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#
# 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.
# ============================================================================
# ZDTaichu-5.0 — Top-Level Configuration
#
# Architecture:
# - LLM backbone: Qwen3 (pure Transformer) → Qwen3.5 (hybrid DeltaNet/Transformer)
# · 3:1 linear-to-full attention ratio (Gated DeltaNet + full attention)
# · Custom Qwen3_5DynamicCache for hybrid KV / recurrent states
# · head_dim=256 (was 128), partial_rotary_factor=0.25
# · Interleaved M-RoPE with 4D position IDs
# · Attention output gating (sigmoid gate on q_proj)
# - Vision encoder: C-RADIOv4-H (unchanged)
# - Token IDs updated for Qwen3.5 vocabulary (vocab_size=248320)
# · img_context_token_id: 151655 → 248056 (<|image_pad|>)
# · video_context_token_id: 151656 → 248057 (<|video_pad|>)
# - Projector output adapts to Qwen3.5 hidden_size (4096 for 9B variant)
# ============================================================================
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from .cradio_config import RADIOConfig
logger = logging.get_logger(__name__)
# ---------------------------------------------------------------------------
# Import Qwen3.5 text config — requires transformers >= 5.3.0
# ---------------------------------------------------------------------------
try:
from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig
except ImportError:
Qwen3_5TextConfig = None
logger.warning(
"Could not import Qwen3_5TextConfig from transformers. "
"Ensure transformers >= 5.3.0 is installed. "
"Falling back to PretrainedConfig with manual attributes."
)
# ---------------------------------------------------------------------------
# Default Qwen3.5 text configuration (9B-class variant)
# ---------------------------------------------------------------------------
_LAYER_TYPES_32 = [
"linear_attention" if bool((i + 1) % 4) else "full_attention"
for i in range(32)
]
# Result: [lin, lin, lin, full, lin, lin, lin, full, ... lin, lin, lin, full]
# 24 linear + 8 full attention layers
def _default_qwen3_5_text_dict() -> dict:
"""Return a dict of Qwen3.5 text config values (9B-class)."""
return dict(
vocab_size=248320,
hidden_size=4096,
intermediate_size=12288,
num_hidden_layers=32,
num_attention_heads=16,
num_key_value_heads=4,
head_dim=256,
hidden_act="silu",
max_position_embeddings=262144,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
attention_bias=False,
attention_dropout=0.0,
torch_dtype="bfloat16",
# --- Hybrid layer architecture ---
layer_types=list(_LAYER_TYPES_32), # copy to avoid mutation
full_attention_interval=4,
# --- Linear attention (Gated DeltaNet) ---
linear_conv_kernel_dim=4,
linear_key_head_dim=128,
linear_value_head_dim=128,
linear_num_key_heads=16,
linear_num_value_heads=32,
# --- RoPE ---
rope_parameters={
"rope_type": "default",
"rope_theta": 10000000,
"partial_rotary_factor": 0.25,
"mrope_interleaved": True,
"mrope_section": [11, 11, 10],
},
)
def _build_llm_config(cfg_dict: dict = None) -> PretrainedConfig:
"""
Construct the LLM sub-config from a dict or defaults.
Uses Qwen3_5TextConfig when available (transformers >= 5.3);
otherwise falls back to a plain PretrainedConfig with the correct
model_type so that AutoModelForCausalLM can still resolve it.
"""
if cfg_dict is None:
cfg_dict = _default_qwen3_5_text_dict()
if Qwen3_5TextConfig is not None:
return Qwen3_5TextConfig(**cfg_dict)
else:
config = PretrainedConfig(**cfg_dict)
config.model_type = "qwen3_5_text"
return config
class ZDTaichu5_0_Config(PretrainedConfig):
"""
Configuration for ZDTaichu-5.0-9B:
Vision encoder : C-RADIOv4-H (ViT-H/16, 653 M params)
LLM decoder : Qwen3.5 (hybrid DeltaNet/Transformer)
Projector : RMSNorm → Linear(5120→20480) → SquaredReLU → Linear(20480→H)
The projector input side is unchanged (C-RADIOv4-H ViT-H features at 1280,
pixel-shuffled to 5120). Only the final projection layer adapts to the
target LLM hidden_size (4096 for the 9B variant, vs 5120 for Qwen3-14B).
