rocm_jax/jax/experimental/array.py

# Copyright 2021 Google LLC
#
# 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

import operator as op
import numpy as np
from typing import Sequence, Tuple, Callable, Union, Optional, cast, List

from jax import core
from jax._src import abstract_arrays
from jax._src import ad_util
from jax._src import api_util
from jax._src import dispatch
from jax._src import dtypes
from jax._src.lax import lax as lax_internal
from jax._src.config import config
from jax._src.util import prod, safe_zip
from jax._src.lib import xla_client as xc
from jax._src.api import device_put
from jax._src.numpy.ndarray import ndarray
from jax.interpreters import pxla, xla, mlir
from jax.experimental.sharding import (Sharding, SingleDeviceSharding,
                                       XLACompatibleSharding)

Shape = Tuple[int, ...]
Device = xc.Device
DeviceArray = xc.Buffer
Index = Tuple[slice, ...]
ArrayLike = Union[np.ndarray, DeviceArray]


class Shard:
  """A single data shard of an Array.

  Attributes:
    device : Which device this shard resides on.
    index : The index into the global array of this shard.
    replica_id : Integer id indicating which replica of the global array this
      shard is part of. Always 0 for fully sharded data
      (i.e. when there’s only 1 replica).
    data : The data of this shard. None if ``device`` is non-local.
  """

  def __init__(self, device: Device, sharding: Sharding, global_shape: Shape,
               data: Optional[Array] = None):
    self.device = device
    self._sharding = sharding
    self._global_shape = global_shape
    self.data = data

  def __repr__(self):
    try:
      return (f'Shard(device={repr(self.device)}, index={self.index}, '
              f'replica_id={self.replica_id}, data={self.data})')
    except ValueError:
      return f'Shard(device={repr(self.device)}, data={self.data})'

  @property
  def index(self) -> Index:
    try:
      device_indices_fn = self._sharding.device_indices
    except AttributeError:
      raise ValueError('Cannot calculate indices from sharding: '
                       f'{self._sharding}. Please create a device to index '
                       'mapping for your sharding.') from None
    index = device_indices_fn(self.device, self._global_shape)
    assert index is not None
    return index

  @property
  def replica_id(self) -> int:
    try:
      device_replica_id_fn = self._sharding.device_replica_id_map  # pytype: disable=attribute-error
    except AttributeError:
      raise ValueError('Cannot calculate replica ids from sharding: '
                       f'{self._sharding}. Please create a device to replica id '
                       'mapping for your sharding.') from None
    return device_replica_id_fn(self._global_shape)[self.device]


def _reconstruct_array(fun, args, arr_state, aval_state):
  """Method to reconstruct a device array from a serialized state."""
  np_value = fun(*args)
  np_value.__setstate__(arr_state)
  jnp_value = device_put(np_value)
  jnp_value.aval = jnp_value.aval.update(**aval_state)
  return jnp_value


class Array:
  # TODO(yashkatariya): Add __slots__ here.

  def __init__(self, aval: core.ShapedArray, sharding: Sharding,
               arrays: Union[Sequence[DeviceArray], Sequence[Array]],
               committed: bool, _skip_checks: bool = False):
    self.aval = aval
    self._sharding = sharding
    # Extract DeviceArrays from arrays with `SingleDeviceSharding` to keep the
    # code handling `self._arrays` simpler.
    # TODO(yashkatariya): This will be slower as it will happen during
    # `__init__` on single controller environment. Make it lazy.
    self._arrays: List[DeviceArray] = [a if isinstance(a, DeviceArray) else a._arrays[0]
                                       for a in arrays]
    # See https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
    # for what committed means.
    self._committed = committed
    self._npy_value = None

    # TODO(yashkatariya): Add a check here which checks if the expected shard
    # shape matches the shape of _arrays. A similar check exists for GDA.

    if not _skip_checks or config.jax_enable_checks:
      assert all(db.dtype == self.dtype for db in self._arrays), (
          "Input arrays to `Array` must have matching dtypes, "
          f"got: {[db.dtype for db in self._arrays]}, aval type: {self.dtype}")

