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fb11af9 | 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 | # Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# 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.
# Adapted from https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/model_loader/loader.py
from abc import ABC
import torch
from transformers import AutoModel, AutoModelForCausalLM, AutoModelForVision2Seq, PreTrainedModel
from transformers.modeling_utils import no_init_weights
from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
from ..utils import logging
from ..utils.import_utils import is_torch_npu_available, is_vescale_available
from .module_utils import init_empty_weights, load_model_weights
from .registry import get_registry
logger = logging.get_logger(__name__)
class BaseModelLoader(ABC):
def __init__(self):
pass
def load_model(self, model_config, **kwargs):
raise NotImplementedError
class HuggingfaceLoader(BaseModelLoader):
def __init__(self):
super().__init__()
def load_model(self, init_kwargs: dict, **kwargs):
model_config = init_kwargs["config"]
architecture = _get_model_arch_from_config(model_config)
if type(model_config) in AutoModelForVision2Seq._model_mapping.keys(): # assume built-in models
load_class = AutoModelForVision2Seq
elif "ForCausalLM" in architecture and type(model_config) in AutoModelForCausalLM._model_mapping.keys():
load_class = AutoModelForCausalLM
else:
load_class = AutoModel
init_device = kwargs.pop("init_device", "cuda")
weights_path = kwargs.pop("weights_path", None)
empty_init = kwargs.pop("empty_init", False)
logger.info_rank0(
f"Loading model from Huggingface modeling.\n"
f"init_device: {init_device}\n"
f"empty_init: {empty_init}\n"
f"weights_path: {weights_path}"
)
if weights_path is None: # init empty model from config
if is_torch_npu_available() and init_device == "cuda":
init_device = "npu"
if init_device == "meta":
with torch.device(init_device), no_init_weights():
logger.info_rank0("Init empty model on meta device from config without init_weights.")
model = load_class.from_config(**init_kwargs)
else:
with torch.device(init_device):
logger.info_rank0("Init empty model from config.")
model = load_class.from_config(**init_kwargs)
else:
if is_vescale_available() and init_device == "meta":
from vescale.initialize.meta_init import meta_device_init
with meta_device_init():
model = load_class.from_config(**init_kwargs)
else:
with init_empty_weights(), no_init_weights():
model = load_class.from_config(**init_kwargs)
if not empty_init:
load_model_weights(model, weights_path, init_device)
return model
class CustomizedModelingLoader(BaseModelLoader):
def __init__(self, model_cls: PreTrainedModel):
super().__init__()
self.model_cls = model_cls # model class from code_path
def load_model(self, init_kwargs: dict, **kwargs):
init_kwargs.pop("trust_remote_code", True)
init_device = kwargs.pop("init_device", "cuda")
weights_path = kwargs.pop("weights_path", None)
empty_init = kwargs.pop("empty_init", False)
vlm_repo_id = kwargs.pop("vlm_repo_id", None)
enable_expert_vision = kwargs.pop("enable_expert_vision", False)
expert_vision_path = kwargs.pop("expert_vision_path", None)
post_training = kwargs.pop("post_training", False)
adanorm_time = kwargs.pop("adanorm_time", False)
incremental_training = kwargs.pop("incremental_training", False)
depth_incremental_training = kwargs.pop("depth_incremental_training", False)
norm_qkv = kwargs.pop("norm_qkv", False)
logger.info_rank0(
f"Loading model from customized modeling.\n"
f"init_device: {init_device}\n"
f"empty_init: {empty_init}\n"
f"weights_path: {weights_path}"
)
if weights_path is None: # init empty model from config
if is_torch_npu_available() and init_device == "cuda":
init_device = "npu"
if init_device == "meta":
with torch.device(init_device), no_init_weights():
logger.info_rank0("Init empty model on meta device from config without init_weights.")
model = self.model_cls._from_config(**init_kwargs)
else:
with torch.device(init_device):
logger.info_rank0("Init empty model from config.")
model = self.model_cls._from_config(**init_kwargs)
else:
load_vlm_only = False
if is_vescale_available() and init_device == "meta":
from vescale.initialize.meta_init import meta_device_init
with meta_device_init():
model = self.model_cls._from_config(**init_kwargs)
else:
with init_empty_weights(), no_init_weights():
if (self.model_cls.__name__ == "PI0Policy" and
self.model_cls.__module__ == "lingbotvla.models.vla.pi0.modeling_pi0"):
model = self.model_cls(config=init_kwargs['config'], tokenizer_path=init_kwargs['config'].tokenizer_path).to(init_kwargs['torch_dtype'])
if vlm_repo_id is not None:
load_vlm_only = True
elif (self.model_cls.__name__ == "LingbotVlaPolicy" and
self.model_cls.__module__ == "lingbotvla.models.vla.pi0.modeling_lingbot_vla"):
model = self.model_cls(config=init_kwargs['config'], tokenizer_path=init_kwargs['config'].tokenizer_path).to(init_kwargs['torch_dtype'])
if vlm_repo_id is not None and incremental_training:
load_vlm_only = True
else:
model = self.model_cls._from_config(**init_kwargs)
if not empty_init:
load_model_weights(model, weights_path, init_device, load_vlm_only=load_vlm_only, enable_expert_vision=enable_expert_vision, expert_vision_path=expert_vision_path, post_training=post_training, incremental_training=incremental_training, depth_incremental_training=depth_incremental_training, norm_qkv=norm_qkv, adanorm_time=adanorm_time)
# we should tie embeddings after loading weights because init_empty_weights() leads to untied weights,
if getattr(model.config, "tie_word_embeddings", True):
try:
input_embeddings = model.get_input_embeddings()
output_embeddings = model.get_output_embeddings()
output_embeddings._parameters["weight"] = input_embeddings._parameters["weight"]
except Exception as e:
logger.info_rank0(f"Failed to tie embeddings: {e}")
return model
def _get_model_arch_from_config(model_config):
arch_name = model_config.architectures
if isinstance(arch_name, list):
arch_name = arch_name[0]
return arch_name
def get_loader(model_config, force_use_huggingface):
if isinstance(model_config, PI0Config):
if 'qwen' not in model_config.tokenizer_path.lower():
model_arch = 'PI0Policy'
elif 'qwen2' in model_config.tokenizer_path.lower():
model_arch = 'LingbotVlaPolicy'
else:
model_arch = _get_model_arch_from_config(model_config) # Qwen2VLForConditionalGeneration
loader = HuggingfaceLoader()
if not force_use_huggingface:
model_registry = get_registry()
if model_arch in model_registry.supported_models:
model_cls = model_registry.get_model_cls_from_model_arch(model_arch)
loader = CustomizedModelingLoader(model_cls=model_cls)
return loader
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