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a779cb6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 | """HuggingFace PretrainedConfig for VisionPsyNano."""
from __future__ import annotations
import os
from typing import Any, Optional
from transformers import PretrainedConfig
FLASH_MIN_SIDE_LEN = 512
def resolve_is_flash(
*,
is_flash: Optional[bool] = None,
variant: Optional[str] = None,
resize_to_max_side_len: Optional[bool] = None,
) -> bool:
"""Resolve Flash preprocess mode from explicit flag, legacy variant, or resize policy."""
if is_flash is not None:
return bool(is_flash)
if variant is not None:
v = str(variant).lower().strip()
if v in ("flash", "nano-flash", "visionpsy-nano-flash"):
return True
if v in ("nano", "plain", "nano-plain", "visionpsy-nano"):
return False
raise ValueError(
"legacy variant must be 'nano' or 'flash' "
f"(or aliases); got {variant!r}. Prefer is_flash=True/False."
)
if resize_to_max_side_len is not None:
return not bool(resize_to_max_side_len)
return False
def apply_flash_preprocess(
*,
is_flash: bool,
resize_to_max_side_len: Optional[bool] = None,
resize_min_side_len: Optional[int] = None,
) -> tuple[bool, Optional[int]]:
"""Return (resize_to_max_side_len, resize_min_side_len) for the given Flash mode."""
if resize_to_max_side_len is None:
resize_to_max_side_len = not is_flash
resize_to_max_side_len = bool(resize_to_max_side_len)
if is_flash:
resize_min_side_len = max(int(resize_min_side_len or 0), FLASH_MIN_SIDE_LEN)
elif resize_to_max_side_len:
resize_min_side_len = None
return resize_to_max_side_len, resize_min_side_len
_DEFAULT_CHAT_TEMPLATE = (
"{% for message in messages %}"
"{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}"
"{% endfor %}"
"{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}"
)
_DEFAULT_EXTRA_TOKENS = {
"image_token": "<|image|>",
"global_image_token": "<|global_image|>",
"r1c1": "<row_1_col_1>",
"r1c2": "<row_1_col_2>",
"r1c3": "<row_1_col_3>",
"r1c4": "<row_1_col_4>",
"r1c5": "<row_1_col_5>",
"r1c6": "<row_1_col_6>",
"r1c7": "<row_1_col_7>",
"r1c8": "<row_1_col_8>",
"r2c1": "<row_2_col_1>",
"r2c2": "<row_2_col_2>",
"r2c3": "<row_2_col_3>",
"r2c4": "<row_2_col_4>",
"r2c5": "<row_2_col_5>",
"r2c6": "<row_2_col_6>",
"r2c7": "<row_2_col_7>",
"r2c8": "<row_2_col_8>",
"r3c1": "<row_3_col_1>",
"r3c2": "<row_3_col_2>",
"r3c3": "<row_3_col_3>",
"r3c4": "<row_3_col_4>",
"r3c5": "<row_3_col_5>",
"r3c6": "<row_3_col_6>",
"r3c7": "<row_3_col_7>",
"r3c8": "<row_3_col_8>",
"r4c1": "<row_4_col_1>",
"r4c2": "<row_4_col_2>",
"r4c3": "<row_4_col_3>",
"r4c4": "<row_4_col_4>",
"r4c5": "<row_4_col_5>",
"r4c6": "<row_4_col_6>",
"r4c7": "<row_4_col_7>",
"r4c8": "<row_4_col_8>",
"r5c1": "<row_5_col_1>",
"r5c2": "<row_5_col_2>",
"r5c3": "<row_5_col_3>",
"r5c4": "<row_5_col_4>",
"r5c5": "<row_5_col_5>",
"r5c6": "<row_5_col_6>",
"r5c7": "<row_5_col_7>",
"r5c8": "<row_5_col_8>",
"r6c1": "<row_6_col_1>",
"r6c2": "<row_6_col_2>",
"r6c3": "<row_6_col_3>",
"r6c4": "<row_6_col_4>",
"r6c5": "<row_6_col_5>",
"r6c6": "<row_6_col_6>",
"r6c7": "<row_6_col_7>",
"r6c8": "<row_6_col_8>",
"r7c1": "<row_7_col_1>",
"r7c2": "<row_7_col_2>",
"r7c3": "<row_7_col_3>",
"r7c4": "<row_7_col_4>",
"r7c5": "<row_7_col_5>",
"r7c6": "<row_7_col_6>",
"r7c7": "<row_7_col_7>",
"r7c8": "<row_7_col_8>",
"r8c1": "<row_8_col_1>",
"r8c2": "<row_8_col_2>",
"r8c3": "<row_8_col_3>",
"r8c4": "<row_8_col_4>",
"r8c5": "<row_8_col_5>",
"r8c6": "<row_8_col_6>",
"r8c7": "<row_8_col_7>",
"r8c8": "<row_8_col_8>",
}
class VisionPsyNanoConfig(PretrainedConfig):
"""Config for VisionPsyNano (``is_flash`` selects preprocess mode)."""
