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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
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