                manual_axes=manual_axes)
  f_jvp, out_tree = ad.traceable(f_jvp, in_tree)
  result = shard_map_p.bind_with_trace(trace.parent_trace, (f_jvp,) + tuple(args), params)
  primal_out, tangent_out = tree_unflatten(out_tree(), result)
  tangent_out = [ad.Zero(core.get_aval(p).to_tangent_aval()) if t is None else t
                 for p, t in zip(primal_out, tangent_out)]
  return [ad.JVPTracer(trace, p, t) for p, t in zip(primal_out, tangent_out)]
ad.JVPTrace.process_shard_map = _shard_map_jvp

def _shard_map_partial_eval(trace: pe.JaxprTrace, shard_map_p,
                            f: lu.WrappedFun, tracers, mesh, in_specs,
                            out_specs_thunk, check_vma, manual_axes):
  tracers = map(trace.to_jaxpr_tracer, tracers)
  in_pvals = [t.pval for t in tracers]
  in_knowns, in_avals, in_consts = pe.partition_pvals(in_pvals)
  unk_in_specs, known_in_specs = pe.partition_list(in_knowns, in_specs)
  in_avals_sharded = map(partial(_shard_aval, mesh, manual_axes, check_vma),
                         unk_in_specs, in_avals)
  f = pe.trace_to_subjaxpr_nounits_fwd2(f, trace.tag, f.debug_info, False)
  f = _promote_scalar_residuals(f)
  f_known, aux = pe.partial_eval_wrapper_nounits2(
      f, (*in_knowns,), (*in_avals_sharded,))
  all_names = _all_newly_manual_mesh_names(mesh, manual_axes)

  @as_hashable_function(closure=out_specs_thunk)
  def known_out_specs():
    _, _, out_knowns, res_avals, _, _ = aux()
    _, out_known_specs = pe.partition_list(out_knowns, out_specs_thunk())
    if check_vma:
      res_specs = [P(order_wrt_mesh(mesh, a.vma)) for a in res_avals]
    else:
      res_specs = [P(all_names)] * len(res_avals)
    return (*out_known_specs, *res_specs)

  known_params = dict(mesh=mesh, in_specs=(*known_in_specs,),
                      out_specs_thunk=known_out_specs, check_vma=check_vma,
                      manual_axes=manual_axes)
  out = shard_map_p.bind_with_trace(trace.parent_trace, (f_known, *in_consts),
                                    known_params)
  in_fwd, out_fwd, out_knowns, res_avals, jaxpr, env = aux()
  num_res = sum(f1 is None and f2 is None for f1, f2 in zip(in_fwd, out_fwd))
  out_consts, non_fwd_res = split_list(out, [len(out) - num_res])
  assert not jaxpr.constvars
  unk_out_specs, _ = pe.partition_list(out_knowns, out_specs_thunk())
  known_out_specs_ = known_out_specs()
  res = subs_list2(in_fwd, out_fwd, in_consts, out_consts, non_fwd_res)
  # TODO make res_avals be the full set, not just the non-fwd ones
  res_avals_iter = iter(res_avals)
  res_specs = []
  for f1, f2 in zip(in_fwd, out_fwd):
    if f1 is not None:
      res_specs.append(known_in_specs[f1])
    elif f2 is not None:
      res_specs.append(known_out_specs_[f2])
    else:
      if check_vma:
        res_vma = next(res_avals_iter).vma
        res_specs.append(P(order_wrt_mesh(mesh, res_vma)))
      else:
        res_specs.append(P(all_names))
  unk_in_specs = (*res_specs,) + (P(),) * len(env) + (*unk_in_specs,)  # type: ignore[assignment]
  const_tracers = map(trace.new_instantiated_const, res)
  env_tracers = map(trace.to_jaxpr_tracer, env)
  unk_arg_tracers = [t for t in tracers if not t.is_known()]
  out_avals_sharded = [v.aval for v in jaxpr.outvars]
  unk_params = dict(mesh=mesh, in_specs=unk_in_specs,
                    out_specs=unk_out_specs, jaxpr=jaxpr,
                    check_vma=check_vma, manual_axes=manual_axes)
  out_avals = map(partial(_unshard_aval, mesh, check_vma), unk_out_specs,
                  out_avals_sharded)
  out_tracers = [pe.JaxprTracer(trace, pe.PartialVal.unknown(a), None)
                 for a in out_avals]
  effs = core.filter_named_axis_effects(jaxpr.effects, mesh.axis_names)
  eqn = pe.new_eqn_recipe(trace, (*const_tracers, *env_tracers, *unk_arg_tracers),
                          out_tracers, shard_map_p, unk_params,
                          effs, source_info_util.current())
  for t in out_tracers: t.recipe = eqn
  return merge_lists(out_knowns, out_tracers, out_consts)
pe.JaxprTrace.process_shard_map = _shard_map_partial_eval

