# Copyright 2023 The T5X 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
#
# http://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.
"""Utilities for partitioning."""
import abc
import collections
import dataclasses
import functools
import typing
from typing import Any, Callable, Optional, Sequence, Set, Tuple, Union
from absl import logging
import cached_property
from flax import traverse_util
from flax.linen import partitioning as flax_partitioning
import jax
from jax import numpy as jnp
from jax import random
from jax.experimental import multihost_utils
from jax.experimental.mesh_utils import create_hybrid_device_mesh
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
from jax.sharding import PartitionSpec
import numpy as np
from t5x import train_state as train_state_lib
JaxDevice = jax.Device
TpuMesh = Tuple[int, int, int, int] # (x, y, z, num_cores).
OtherMesh = Tuple[int, int]
HardwareMesh = Union[TpuMesh, OtherMesh]
TrainState = train_state_lib.TrainState
LogicalAxisRules = Sequence[Tuple[str, Optional[str]]]
if typing.TYPE_CHECKING: # See b/163639353
cached_property = property # pylint: disable=invalid-name
else:
cached_property = cached_property.cached_property
[docs]class AxisNames(tuple):
"""Tuple of strings specifying name for each axis.
We create a separate class for this so JAX's pytree utilities can distinguish
it from a tuple that should be treated as a pytree, instead treating it as a
leaf.
"""
def __new__(cls, *names):
return tuple.__new__(AxisNames, names)
def __repr__(self):
return 'AxisNames%s' % tuple.__repr__(self)
[docs]def with_sharding_constraint(x, axis_resources):
"""Wrapper for lax.with_sharding_constraint, no-op on cpu or outside pjit."""
if jax.devices()[0].platform == 'cpu' or not global_mesh_defined():
return x
else:
return jax.lax.with_sharding_constraint(x, axis_resources)
# pjit Mesh creation functions.
# -----------------------------------------------------------------------------
[docs]def bounds_from_last_device(last_device: jax.Device) -> HardwareMesh:
"""Get the bound from the given last device."""
# Must be passed the device at the highest-coordinate corner of the
# relevant mesh, which is a requirement we know is satisfied by the last
# device in jax.devices().
if hasattr(last_device, 'coords'):
x, y, z = last_device.coords
return x + 1, y + 1, z + 1, last_device.core_on_chip + 1
else:
# On non-TPU platforms, the "mesh" is hosts x devices per host in order
# to take advantage of faster within-host interconnect.
return jax.process_count(), jax.local_device_count()
[docs]def get_coords(device: jax.Device) -> HardwareMesh:
"""Returns the coordinates of the given device."""
if hasattr(device, 'coords'):
return (*device.coords, device.core_on_chip)
return (device.process_index, device.id % jax.local_device_count())
[docs]def global_mesh_defined():
"""Checks if global xmap/pjit mesh resource environment is defined."""
maps_env = jax.experimental.maps.thread_resources.env
return maps_env.physical_mesh.devices.shape != () # pylint: disable=g-explicit-bool-comparison
[docs]def get_mesh(model_parallel_submesh: HardwareMesh,
input_devices: Sequence[JaxDevice] = (),
input_local_devices: Sequence[JaxDevice] = (),
tile_by_host_if_needed: bool = True,
backend: Optional[str] = None) -> Mesh:
"""Construct an xmap/pjit Mesh for the given model-parallel submesh.
The resulting mesh has two resource axes: 'model', with the provided submesh
shape, and 'data', which covers the rest of the mesh.
Args:
model_parallel_submesh: a HardwareMesh spec, namely (x,y,z,core) on TPU for
a single model-parallel replica's "tile" in the physical device mesh. The
first three elements (`x`, `y`, and `z`) should be factors of the pod
slice; e.g., if you are using df_4x8, then `x` should be a factor of 4
(one of 1, 2, 4), `y` should be a factor of 8 (one of 1, 2, 4, 8), and `z`
must be 1, because TPU v3 slices are only 2D. `z` can be >1 for TPU v4
(and maybe later TPUs) that allow 3D slices. `core` is the number of cores
to use from each TPU node. As communication is usually fastest inside the
same node, if you need a tile of more than 1 core, then
you should first increase `core`: e.g., for TPU v3, (1,1,1,2) is better
than (2,1,1,1). To pick a good spec, try a few possible values until you
get high TPU utilization.
input_devices: the devices to use, will use jax.devices() if this is not
set.
input_local_devices: the local devices to use, will use jax.local_devices()
if this is not set.
tile_by_host_if_needed: JAX currently requires that the parts of any sharded
array that are located on one host's local devices form a single
contiguous slice. A best effort will be made to achieve this without
"tiling" the device assignment over hosts (which can reduce XLA collective
performance). If this flag is True, then the device assignment will be
tiled over hosts if necessary to satisfy this constraint and create a
buildable mesh; if false, mesh construction will fail instead.
backend: get devices from the pinned backend, if specified. This is
useful for explicitly specifying the devices other than relying on
jax_platform_name.
Returns:
A xmap / pjit Mesh containing the virtual device mesh with data, model axes.
