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model: support Kimi-K2.5 (#17789)

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Co-authored-by: Mick <mickjagger19@icloud.com>
This commit is contained in:
Yuhao Yang
2026-01-27 10:57:00 +08:00
committed by GitHub
co-authored by Mick
parent 0519b0935f
commit 479ab7a4e7
11 changed files with 1053 additions and 12 deletions
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python/sglang/srt/models/kimi_k25.py
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import logging
from copy import deepcopy
from typing import Iterable, List, Optional, Sequence, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import activations
from sglang.srt.configs.kimi_k25 import KimiK25Config, KimiK25VisionConfig
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.managers.mm_utils import (
MultiModalityDataPaddingPatternMultimodalTokens,
general_mm_embed_routine,
)
try:
from transformers.activations import PytorchGELUTanh
except ImportError:
from transformers.activations import GELUTanh
activations.PytorchGELUTanh = GELUTanh
PytorchGELUTanh = GELUTanh
from sglang.srt.layers.attention.vision import VisionAttention
from sglang.srt.layers.linear import ReplicatedLinear
from sglang.srt.managers.schedule_batch import (
Modality,
MultimodalDataItem,
MultimodalInputs,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.deepseek_v2 import DeepseekV3ForCausalLM
from sglang.srt.models.kimi_vl_moonvit import MLP2
from sglang.srt.utils import add_prefix
KIMIV_VT_INFER_MAX_PATCH_NUM = 16328
logger = logging.getLogger(__name__)
def apply_rope(
xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, x_shape=None
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Args: (The leading dimensions of all inputs should be the same)
xq: query, tensor of shape (..., num_heads, head_dim)
xk: key, tensor of shape (..., num_heads, head_dim)
freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid.
Returns:
xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
"""
freqs_cis = freqs_cis.unsqueeze(-2) # ..., 1, head_dim/2
# ..., num_heads, head_dim/2
xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim
return xq_out.type_as(xq), xk_out.type_as(xk)
def tpool_patch_merger(
x: torch.Tensor,
grid_thws: torch.Tensor,
merge_kernel_size: tuple[int, int] = (2, 2),
) -> list[torch.Tensor]:
d_model = x.size(-1)
outputs = []
pre_sum = 0
for t, h, w in grid_thws.tolist():
# Get the current sequence
seq = x[pre_sum : pre_sum + t * h * w]
# Reshape along self.merge_kernel_size and concat to the last dimension
kernel_height, kernel_width = merge_kernel_size
new_height, new_width = h // kernel_height, w // kernel_width
reshaped_seq = seq.view(
t, new_height, kernel_height, new_width, kernel_width, d_model
)
reshaped_seq = (
reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean(dim=0)
) # temporal pooling
padded_seq = reshaped_seq.view(
new_height * new_width, kernel_height * kernel_width, -1
)
outputs.append(padded_seq)
pre_sum += t * h * w
return outputs
class MoonViTEncoderLayer(nn.Module):
def __init__(
self,
num_heads: int,
hidden_dim: int,
mlp_dim: int,
*,
activation=F.gelu,
attn_bias: bool = False,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
use_data_parallel: bool = False,
):
super().__init__()
self.num_heads = num_heads
self.hidden_dim = hidden_dim
self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
self.norm0 = nn.LayerNorm(hidden_dim)
self.norm1 = nn.LayerNorm(hidden_dim)
self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation)
self.attn = VisionAttention(
embed_dim=hidden_dim,
num_heads=num_heads,
projection_size=hidden_dim,
use_qkv_parallel=True,
qkv_bias=attn_bias,
proj_bias=attn_bias,
flatten_batch=True,
quant_config=quant_config,
prefix=add_prefix("attn", prefix),
use_data_parallel=use_data_parallel,
customized_position_embedding_applier=apply_rope,
