File size: 40,248 Bytes
1d2ae3c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
# coding=utf-8
# Copyright 2023 The T5X Authors and The HuggingFace Inc. team.
#
# 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 typing
from typing import Any, Callable, Optional, Sequence, Tuple, Union

import cached_property
import jax
import numpy as np
from absl import logging
from flax import traverse_util
from flax.linen import partitioning as flax_partitioning
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 as jax_pjit
from jax.sharding import Mesh, PartitionSpec


JaxDevice = Any
TpuMesh = Tuple[int, int, int, int]  # (x, y, z, num_cores).
OtherMesh = Tuple[int, int]
HardwareMesh = Union[TpuMesh, OtherMesh]
PyTreeDef = type(jax.tree_util.tree_structure(None))
TrainState = Any
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


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)


# pjit wrappers for cpu fallback.
# ----------------------------------------------------------------------------
# TODO(levskaya): This function is now no different than jax_pjit, but callers
# currently depend on `backend` argument
def pjit(
    fun: Callable,  # pylint: disable=g-bare-generic
    in_axis_resources,
    out_axis_resources,
    static_argnums: Union[int, Sequence[int]] = (),
    donate_argnums: Union[int, Sequence[int]] = (),
    backend: Optional[str] = None,
):
    """Wrapper for pjit."""
    del backend
    return jax_pjit(
        fun, in_axis_resources, out_axis_resources, static_argnums=static_argnums, donate_argnums=donate_argnums
    )


# pjit wrappers for cpu fallback.
# -----------------------------------------------------------------------------
# TODO(levskaya): upstream this fallback behavior to jax pjit.
def pjit_with_cpu_fallback(
    fun: Callable,  # pylint: disable=g-bare-generic
    in_axis_resources,
    out_axis_resources,
    static_argnums: Union[int, Sequence[int]] = (),
    donate_argnums: Union[int, Sequence[int]] = (),
    backend: Optional[str] = None,
):
    """Wrapper for pjit that calls normal jit on cpu."""
    if jax.devices(backend)[0].platform == "cpu":
        return jax.jit(fun, static_argnums=static_argnums, donate_argnums=donate_argnums)
    else:
        return jax_pjit(
            fun, in_axis_resources, out_axis_resources, static_argnums=static_argnums, donate_argnums=donate_argnums
        )


def with_sharding_constraint(x, axis_resources):
    """Wrapper for pjit 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.experimental.pjit.with_sharding_constraint(x, axis_resources)


# pjit Mesh creation functions.
# -----------------------------------------------------------------------------
def bounds_from_last_device(last_device: JaxDevice) -> 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.host_count(), jax.local_device_count()


def get_coords(device: JaxDevice) -> 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())


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


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


def get_cpu_mesh() -> Mesh:
    """Trivial mesh for CPU Testing."""
    devices = np.empty((jax.host_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"])


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


def default_mesh(
    num_partitions: int, model_parallel_submesh: Optional[HardwareMesh] = None, backend: Optional[str] = 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.

    Returns:
      xmap/pjit 2D Mesh with 'data', 'model' mesh axes.
    """
    last_device = jax.devices(backend)[-1]
    platform = last_device.platform
    device_kind = last_device.device_kind
    bounds = bounds_from_last_device(last_device)

    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."
        )
    return get_mesh(mps, backend=backend)


# Data chunking helper.
# -----------------------------------------------------------------------------
@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


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

    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)

        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 LocalChunkInfo(tuple(local_slice), replica_id)


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


@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]


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,
    ):
        """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.
        """

        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`=%s. 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=%s` and `model_parallel_submesh`=%s. 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
        self._data_axis = "data"
        self._backend = backend

    @property
    def mesh(self) -> Mesh:
        raise NotImplementedError

    @property
    def data_partition_spec(self) -> PartitionSpec:
        return PartitionSpec(self._data_axis)

    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),
            )
        mesh_size = self._local_chunker.global_mesh.shape[self._data_axis]
        batch_size = batch_size or mesh_size
        if batch_size % mesh_size:
            raise ValueError(
                f"Batch size ({batch_size}) must be divisible by corresponding " f"mesh size ({mesh_size})."
            )
        num_shards = self._local_chunker.num_chunks[self._data_axis]
        if batch_size % num_shards:
            raise ValueError(f"Batch size ({batch_size}) must be divisible by number of " f"replicas ({num_shards}).")
        replica_id = self._local_chunker.get_local_chunk_info((batch_size,), [self._data_axis]).replica_id
        return DataLayout(
            batch_size=int(batch_size),
            shard_id=int(self._local_chunker.chunk_ids[self._data_axis]),
            num_shards=int(num_shards),
            is_first_host_in_replica_set=(replica_id == 0),
        )

    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

    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.config.jax_array and jax.process_count() > 1:
            train_state = multihost_utils.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

    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()))

    def get_mesh_axes(self, train_state: TrainState) -> TrainState:
        """Returns a copy of TrainState with Optional[PartitionSpecs] as leaves."""
        raise NotImplementedError

    @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

    @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


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):
        with Mesh(self._mesh.devices, self._mesh.axis_names), flax_partitioning.axis_rules(self._logical_axis_rules):
            return self._pjitted_fn(*args)

    def lower(self, *args):
        with Mesh(self._mesh.devices, self._mesh.axis_names), flax_partitioning.axis_rules(self._logical_axis_rules):
            return self._pjitted_fn.lower(*args)


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)

    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_axis_resources=in_axis_resources,
            out_axis_resources=out_axis_resources,
            static_argnums=static_argnums,
            donate_argnums=donate_argnums,
            backend=self._backend,
        )

        return PjittedFnWithContext(pjitted, self.mesh)

    def compile(self, partitioned_fn: PjittedFnWithContext, *args) -> CompiledPartitionedCallable:
        return partitioned_fn.lower(*args).compile()


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,
        logical_axis_rules: Optional[LogicalAxisRules] = None,
        use_cpu_pjit: Optional[bool] = False,
    ):
        """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 exlusive 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.
          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).
          use_cpu_pjit: enables wrapper function for pjit which just jits the
            function if using CPU backend.
        """
        super().__init__(
            num_partitions=num_partitions,
            model_parallel_submesh=model_parallel_submesh,
            params_on_devices=params_on_devices,
            backend=backend,
        )
        if logical_axis_rules is None:
            logical_axis_rules = standard_logical_axis_rules()
        self._logical_axis_rules = tuple(logical_axis_rules)
        (self._data_axis,) = flax_partitioning.logical_to_mesh_axes(["batch"], logical_axis_rules)
        self._use_cpu_pjit = use_cpu_pjit

    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."""
        if self._use_cpu_pjit:
            pjit_fn = pjit_with_cpu_fallback
        else:
            pjit_fn = pjit
        pjitted = pjit_fn(
            fn,
            in_axis_resources=in_axis_resources,
            out_axis_resources=out_axis_resources,
            static_argnums=static_argnums,
            donate_argnums=donate_argnums,
            backend=self._backend,
        )

        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

    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()

    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="/"))