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# Copyright 2024 X.AI Corp.
#
# 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.
from __future__ import annotations
import contextlib
import logging
import math
import os
import pickle
import re
import shutil
import sys
import tempfile
from concurrent.futures import ThreadPoolExecutor, wait
from typing import Any, Optional
import jax
import numpy as np
from jax.experimental import multihost_utils
from model import QuantizedWeight8bit
logger = logging.getLogger(__name__)
rank_logger = logging.getLogger("rank")
# Needed for loading the checkpoint with pickle.
sys.modules['__main__'].QuantizedWeight8bit = QuantizedWeight8bit
@contextlib.contextmanager
def copy_to_shm(file: str):
if file.startswith("/dev/shm/"):
# Nothing to do, the file is already in shared memory.
yield file
return
tmp_dir = "/dev/shm/"
fd, tmp_path = tempfile.mkstemp(dir=tmp_dir)
try:
shutil.copyfile(file, tmp_path)
yield tmp_path
finally:
os.remove(tmp_path)
os.close(fd)
@contextlib.contextmanager
def copy_from_shm(file: str):
tmp_dir = "/dev/shm/"
fd, tmp_path = tempfile.mkstemp(dir=tmp_dir)
try:
yield tmp_path
shutil.copyfile(tmp_path, file)
finally:
os.remove(tmp_path)
os.close(fd)
def fast_unpickle(path: str) -> Any:
with copy_to_shm(path) as tmp_path:
with open(tmp_path, "rb") as f:
return pickle.load(f)
def fast_pickle(obj: Any, path: str) -> None:
with copy_from_shm(path) as tmp_path:
with open(tmp_path, "wb") as f:
pickle.dump(obj, f)
def load_tensors(shaped_arrays, directory, mesh_config, tensor_indices=None):
"""Loads a set of arrays."""
pool = ThreadPoolExecutor(max_workers=32)
fs = list()
num_tensors = 0
num_replicas = 1
data_model_shards = math.prod(mesh_config)
if tensor_indices is None:
iterator = enumerate(shaped_arrays)
else:
iterator = zip(tensor_indices, shaped_arrays)
for i, t in iterator:
if (i % num_replicas) == ((jax.process_index() // data_model_shards) % num_replicas):
idx = (
jax.process_index() // (num_replicas * data_model_shards) * data_model_shards
+ jax.process_index() % data_model_shards
)
fs.append(
pool.submit(fast_unpickle, os.path.join(directory, f"tensor{i:05d}_{idx:03d}"))
)
num_tensors += 1
else:
fs.append(pool.submit(np.zeros, t.shape, dtype=t.dtype))
wait(fs)
return [f.result() for f in fs]
def path_tuple_to_string(path: tuple) -> str:
pieces = []
for elem in path:
if isinstance(elem, jax.tree_util.DictKey):
pieces.append(elem.key)
elif isinstance(elem, jax.tree_util.GetAttrKey):
pieces.append(elem.name)
else:
assert isinstance(elem, (jax.tree_util.FlattenedIndexKey, jax.tree_util.SequenceKey))
return "/".join(pieces)
def get_load_path_str(
init_path_str: str,
load_rename_rules: Optional[list[tuple[str, str]]] = None,
load_exclude_rules: Optional[list[str]] = None,
) -> Optional[str]:
# Exclusion
if load_exclude_rules is not None:
for search_pattern in load_exclude_rules:
if re.search(search_pattern, init_path_str):
return None
# Renaming
load_path_str = init_path_str
if load_rename_rules is not None:
for search_pattern, replacement_pattern in load_rename_rules:
if re.search(search_pattern, load_path_str):
load_path_str = re.sub(search_pattern, replacement_pattern, load_path_str)
break
return load_path_str
def replace_with_load_state(
init_state: Any,
load_state: Any,
load_rename_rules: Optional[list[tuple[str, str]]] = None,
load_exclude_rules: Optional[list[str]] = None,
mesh_config: tuple = (1, 1),
) -> Any:
flatten_load, _ = jax.tree_util.tree_flatten_with_path(load_state)
flatten_init, structure_init = jax.tree_util.tree_flatten_with_path(init_state)
load_map = {path_tuple_to_string(path): tensor for path, tensor in flatten_load}
replaced = []
num_replicas = 1
data_model_shards = math.prod(mesh_config)
for i, (init_path, tensor) in enumerate(flatten_init):
init_path_str = path_tuple_to_string(init_path)
load_path_str = get_load_path_str(init_path_str, load_rename_rules, load_exclude_rules)
if load_path_str is None:
rank_logger.info(f"Excluded from restore: {init_path_str}.")
replaced.append(tensor)
elif load_path_str in load_map:
if load_path_str == init_path_str:
rank_logger.info(f"Restored from ckpt: {init_path_str}.")
else:
rank_logger.info(f"Restored from ckpt: {init_path_str} <-- {load_path_str}.")
replaced.append(load_map[load_path_str])
else:
rank_logger.info(f"Not found in ckpt: {init_path_str}.")
if (i % num_replicas) == ((jax.process_index() // data_model_shards) % num_replicas):
replaced.append(tensor)
else:
replaced.append(np.zeros_like(tensor))
return jax.tree_util.tree_unflatten(structure_init, replaced)
def restore(
checkpoint_path: str,
state_shapes: Any,
mesh,
between_hosts_config,
params_only,
state_sharding,
init_state: Optional[Any] = None,
) -> Any:
ckpt_path = os.path.join(checkpoint_path, "ckpt-0")
rank_logger.info("Loading checkpoint at {}".format(ckpt_path))
ckpt_shapes = state_shapes
ckpt_shapes_with_path, structure = jax.tree_util.tree_flatten_with_path(ckpt_shapes)
ckpt_shapes_flat = [elem[1] for elem in ckpt_shapes_with_path]
loaded_tensors = load_tensors(ckpt_shapes_flat, ckpt_path, between_hosts_config)
state = jax.tree_util.tree_unflatten(structure, loaded_tensors)
# Sanity check to give a better error message.
ckpt_keys = set(state.params.keys())
code_keys = set(state_sharding.params.keys())
if ckpt_keys != code_keys and init_state is None:
missing_in_ckpt = code_keys - ckpt_keys
missing_locally = ckpt_keys - code_keys
raise ValueError(
"Parameters in the code are not matching checkpoint parameters.\n"
"Params missing in checkpoint: {}\nParams missing in code: {}".format(
missing_in_ckpt, missing_locally
)
)
state_sharding = jax.tree_util.tree_map(
lambda x: jax.sharding.PartitionSpec() if x is None else x,
state_sharding,
is_leaf=lambda x: x is None,
)
state = multihost_utils.host_local_array_to_global_array(state, mesh, state_sharding)
if params_only:
state = state.params
return state
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