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import torch
import torch.nn as nn
import torch.nn.functional as F
LLM_TOKEN_INDICATOR = 3
QWEN3_VL_ACTIVATION_LAYERS = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 35)
FP8_E4M3_MAX = 448.0
FP8_WEIGHT_DTYPE = torch.float8_e4m3fn
FP8_SCALE_SUFFIX = ".weight_scale"
_BNB_SIBLING_SUFFIXES = (
".absmax",
".quant_map",
".nested_absmax",
".nested_quant_map",
)
class Fp8Linear(nn.Module):
"""Linear layer holding an e4m3 float8 weight + per-row float32 scale."""
weight: torch.Tensor
weight_scale: torch.Tensor
bias: torch.Tensor | None
def __init__(
self,
in_features: int,
out_features: int,
bias: bool,
compute_dtype: torch.dtype,
) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.compute_dtype = compute_dtype
self.register_buffer(
"weight",
torch.empty(out_features, in_features, dtype=FP8_WEIGHT_DTYPE),
)
self.register_buffer("weight_scale", torch.empty(out_features, dtype=torch.float32))
if bias:
self.register_buffer("bias", torch.empty(out_features, dtype=compute_dtype))
else:
self.bias = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
w = self.weight.to(x.dtype) * self.weight_scale.to(x.dtype).unsqueeze(1)
bias = self.bias.to(x.dtype) if self.bias is not None else None
return F.linear(x, w, bias)
def is_fp8_state_dict(state_dict: dict[str, torch.Tensor]) -> bool:
return any(k.endswith(FP8_SCALE_SUFFIX) for k in state_dict) or any(
v.dtype == FP8_WEIGHT_DTYPE for v in state_dict.values()
)
def is_bnb4bit_state_dict(state_dict: dict[str, torch.Tensor]) -> bool:
return any(".quant_state.bitsandbytes__" in k for k in state_dict)
def swap_linears_to_fp8(
module: nn.Module,
state_dict: dict[str, torch.Tensor],
compute_dtype: torch.dtype,
*,
prefix: str = "",
) -> None:
for name, child in list(module.named_children()):
child_prefix = f"{prefix}{name}"
if (
isinstance(child, nn.Linear) and f"{child_prefix}{FP8_SCALE_SUFFIX}" in state_dict
):
setattr(
module,
name,
Fp8Linear(
child.in_features,
child.out_features,
bias=child.bias is not None,
compute_dtype=compute_dtype,
),
)
else:
swap_linears_to_fp8(child, state_dict, compute_dtype, prefix=f"{child_prefix}.")
def load_fp8_state_dict(
model: nn.Module,
state_dict: dict[str, torch.Tensor],
device: torch.device,
dtype: torch.dtype,
*,
assign: bool = False,
strict: bool = True,
) -> None:
prepared: dict[str, torch.Tensor] = {}
for k, v in state_dict.items():
if v.dtype == FP8_WEIGHT_DTYPE:
prepared[k] = v.to(device=device)
elif k.endswith(FP8_SCALE_SUFFIX):
prepared[k] = v.to(device=device, dtype=torch.float32)
elif v.is_floating_point():
prepared[k] = v.to(device=device, dtype=dtype)
else:
prepared[k] = v.to(device=device)
missing, unexpected = model.load_state_dict(prepared, strict=False, assign=assign)
if unexpected:
raise RuntimeError(f"unexpected keys after fp8 load: {unexpected[:10]}")
if missing:
if strict:
raise RuntimeError(f"missing keys after fp8 load: {missing[:10]}")
warnings.warn(f"missing keys after fp8 load: {missing[:10]}", stacklevel=2)
model.to(device)
def swap_linears_to_bnb4bit(
module: nn.Module,
compute_dtype: torch.dtype,
*,
quant_type: str = "nf4",
compress_statistics: bool = False,
) -> None:
import bitsandbytes as bnb
for name, child in list(module.named_children()):
if isinstance(child, nn.Linear):
new_linear = bnb.nn.Linear4bit(
child.in_features,
child.out_features,
bias=child.bias is not None,
compute_dtype=compute_dtype,
compress_statistics=compress_statistics,
quant_type=quant_type,
)
setattr(module, name, new_linear)
else:
swap_linears_to_bnb4bit(
child,
compute_dtype,
quant_type=quant_type,
compress_statistics=compress_statistics,
)
def load_bnb4bit_state_dict(
model: nn.Module,
state_dict: dict[str, torch.Tensor],
device: torch.device,
dtype: torch.dtype,
) -> None:
import bitsandbytes as bnb
consumed: set[str] = set()
for full_name, tensor in state_dict.items():
if ".quant_state." in full_name or full_name.endswith(_BNB_SIBLING_SUFFIXES):
continue
parent_path, _, param_name = full_name.rpartition(".")
parent = model.get_submodule(parent_path) if parent_path else model
current = parent._parameters.get(param_name)
if not isinstance(current, bnb.nn.Params4bit):
continue
prefix = full_name + "."