def _shard_map_linearize(trace, shard_map_p, f: lu.WrappedFun,
                         tracers, mesh, in_specs, out_specs_thunk, check_vma,
                         manual_axes):
  primals, tangents = unzip2(map(trace.to_primal_tangent_pair, tracers))
  nzs_in = tuple(type(t) is not ad.Zero for t in tangents)
  f_primal, linearize_outs_thunk = ad.linearize_subtrace(f, trace.tag, nzs_in, f.debug_info)
  f_primal = _promote_scalar_residuals_lin(f_primal, linearize_outs_thunk)
  all_names = _all_newly_manual_mesh_names(mesh, manual_axes)

  @as_hashable_function(closure=linearize_outs_thunk)
  def fwd_out_specs_thunk():
    res_avals, _, _, _, in_fwd, out_fwd = linearize_outs_thunk()
    res_avals = [r for r, f1, f2 in zip(res_avals, in_fwd, out_fwd)
                 if f1 is None and f2 is None]
    out_specs = out_specs_thunk()
    if check_vma:
      res_specs = [P(order_wrt_mesh(mesh, a.vma)) for a in res_avals]
    else:
      res_specs = [P(all_names)] * len(res_avals)
    return (*res_specs, *out_specs)
  fwd_params = dict(
      mesh=mesh, in_specs=in_specs,
      out_specs_thunk=fwd_out_specs_thunk, check_vma=check_vma,
      manual_axes=manual_axes)
  all_fwd_results = shard_map_p.bind_with_trace(
      trace.parent_trace, (f_primal, *primals), fwd_params)
  res_avals, nzs_out, lin_jaxpr, env, in_fwd, out_fwd = linearize_outs_thunk()
  num_res_out = sum(f1 is None and f2 is None for f1, f2 in zip(in_fwd, out_fwd))
  non_fwd_res = all_fwd_results[:num_res_out]
  primals_out = all_fwd_results[num_res_out:]
  residuals = subs_list2(in_fwd, out_fwd, primals, primals_out, non_fwd_res)
  args_to_promote = [getattr(aval, 'shape', ()) == () and f1 is None and f2 is None
                     for aval, f1, f2 in zip(res_avals, in_fwd, out_fwd)]
  with (_extend_axis_env(mesh, manual_axes),
        use_abstract_mesh(_as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes))),
        config._check_vma(check_vma)):
    lin_jaxpr = _promote_scalar_residuals_jaxpr(lin_jaxpr, args_to_promote)
  out_specs = out_specs_thunk()
  res_avals2 = [r for r, f1, f2 in zip(res_avals, in_fwd, out_fwd)
                if f1 is None and f2 is None]
  res_avals_iter = iter(res_avals2)
  res_specs = []
  for f1, f2 in zip(in_fwd, out_fwd):
    if f1 is not None:
      res_specs.append(in_specs[f1])
    elif f2 is not None:
      res_specs.append(out_specs[f2])
    else:
      if check_vma:
        res_vma = next(res_avals_iter).vma
        res_specs.append(P(order_wrt_mesh(mesh, res_vma)))
      else:
        res_specs.append(P(all_names))
  new_in_specs = (*res_specs, *(P(),) * len(env),
                  *(ax for ax, nz in zip(in_specs, nzs_in) if nz))
  tangent_out_specs = tuple(ax for ax, nz in zip(out_specs_thunk(), nzs_out)
                            if nz)
  @as_hashable_function(closure=tangent_out_specs)
  def tangent_out_specs_thunk():
    return tangent_out_specs
  tangent_params = dict(
      mesh=mesh, in_specs=new_in_specs, out_specs_thunk=tangent_out_specs_thunk,
      check_vma=check_vma, manual_axes=manual_axes)