)
def forward(
self,
hidden_states: torch.Tensor,
cu_seqlens: torch.Tensor,
max_seqlen: int,
rope_freqs_cis: torch.Tensor | None = None,
):
residual = hidden_states
hidden_states = self.norm0(hidden_states)
hidden_states = self.attn(
hidden_states,
cu_seqlens=cu_seqlens,
position_embeddings=rope_freqs_cis,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
def get_rope_shape_decorate(func):
_get_rope_shape_first_call_flag = set()
def wrapper(org, interpolation_mode, shape):
key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
if key not in _get_rope_shape_first_call_flag:
_get_rope_shape_first_call_flag.add(key)
_ = func(org, interpolation_mode, shape=(64, 64))
return func(org, interpolation_mode, shape)
return wrapper
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
From:
https://github.com/OpenGVLab/InternVideo/blob/421f6d2361fc8f61a3394244571f2601a4e99e29/InternVideo2/multi_modality/models/backbones/internvideo2/pos_embed.py#L86
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
@get_rope_shape_decorate
@torch.compile(dynamic=True)
def get_rope_shape(org, interpolation_mode, shape):
return (
F.interpolate(
org.permute((2, 0, 1)).unsqueeze(0),
size=shape,
mode=interpolation_mode,
)
.squeeze(0)
.permute((1, 2, 0))
.flatten(end_dim=1)
)
def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
"""
t_size: int of the temporal size
return:
pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token)
"""
grid_t = np.arange(t_size, dtype=np.float32)
pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
def __init__(
self,
height: int,
width: int,
num_frames: int,
dim: int,
interpolation_mode: str = "bicubic",
) -> None:
super().__init__()
self.height = height
self.width = width
self.num_frames = num_frames
self.dim = dim
self.interpolation_mode = interpolation_mode
self.weight = nn.Parameter(torch.empty(height, width, dim))
self.register_buffer(
"time_weight",
torch.from_numpy(get_1d_sincos_pos_embed(self.dim, self.num_frames))
.float()
.unsqueeze(1),
persistent=False,
)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.weight)
def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
pos_embs = []
for t, h, w in grid_thws.tolist():
assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
if (h, w) == self.weight.shape[:-1]:
pos_emb_2d = self.weight.flatten(end_dim=1)
else:
pos_emb_2d = get_rope_shape(
self.weight,
interpolation_mode=self.interpolation_mode,
shape=(h, w),
)
if t == 1:
pos_emb_3d = pos_emb_2d
else:
pos_emb_3d = (
pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
)
pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
out = x + torch.cat(pos_embs)
return out
class Rope2DPosEmbRepeated(nn.Module):
"""2D rotary position embedding with multi-resolution support.
This class is intended to be used in the following way:
1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis.
2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration.
3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation.
The rope is shared across all attention layers and all heads.
Refs:
- RoFormer: https://arxiv.org/abs/2104.09864
- VisionLLaMA: https://arxiv.org/abs/2403.00522
- https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py
Args:
dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed)
max_height (int): the maximum height of the 2D grid
max_width (int): the maximum width of the 2D grid
theta_base (float): the base of the theta
"""
def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000):
super().__init__()
self.dim = dim
assert self.dim % 4 == 0, "dim must be divisible by 4"
self.max_height = max_height
self.max_width = max_width
self.theta_base = theta_base
def extra_repr(self):
return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}"
def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
"""Calculate the cis(freqs) for each position in the 2D grid.