"""
input_devices = input_devices or jax.devices(backend)
input_local_devices = input_local_devices or jax.local_devices(0, backend)
# Sort input_devices based on coords, as backends might not return devices
# in order.
last_device = sorted(input_devices, key=get_coords)[-1]
last_input_local_devices = sorted(input_local_devices, key=get_coords)[-1]
logging.info('last device coords : %r\nlast local device coords: %r',
get_coords(last_device), get_coords(last_input_local_devices))
global_hardware_mesh = bounds_from_last_device(last_device)
mesh_ndim = len(global_hardware_mesh)
local_hardware_mesh = bounds_from_last_device(last_input_local_devices)
mesh_err = (
f'each dimension of the model parallel submesh {model_parallel_submesh} '
'must be a factor of the corresponding dimension of the global device '
f'mesh {global_hardware_mesh}')
assert not any(
g % m
for g, m in zip(global_hardware_mesh, model_parallel_submesh)), mesh_err
assert not any(
g % l for g, l in zip(global_hardware_mesh, local_hardware_mesh))
devices = np.empty(global_hardware_mesh, dtype=object)
for device in input_devices:
device_coords = get_coords(device)
devices[device_coords] = device
tile_by_host = tile_by_host_if_needed
if len(global_hardware_mesh) == 4:
# enable contiguous local chunks without host tiling by making Z major
global_hardware_mesh = typing.cast(Tuple[int, int, int, int],
global_hardware_mesh)
model_parallel_submesh = typing.cast(Tuple[int, int, int, int],
model_parallel_submesh)
gx, gy, gz, gc = global_hardware_mesh
mx, my, mz, mc = model_parallel_submesh
if (mx == gx > 1 and my == mz == 1) or (mx == 1 and my == gy > 1 and
mz == gz > 1):
logging.info('ensuring YZ plane has a Z-major device order')
# YZ should be ZY
assert mc == gc, (mc, gc)
global_hardware_mesh = gx, gz, gy, gc
model_parallel_submesh = mx, mz, my, mc
devices = devices.swapaxes(1, 2)
tile_by_host = False
if (my == gy > 1 and mx == mz == 1) or (my == 1 and mx == gx > 1 and
mz == gz > 1):
logging.info('ensuring XZ plane has a Z-major device order')
# XZ should be ZX
assert mc == gc, (mc, gc)
global_hardware_mesh = gz, gy, gx, gc
model_parallel_submesh = mz, my, mx, mc
devices = devices.swapaxes(0, 2)
tile_by_host = False
if tile_by_host:
logging.warning(
'Tiling device assignment mesh by hosts, which may lead to '
'reduced XLA collective performance. To avoid this, modify '
'the model parallel submesh or run with more tasks per host.')
tile_err = (
'to tile the mesh by hosts, each dimension of the model parallel '
'submesh must be either a factor or a multiple of the corresponding '
'dimension of the per-host submesh')
def dh_dd_mh_md(g: int, m: int, l: int) -> Tuple[int, int, int, int]:
"""Split a global mesh dimension into four tiling components.
Args:
g: global mesh bounds dimension size
m: model-parallel submesh bounds dimension size
l: local submesh bounds dimension size
Returns:
The resulting tuple divides the dimension into the hosts component of
the data-parallel submesh, the devices component of the data-parallel
submesh, the hosts component of the model-parallel submesh, and the
devices component of the model-parallel submesh.
"""
d = g // m
if m >= l:
assert not m % l, tile_err
return (d, 1, m // l, l)
else:
assert not l % m, tile_err
return (d // (l // m), l // m, 1, m)
# e.g. [(x_data_hosts, x_data_devs, x_model_hosts, x_model_devs), ...]
dh_dd_mh_md_tups = map(dh_dd_mh_md, global_hardware_mesh,
model_parallel_submesh, local_hardware_mesh)
# reshape to e.g. (x_dh, x_dd, x_mh, x_md, y_dh, ...)
devices = devices.reshape(*(s for t in dh_dd_mh_md_tups for s in t)) # pylint: disable=g-complex-comprehension
# TODO(jekbradbury): reorder local subgroups for ring locality
# Transpose to [data_host], [data_device], [model_host], [model_device]
# block ordering e.g. (x_dh, y_dh, ..., x_dd, y_dd, ...)
devices = devices.transpose(*(4 * i for i in range(mesh_ndim)),
*(4 * i + 1 for i in range(mesh_ndim)),
*(4 * i + 2 for i in range(mesh_ndim)),
*(4 * i + 3 for i in range(mesh_ndim)))
else:
# e.g. [(x_data, x_model), (y_data, y_model), ...]
model_data_tups = [
(g // m, m)
for g, m in zip(global_hardware_mesh, model_parallel_submesh)
]
# reshape to e.g. (x_data, x_model, y_data, y_model...)
devices = devices.reshape(*(s for t in model_data_tups for s in t)) # pylint: disable=g-complex-comprehension
# TODO(jekbradbury): reorder small subgroups for ring locality
# transpose to e.g. (x_data, y_data, ..., x_model, ...)
devices = devices.transpose(*(2 * i for i in range(mesh_ndim)),
*(2 * i + 1 for i in range(mesh_ndim)))
# reshape to (data, model)
devices = devices.reshape(-1, np.prod(model_parallel_submesh))
global_mesh = Mesh(devices, ['data', 'model'])
logging.info('global_mesh axis_names: %s', global_mesh.axis_names)
logging.info('global_mesh devices: %s', global_mesh.devices)
logging.info('global_mesh devices shape: %s', global_mesh.devices.shape)
return global_mesh
[docs]def get_cpu_mesh() -> Mesh:
"""Trivial mesh for CPU Testing."""