  # TODO(mattjj): avoid round-tripping the jaxpr through eval_jaxpr here
  def f_tangent(*args):
    return core.eval_jaxpr(lin_jaxpr, (), *args)

  nz_tangents_in = [t for (t, nz) in zip(tangents, nzs_in) if nz]
  nz_tangents_out = shard_map_p.bind_with_trace(
      trace.tangent_trace,
      (lu.wrap_init(f_tangent, debug_info=lin_jaxpr.debug_info),
       *residuals, *env, *nz_tangents_in), tangent_params)
  nz_tangents_out_iter = iter(nz_tangents_out)
  tangents_out = [next(nz_tangents_out_iter) if nz else ad.Zero.from_primal_value(primal)
                  for nz, primal in zip(nzs_out, primals_out)]
  return map(partial(ad.maybe_linearize_tracer, trace), primals_out, nzs_out, tangents_out)
ad.LinearizeTrace.process_shard_map = _shard_map_linearize

@lu.transformation2
def _promote_scalar_residuals_lin(f, linearize_outs_thunk, *args, **kwargs):
  ans = f(*args, **kwargs)
  _, _, _, _, in_fwd, out_fwd = linearize_outs_thunk()
  num_res_out = sum(f1 is None and f2 is None for f1, f2 in zip(in_fwd, out_fwd))
  residuals = ans[:num_res_out]
  primals = ans[num_res_out:]
  residuals = [jax.lax.broadcast(x, (1,)) if not getattr(x, 'shape', ()) else x
               for x in residuals]
  return *residuals, *primals

@lu.transformation2
def _promote_scalar_residuals(f: Callable, *args, **kwargs):
  jaxpr, (in_fwds, out_fwds, out_pvals, out_consts, env) = f(*args, **kwargs)
  which = [f1 is None and f2 is None and not v.aval.shape
           for f1, f2, v in zip(in_fwds, out_fwds, jaxpr.constvars)]
  jaxpr = _promote_scalar_residuals_jaxpr(jaxpr, which)
  out_consts = [jax.lax.broadcast(x, (1,)) if not getattr(x, 'shape', ()) else x
                for x in out_consts]
  return jaxpr, (in_fwds, out_fwds, out_pvals, out_consts, env)

def _promote_scalar_residuals_jaxpr(jaxpr: core.Jaxpr, which: Sequence[bool]):
  def fun(*res_and_args):
    res, args = split_list(res_and_args, [len(jaxpr.constvars)])
    res = [_rem_singleton(x) if w else x for x, w in zip(res, which)]
    return core.eval_jaxpr(jaxpr, res, *args)
  res_avals = [core.unmapped_aval(1, 0, v.aval) if w else v.aval
               for v, w in zip(jaxpr.constvars, which)]
  in_avals = [*res_avals, *[v.aval for v in jaxpr.invars]]
  jaxpr, _, _, () = pe.trace_to_jaxpr_dynamic(
      lu.wrap_init(fun, debug_info=jaxpr.debug_info), in_avals)
  return jaxpr


def _unmentioned2(mesh: Mesh, spec, manual_axes: frozenset[AxisName]
                  ) -> list[AxisName]:
  # We use a filtered-down version of unmentioned to avoid defensive-psum over
  # more chips than required in the transpose-no-check-vma case.
  name_set = _spec_to_vma(spec)
  return [n for n in _all_mesh_names_except_spmd(mesh, manual_axes)
          if n not in name_set]


def _shard_map_transpose(out_cts, *args,
                         jaxpr: core.Jaxpr, mesh, in_specs, out_specs,
                         check_vma, manual_axes):
  mb_div = lambda x, y: x / y if y != 1 else x
  out_cts = [
      ad.Zero(_shard_aval(mesh, manual_axes, check_vma, sp, x.aval))
      if type(x) is ad.Zero else x if check_vma or dtypes.dtype(x) == dtypes.float0
      else mb_div(x, prod(map(mesh.shape.get, _unmentioned2(mesh, sp, manual_axes))))
      for sp, x in zip(out_specs, out_cts)
  ]
  args = tuple(x if type(x) is not ad.UndefinedPrimal else
               ad.UndefinedPrimal(
                   _shard_aval(mesh, manual_axes, check_vma, sp, x.aval))
               for sp, x in zip(in_specs, args))
  all_args, in_tree = tree_flatten((out_cts, args))