Return: complex tensor of shape (max_height, max_width, dim//2) and value:
height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim))
weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim)) with (i in [0, dim//4))
note: `cis` is a mathematical notation defined by cis x = cos x + i sin x,
"""
N = self.max_height * self.max_width
flat_pos = torch.arange(0, N).float().to(device)
x_pos = flat_pos % self.max_width
y_pos = flat_pos // self.max_width
dim_range = (
torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device)
) # C/4
freqs = 1.0 / (self.theta_base ** (dim_range / self.dim))
x_freqs = torch.outer(x_pos, freqs).float() # N, C/4
y_freqs = torch.outer(y_pos, freqs).float() # N, C/4
x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs) # N, C/4
y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs) # N, C/4
# N, C/4, 2
freqs_cis = torch.cat(
[x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1
)
# max_height, max_width, C/2
freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
return freqs_cis
def get_freqs_cis(
self, grid_thws: torch.Tensor, device: torch.device
) -> torch.Tensor:
"""
Args:
grid_thws (torch.Tensor): grid time, height and width
Returns:
freqs_cis: tensor of shape (sum(t * height * width), dim//2)
"""
if not hasattr(self, "freqs_cis"):
self.register_buffer(
"freqs_cis", self._precompute_freqs_cis(device), persistent=False
)
shapes = grid_thws.tolist()
assert all(
1 <= h <= self.max_height and 1 <= w <= self.max_width for t, h, w in shapes
), (
shapes,
self.max_height,
self.max_width,
)
freqs_cis = torch.cat(
[
self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
for t, h, w in shapes
],
dim=0,
)
return freqs_cis
class MoonVision3dPatchEmbed(nn.Module):
def __init__(
self,
out_dim: int,
in_dim: int = 3,
patch_size: int | tuple[int, int] = (14, 14),
pos_emb_height: int = 14,
pos_emb_width: int = 14,
pos_emb_time: int = 4,
pos_emb_type: str = "divided_fixed",
):
super().__init__()
assert isinstance(
patch_size, int | Sequence
), f"Invalid patch_size type: {type(patch_size)}"
if isinstance(patch_size, int):
patch_size = (patch_size, patch_size)
assert (
len(patch_size) == 2
), f"Expected patch_size to be a tuple of 2, got {patch_size}"
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_dim, out_dim, kernel_size=patch_size, stride=patch_size
)
if pos_emb_type == "divided_fixed":
self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
height=pos_emb_height,
width=pos_emb_width,
num_frames=pos_emb_time,
dim=out_dim,
)
else:
raise NotImplementedError(f"Not support pos_emb_type: {pos_emb_type}")
def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
"""
Args:
x (L, Channels): input tensor
grid_hws (N, 3): temporal, height and width
Returns:
(L, Cout) tensor
"""
x = self.proj(x).view(x.size(0), -1)
# apply positional embedding
x = self.pos_emb(x, grid_thws)
return x
class MoonViT3dEncoder(nn.Module):
def __init__(
self,
hidden_dim: int,
num_layers: int,
block_cfg: dict,
video_attn_type: str = "spatial_temporal",
) -> None:
super().__init__()
assert (
video_attn_type == "spatial_temporal"
), f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
self.video_attn_type = video_attn_type
self.rope_2d = Rope2DPosEmbRepeated(
block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512
)
self.blocks = nn.ModuleList(
[MoonViTEncoderLayer(**block_cfg) for _ in range(num_layers)]
)
self.final_layernorm = nn.LayerNorm(hidden_dim)
def forward(
self,
hidden_states: torch.Tensor,
grid_thws: torch.Tensor,
) -> torch.Tensor:
rope_freqs_cis = self.rope_2d.get_freqs_cis(
grid_thws=grid_thws, device=hidden_states.device
)
lengths = torch.cat(
(
torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
)
)
max_seqlen = lengths.max()
cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
for block in self.blocks:
hidden_states = block(
hidden_states, cu_seqlens, max_seqlen, rope_freqs_cis=rope_freqs_cis
)
hidden_states = self.final_layernorm(hidden_states)
return hidden_states
class MoonViT3dPretrainedModel(nn.Module):
model_type = "moonvit3d"
_no_split_modules = ["PackingTransformer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def __init__(self, config, *inputs, **kwargs):
super().__init__()
config = deepcopy(config)
self.merge_kernel_size = config.merge_kernel_size
self.patch_size = config.patch_size
self.merge_type = config.merge_type
self.patch_embed = MoonVision3dPatchEmbed(
out_dim=config.hidden_size,
patch_size=config.patch_size,
pos_emb_height=config.init_pos_emb_height,
pos_emb_width=config.init_pos_emb_width,
pos_emb_time=config.init_pos_emb_time,
pos_emb_type=config.pos_emb_type,
)
self.encoder = MoonViT3dEncoder(
hidden_dim=config.hidden_size,
num_layers=config.num_hidden_layers,
block_cfg={
"num_heads": config.num_attention_heads,
"hidden_dim": config.hidden_size,
"mlp_dim": config.intermediate_size,
"activation": PytorchGELUTanh(),
"attn_bias": True,
},
video_attn_type=config.video_attn_type,
)
@property
def dtype(self) -> torch.dtype:
return self.patch_embed.proj.weight.dtype
@property
def device(self) -> torch.device:
return self.patch_embed.proj.weight.device
def forward(
self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
) -> torch.Tensor:
"""
Args:
pixel_values (torch.Tensor): The input pixel values.
