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c645013 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | """Self-contained Transformers wrapper around the FALCON architecture."""
from __future__ import annotations
from copy import deepcopy
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
import torch
from torch import nn
from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig, PreTrainedModel
from transformers.models.auto.image_processing_auto import get_image_processor_class_from_name
from transformers.utils import ModelOutput
# Transformers 4.49 copies only direct relative imports for local model folders.
from .artifacts import atomic_json as atomic_json
from .capabilities import SafetyCapabilities
from .config import load_config as load_config
from .configuration_falcon import FalconConfig
from .detector import RFDETRDetector
from .grounding import resolve_segmentation as resolve_segmentation
from .model import FalconConfig as CoreConfig
from .model import FalconModel as CoreModel
from .prediction import predict, predict_panoptic, predict_segmentation
from .regions import MaskAwareRegionEncoder as MaskAwareRegionEncoder
from .safety import StructuredSafetyAdapter as StructuredSafetyAdapter
def _portable(value: Any) -> Any:
if isinstance(value, dict):
return {
key: _portable(item)
for key, item in value.items()
if key not in {
"_name_or_path", "name_or_path", "auto_map", "custom_pipelines",
"base_model_name_or_path",
}
}
if isinstance(value, (list, tuple)):
return [_portable(item) for item in value]
return deepcopy(value)
def _backbone_config(values: dict[str, Any]) -> Any:
values = deepcopy(values)
model_type = values.pop("model_type")
return AutoConfig.for_model(model_type, **values)
def _dtype(value: str | torch.dtype | None) -> torch.dtype:
if isinstance(value, torch.dtype):
return value
if value not in (None, "float32", "float16", "bfloat16"):
raise ValueError(f"Unsupported backbone dtype: {value!r}")
return getattr(torch, value or "float32")
@dataclass
class FalconOutput(ModelOutput):
loss: Any = None
language_loss: Any = None
structured_losses: Any = None
language_output: Any = None
safety: Any = None
region_embeddings: Any = None
detector_output: Any = None
prefix: Any = None
class FalconModel(PreTrainedModel):
config_class = FalconConfig
base_model_prefix = "core"
main_input_name = "images"
def __init__(self, config: FalconConfig, *, core: CoreModel | None = None) -> None:
config.validate()
super().__init__(config)
if core is None:
core_values = deepcopy(config.core_config)
core_values["safety_capabilities"] = SafetyCapabilities.from_dict(
core_values["safety_capabilities"]
)
core_config = CoreConfig(**core_values)
vision = AutoModel.from_config(
_backbone_config(config.vision_config),
torch_dtype=_dtype(config.vision_config.get("torch_dtype")),
)
language = AutoModelForCausalLM.from_config(
_backbone_config(config.language_config),
torch_dtype=_dtype(config.language_config.get("torch_dtype")),
)
processor_values = deepcopy(config.image_processor_config)
processor_class = get_image_processor_class_from_name(
processor_values.pop("image_processor_type", "")
)
if processor_class is None:
raise ValueError("Unknown image_processor_type in FALCON package")
core = CoreModel(
core_config,
vision_encoder=vision,
language_model=language,
detector=RFDETRDetector.from_config(config.detector_config, device="cpu"),
image_processor=processor_class.from_dict(processor_values),
)
core.enable_lora(**config.lora_config)
core.set_training_stage(3)
if config.language_generation_config:
core.language_model.generation_config = GenerationConfig.from_dict(
config.language_generation_config
)
# This is an explicit saved generation policy, not a policy to
# regenerate later from the language architecture's defaults.
core.language_model.generation_config._from_model_config = False
if core.training_stage != 3:
raise ValueError("A Hugging Face FALCON model requires Stage 3 weights")