  def fun_trans_callable(out_cts, args):
    # TODO(mattjj): when #26811 lands, delete this and just run backward_pass
    in_undef = map(ad.is_undefined_primal, args)
    res, undefs = partition_list(in_undef, args)
    jaxpr_known, jaxpr_unknown, _, _ = pe.partial_eval_jaxpr_nounits(
        pe.close_jaxpr(jaxpr), in_undef, False)
    res_reshaped = core.jaxpr_as_fun(jaxpr_known)(*res)
    in_cts = ad.backward_pass(
        jaxpr_unknown.jaxpr, False, (), (*res_reshaped, *undefs), out_cts
    )[len(res_reshaped):]
    _, in_ct_specs = partition_list(in_undef, in_specs)
    in_cts = [ad.Zero(_unshard_aval(mesh, check_vma, sp, x.aval))
              if type(x) is ad.Zero else x if check_vma
              else jax.lax.psum(x, tuple(_unmentioned2(mesh, sp, manual_axes)))
              for sp, x in zip(in_ct_specs, in_cts)]
    res_zeros = [ad_util.zero_from_primal(r) for r in res]
    return merge_lists(in_undef, res_zeros, in_cts)

  fun_trans = lu.wrap_init(fun_trans_callable, debug_info=jaxpr.debug_info)
  fun_trans, nz_arg_cts = ad.nonzero_outputs(fun_trans)
  fun_trans_flat, out_tree = api_util.flatten_fun_nokwargs(fun_trans, in_tree)

  new_in_specs = (
      [n for n, x in zip(out_specs, out_cts) if type(x) is not ad.Zero] +
      [n for n, x in zip(in_specs, args) if type(x) is not ad.UndefinedPrimal])

  def new_out_specs_thunk():
    return tuple(sp for sp, nz in zip(in_specs, nz_arg_cts()) if nz)

  try:
    out_flat = shard_map_p.bind(
        fun_trans_flat, *all_args, mesh=mesh, in_specs=tuple(new_in_specs),
        out_specs_thunk=new_out_specs_thunk, check_vma=check_vma,
        manual_axes=manual_axes)
  except (FloatingPointError, ZeroDivisionError) as e:
    print("Invalid nan value encountered in the backward pass of a shard_map "
          "function. Calling the de-optimized backward pass.")
    try:
      # TODO(mattjj): Remove this and do `fun_trans.call_wrapped(out_cts, args)`
      # in eager mode so that output of shmap are not manual.
      with jax.disable_jit(True):
        _ = shard_map_p.bind(
            fun_trans_flat, *all_args, mesh=mesh, in_specs=tuple(new_in_specs),
            out_specs_thunk=new_out_specs_thunk, check_vma=check_vma,
            manual_axes=manual_axes)
    except (FloatingPointError, ZeroDivisionError) as e2:
      raise e2 from None
    else:
      api_util._raise_no_nan_in_deoptimized(e)
  return tree_unflatten(out_tree(), out_flat)
ad.primitive_transposes[shard_map_p] = _shard_map_transpose