devices = np.empty(
(jax.process_count(), jax.local_device_count()), dtype=object
)
for device in jax.devices():
devices[device.process_index, device.id % jax.local_device_count()] = device
return Mesh(devices, ['data', 'model'])
[docs]def get_gpu_mesh(num_partitions: int) -> Mesh:
"""Mesh for GPUs that preferentially places 'model' on NVLink."""
nvlink_size = jax.local_device_count()
dcn_size = jax.process_count()
nvlink_mp = min(num_partitions, nvlink_size)
nvlink_dp, extra1 = divmod(nvlink_size, nvlink_mp)
dcn_mp, extra2 = divmod(num_partitions, nvlink_mp)
assert not (extra1 or extra2), ('number of partitions on GPU must be a factor'
' or multiple of the number of local devices')
dcn_dp = dcn_size // dcn_mp
devices = create_hybrid_device_mesh(
mesh_shape=[nvlink_dp, nvlink_mp],
dcn_mesh_shape=[dcn_dp, dcn_mp],
process_is_granule=True)
global_mesh = Mesh(devices, ['data', 'model'])
logging.info('global_mesh axis_names: %s', global_mesh.axis_names)
logging.info('global_mesh devices: %s', global_mesh.devices)
return global_mesh
[docs]def default_mesh(
num_partitions: int,
model_parallel_submesh: Optional[HardwareMesh] = None,
backend: Optional[str] = None,
ici_mesh_shape: Optional[HardwareMesh] = None,
dcn_mesh_shape: Optional[HardwareMesh] = None,
) -> Mesh:
"""Attempt to return a default mesh for simple cases.
Args:
num_partitions: number of partitions to use, will be ignored if
model_parallel_submesh is provided.
model_parallel_submesh: 4-tuple that specifies the x,y,z,c submesh to use as
the model-parallel device tile.
backend: get devices from the pinned backend, if specified. This is useful
for explicitly specifying the devices other than relying on
jax_platform_name.
ici_mesh_shape: Shape of the logical mesh used for SPMD parallelism in each
slice. The meaning of each mesh axis is defined by mesh_axis_names, so
these two params must be the same length. If dcn_mesh_shape is present,
the overall mesh is the product of ici_mesh_shape and dcn_mesh_shape. For
example, an ici_mesh_shape of [2, 3, 4] with mesh_axis_names ['replica',
'data', 'model'] indicates 2-way replica parallelism, 3-way data
parallelism, and 4-way model parallelism over 24 devices. None, the
default, is equivalent to a sequence of ones and means that the model is
placed on a single device.
dcn_mesh_shape: Shape of the logical mesh used for SPMD parallelism over
multiple slices. The overall mesh is the product of ici_mesh_shape and
dcn_mesh_shape, and the meaning of each mesh axis is defined by
mesh_axis_names, so these three params must be the same length.
Returns:
xmap/pjit 2D Mesh with 'data', 'model' mesh axes if single-slice, otherwise
3D Mesh with 'replica', 'data', and 'model' mesh axes.
"""
devices = jax.devices(backend)
last_device = devices[-1]
platform = last_device.platform
device_kind = last_device.device_kind
bounds = bounds_from_last_device(last_device)
if ici_mesh_shape is not None and dcn_mesh_shape is not None:
device_mesh = create_hybrid_device_mesh(
ici_mesh_shape,
dcn_mesh_shape,
devices=devices,
)
multi_slice_global_mesh = Mesh(device_mesh, ['replica', 'data', 'model'])
logging.info(
'multi_slice_global_mesh axis_names: %s',
multi_slice_global_mesh.axis_names,
)
logging.info(
'multi_slice_global_mesh devices: %s', multi_slice_global_mesh.devices
)
logging.info(
'multi_slice_global_mesh devices shape: %s',
multi_slice_global_mesh.devices.shape,
)
return multi_slice_global_mesh
if model_parallel_submesh:
return get_mesh(model_parallel_submesh, backend=backend)
if platform == 'cpu':
return get_cpu_mesh()
elif platform == 'gpu':
return get_gpu_mesh(num_partitions)
mps = None
if device_kind in ('TPU v2', 'TPU v3'):
if num_partitions == 1:
mps = (1, 1, 1, 1)
elif num_partitions == 2:
mps = (1, 1, 1, 2)
elif num_partitions == 4:
mps = (2, 1, 1, 2)
elif num_partitions == 8:
mps = (2, 2, 1, 2)
elif num_partitions == 16:
mps = (4, 2, 1, 2)
# assume the use of megacore on TPU v4
elif (device_kind == 'TPU v4' or
device_kind == 'TPU v4 lite') and bounds[3] == 1:
if num_partitions == 1:
mps = (1, 1, 1, 1)
elif num_partitions == 2:
mps = (1, 2, 1, 1)
elif num_partitions == 4:
if bounds[0] >= 4:
mps = (4, 1, 1, 1)
else:
mps = (2, 2, 1, 1)
elif num_partitions == 8:
if bounds[2] >= 8:
mps = (1, 1, 8, 1)
else:
mps = (4, 2, 1, 1)
elif num_partitions == 16:
if bounds[2] >= 16:
mps = (1, 1, 16, 1)
elif bounds[0] >= 8:
mps = (8, 2, 1, 1)
elif bounds[0] >= 4:
mps = (4, 4, 1, 1)
else:
mps = (2, 2, 4, 1)
if mps is None:
raise ValueError(
'No default mesh for this configuration: specify '
'config.model_parallel_submesh explicitly. \n'
f'Platform: {platform}\n'
f'Device kind: {device_kind}\n'
f'Num partitions: {num_partitions}\n'
f'Bounds: {bounds}'
)
return get_mesh(mps, backend=backend)
# Data chunking helper.
# -----------------------------------------------------------------------------
[docs]@dataclasses.dataclass
class LocalChunkInfo:
# The logical slice of an array located on this host's local devices.
slice: Tuple[slice, ...]