grid_thws (torch.Tensor): Temporal, height and width.
Returns:
torch.Tensor: The output tokens.
"""
assert grid_thws.ndim == 2, f"grid_thws should be 2D, got {grid_thws.ndim}"
assert grid_thws.size(1) == 3, f"No support for _thw: {grid_thws}"
hidden_states = self.patch_embed(pixel_values, grid_thws)
hidden_states = self.encoder(hidden_states, grid_thws)
hidden_states = hidden_states.squeeze(0)
# spatial downsampling 2x with temporal pooling all
hidden_states = tpool_patch_merger(
hidden_states, grid_thws, merge_kernel_size=self.merge_kernel_size
)
return hidden_states
class K2VLMultiModalProjector(nn.Module):
"""Multi-modal projector with patch merging for K2-VL."""
def __init__(
self,
config: KimiK25VisionConfig,
use_data_parallel: bool = False,
prefix: str = "",
):
super().__init__()
self.use_data_parallel = use_data_parallel
# Hidden size after patch merging
merge_h, merge_w = config.merge_kernel_size
self.hidden_size = config.vt_hidden_size * merge_h * merge_w
self.pre_norm = torch.nn.LayerNorm(config.vt_hidden_size, eps=1e-5)
self.linear_1 = ReplicatedLinear(
self.hidden_size,
self.hidden_size,
bias=True,
prefix=add_prefix(prefix, "linear_1"),
)
self.linear_2 = ReplicatedLinear(
self.hidden_size,
config.text_hidden_size,
bias=True,
prefix=add_prefix(prefix, "linear_2"),
)
self.act = nn.GELU()
def forward(self, image_features: torch.Tensor) -> torch.Tensor:
hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
hidden_states, _ = self.linear_1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states, _ = self.linear_2(hidden_states)
return hidden_states
@torch.inference_mode()
def mm_projection_auto(
mm_projector: torch.nn.Module | None, vt_output: list[torch.Tensor]
):
"""Apply MM projector to vision tower outputs."""
if mm_projector is None:
return vt_output
num_embedding_list = [x.shape[0] for x in vt_output]
batched = torch.cat(vt_output, dim=0)
proj_out = mm_projector(batched) if mm_projector else batched
proj_out = proj_out.reshape(-1, proj_out.shape[-1])
proj_out = torch.split(proj_out, num_embedding_list)
return proj_out
@torch.inference_mode()
def vision_tower_forward_auto(
vision_tower: torch.nn.Module,
pixel_values: torch.Tensor,
grid_thw: torch.Tensor,
mm_projector: torch.nn.Module | None = None,
) -> list[torch.Tensor]:
"""Auto-batched vision tower forward."""