# Remat

def _partial_eval_jaxpr_custom_rule(
    saveable: Callable[..., pe.RematCases_], unks_in: Sequence[bool],
    inst_in: Sequence[bool], eqn: core.JaxprEqn
) -> tuple[core.JaxprEqn, core.JaxprEqn, Sequence[bool], Sequence[bool],
           list[core.Var]]:
  jaxpr, mesh = eqn.params['jaxpr'], eqn.params['mesh']
  check_vma, manual_axes = eqn.params['check_vma'], eqn.params['manual_axes']
  with (_extend_axis_env(mesh, manual_axes), config._check_vma(check_vma),
        use_abstract_mesh(_as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes)))):
    jaxpr_known, jaxpr_staged, unks_out, inst_out, num_res = \
        pe.partial_eval_jaxpr_custom(jaxpr, unks_in, inst_in, False, False, saveable)
  num_out_primals = len(jaxpr_known.outvars) - num_res
  in_fwd = pe._jaxpr_forwarding(jaxpr_known)[num_out_primals:]
  out_vars, res_vars = split_list(jaxpr_known.outvars, [num_out_primals])
  idx_map = {id(v): i for i, v in enumerate(out_vars)}
  out_fwd = [idx_map.get(id(v)) for v in res_vars]
  which = [f1 is None and f2 is None for f1, f2 in zip(in_fwd, out_fwd)]
  mesh = eqn.params['mesh']
  with (_extend_axis_env(mesh, manual_axes),
        use_abstract_mesh(_as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes))),
        config._check_vma(check_vma)):
    jaxpr_known = pe.prune_jaxpr_outputs(jaxpr_known, [True] * num_out_primals + which)
    jaxpr_known, jaxpr_staged = _add_reshapes(which, jaxpr_known, jaxpr_staged)
  jaxpr_known = core.remove_named_axis_effects(jaxpr_known, mesh.axis_names)
  jaxpr_staged = core.remove_named_axis_effects(jaxpr_staged, mesh.axis_names)
  ins_known, _ = partition_list(unks_in, eqn.invars)
  out_binders_known, _ = partition_list(unks_out, eqn.outvars)
  _, ins_staged = partition_list(inst_in, eqn.invars)
  _, out_binders_staged = partition_list(inst_out, eqn.outvars)
  newvar = core.gensym()
  residuals, staged_in_res_specs = [], []
  for var, w in zip(jaxpr_staged.invars[:num_res], which):
    if w:
      rn = (P(order_wrt_mesh(mesh, var.aval.vma))  # type: ignore
            if check_vma else P(_all_newly_manual_mesh_names(mesh, manual_axes)))
      residuals.append(newvar(_unshard_aval(mesh, check_vma, rn, var.aval)))
      staged_in_res_specs.append(rn)
  if check_vma:
    out_res_specs_known = [P(order_wrt_mesh(mesh, var.aval.vma))  # type: ignore
                           for var, o in zip(res_vars, out_fwd) if o is None]
  else:
    out_res_specs_known = [
        P(_all_newly_manual_mesh_names(mesh, manual_axes))] * sum(which)
  params_known, params_staged = _pe_custom_params(
      unks_in, inst_in, map(op.not_, unks_out), inst_out, in_fwd, out_fwd,
      out_res_specs_known, staged_in_res_specs,
      dict(eqn.params, jaxpr=jaxpr_known), dict(eqn.params, jaxpr=jaxpr_staged))
  eqn_known = pe.new_jaxpr_eqn(ins_known, [*out_binders_known, *residuals],
                               eqn.primitive, params_known, jaxpr_known.effects,
                               eqn.source_info, eqn.ctx)
  full_res = subs_list2(in_fwd, out_fwd, ins_known, out_binders_known, residuals)
  eqn_staged = pe.new_jaxpr_eqn([*full_res, *ins_staged], out_binders_staged,
                                eqn.primitive, params_staged,
                                jaxpr_staged.effects, eqn.source_info, eqn.ctx)
  assert len(eqn_staged.invars) == len(jaxpr_staged.invars)
  new_inst = [x for x, inst in zip(eqn.invars, inst_in)
              if type(x) is core.Var and not inst]
  new_inst += [out_binders_known[f] for f in {i for i in out_fwd if i is not None}]
  return eqn_known, eqn_staged, unks_out, inst_out, new_inst + residuals
pe.partial_eval_jaxpr_custom_rules[shard_map_p] = \
    _partial_eval_jaxpr_custom_rule

def _add_reshapes(which: Sequence[bool],
                  jaxpr_known: core.Jaxpr,
                  jaxpr_staged: core.Jaxpr) -> tuple[core.Jaxpr, core.Jaxpr]:
  # add singleton axes to residuals which are from jaxpr_known and are scalars
  which_ = [w and not v.aval.shape  # pytype: disable=attribute-error
            for w, v in zip(which, jaxpr_staged.invars[:len(which)])]
  if not any(which_): return jaxpr_known, jaxpr_staged
  assert not jaxpr_known.constvars and not jaxpr_staged.constvars

  def known(*args):
    out = core.eval_jaxpr(jaxpr_known, (), *args)
    out_known, res = split_list(out, [len(out) - sum(which)])
    res = [_add_singleton(x) if not x.shape else x for x in res]
    return [*out_known, *res]
  avals_in = [v.aval for v in jaxpr_known.invars]
  jaxpr_known, _, (), () = pe.trace_to_jaxpr_dynamic(
      lu.wrap_init(known, debug_info=jaxpr_known.debug_info), avals_in)