quantized_stats = {k: v for k, v in state_dict.items() if k.startswith(prefix)}
consumed.add(full_name)
consumed.update(quantized_stats.keys())
parent._parameters[param_name] = bnb.nn.Params4bit.from_prequantized(
data=tensor,
quantized_stats=quantized_stats,
requires_grad=False,
device=device,
)
remaining = {k: v for k, v in state_dict.items() if k not in consumed}
for k in list(remaining):
if remaining[k].is_floating_point():
remaining[k] = remaining[k].to(device=device, dtype=dtype)
else:
remaining[k] = remaining[k].to(device=device)
missing, unexpected = model.load_state_dict(remaining, strict=False)
real_missing = [m for m in missing if m not in consumed]
if real_missing:
raise RuntimeError(f"missing keys after quantized load: {real_missing[:10]}")
if unexpected:
raise RuntimeError(f"unexpected keys after quantized load: {unexpected[:10]}")
for p in model.parameters():
if isinstance(p, bnb.nn.Params4bit):
continue
if p.is_floating_point() and p.dtype != dtype:
p.data = p.data.to(dtype=dtype)
if p.device != device:
p.data = p.data.to(device=device)
for name, b in list(model.named_buffers()):
if b.is_floating_point() and b.dtype != dtype:
parent_path, _, leaf = name.rpartition(".")
parent = model.get_submodule(parent_path) if parent_path else model
parent.register_buffer(
leaf,
b.to(device=device, dtype=dtype),
persistent=leaf not in parent._non_persistent_buffers_set,
)
elif b.device != device:
parent_path, _, leaf = name.rpartition(".")
parent = model.get_submodule(parent_path) if parent_path else model
parent.register_buffer(
leaf,
b.to(device=device),
persistent=leaf not in parent._non_persistent_buffers_set,
)
_DEFAULT_TEXT_ENCODER_CONFIG = {
"architectures": ["Qwen3VLModel"],
"dtype": "bfloat16",
"image_token_id": 151655,
"model_type": "qwen3_vl",
"text_config": {
"attention_bias": False,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"max_position_embeddings": 262144,
"model_type": "qwen3_vl_text",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": None,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"mrope_interleaved": True,
"mrope_section": [24, 20, 20],
"rope_theta": 5000000,
"rope_type": "default",
},
"use_cache": True,
"vocab_size": 151936,
},
"tie_word_embeddings": False,
"transformers_version": "5.8.0",
"video_token_id": 151656,
"vision_config": {
"deepstack_visual_indexes": [8, 16, 24],
"depth": 27,
"dtype": "bfloat16",
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 4304,
"model_type": "qwen3_vl_vision",
"num_heads": 16,
"num_position_embeddings": 2304,
"out_hidden_size": 4096,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2,
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"ideogram_fp8_weight_only": True,
}
class Ideogram4TextEncoder(nn.Module):
"""Qwen3-VL-8B-Instruct wrapper that extracts hidden states from specific layers."""
def __init__(self, config_path: str = None, **kwargs) -> None:
super().__init__()
from transformers import AutoConfig, AutoModel
if config_path is None:
config_kwargs = {k: v for k, v in _DEFAULT_TEXT_ENCODER_CONFIG.items() if k != "model_type"}
config = AutoConfig.for_model("qwen3_vl", **config_kwargs)
else:
config = AutoConfig.from_pretrained(config_path, trust_remote_code=True)
self.model = AutoModel.from_config(config, trust_remote_code=True)
self.config = config
def load_state_dict(self, state_dict, strict=True, assign=False):
if is_fp8_state_dict(state_dict):
swap_linears_to_fp8(self.model, state_dict, torch.bfloat16)
return self.model.load_state_dict(state_dict, strict=False, assign=assign)
return self.model.load_state_dict(state_dict, strict=strict, assign=assign)
def forward(
self,
token_ids: torch.Tensor,
attention_mask: torch.Tensor,
text_position_ids: torch.Tensor,
) -> torch.Tensor:
"""Extract hidden states from specific layers of Qwen3-VL.
Args:
token_ids: (B, L) token ids
attention_mask: (B, L) attention mask
text_position_ids: (B, L) position ids for text tokens
Returns:
(B, L, hidden_size * num_activation_layers) concatenated hidden states
"""
from transformers.masking_utils import create_causal_mask
language_model = self.model.language_model
inputs_embeds = language_model.embed_tokens(token_ids)
position_ids_4d = text_position_ids[None, ...].expand(4, text_position_ids.shape[0], -1)
text_position_ids_4d = position_ids_4d[0]
mrope_position_ids = position_ids_4d[1:]
causal_mask = create_causal_mask(
config=language_model.config,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
past_key_values=None,
position_ids=text_position_ids_4d,
)
position_embeddings = language_model.rotary_emb(inputs_embeds, mrope_position_ids)
tap_set = set(QWEN3_VL_ACTIVATION_LAYERS)
captured: dict[int, torch.Tensor] = {}
hidden_states = inputs_embeds
for layer_idx, decoder_layer in enumerate(language_model.layers):
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=text_position_ids_4d,
past_key_values=None,
position_embeddings=position_embeddings,
)
if layer_idx in tap_set:
captured[layer_idx] = hidden_states
selected = [captured[i] for i in QWEN3_VL_ACTIVATION_LAYERS]
stacked = torch.stack(selected, dim=0)
stacked = torch.permute(stacked, (1, 2, 3, 0))
batch_size, seq_len, hidden_size = stacked.shape[:3]
stacked = stacked.reshape(batch_size, seq_len, -1)
text_mask = attention_mask.to(stacked.dtype).unsqueeze(-1)
stacked = stacked * text_mask
return stacked.to(torch.float32)
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