File size: 8,262 Bytes
a85f909 |
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 |
import os
import numpy as np
from ml_collections import ConfigDict
import mlxu
import jax
import jax.numpy as jnp
import flax
from flax.serialization import (
from_bytes, to_bytes, to_state_dict, from_state_dict
)
from flax.traverse_util import flatten_dict, unflatten_dict, empty_node
import msgpack
from EasyLM.jax_utils import tree_apply, float_tensor_to_dtype
class StreamingCheckpointer(object):
""" Custom msgpack checkpointer that saves large train states by serializing
and saving tensors one by one in a streaming fashion. Avoids running
out of memory or local TPU disk with default flax checkpointer.
"""
@staticmethod
def get_default_config(updates=None):
config = ConfigDict()
config.float_dtype = 'bf16'
config.save_optimizer_state = False
if updates is not None:
config.update(ConfigDict(updates).copy_and_resolve_references())
return config
def __init__(self, config, checkpoint_dir, enable=True):
self.config = self.get_default_config(config)
self.checkpoint_dir = checkpoint_dir
self.enable = enable
def save_checkpoint(self, train_state, filename, gather_fns=None):
if self.enable:
path = os.path.join(self.checkpoint_dir, filename)
else:
path = '/dev/null'
self.save_train_state_to_file(
train_state, path, gather_fns, self.config.float_dtype
)
@staticmethod
def save_train_state_to_file(train_state, path, gather_fns=None, float_dtype=None):
train_state = to_state_dict(train_state)
packer = msgpack.Packer()
flattend_train_state = flatten_dict(train_state)
if gather_fns is not None:
gather_fns = flatten_dict(to_state_dict(gather_fns))
with mlxu.open_file(path, "wb") as fout:
for key, value in flattend_train_state.items():
if gather_fns is not None:
value = gather_fns[key](value)
value = float_tensor_to_dtype(value, float_dtype)
fout.write(packer.pack((key, to_bytes(value))))
def save_pickle(self, obj, filename):
if self.enable:
path = os.path.join(self.checkpoint_dir, filename)
else:
path = '/dev/null'
mlxu.save_pickle(obj, path)
def save_all(self, train_state, gather_fns, metadata=None, dataset=None, milestone=False):
step = int(jax.device_get(train_state.step))
if self.config.save_optimizer_state:
checkpoint_state = train_state
checkpoint_name = 'streaming_train_state'
checkpoint_gather_fns = gather_fns
else:
checkpoint_state = train_state.params['params']
checkpoint_name = 'streaming_params'
checkpoint_gather_fns = gather_fns.params['params']
if milestone:
# Save a milestone checkpoint that will not be overwritten
self.save_pickle(metadata, f'metadata_{step}.pkl')
self.save_pickle(dataset, f'dataset_{step}.pkl')
self.save_checkpoint(
checkpoint_state, f'{checkpoint_name}_{step}', checkpoint_gather_fns
)
else:
# Save a normal checkpoint that can be overwritten
self.save_pickle(metadata, 'metadata.pkl')
self.save_pickle(dataset, 'dataset.pkl')
self.save_checkpoint(
checkpoint_state, f'{checkpoint_name}', checkpoint_gather_fns
)
@staticmethod
def load_checkpoint(path, target=None, shard_fns=None, remove_dict_prefix=None):
if shard_fns is not None:
shard_fns = flatten_dict(
to_state_dict(shard_fns)
)
if remove_dict_prefix is not None:
remove_dict_prefix = tuple(remove_dict_prefix)
flattend_train_state = {}
with mlxu.open_file(path) as fin:
# 83886080 bytes = 80 MB, which is 16 blocks on GCS
unpacker = msgpack.Unpacker(fin, read_size=83886080, max_buffer_size=0)
for key, value in unpacker:
key = tuple(key)
if remove_dict_prefix is not None:
if key[:len(remove_dict_prefix)] == remove_dict_prefix:
key = key[len(remove_dict_prefix):]
else:
continue
tensor = from_bytes(None, value)
if shard_fns is not None:
tensor = shard_fns[key](tensor)
flattend_train_state[key] = tensor
if target is not None:
flattened_target = flatten_dict(
to_state_dict(target), keep_empty_nodes=True
)
for key, value in flattened_target.items():
if key not in flattend_train_state and value == empty_node:
flattend_train_state[key] = value
train_state = unflatten_dict(flattend_train_state)
if target is None:
return train_state
return from_state_dict(target, train_state)
@staticmethod
def load_flax_checkpoint(path, target=None, shard_fns=None):
""" Load a standard flax checkpoint that's not saved with the
msgpack streaming format.
"""
with mlxu.open_file(path, "rb") as fin:
encoded_bytes = fin.read()
state_dict = flax.serialization.msgpack_restore(encoded_bytes)
if shard_fns is not None:
shard_fns = to_state_dict(shard_fns)
state_dict = tree_apply(shard_fns, state_dict)
if target is None:
return state_dict
return from_state_dict(target, state_dict)
@classmethod
def load_trainstate_checkpoint(cls, load_from, trainstate_target=None,
trainstate_shard_fns=None,
disallow_trainstate=False):
if trainstate_target is not None:
params_target = trainstate_target.params['params']
else:
params_target = None
if trainstate_shard_fns is not None:
params_shard_fns = trainstate_shard_fns.params['params']
else:
params_shard_fns = None
load_type, load_path = load_from.split('::', 1)
if disallow_trainstate:
assert load_type != 'trainstate', 'Loading full trainstate is not allowed!'
train_state = None
restored_params = None
if load_type == 'trainstate':
# Load the entire train state in the streaming format
train_state = cls.load_checkpoint(
path=load_path,
target=trainstate_target,
shard_fns=trainstate_shard_fns,
)
elif load_type == 'trainstate_params':
# Load the params part of the train state in the streaming format
restored_params = cls.load_checkpoint(
path=load_path,
target=params_target,
shard_fns=params_shard_fns,
remove_dict_prefix=('params', 'params'),
)
restored_params = flax.core.frozen_dict.freeze(
{'params': restored_params}
)
elif load_type == 'params':
# Load the params in the streaming format
restored_params = cls.load_checkpoint(
path=load_path,
target=params_target,
shard_fns=params_shard_fns,
)
restored_params = flax.core.frozen_dict.freeze(
{'params': restored_params}
)
elif load_type == 'flax_params':
# Load the params in the standard flax format (non-streaming)
# This requires the entire params to fit in memory
restored_params = cls.load_flax_checkpoint(
path=load_path,
target=params_target,
shard_fns=params_shard_fns
)
restored_params = flax.core.frozen_dict.freeze(
{'params': restored_params}
)
else:
raise ValueError(f'Invalid load_from type: {load_type}')
return train_state, restored_params
|