# A unique index for this host/local chunk among chunks with the same slice.
replica_id: int
[docs]class LocalChunker:
"""Utility class to aid chunking of sharded arrays in multihost settings."""
def __init__(self, global_mesh: Mesh):
self.global_mesh = global_mesh
local_mesh = global_mesh.local_mesh
first_local_device = local_mesh.devices.reshape(-1)[0]
host_location = collections.OrderedDict(
zip(
global_mesh.shape.keys(),
list(zip(*np.nonzero(
global_mesh.devices == first_local_device)))[0]))
self.num_chunks = collections.OrderedDict()
self.chunk_ids = collections.OrderedDict()
self.mesh_axes = list(global_mesh.shape.keys())
for mesh_axis in self.mesh_axes:
num_devices_per_chunk = local_mesh.shape[mesh_axis]
self.num_chunks[mesh_axis] = (
global_mesh.shape[mesh_axis] // num_devices_per_chunk)
self.chunk_ids[mesh_axis] = (
host_location[mesh_axis] // num_devices_per_chunk)
[docs] def get_local_chunk_info(
self, global_shape: Tuple[int, ...],
mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:
"""Get the local chunk info for a given array shape and sharded axes.
Args:
global_shape: the global, unsharded shape of the array to chunk.
mesh_axes: a sequence of names (or None) of equal rank to `global_shape`
that specifies which mesh dimensions the array is sharded along.
Returns:
LocalChunkInfo containing the logical slices of the array found on this
host's local devices, as well as the replica index for this chunk among
chunks with the same slice. The latter is used to determine which
host should write this chunk during checkpointing.
"""
local_slice = [slice(None) for dim in global_shape]
sharded_mesh_axes = set()
for i, (mesh_axis, size) in enumerate(zip(mesh_axes, global_shape)):
if not mesh_axis:
continue
sharded_mesh_axes.add(mesh_axis)
if not isinstance(mesh_axis, str):
raise NotImplementedError('TODO(jekbradbury)')
chunk_id = self.chunk_ids[mesh_axis]
chunk_size = size // self.num_chunks[mesh_axis]
local_slice[i] = slice(chunk_id * chunk_size, (chunk_id + 1) * chunk_size)
replica_id = self.get_replica_id(sharded_mesh_axes)
return LocalChunkInfo(tuple(local_slice), replica_id)
[docs] def get_shard_id(self, sharded_mesh_axes: str | Set[Optional[str]]) -> int:
"""Given mesh axes used for sharding, computes current host's shard id.
To give an example, let's say there are two axes globally: replica, data,
and model, the mesh axes for sharding is ('replica', 'data'), which means we
are going to partition an array along 'replica' and 'data' axes.
The shard_id is to show the index of the current local host along the
sharding axes (in this example, it's 'replica' and 'data' axes).
More concretely, let's say we have 4 local hosts, and we use 'replica' and
'data' axes for data parallel (2 hosts along the replica axis, and 2 host
along the data axis). The host located in ('replica': 0, 'data': 0), we
should assign data shard-0 to it. For host ('replica': 0, 'data': 1), we
assign shard-1. For host ('replica': 1, 'data': 0), we assign shard-2.
For host ('replica': 1, 'data': 1), we assign shard-3.
Note: the host location along 'replica' and 'data' axes, e.g.,
('replica': 0, 'data': 0) is named chunk_id and stored in
self._local_chunker.chunk_ids[axis].
Args:
sharded_mesh_axes: the mesh axes for sharding.
Returns:
the index of the current local host along the sharding axes.
"""
if isinstance(sharded_mesh_axes, str):
sharded_mesh_axes = (sharded_mesh_axes,)
shard_id = 0
for mesh_axis in sharded_mesh_axes:
chunk_id = self.chunk_ids[mesh_axis]
shard_id = shard_id * self.num_chunks[mesh_axis] + chunk_id
return shard_id
[docs] def get_replica_id(self, sharded_mesh_axes: str | Set[Optional[str]]) -> int:
"""Given mesh axes used for sharding, computes current host's replica id.
To give an example, let's say there are two axes globally: data, and model,
the mesh axes for sharding is ('data', ), which means we are going to
partition an array along 'data' axis and replicate it along 'model' axis.
The replica_id is to show the index of the current local host along the
'model' axis.
Args:
sharded_mesh_axes: the mesh axes for sharding.
Returns:
the index of the current local host along the non-sharding axes (i.e.,
replicating axes).
"""
if isinstance(sharded_mesh_axes, str):
sharded_mesh_axes = (sharded_mesh_axes,)
replicated_mesh_axes = [
mesh_axis for mesh_axis in self.mesh_axes
if mesh_axis not in sharded_mesh_axes
]
replica_id = 0
for mesh_axis in replicated_mesh_axes:
chunk_id = self.chunk_ids[mesh_axis]
replica_id = replica_id * self.num_chunks[mesh_axis] + chunk_id
return replica_id
[docs]def standard_logical_axis_rules(
activation_partitioning_dims: int = 1,
parameter_partitioning_dims: int = 1,
additional_rules: Optional[LogicalAxisRules] = None) -> LogicalAxisRules:
"""Default sharding rules for T5X model in terms of logical axis names.