  def staged(*args):
    res_, ins = split_list(args, [len(which)])
    res = [_rem_singleton(x) if w else x for x, w in zip(res_, which_)]
    return core.eval_jaxpr(jaxpr_staged, (), *res, *ins)
  res_avals = [core.unmapped_aval(1, 0, v.aval) if w else v.aval
               for w, v in zip(which_, jaxpr_staged.invars[:len(which)])]
  avals_in = [*res_avals, *[v.aval for v in jaxpr_staged.invars[len(which):]]]
  jaxpr_staged, _, (), () = pe.trace_to_jaxpr_dynamic(
      lu.wrap_init(staged, debug_info=jaxpr_staged.debug_info), avals_in)

  return jaxpr_known, jaxpr_staged

def _pe_custom_params(unks_in, inst_in, kept_outs_known, kept_outs_staged,
                      in_fwd, out_fwd, out_res_specs_known, staged_in_res_specs,
                      params_known, params_staged):
  # prune inputs to jaxpr_known according to unks_in
  in_specs_known, _ = partition_list(unks_in, params_known['in_specs'])
  _, out_specs_known = partition_list(kept_outs_known, params_known['out_specs'])
  out_specs_known = out_specs_known + out_res_specs_known
  assert len(out_specs_known) == len(params_known['jaxpr'].outvars)
  new_params_known = dict(params_known, in_specs=tuple(in_specs_known),
                          out_specs=tuple(out_specs_known))

  # added num_res new inputs to jaxpr_staged, pruning according to inst_in
  _, in_specs_staged = partition_list(inst_in, params_staged['in_specs'])
  iter_staged = iter(staged_in_res_specs)
  res_specs = [in_specs_known[f1] if f1 is not None else
               out_specs_known[f2] if f2 is not None else
               next(iter_staged) for f1, f2 in zip(in_fwd, out_fwd)]

  in_specs_staged = res_specs + in_specs_staged
  _, out_specs_staged = partition_list(kept_outs_staged, params_staged['out_specs'])
  new_params_staged = dict(params_staged, in_specs=tuple(in_specs_staged),
                           out_specs=tuple(out_specs_staged))
  return new_params_known, new_params_staged

# TODO(mattjj): remove this mechanism when we revise mesh scopes
def _all_mesh_names_except_spmd(
    mesh: Mesh, manual_axes: frozenset[AxisName]) -> tuple[AxisName, ...]:
  axis_env = core.get_axis_env()
  spmd_names = axis_env.spmd_axis_names
  return tuple(name for name in mesh.axis_names
               if name not in spmd_names and name in manual_axes)

def _all_newly_manual_mesh_names(
    mesh: Mesh, manual_axes: frozenset[AxisName]) -> tuple[AxisName, ...]:
  axis_env = core.get_axis_env()
  vmap_spmd_names = set(axis_env.spmd_axis_names)
  if not (ctx_mesh := get_abstract_mesh()).empty:
    mesh = ctx_mesh
    already_manual_names = set(ctx_mesh.manual_axes)
  else:
    # TODO(mattjj): remove this mechanism when we revise mesh scopes
    already_manual_names = set(axis_env.axis_sizes)  # may include vmap axis_names
  return tuple(name for name in mesh.axis_names
               if (name not in vmap_spmd_names | already_manual_names and
                   name in manual_axes))