    # Don't rearrange if skip_checks is enabled because this assumes that the
    # input buffers are already arranged properly. This usually happens when
    # Array's are created as output of a JAX transformation
    # (like pjit, xmap, etc).
    if not _skip_checks:
      addressable_device_assignment = self.sharding._addressable_device_assignment
      # Rearrange arrays based on the device assignment.
      if isinstance(sharding, XLACompatibleSharding):
        if len(self._arrays) != len(addressable_device_assignment):
          raise ValueError(
              f"Expected {len(addressable_device_assignment)} per-device arrays "
              "(this is how many devices are addressable by the sharding), but "
              f"got {len(self._arrays)}")
        device_to_buffer = {db.device().id: db for db in self._arrays}
        try:
          self._arrays = [device_to_buffer[device.id]
                          for device in addressable_device_assignment]
        except KeyError as e:
          array_device_ids = set(a.device().id for a in self._arrays)
          addressable_device_ids = set(d.id for d in addressable_device_assignment)
          diff = set(array_device_ids) - set(addressable_device_ids)
          raise ValueError(
              f"Some per-device arrays are placed on devices {diff}, which are "
              f"not used in the specified sharding {self.sharding}") from e

  @property
  def shape(self) -> Shape:
    return self.aval.shape

  @property
  def dtype(self):
    return self.aval.dtype

  @property
  def ndim(self):
    return len(self.shape)

  @property
  def size(self):
    return prod(self.shape)

  @property
  def sharding(self):
    return self._sharding

  def __str__(self):
    return str(self._value)

  def __len__(self):
    try:
      return self.shape[0]
    except IndexError as err:
      raise TypeError("len() of unsized object") from err  # same as numpy error

  def __bool__(self):
    return bool(self._value)

  def __nonzero__(self):
    return bool(self._value)

  def __float__(self):
    return self._value.__float__()

  def __int__(self):
    return self._value.__int__()

  def __complex__(self):
    return self._value.__complex__()

  def __hex__(self):
    assert self.ndim == 0, 'hex only works on scalar values'
    return hex(self._value)  # type: ignore

  def __oct__(self):
    assert self.ndim == 0, 'oct only works on scalar values'
    return oct(self._value)  # type: ignore

  def __index__(self):
    return op.index(self._value)

  def tobytes(self, order="C"):
    return self._value.tobytes(order)

  def tolist(self):
    return self._value.tolist()

  def __format__(self, format_spec):
    # Simulates behavior of https://github.com/numpy/numpy/pull/9883
    if self.ndim == 0:
      return format(self._value[()], format_spec)
    else:
      return format(self._value, format_spec)

  def __iter__(self):
    if self.ndim == 0:
      raise TypeError("iteration over a 0-d array")  # same as numpy error
    else:
      # chunk_iter is added to Array in lax_numpy.py similar to DA.
      return (sl for chunk in self._chunk_iter(100) for sl in chunk._unstack())  # type: ignore

  def item(self):
    if dtypes.issubdtype(self.dtype, np.complexfloating):
      return complex(self)
    elif dtypes.issubdtype(self.dtype, np.floating):
      return float(self)
    elif dtypes.issubdtype(self.dtype, np.integer):
      return int(self)
    elif dtypes.issubdtype(self.dtype, np.bool_):
      return bool(self)
    else:
      raise TypeError(self.dtype)

  def is_fully_replicated(self) -> bool:
    return self.shape == self._arrays[0].shape

  def __repr__(self):
    prefix = '{}('.format(self.__class__.__name__.lstrip('_'))
    if self.aval is not None and self.aval.weak_type:
      dtype_str = f'dtype={self.dtype.name}, weak_type=True)'
    else:
      dtype_str = f'dtype={self.dtype.name})'