Args:
activation_partitioning_dims: enables 2-D activation sharding when set to 2.
parameter_partitioning_dims: enables 2-D parameter sharding when set to 2.
additional_rules: additional rules (a sequence of tuples) that will be
appended to the standard rules.
Returns:
Sequence of logical axis rules
"""
logging.info(
'`activation_partitioning_dims` = %d, `parameter_partitioning_dims` = %d',
activation_partitioning_dims, parameter_partitioning_dims)
if activation_partitioning_dims == 1 and parameter_partitioning_dims == 1:
rules = [
('batch', 'data'),
('vocab', 'model'),
('embed', None),
('mlp', 'model'),
('heads', 'model'),
('kv', None),
('joined_kv', 'model'), # joined heads+kv dim in 2D attn param layouts
]
elif activation_partitioning_dims == 2 and parameter_partitioning_dims == 1:
rules = [
('batch', 'data'),
('vocab', 'model'),
('mlp', 'model'),
('heads', 'model'),
('kv', None),
('joined_kv', 'model'),
('embed', 'model'),
]
elif activation_partitioning_dims == 1 and parameter_partitioning_dims == 2:
rules = [
('batch', 'data'),
('vocab', 'model'),
('mlp', 'model'),
('heads', 'model'),
('kv', None),
('joined_kv', 'model'),
('embed', 'data'),
]
elif activation_partitioning_dims == 2 and parameter_partitioning_dims == 2:
rules = [
('batch', 'data'),
('vocab', 'model'),
('mlp', 'model'),
('heads', 'model'),
('kv', None),
('joined_kv', 'model'),
('embed', 'model'),
('embed', 'data'),
]
else:
raise ValueError(
f'`activation_partitioning_dims` = {activation_partitioning_dims} '
f'`parameter_partitioning_dims` = {parameter_partitioning_dims} '
'is not supported.')
# Add the common rules for the replicated logical axes names.
replicated_rules = [
('relpos_buckets', None),
('abspos_buckets', None),
('length', None),
('layers', None),
('stack', None),
('mlp_activations', None),
]
rules.extend(replicated_rules)
if additional_rules:
rules.extend(additional_rules)
return rules
# NB: This needs to be top-level for the jax compilation cache.
def _id_fn(x, ix):
"""Identity function for copying parameters to the devices, sharded."""
# A pure identity such as `lambda x, *: x` can get optimized away, so we
# include a random.split as a cheap function that cannot be optimized away.
y = random.split(random.PRNGKey(jnp.array(ix, dtype=jnp.uint32)))
return x, y
[docs]@dataclasses.dataclass
class DataLayout:
"""Represents data layout for the partitioned model."""
batch_size: int
shard_id: int
num_shards: int
is_first_host_in_replica_set: bool
PartitionedCallable = Callable[..., Any]
CompiledPartitionedCallable = Callable[..., Any]
[docs]class BasePartitioner(metaclass=abc.ABCMeta):
"""Interface for partitioning computations across hardware devices."""
def __init__(
self,
num_partitions: Optional[int] = None,
model_parallel_submesh: Optional[HardwareMesh] = None,
params_on_devices: bool = True,
backend: Optional[str] = None,
ici_mesh_shape: Optional[HardwareMesh] = None,
dcn_mesh_shape: Optional[HardwareMesh] = None,
):
"""Configures the partitioner.
Args:
num_partitions: the number of partitions to use. Ignored if
`model_parallel_submesh` is provided.
model_parallel_submesh: 4-tuple that specifies the x,y,z,c submesh to use
as the model-parallel device tile. This submesh is used for the larger
of the two parameter dimensions, and, if 2-D activation sharding is
enabled, for the model dimension of activations. The rest of the mesh is
used for data parallelism and, if 2-D parameter sharding is enabled, the
other parameter dimension.
params_on_devices: whether to keep the params on devices, if False -
params stay in the host memory. Note that some partitioners might ignore
this setting, for example if they don't support storing all params on
device memory.
backend: get devices from the pinned backend, if specified. This is useful
for explicitly specifying the devices other than relying on
jax_platform_name.
ici_mesh_shape: Shape of the logical mesh used for SPMD parallelism in
each slice. The meaning of each mesh axis is defined by mesh_axis_names,
so these two params must be the same length. If dcn_mesh_shape is
present, the overall mesh is the product of ici_mesh_shape and
dcn_mesh_shape. For example, an ici_mesh_shape of [2, 3, 4] with
mesh_axis_names ['replica', 'data', 'mdl'] indicates 2-way replica
parallelism, 3-way data parallelism, and 4-way model parallelism over 24
devices. None, the default, is equivalent to a sequence of ones and
means that the model is placed on a single device.
dcn_mesh_shape: Shape of the logical mesh used for SPMD parallelism over
multiple slices. The overall mesh is the product of ici_mesh_shape and
dcn_mesh_shape, and the meaning of each mesh axis is defined by
mesh_axis_names, so these three params must be the same length.
"""
if not num_partitions and not model_parallel_submesh:
raise ValueError('At least one of `num_partitions` or '
'`model_parallel_submesh` must be set.')
if model_parallel_submesh is not None and len(model_parallel_submesh) != 4:
logging.error(
(
'`model_parallel_submesh` must be either None or a 4-tuple. Got'
' `model_parallel_submesh`=%r. A ValueError will be raised'
' beginning March 1, 2022.'