self.core = core
# RFDETRDetector is not an nn.Module. Register its actual backend module
# explicitly so saving, loading, and device movement include every weight.
self.detector_model = core.detector.torch_model
self.detector_model.requires_grad_(False).eval()
self.core.checkpoint_metadata = deepcopy(config.checkpoint_metadata)
self.core.runtime_config = deepcopy(config.runtime_config)
self.core.trained_safety_capabilities = core.config.safety_capabilities
self.core.inference_safety_capabilities = core.config.safety_capabilities
self.core.inference_ssa_ablation = "none"
def __getattr__(self, name: str) -> Any:
try:
return super().__getattr__(name)
except AttributeError:
core = self.__dict__.get("_modules", {}).get("core")
if core is not None and hasattr(core, name):
return getattr(core, name)
raise
@classmethod
def from_falcon(cls, core: CoreModel) -> FalconModel:
"""Wrap loaded final weights without reallocating the frozen backbones."""
source = deepcopy(getattr(core, "checkpoint_metadata", {}))
if core.training_stage != 3 or source.get("training_complete") is not True:
raise ValueError("Export requires a completed Stage 3 model")
if source.get("partial_initialization_allowed") or source.get(
"legacy_initialization_allowed"
) or source.get("oracle_detector", "none") != "none":
raise ValueError("Cannot export partial initialization or oracle models")
runtime = _portable(core.runtime_config)
runtime["model"]["vision_model"] = "facebook/dinov2-large"
runtime["model"]["language_model"] = "lmsys/vicuna-7b-v1.5"
runtime["experiment"]["name"] = "falcon-x"
retained = {
"stage", "training_complete", "checkpoint_format", "safety_capabilities",
"supervision_capabilities", "ssa_ablation", "require_observed_supervision",
"observed_supervision_coverage", "dataset_categories",
"diagnostic_training_reasons", "official_result_eligible", "precision",
"structured_loss_recipe", "grounding_policy", "grounding_coverage",
}
metadata = {key: _portable(value) for key, value in source.items() if key in retained}
metadata["stage"] = 3
metadata["config"] = runtime
vision_config = _portable(core.vision_encoder.config.to_dict())
language_config = _portable(core.language_model.config.to_dict())
for values, model in ((vision_config, core.vision_encoder),
(language_config, core.language_model)):
values["torch_dtype"] = str(next(model.parameters()).dtype).removeprefix("torch.")
values["attn_implementation"] = model.config._attn_implementation
adapters = core.language_model.peft_config
if set(adapters) != {"default"}:
raise ValueError("FALCON export requires exactly one default LoRA adapter")
lora = adapters["default"]
if lora.bias != "none" or lora.modules_to_save or lora.use_dora or lora.use_rslora:
raise ValueError("Unsupported LoRA variant in FALCON export")
config = FalconConfig(
core_config=asdict(core.config),
vision_config=vision_config,
language_config=language_config,
detector_config=core.detector.export_config(),
image_processor_config=_portable(core.image_processor.to_dict()),
lora_config={
"rank": lora.r, "alpha": lora.lora_alpha,
"dropout": lora.lora_dropout, "target_modules": sorted(lora.target_modules),
},
runtime_config=runtime,
checkpoint_metadata=metadata,
language_generation_config=_portable(core.language_model.generation_config.to_dict()),
)
return cls(config, core=core)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Any, *args: Any, **kwargs: Any):
if kwargs.pop("torch_dtype", None) not in (None, "auto"):
raise ValueError("FALCON preserves each component's saved dtype; omit torch_dtype")
for name in ("quantization_config", "load_in_8bit", "load_in_4bit", "ignore_mismatched_sizes"):
if kwargs.get(name):
raise ValueError(f"FALCON does not support {name}")
device = kwargs.pop("device", None)
device_map = kwargs.pop("device_map", None)
if device_map is not None:
if isinstance(device_map, dict) and set(device_map) == {""}:
mapped_device = device_map[""]
elif isinstance(device_map, str | torch.device | int) and device_map not in (
"auto", "balanced", "balanced_low_0", "sequential", "disk",
):
mapped_device = device_map
else:
raise ValueError("FALCON supports one device; use device='cuda' or .to('cuda')")
if isinstance(mapped_device, int):
mapped_device = f"cuda:{mapped_device}"
if device is not None and torch.device(device) != torch.device(mapped_device):
raise ValueError("device and device_map disagree")
device = mapped_device
tokenizer_kwargs = {
name: kwargs[name] for name in (
"cache_dir", "force_download", "local_files_only", "token", "revision",
) if name in kwargs
}
package_subfolder = kwargs.get("subfolder", "")
tokenizer_kwargs["subfolder"] = "/".join(
part for part in (package_subfolder, "tokenizer") if part
)
requested_info = kwargs.pop("output_loading_info", False)
model, info = super().from_pretrained(
pretrained_model_name_or_path, *args, output_loading_info=True, **kwargs
)
issues = {name: info[name] for name in (
"missing_keys", "unexpected_keys", "mismatched_keys", "error_msgs"
) if info.get(name)}
if issues:
raise ValueError(f"Incomplete or incompatible FALCON weights: {issues}")
resolved_revision = getattr(model.config, "_commit_hash", None)
if resolved_revision:
tokenizer_kwargs["revision"] = resolved_revision
model.core.tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, use_fast=False, trust_remote_code=False,
**tokenizer_kwargs,
)
if model.core.tokenizer.pad_token_id is None:
model.core.tokenizer.pad_token = model.core.tokenizer.eos_token
if device is not None:
model.to(device)
model.eval()
return (model, info) if requested_info else model
def save_pretrained(self, save_directory: str | Path, **kwargs: Any) -> None:
if kwargs.get("push_to_hub"):
raise ValueError("Save the complete folder first, then use push_to_hub() or upload_folder()")
if self.core.tokenizer is None:
raise ValueError("A complete FALCON package requires its tokenizer")
self.config.validate()
super().save_pretrained(save_directory, **kwargs)
self.core.tokenizer.save_pretrained(Path(save_directory) / "tokenizer")
self.core.image_processor.save_pretrained(save_directory)
def _apply(self, fn: Any, recurse: bool = True):
for dtype in (torch.float32, torch.bfloat16):
if fn(torch.empty(0, dtype=dtype)).dtype != dtype:
raise ValueError("FALCON uses mixed component dtypes; move devices without casting")
result = super()._apply(fn, recurse=recurse)
detector = self.__dict__.get("_modules", {}).get("detector_model")
if detector is not None:
self.core.detector.to(next(detector.parameters()).device)
return result
def train(self, mode: bool = True):
super().train(mode)
self.detector_model.eval()
return self
def get_input_embeddings(self) -> nn.Module:
return self.core.language_model.get_input_embeddings()
def get_output_embeddings(self) -> nn.Module:
return self.core.language_model.get_output_embeddings()
def forward(self, **kwargs: Any) -> FalconOutput:
return FalconOutput(**vars(self.core(**kwargs)))
def generate(self, **kwargs: Any):
return self.core.generate(**kwargs)
@torch.inference_mode()
def predict(self, image: Any, prompt: str, *, task: str = "text", **kwargs: Any):
methods = {
"text": predict, "segmentation": predict_segmentation, "panoptic": predict_panoptic,
}
if task not in methods:
raise ValueError("task must be text, segmentation, or panoptic")
return methods[task](self.core, image=image, prompt=prompt, **kwargs)
@torch.inference_mode()
def predict_segmentation(self, image: Any, prompt: str, **kwargs: Any):
return predict_segmentation(self.core, image=image, prompt=prompt, **kwargs)
@torch.inference_mode()
def predict_panoptic(self, image: Any, prompt: str, **kwargs: Any):
return predict_panoptic(self.core, image=image, prompt=prompt, **kwargs)
FalconModel.register_for_auto_class("AutoModel")
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