model_type = "visionpsynano"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
is_flash: Optional[bool] = None,
variant: Optional[str] = None,
vit_hidden_dim: int = 768,
vit_inter_dim: int = 3072,
vit_patch_size: int = 16,
vit_img_size: int = 512,
vit_n_heads: int = 12,
vit_dropout: float = 0.0,
vit_n_blocks: int = 12,
vit_ln_eps: float = 1e-6,
vit_cls_flag: bool = False,
vit_model_type: str = "google/siglip2-base-patch16-512",
lm_hidden_dim: int = 960,
lm_inter_dim: int = 2560,
lm_rms_eps: float = 1e-5,
lm_re_base: int = 100000,
lm_max_position_embeddings: int = 8192,
lm_base_vocab_size: int = 49152,
extra_token_amount: int = 66,
lm_vocab_size: Optional[int] = None,
lm_n_heads: int = 15,
lm_n_kv_heads: int = 5,
lm_dropout: float = 0.0,
lm_n_blocks: int = 32,
lm_attn_scaling: float = 1.0,
lm_max_length: int = 4096,
lm_use_tokens: bool = False,
lm_tie_weights: bool = True,
lm_model_type: str = "HuggingFaceTB/SmolLM2-360M-Instruct",
lm_tokenizer: str = "HuggingFaceTB/SmolLM2-360M-Instruct",
lm_chat_template: str = _DEFAULT_CHAT_TEMPLATE,
mp_pixel_shuffle_factor: int = 4,
mp_image_token_length: int = 64,
max_img_size: int = 2048,
resize_to_max_side_len: Optional[bool] = None,
resize_min_side_len: Optional[int] = None,
inference_max_img_size: Optional[int] = None,
vlm_extra_tokens: Optional[dict] = None,
vlm_load_backbone_weights: bool = True,
vlm_checkpoint_path: str = "checkpoints",
hf_repo_name: str = "qvac/VisionPsy-Nano-460M",
compile_inference: bool = True,
compile_inference_mode: str = "reduce-overhead",
cuda_graphs_cache_quantum: int = 128,
eos_check_interval: int = 16,
**kwargs: Any,
):
is_flash = resolve_is_flash(
is_flash=is_flash,
variant=variant,
resize_to_max_side_len=resize_to_max_side_len,
)
resize_to_max_side_len, resize_min_side_len = apply_flash_preprocess(
is_flash=is_flash,
resize_to_max_side_len=resize_to_max_side_len,
resize_min_side_len=resize_min_side_len,
)
if lm_vocab_size is None:
lm_vocab_size = lm_base_vocab_size + extra_token_amount
self.is_flash = bool(is_flash)
self.vit_hidden_dim = vit_hidden_dim
self.vit_inter_dim = vit_inter_dim
self.vit_patch_size = vit_patch_size
self.vit_img_size = vit_img_size
self.vit_n_heads = vit_n_heads
self.vit_dropout = vit_dropout
self.vit_n_blocks = vit_n_blocks
self.vit_ln_eps = vit_ln_eps
self.vit_cls_flag = vit_cls_flag
self.vit_model_type = vit_model_type
self.lm_hidden_dim = lm_hidden_dim
self.lm_inter_dim = lm_inter_dim
self.lm_rms_eps = lm_rms_eps
self.lm_re_base = lm_re_base
self.lm_max_position_embeddings = lm_max_position_embeddings
self.lm_base_vocab_size = lm_base_vocab_size
self.extra_token_amount = extra_token_amount
self.lm_vocab_size = lm_vocab_size
self.lm_n_heads = lm_n_heads
self.lm_n_kv_heads = lm_n_kv_heads
self.lm_dropout = lm_dropout
self.lm_n_blocks = lm_n_blocks
self.lm_attn_scaling = lm_attn_scaling