),
model_parallel_submesh,
)
if bool(num_partitions) and bool(model_parallel_submesh):
logging.error(
'At most one of `num_partitions` or `model_parallel_submesh` can be '
'set. Got `num_partitions=%r` and `model_parallel_submesh`=%r. A '
'ValueError will be raised beginning March 21, 2022.',
num_partitions,
model_parallel_submesh,
)
self._num_partitions = num_partitions
self._model_parallel_submesh = model_parallel_submesh
self._params_on_devices = params_on_devices
if ici_mesh_shape is None or dcn_mesh_shape is None:
self._data_axis = 'data'
else:
self._data_axis = ('replica', 'data')
self._backend = backend
self._ici_mesh_shape = ici_mesh_shape
self._dcn_mesh_shape = dcn_mesh_shape
@property
def mesh(self) -> Mesh:
raise NotImplementedError
@property
def data_partition_spec(self) -> PartitionSpec:
return PartitionSpec(self._data_axis)
@property
def data_mesh_size(self) -> int:
"""Data mesh size.
Data mesh size is defined as the number of global devices involved to
carry out data parallel. Let's say we have a global mesh: ('replica': 2,
'data': 4, 'model': 2), and axes 'replica' and 'data' are responsible for
the data parallel, that means we have 2*4 = 8 devices involved - i.e., data
mesh size is 8.
Returns:
the id of the shard for the axes being replicated among the devices used
to shard the sharded_mesh_axes.
"""
data_submesh_sizes = (
[self.mesh.shape[self._data_axis]]
if isinstance(self._data_axis, str)
else [self.mesh.shape[axis] for axis in self._data_axis]
)
data_mesh_size = functools.reduce(lambda x, y: x * y, data_submesh_sizes)
return data_mesh_size
@property
def data_shards(self) -> int:
"""Number of data shards.
Let's say we are dealing with 2 slices of df4x2 TPUs. In data pipeline
we need prepare / send one data shard to each local host. This means, we
need 4 shards since we have 4 local hosts. How to infer the number of hosts
from mesh information? In this case, we have a global mesh: ('replica': 2,
'data': 8, 'model': 2). Each local host (i.e., df2x2) has this local mesh:
('replica': 1, 'data': 4, 'model': 2). By dividing global mesh with local
mesh, we can get the count of hosts.
Returns:
Number of data shards. Each shard will be sent to one local host.
"""
data_chunks = (
[self._local_chunker.num_chunks[self._data_axis]]
if isinstance(self._data_axis, str)
else [self._local_chunker.num_chunks[axis] for axis in self._data_axis]
)
data_shards = functools.reduce(lambda x, y: x * y, data_chunks)
return data_shards
@property
def data_shard_id(self) -> int:
"""Data shard id for the current host.
Returns:
Index of data shard that will be sent to the current local host.
"""
return self._local_chunker.get_shard_id(self._data_axis)
[docs] def get_data_layout(
self, batch_size: Optional[int] = None, host_index: Optional[int] = None
) -> DataLayout:
"""Returns filled `DataLayout` based on the partitioned model layout.
Args:
batch_size: if set, indicates the requested batch size. The exception will
be raised if this batch size is not compatible with the layout. If not
set, the batch size is inferred from the layout.
host_index: indicates the host index to use for the calculations, if not
set - use JAX-provided one. Should be in [0, num_hosts) interval and the
order should match the order of corresponding CPU devices in
`jax.devices()`.
Returns:
Filled `DataLayout` structure.
"""
if host_index is not None:
raise NotImplementedError('Explicit host_index is not yet implemented.')
if self._data_axis is None:
return DataLayout(
batch_size=batch_size,
shard_id=0,
num_shards=1,
is_first_host_in_replica_set=(jax.process_index() == 0))
batch_size = batch_size or self.data_mesh_size
if batch_size % self.data_mesh_size:
raise ValueError(
f'Batch size ({batch_size}) must be divisible by corresponding '
f'data mesh size ({self.data_mesh_size}).'
)
if batch_size % self.data_shards:
raise ValueError(
f'Batch size ({batch_size}) must be divisible by number of '
f'data shards ({self.data_shards}).'
)
replica_id = self._local_chunker.get_replica_id(self._data_axis)
return DataLayout(
batch_size=int(batch_size),
shard_id=int(self.data_shard_id),
num_shards=int(self.data_shards),
is_first_host_in_replica_set=(replica_id == 0),
)
[docs] def get_local_chunk_info(
self, global_shape: Tuple[int, ...],
mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:
"""Returns the local chunk info for a given array shape and sharded axes."""
return self._local_chunker.get_local_chunk_info(global_shape, mesh_axes)
@property
def params_on_devices(self):
return self._params_on_devices
@params_on_devices.setter
def params_on_devices(self, value):
self._params_on_devices = value
[docs] def move_params_to_devices(self, train_state: TrainState,
train_state_axes: TrainState) -> TrainState:
"""Moves the optimizer parameters to devices."""
p_id_fn = self.partition(
_id_fn,
in_axis_resources=(train_state_axes, None),
out_axis_resources=(train_state_axes, None),
donate_argnums=(0,))
if jax.process_count() > 1:
train_state = host_local_array_to_global_array(
train_state, self.mesh, train_state_axes
)
train_state, _ = p_id_fn(train_state, jnp.ones((), dtype=jnp.uint32))
return train_state
@property
@abc.abstractmethod
def _local_chunker(self):
"""Returns the chunker that matches the parameters of this partitioner."""
raise NotImplementedError
[docs] def get_logical_axes(self, train_state: TrainState) -> TrainState:
"""Returns a copy of TrainState with Optional[AxisNames] as leaves."""