    if self.is_fully_addressable() or self.is_fully_replicated():
      line_width = np.get_printoptions()["linewidth"]
      s = np.array2string(self._value, prefix=prefix, suffix=',',
                          separator=', ', max_line_width=line_width)
      last_line_len = len(s) - s.rfind('\n') + 1
      sep = ' '
      if last_line_len + len(dtype_str) + 1 > line_width:
        sep = ' ' * len(prefix)
      return f"{prefix}{s},{sep}{dtype_str}"
    else:
      return f"{prefix}{self.shape}, {dtype_str}"

  def is_fully_addressable(self) -> bool:
    return self.sharding.is_fully_addressable()

  def __array__(self, dtype=None, context=None):
    return np.asarray(self._value, dtype=dtype)

  def __dlpack__(self):
    from jax.dlpack import to_dlpack  # pylint: disable=g-import-not-at-top
    return to_dlpack(self)

  def __reduce__(self):
    fun, args, arr_state = self._value.__reduce__()
    aval_state = {'weak_type': self.aval.weak_type,
                  'named_shape': self.aval.named_shape}
    return (_reconstruct_array, (fun, args, arr_state, aval_state))

  # TODO(yashkatariya): Remove this method when everyone is using devices().
  def device(self) -> Device:
    self._check_if_deleted()
    device_set = self.sharding.device_set
    if len(device_set) == 1:
      single_device, = device_set
      return single_device
    raise ValueError('Length of devices is greater than 1. '
                     'Please use `.devices()`.')

  def devices(self) -> List[Device]:
    self._check_if_deleted()
    return list(self.sharding.device_set)

  @pxla.maybe_cached_property
  def addressable_shards(self) -> Sequence[Shard]:
    self._check_if_deleted()
    out = []
    for db in self._arrays:
      db = pxla._set_aval(db)
      device = db.device()
      # Wrap the device arrays in `Array` until C++ returns an Array instead
      # of a DA.
      array = Array(db.aval, SingleDeviceSharding(device), [db], committed=True,
                    _skip_checks=True)
      out.append(Shard(device, self.sharding, self.shape, array))
    return out

  def delete(self):
    if self._arrays is None:
      return
    for buf in self._arrays:
      buf.delete()
    self._arrays = None
    self._npy_value = None

  def is_deleted(self):
    return all(buf.is_deleted() for buf in self._arrays)

  def _check_if_deleted(self):
    if self._arrays is None:
      raise RuntimeError("Array has been deleted.")

  def block_until_ready(self):
    self._check_if_deleted()
    for db in self._arrays:
      db.block_until_ready()
    return self

  def copy_to_host_async(self):
    self._check_if_deleted()
    if self._npy_value is None:
      try:
        self.addressable_shards[0].replica_id
        replica_id_exists = True
      except ValueError:
        replica_id_exists = False

      for s in self.addressable_shards:
        if not replica_id_exists or s.replica_id == 0:
          s.data._arrays[0].copy_to_host_async()  # pytype: disable=attribute-error

  @property
  def _value(self) -> np.ndarray:
    self._check_if_deleted()

    if self._npy_value is None:
      if self.is_fully_replicated():
        self._npy_value = np.asarray(self._arrays[0])  # type: ignore
        return cast(np.ndarray, self._npy_value)

      if not self.is_fully_addressable():
        raise RuntimeError("Fetching value for `jax.Array` that spans "
                           "non-addressable devices is not possible. You can use "
                           "`jax.experimental.multihost_utils.process_allgather` "
                           "for this use case.")

      self.copy_to_host_async()
      npy_value = np.empty(self.shape, self.dtype)

      try:
        self.addressable_shards[0].replica_id
        replica_id_exists = True
      except ValueError:
        replica_id_exists = False

      for s in self.addressable_shards:
        if not replica_id_exists or s.replica_id == 0:
          npy_value[s.index] = np.asarray(s.data._arrays[0])  # type: ignore  # [union-attr]
      self._npy_value = npy_value  # type: ignore
    # https://docs.python.org/3/library/typing.html#typing.cast
    return cast(np.ndarray, self._npy_value)