assert isinstance(
pixel_values, torch.Tensor
), "expect pixel_values to be a tensor, get {}".format(type(pixel_values))
n = grid_thw.shape[0]
n_patches_each_media = grid_thw.prod(-1)
max_infer_batch = max(n_patches_each_media.max(), KIMIV_VT_INFER_MAX_PATCH_NUM)
logger.debug(
"vt max_infer_batch: %s, KIMIV_VT_INFER_MAX_PATCH_NUM: %s",
max_infer_batch,
KIMIV_VT_INFER_MAX_PATCH_NUM,
)
tensors = []
pre_sum = 0
current_group_start = 0
current_group_patches = 0
for i in range(n):
current_media_patches = n_patches_each_media[i].item()
if current_group_patches + current_media_patches <= max_infer_batch:
current_group_patches += current_media_patches
else:
if current_group_start < i:
group_grid_thw = grid_thw[current_group_start:i]
group_n_patches = n_patches_each_media[current_group_start:i].sum()
group_input = pixel_values[pre_sum : pre_sum + group_n_patches]
group_output = vision_tower(group_input, group_grid_thw)
proj_out = mm_projection_auto(mm_projector, group_output)
tensors.extend(proj_out)
pre_sum += group_n_patches
current_group_start = i
current_group_patches = current_media_patches
# Process the last group
if current_group_start < n:
group_grid_thw = grid_thw[current_group_start:n]
group_n_patches = n_patches_each_media[current_group_start:n].sum()
group_input = pixel_values[pre_sum : pre_sum + group_n_patches]
group_output = vision_tower(group_input, group_grid_thw)
proj_out = mm_projection_auto(mm_projector, group_output)
tensors.extend(proj_out)
return tensors
class KimiK25ForConditionalGeneration(nn.Module):
def __init__(
self,
config: KimiK25Config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
**kwargs, # fix init_tts argument error
) -> None:
super().__init__()
self.config = config
# Create vision tower
self.vision_tower = MoonViT3dPretrainedModel(config.vision_config)
# Create mm projector
self.mm_projector = K2VLMultiModalProjector(config.vision_config)
self.language_model = DeepseekV3ForCausalLM(config.text_config, quant_config)
# Ensure that the dtype of the vision_tower and mm_projector matches that of the language_model.
# This solves the dtype mismatch issue when using device_map="auto" and torch_dtype.
if hasattr(self.language_model, "dtype"):
target_dtype = self.language_model.dtype
self.vision_tower = self.vision_tower.to(dtype=target_dtype)
self.mm_projector = self.mm_projector.to(dtype=target_dtype)
def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
pixel_values = torch.cat([item.feature for item in items], dim=0).type(
self.vision_tower.dtype
)
grid_thws = torch.concat([item.grid_thws for item in items], dim=0).to(
self.vision_tower.device
)
target_dtype = self.vision_tower.patch_embed.proj.weight.dtype
pixel_values = pixel_values.to(target_dtype)
image_features = vision_tower_forward_auto(
self.vision_tower,
pixel_values,
grid_thws,
mm_projector=self.mm_projector,
)
image_features = torch.cat(image_features, dim=0)
return image_features
def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
pattern = MultiModalityDataPaddingPatternMultimodalTokens()
return pattern.pad_input_tokens(input_ids, mm_inputs)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
get_embedding: bool = False,
):
hidden_states = general_mm_embed_routine(
input_ids=input_ids,
forward_batch=forward_batch,
language_model=self.language_model,
data_embedding_funcs={
Modality.IMAGE: self.get_image_feature,
},
positions=positions,
)
return hidden_states
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
"""Load weights for the model, separating vision and language weights"""
weights = list(weights)
# Separate vision tower weights and language model weights
vision_weights = []
language_weights = []
for name, loaded_weight in weights:
if "vision_tower" in name or "mm_projector" in name:
name = name.replace(r"wqkv.", r"attn.qkv_proj.")
name = name.replace(r"wo.", r"attn.proj.")
name = name.replace("mm_projector.proj.0", "mm_projector.linear_1")
name = name.replace("mm_projector.proj.2", "mm_projector.linear_2")
vision_weights.append((name, loaded_weight))
else:
name = name.replace("language_model.", "")
# All other weights go to language model
language_weights.append((name, loaded_weight))
# Load vision tower weights
vision_state_dict = dict(vision_weights)
params_dict = dict(self.named_parameters(remove_duplicate=False))
for name, loaded_weight in vision_state_dict.items():
if name not in params_dict:
raise ValueError(f"Weight {name} not found in params_dict")
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
# loaded_weight = self._pad_vit_attn_dummy_heads(name, loaded_weight)
weight_loader(param, loaded_weight)
# Load language model weights
if language_weights:
self.language_model.load_weights(language_weights)
EntryClass = [KimiK25ForConditionalGeneration]