# By default, return None for the logical axes.
return train_state.restore_state(
jax.tree_map(lambda x: None, train_state.state_dict()))
[docs] def get_mesh_axes(self, train_state: TrainState) -> TrainState:
"""Returns a copy of TrainState with Optional[PartitionSpecs] as leaves."""
raise NotImplementedError
[docs] @abc.abstractmethod
def partition(
self,
fn: Callable, # pylint: disable=g-bare-generic
in_axis_resources,
out_axis_resources,
static_argnums: Union[int, Sequence[int]] = (),
donate_argnums: Union[int, Sequence[int]] = ()
) -> PartitionedCallable:
"""Partitions the computation using partitioner-specific implementation.
Args:
fn: the function to partition.
in_axis_resources: Pytree of structure matching that of arguments to `fn`,
with all actual arguments replaced by resource assignment
specifications. It is also valid to specify a pytree prefix (e.g. one
value in place of a whole subtree), in which case the leaves get
broadcast to all values in that subtree.
The valid resource assignment specifications are:
`None`: in which case the value will be replicated on all devices
`PartitionSpec`: a tuple of length at most equal to the rank of the
partitioned value. Each element can be a `None`, a mesh axis or a
tuple of mesh axes, and specifies the set of resources assigned to
partition the value's dimension matching its position in the spec.
out_axis_resources: Like `in_axis_resources`, but specifies resource
assignment for function outputs.
static_argnums: an optional int or collection of ints that specify which
positional arguments to treat as static (compile-time constant) in the
partitioned function.
donate_argnums: an optional int or collection of ints that specify which
argument buffers are "donated" to the computation. It is safe to donate
argument buffers if you no longer need them once the computation has
finished.
Returns:
A partitioned version of the input function.
"""
raise NotImplementedError
[docs] @abc.abstractmethod
def compile(self, partitioned_fn: PartitionedCallable,
*args) -> CompiledPartitionedCallable:
"""Compiles and returns the partitioned function, or the original.
Args:
partitioned_fn: The partitioned function.
*args: Sample arguments to the partitioned function matching the input
shapes that will be passed to the compiled function.
Returns:
The compiled function, or the original if this partitioner does not
support compilation.
"""
raise NotImplementedError
[docs]class PjittedFnWithContext(PartitionedCallable):
"""Wraps pjitted function to apply the appropriate contexts."""
def __init__(self,
pjitted_fn,
partition_mesh: Mesh,
logical_axis_rules: flax_partitioning.LogicalRules = ()):
self._pjitted_fn = pjitted_fn
self._mesh = partition_mesh
self._logical_axis_rules = logical_axis_rules
def __call__(self, *args, **kwargs):
with Mesh(self._mesh.devices,
self._mesh.axis_names), flax_partitioning.axis_rules(
self._logical_axis_rules):
return self._pjitted_fn(*args, **kwargs)
def lower(self, *args, **kwargs):
with Mesh(self._mesh.devices,
self._mesh.axis_names), flax_partitioning.axis_rules(
self._logical_axis_rules):
return self._pjitted_fn.lower(*args, **kwargs)
[docs]class BasePjitPartitioner(BasePartitioner):
"""Partitioner that uses T5X version of jax.pjit."""
@cached_property
def _local_chunker(self) -> LocalChunker:
return LocalChunker(self.mesh)
@cached_property
def mesh(self) -> Mesh:
return default_mesh(
self._num_partitions,
self._model_parallel_submesh,
self._backend,
self._ici_mesh_shape,
self._dcn_mesh_shape,
)
[docs] def partition(
self,
fn: Callable, # pylint: disable=g-bare-generic
in_axis_resources,
out_axis_resources,
static_argnums: Union[int, Sequence[int]] = (),
donate_argnums: Union[int, Sequence[int]] = (),
) -> PjittedFnWithContext:
pjitted = pjit(
fn,
in_shardings=in_axis_resources,
out_shardings=out_axis_resources,
static_argnums=static_argnums,
donate_argnums=donate_argnums,
)
return PjittedFnWithContext(pjitted, self.mesh)
[docs] def compile(self, partitioned_fn: PjittedFnWithContext,
*args) -> CompiledPartitionedCallable:
return partitioned_fn.lower(*args).compile()
[docs]class PjitPartitioner(BasePjitPartitioner):
"""Partitioner that uses named axes and jax.pjit."""
def __init__(
self,
num_partitions: Optional[int] = None,
model_parallel_submesh: Optional[HardwareMesh] = None,
params_on_devices: bool = True,
backend: Optional[str] = None,
ici_mesh_shape: Optional[HardwareMesh] = None,
dcn_mesh_shape: Optional[HardwareMesh] = None,
logical_axis_rules: Optional[LogicalAxisRules] = None,
):
"""PjitPartitioner constructor.
See https://github.com/google-research/text-to-text-transfer-transformer/blob/main/README.mdx/usage/partitioning for details.
Args:
num_partitions: an integer that specifies the size of the model parallel
submesh to be automatically selected for the current topology. See
`model_parallel_submesh` for details on how this submesh is used.