# explicitly set to be unhashable. Same as what device_array.py does.
setattr(Array, "__hash__", None)

def make_array_from_callback(shape: Shape, sharding: Sharding,
                             data_callback: Callable[[Optional[Index]], ArrayLike]) -> Array:
  arrays = [
      device_put(data_callback(sharding.device_indices(device, shape)), device)
      for device in sharding.addressable_devices
  ]
  aval = core.ShapedArray(shape, arrays[0].dtype, weak_type=False)
  return Array(aval, sharding, arrays, committed=True)


core.pytype_aval_mappings[Array] = abstract_arrays.canonical_concrete_aval
xla.pytype_aval_mappings[Array] = op.attrgetter('aval')
xla.canonicalize_dtype_handlers[Array] = pxla.identity
api_util._shaped_abstractify_handlers[Array] = op.attrgetter('aval')
ad_util.jaxval_adders[Array] = lax_internal.add
ad_util.jaxval_zeros_likers[Array] = lax_internal.zeros_like_array
ndarray.register(Array)


def _array_mlir_constant_handler(val, canonicalize_types=True):
  return mlir.ir_constants(val._value,
                           canonicalize_types=canonicalize_types)
mlir.register_constant_handler(Array, _array_mlir_constant_handler)


def _device_put_array(x, device: Optional[Device]):
  # TODO(yashkatariya): Remove this restriction and the round trip via host
  # once lowering to XLA goes through `lower_mesh_computation`.
  assert x.is_fully_addressable()
  if isinstance(x.sharding, SingleDeviceSharding):
    x = dispatch._copy_device_array_to_device(pxla._set_aval(x._arrays[0]), device)
    return (x,)
  else:
    # Round trip via host if x is sharded. SDA also does a round trip via host.
    return dispatch._device_put_array(x._value, device)

dispatch.device_put_handlers[Array] = _device_put_array


def _array_pmap_shard_arg(x, devices, indices, mode):
  if isinstance(x.sharding, SingleDeviceSharding):
    return pxla._shard_device_array(x, devices, indices, mode)

  # If the sharding of Array does not match pmap's sharding then take the slow
  # path which is similar to what SDA does. This slow path reroute only happens
  # for `pmap`.
  if indices == tuple(x.sharding.devices_indices_map(x.shape).values()):
    return [buf if buf.device() == d else buf.copy_to_device(d)
            for buf, d in safe_zip(x._arrays, devices)]
  else:
    return pxla._shard_sharded_device_array_slow_path(x, devices, indices, mode)


def _array_shard_arg(x, devices, indices, mode):
  if mode == pxla.InputsHandlerMode.pmap:
    return _array_pmap_shard_arg(x, devices, indices, mode)
  else:
    return x._arrays
pxla.shard_arg_handlers[Array] = _array_shard_arg


def _array_global_result_handler(global_aval, out_sharding):
  if core.aval_has_custom_eltype(global_aval):
    return global_aval.dtype.global_sharded_result_handler(
        global_aval, out_sharding)
  else:
    return lambda bufs: Array(global_aval, out_sharding, bufs, committed=True,
                              _skip_checks=True)
pxla.global_result_handlers[(core.ShapedArray, pxla.OutputType.Array)] = _array_global_result_handler
pxla.global_result_handlers[(core.ConcreteArray, pxla.OutputType.Array)] = _array_global_result_handler


def _array_local_result_handler(aval, sharding, indices):
  if core.aval_has_custom_eltype(aval):
    return aval.dtype.local_sharded_result_handler(aval, sharding, indices)
  else:
    return lambda bufs: Array(aval, sharding, bufs, committed=True,
                              _skip_checks=True)
pxla.local_result_handlers[(core.ShapedArray, pxla.OutputType.Array)] = _array_local_result_handler
pxla.local_result_handlers[(core.ConcreteArray, pxla.OutputType.Array)] = _array_local_result_handler