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4c8c0f5 | 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 | # Copyright (c) 2025 SandAI. All Rights Reserved.
#
# 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.
import io
import json
import os
import re
import subprocess
from collections import OrderedDict
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
import numpy as np
import torch
import torch.distributed
from safetensors.torch import load as load_from_bytes
from safetensors.torch import load_file
from tqdm.auto import tqdm
import inference.infra.distributed.parallel_state as mpu
from inference.common import EngineConfig, ModelConfig, RuntimeConfig, print_per_rank, print_rank_0
def _load_shard(shard_path, param_names, num_threads=None):
zstd_path = shard_path + ".zst"
if os.path.exists(zstd_path):
start_time = datetime.now()
print_per_rank(f"Decompressing {zstd_path} with {num_threads} threads")
cmd = ["zstd", "-d"]
if num_threads:
cmd.extend(["-T", str(num_threads)])
process = subprocess.Popen(cmd + ["-c", zstd_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=-1)
decompressed_data = process.stdout.read()
process.stdout.close()
retcode = process.wait()
if retcode != 0:
raise RuntimeError(f"Decompression failed: {process.stderr.read().decode()}")
print_per_rank(
f"Decompressed {zstd_path} with {num_threads} threads, duration: {(datetime.now() - start_time).total_seconds()}s"
)
buffer = io.BytesIO(decompressed_data)
start_time = datetime.now()
print_per_rank(f"Loading {shard_path} from zstd file, start time: {start_time}")
weights = load_from_bytes(buffer.getvalue())
print_per_rank(f"Loaded {shard_path} from zstd file, duration: {(datetime.now() - start_time).total_seconds()}s")
buffer.close()
else:
weights = load_file(shard_path)
return {name: weights[name] for name in param_names}
def load_sharded_safetensors_parallel_with_progress(checkpoint_dir):
index_path = os.path.join(checkpoint_dir, "model.safetensors.index.json")
if not os.path.exists(index_path):
model_file_path = os.path.join(checkpoint_dir, "model.safetensors")
state_dict = load_file(model_file_path)
return state_dict
with open(index_path, "r") as f:
index = json.load(f)
state_dict = {}
shard_map = {}
# Group parameters by shard file
for param_name, shard_file in index["weight_map"].items():
shard_path = os.path.join(checkpoint_dir, shard_file)
if shard_path not in shard_map:
shard_map[shard_path] = []
shard_map[shard_path].append(param_name)
# Load shards in parallel with a progress bar
with ThreadPoolExecutor() as executor:
futures = {
executor.submit(_load_shard, shard_path, param_names): shard_path for shard_path, param_names in shard_map.items()
}
pbar = tqdm(futures, desc="Loading shards", total=len(futures))
for future in pbar:
result = future.result()
state_dict.update(result)
return state_dict
def unwrap_model(model):
return_list = True
if not isinstance(model, list):
model = [model]
return_list = False
unwrapped_model = []
for model_module in model:
while hasattr(model_module, "module"):
model_module = model_module.module
unwrapped_model.append(model_module)
if not return_list:
return unwrapped_model[0]
return unwrapped_model
def _split_state_dict_for_pp(weight_dict: OrderedDict, model_config: ModelConfig):
num_layers = model_config.num_layers
partition = mpu.get_pp_world_size()
## use partition and num_layers to get current rank layer order
layers_for_each_stage = np.array_split(range(num_layers), partition)
current_stage = mpu.get_pp_rank()
allow_layer_num = layers_for_each_stage[current_stage]
layer_offset = allow_layer_num[0]
new_weight_dict = {}
for k, v in weight_dict.items():
if "videodit_blocks.layers" in k:
layer_num = int(re.search(r"videodit_blocks\.layers\.(\d+)", k).group(1))
if layer_num not in allow_layer_num:
continue
## replace the old key name by new layer number
new_layer_num = layer_num - layer_offset
new_k = k.replace(f"videodit_blocks.layers.{layer_num}", f"videodit_blocks.layers.{new_layer_num}")
new_weight_dict[new_k] = v
else:
new_weight_dict[k] = v
return new_weight_dict
def load_state_dict(runtime_config: RuntimeConfig, engine_config: EngineConfig):
load_dir = runtime_config.load
default_subdir = "inference_weight"
if engine_config.fp8_quant:
default_subdir = f"{default_subdir}.fp8"
if engine_config.distill:
default_subdir = f"{default_subdir}.distill"
inference_weight_dir = os.path.join(load_dir, default_subdir)
print_rank_0(f"load {default_subdir} weight from {inference_weight_dir}")
assert (
os.path.exists(inference_weight_dir) and len(os.listdir(inference_weight_dir)) > 0
), f"Ckpt directory {inference_weight_dir} does not exist or empty. If you are using fp8_quant, please run calibration first."
state_dict = load_sharded_safetensors_parallel_with_progress(inference_weight_dir)
return state_dict
def load_checkpoint(model):
state_dict = load_state_dict(model.runtime_config, model.engine_config)
model = unwrap_model(model)
# if we use pipeline parallelism, we need to load the state dict for each stage
# as it always record layer from 0 -> num_layers//pipeline_parallel_size
# so we need to choose correct layer weight when load_state_dict
if mpu.get_pp_world_size() > 1:
state_dict = _split_state_dict_for_pp(state_dict, model.model_config)
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
model.cuda(torch.cuda.current_device()) # bottleneck for loading
if mpu.get_pp_world_size() > 1:
rank_msg = f"CP_rank={mpu.get_cp_rank()} PP_rank={mpu.get_pp_rank()}"
print_per_rank(
f"""[{rank_msg}] Load Weight Missing Keys: {missing_keys} Load Weight Unexpected Keys: {unexpected_keys} You should see message [missing fianl layer norm weight] except the final pipeline stage"""
)
else:
print_rank_0(f"Load Weight Missing Keys: {missing_keys}")
print_rank_0(f"Load Weight Unexpected Keys: {unexpected_keys}")
return model
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