Mutually exclusive with `model_parallel_submesh`.
model_parallel_submesh: is a 4-tuple that specifies the `(x, y, z, c)`
submesh model-parallel device tile, an axis of accelerator parallelism
orthogonal to data parallelism. Array axes in a model's parameters or
activations can be sharded over this submesh using axis rules (see
`logical_axis_rules`) that map them to 'model'. The effective number of
model sub-partitions is equal to `np.prod(model_parallel_submesh)` and
must evenly divide the total number of devices (i.e.,
`jax.device_count() % np.prod(model_parallel_submesh) == 0`). The rest
of the TPU mesh is the data parallel submesh, providing
`jax.device_count() // np.prod(model_parallel_submesh)` partitions. It
is used for data (batch) parallelism and to shard other array axes that
are mapped to 'data'. This argument is mutually exclusive with
`num_partitions`.
params_on_devices: whether to keep the params on devices, if False -
params stay in the host memory. Note that some partitioners might ignore
this setting, for example if they don't support storing all params on
device memory.
backend: get devices from the pinned backend, if specified. This is useful
for explicitly specifying the devices other than relying on
jax_platform_name.
ici_mesh_shape: Shape of the logical mesh used for SPMD parallelism in
each slice. The meaning of each mesh axis is defined by mesh_axis_names,
so these two params must be the same length. If dcn_mesh_shape is
present, the overall mesh is the product of ici_mesh_shape and
dcn_mesh_shape. For example, an ici_mesh_shape of [2, 3, 4] with
mesh_axis_names ['replica', 'data', 'model'] indicates 2-way replica
parallelism, 3-way data parallelism, and 4-way model parallelism over 24
devices. None, the default, is equivalent to a sequence of ones and
means that the model is placed on a single device.
dcn_mesh_shape: Shape of the logical mesh used for SPMD parallelism over
multiple slices. The overall mesh is the product of ici_mesh_shape and
dcn_mesh_shape, and the meaning of each mesh axis is defined by
mesh_axis_names, so these three params must be the same length.
logical_axis_rules: a priority-ordered sequence of KV tuples that maps
logical axis names to either `None` (not sharded), 'model' (to shard
across the model-parallel submesh), or 'data' (to shard across the
data-parallel submesh).
"""
super().__init__(
num_partitions=num_partitions,
model_parallel_submesh=model_parallel_submesh,
params_on_devices=params_on_devices,
backend=backend,
ici_mesh_shape=ici_mesh_shape,
dcn_mesh_shape=dcn_mesh_shape,
)
if logical_axis_rules is None:
logical_axis_rules = standard_logical_axis_rules()
if ici_mesh_shape is not None and dcn_mesh_shape is not None:
# Split batch over new replica axis.
logical_axis_rules = (
(k, ('replica', 'data') if k == 'batch' else v)
for k, v in logical_axis_rules
)
self._logical_axis_rules = tuple(logical_axis_rules)
(self._data_axis,) = flax_partitioning.logical_to_mesh_axes(
['batch'], self._logical_axis_rules
)
[docs] def partition(
self,
fn: Callable, # pylint: disable=g-bare-generic
in_axis_resources,
out_axis_resources,
static_argnums: Union[int, Sequence[int]] = (),
donate_argnums: Union[int, Sequence[int]] = ()
) -> PjittedFnWithContext:
"""Partitions the function using jax.pjit."""
pjitted = pjit(
fn,
in_shardings=in_axis_resources,
out_shardings=out_axis_resources,
static_argnums=static_argnums,
donate_argnums=donate_argnums,
)
return PjittedFnWithContext(pjitted, self.mesh, self._logical_axis_rules)
@property
def logical_axis_rules(self):
"""Returns the logical axis rules."""
return self._logical_axis_rules
[docs] def get_logical_axes(self, train_state: TrainState) -> TrainState:
"""Returns a copy of TrainState with Optional[AxisNames] as leaves."""
return train_state.as_logical_axes()
[docs] def get_mesh_axes(self, train_state: TrainState) -> TrainState:
"""Returns a copy of TrainState with Optional[PartitionSpecs] as leaves."""
logical_axes = self.get_logical_axes(train_state)
def _logical_to_mesh_axes(param_name, logical_axes):
if logical_axes is None:
return None
elif logical_axes is traverse_util.empty_node:
return traverse_util.empty_node
try:
return flax_partitioning.logical_to_mesh_axes(logical_axes,
self._logical_axis_rules)
except ValueError as e:
raise ValueError(f'Failed to map logical axes for {param_name}') from e
flat_logical_axes = traverse_util.flatten_dict(
logical_axes.state_dict(), keep_empty_nodes=True, sep='/')
flat_mesh_axes = {
k: _logical_to_mesh_axes(k, v) for k, v in flat_logical_axes.items()
}
return logical_axes.restore_state(
traverse_util.unflatten_dict(flat_mesh_axes, sep='/'))
# arr_tree is a PyTree of jax.Array or np.ndarray and
# pspecs is PyTree[jax.sharding.PartitionSpec]
def host_local_array_to_global_array(arr_tree, mesh: jax.sharding.Mesh, pspecs):
pspecs = jax.tree_map(
lambda x: PartitionSpec() if x is None else x,
pspecs,
is_leaf=lambda x: x is None,
)
return multihost_utils.host_local_array_to_global_array(
arr_tree, mesh, pspecs
)