# DCE

# TODO(mattjj): de-duplicate with pe.dce_jaxpr_call_rule, and/or _pmap_dce_rule?
def _shard_map_dce(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
  mesh = eqn.params["mesh"]
  manual_axes = eqn.params["manual_axes"]
  check_vma = eqn.params["check_vma"]
  with (_extend_axis_env(mesh, manual_axes), config._check_vma(check_vma),
        use_abstract_mesh(_as_manual_mesh(mesh, manual_axes | set(mesh.manual_axes)))):
    jaxpr, used_inputs = pe.dce_jaxpr(eqn.params['jaxpr'], used_outputs)
  if not any(used_inputs) and not any(used_outputs) and not jaxpr.effects:
    return used_inputs, None
  else:
    _, in_specs = partition_list(used_inputs, eqn.params['in_specs'])
    _, out_specs = partition_list(used_outputs, eqn.params['out_specs'])
    new_params = dict(eqn.params, jaxpr=jaxpr, in_specs=tuple(in_specs),
                      out_specs=tuple(out_specs))
    effs = core.filter_named_axis_effects(jaxpr.effects, mesh.axis_names)
    new_eqn = pe.new_jaxpr_eqn(
        [v for v, used in zip(eqn.invars, used_inputs) if used],
        [x for x, used in zip(eqn.outvars, used_outputs) if used],
        eqn.primitive, new_params, effs, eqn.source_info, eqn.ctx)
    return used_inputs, new_eqn
pe.dce_rules[shard_map_p] = _shard_map_dce

# Implementing pmap in terms of shard_map

def pmap(f, axis_name=None, *, in_axes=0, out_axes=0,
         static_broadcasted_argnums=(), devices=None, backend=None,
         axis_size=None, donate_argnums=(), global_arg_shapes=None):
  devices = tuple(devices) if devices is not None else devices
  axis_name, static_broadcasted_tuple, donate_tuple = _shared_code_pmap(
      f, axis_name, static_broadcasted_argnums, donate_argnums, in_axes, out_axes)

  def infer_params(*args, **kwargs):
    p = _prepare_pmap(f, in_axes, out_axes, static_broadcasted_tuple,
                      donate_tuple, devices, backend, axis_size, args, kwargs)
    for arg in p.flat_args:
      dispatch.check_arg(arg)
    mesh = Mesh(_get_devices(p, backend), (axis_name,))
    _pmapped, in_specs, out_specs = _cached_shard_map(
        p.flat_fun, mesh, p.in_axes_flat, p.out_axes_thunk, axis_name)
    flat_global_args = host_local_array_to_global_array(
        p.flat_args, mesh, list(in_specs))
    jitted_f = jax.jit(
        _pmapped,
        donate_argnums=[i for i, val in enumerate(p.donated_invars) if val])
    return jitted_f, flat_global_args, p.out_tree, mesh, out_specs

  def wrapped(*args, **kwargs):
    (jitted_f, flat_global_args, out_tree, mesh,
     out_specs) = infer_params(*args, **kwargs)
    outs = jitted_f(*flat_global_args)
    outs = global_array_to_host_local_array(outs, mesh, out_specs())
    return tree_unflatten(out_tree(), outs)

  def lower(*args, **kwargs):
    jitted_f, _, _, _, _ = infer_params(*args, **kwargs)
    return jitted_f.lower(*args, **kwargs)
  wrapped.lower = lower

  return wrapped


@lu.cache
def _cached_shard_map(flat_fun, mesh, in_axes_flat, out_axes_thunk, axis_name):
  in_specs = tuple(map(partial(_axis_to_spec, axis_name), in_axes_flat))
  out_specs = lambda: map(partial(_axis_to_spec, axis_name), out_axes_thunk())
  fun = _handle_reshapes(flat_fun, in_axes_flat, out_axes_thunk)
  return (_shard_map(fun.call_wrapped, mesh=mesh, in_specs=in_specs,
                     out_specs=out_specs, check_vma=False,
                     axis_names=set(mesh.axis_names)),
          in_specs, out_specs)

@lu.transformation2
def _handle_reshapes(f, in_axes, out_axes_thunk, *args, **kwargs):
  args = tree_map(lambda x, ax: x if ax is None else jnp.squeeze(x, axis=ax),
                  list(args), list(in_axes))
  out = f(*args)
  return tree_map(lambda x, ax: x if ax is None else jnp.expand_dims(x, axis=ax),
                  list(out), list(out_axes_thunk()))

def _axis_to_spec(axis_name, ax):
  if isinstance(ax, int):
    specs = [None] * ax + [axis_name]
    return P(*specs)
  elif ax is None:
    return P()
  else:
    raise TypeError(ax)

def _get_devices(p, backend):
  if backend is not None and p.devices is None:
    devs = jax.devices(backend=backend)
  else:
    devs = jax.devices() if p.devices is None else p.devices
  if jax.process_count() > 1:
    return devs[:p.global_axis_size]
  return devs[:p.local_axis_size]