self.lm_max_length = lm_max_length
self.lm_use_tokens = lm_use_tokens
self.lm_tie_weights = lm_tie_weights
self.lm_model_type = lm_model_type
self.lm_tokenizer = lm_tokenizer
self.lm_chat_template = lm_chat_template
self.mp_pixel_shuffle_factor = mp_pixel_shuffle_factor
self.mp_image_token_length = mp_image_token_length
self.max_img_size = max_img_size
self.resize_to_max_side_len = bool(resize_to_max_side_len)
self.resize_min_side_len = resize_min_side_len
self.inference_max_img_size = inference_max_img_size
self.vlm_extra_tokens = dict(vlm_extra_tokens or _DEFAULT_EXTRA_TOKENS)
self.vlm_load_backbone_weights = vlm_load_backbone_weights
self.vlm_checkpoint_path = vlm_checkpoint_path
if self.is_flash and hf_repo_name == "qvac/VisionPsy-Nano-460M":
self.hf_repo_name = "qvac/VisionPsy-Nano-460M-Flash"
else:
self.hf_repo_name = hf_repo_name
self.compile_inference = compile_inference
self.compile_inference_mode = compile_inference_mode
self.cuda_graphs_cache_quantum = cuda_graphs_cache_quantum
self.eos_check_interval = eos_check_interval
kwargs.pop("text_config", None)
kwargs.pop("vision_config", None)
super().__init__(**kwargs)
def get_text_config(self, decoder: bool = False, **kwargs):
"""VisionPsyNano is a single flat config (not text+vision composite)."""
return self
@property
def variant(self) -> str:
return "flash" if self.is_flash else "nano"
def to_vlm_config(self):
"""Convert to the internal VLMConfig dataclass used by the core modules."""
from dataclasses import fields as dc_fields
try:
from .vlm_config import VLMConfig
except ImportError:
from vlm_config import VLMConfig
valid = {f.name for f in dc_fields(VLMConfig)}
payload = {k: getattr(self, k) for k in valid if hasattr(self, k)}
return VLMConfig(**payload)
@classmethod
def from_vlm_config(
cls, cfg, *, is_flash: Optional[bool] = None, variant: Optional[str] = None
) -> "VisionPsyNanoConfig":
from dataclasses import asdict
data = asdict(cfg)
data["is_flash"] = resolve_is_flash(
is_flash=is_flash,
variant=variant,
resize_to_max_side_len=data.get("resize_to_max_side_len"),
)
data.pop("variant", None)
return cls(**data)
@classmethod
def from_legacy_dict(
cls,
raw: dict,
*,
is_flash: Optional[bool] = None,
variant: Optional[str] = None,
) -> "VisionPsyNanoConfig":
"""Load an existing VisionPsyNano / nanoVLM config.json (without model_type)."""
raw = dict(raw)
for key in (
"model_type",
"architectures",
"auto_map",
"transformers_version",
"text_config",
"vision_config",
):
raw.pop(key, None)
raw["is_flash"] = resolve_is_flash(
is_flash=is_flash if is_flash is not None else raw.get("is_flash"),
variant=variant if variant is not None else raw.get("variant"),
resize_to_max_side_len=raw.get("resize_to_max_side_len"),
)
raw.pop("variant", None)
return cls(**raw)
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