Qwen3.5 hybrid architecture
----------------------------
The text backbone alternates Gated DeltaNet (linear attention) and standard
multi-head attention layers in a 3:1 ratio. Linear layers use a causal 1D
convolution + gated delta rule recurrence for O(1) per-token memory during
generation, while every 4th layer uses full quadratic attention to preserve
global context. A custom DynamicCache handles both attention KV states and
recurrent states.
"""
model_type = "zdtaichu5_0"
is_composition = True
def __init__(
self,
vision_config=None,
llm_config=None,
force_image_size=None,
downsample_ratio=0.5,
template=None,
ps_version="v2",
image_tag_type="internvl",
projector_hidden_size=20480, # 4 × pixel_shuffle_dim (5120)
vit_hidden_size=1280, # ViT-H feature dim — same for C-RADIOv4-H
attn_implementation="flash_attention_2",
# Special token IDs for Qwen3.5 vocabulary (vocab_size=248320)
img_context_token_id: int = 248056, # <|image_pad|>
video_context_token_id: int = 248057, # <|video_pad|>
**kwargs,
):
# ------------------------------------------------------------------
# Transformers 5.5.x compatibility:
# PretrainedConfig.__init__ may call self.get_text_config()
# during token-id validation. Therefore llm_config must exist
# before calling super().__init__().
# ------------------------------------------------------------------
# ── Vision encoder ───────────────────────────────────────────────────
if vision_config is not None:
if isinstance(vision_config, dict):
self.vision_config = RADIOConfig(**vision_config)
else:
self.vision_config = vision_config
else:
self.vision_config = RADIOConfig(version="c-radio_v4-h")
# ── Language model (Qwen3.5 hybrid) ──────────────────────────────────
if llm_config is not None:
if isinstance(llm_config, PretrainedConfig):
self.llm_config = llm_config
elif isinstance(llm_config, dict):
self.llm_config = _build_llm_config(llm_config)
else:
raise TypeError(
f"llm_config must be a dict or PretrainedConfig, got {type(llm_config)}"
)
else:
self.llm_config = _build_llm_config(None)
# Make tokenizer/generation token ids visible early.
# Transformers 5.5.x may validate these during super().__init__().
kwargs.setdefault("bos_token_id", getattr(self.llm_config, "bos_token_id", 248040))
kwargs.setdefault("eos_token_id", getattr(self.llm_config, "eos_token_id", 248044))
kwargs.setdefault("pad_token_id", getattr(self.llm_config, "pad_token_id", 248040))
super().__init__(**kwargs)
self.tie_word_embeddings = getattr(self.llm_config, "tie_word_embeddings", False)
# ── VL configuration ─────────────────────────────────────────────────
self.force_image_size = force_image_size
self.downsample_ratio = downsample_ratio
self.template = template
self.ps_version = ps_version
self.image_tag_type = image_tag_type
self.projector_hidden_size = projector_hidden_size
self.vit_hidden_size = vit_hidden_size
# Special token IDs
self.img_context_token_id = img_context_token_id
self.video_context_token_id = video_context_token_id
# Attention implementation propagation
self._attn_implementation = attn_implementation
self.vision_config.use_flash_attn = (
self._attn_implementation is not None
and "flash_attention" in self._attn_implementation
)
self.llm_config._attn_implementation = self._attn_implementation
def get_text_config(self, decoder=False):
# Robust fallback for Transformers 5.5.x validation.
if hasattr(self, "llm_config"):
return self.llm_config
return _build_llm_config(None)
@property
def text_config(self):
return self.get_text_config(decoder=True)
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