745 lines
26 KiB
Python
745 lines
26 KiB
Python
import logging
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from copy import deepcopy
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from typing import Iterable, List, Optional, Sequence, Tuple
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import activations
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from sglang.srt.configs.kimi_k25 import KimiK25Config, KimiK25VisionConfig
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.mm_utils import (
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MultiModalityDataPaddingPatternMultimodalTokens,
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general_mm_embed_routine,
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)
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try:
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from transformers.activations import PytorchGELUTanh
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except ImportError:
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from transformers.activations import GELUTanh
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activations.PytorchGELUTanh = GELUTanh
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PytorchGELUTanh = GELUTanh
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from sglang.srt.layers.attention.vision import VisionAttention
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from sglang.srt.layers.linear import ReplicatedLinear
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from sglang.srt.managers.schedule_batch import (
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Modality,
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MultimodalDataItem,
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MultimodalInputs,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.deepseek_v2 import DeepseekV3ForCausalLM
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from sglang.srt.models.kimi_vl_moonvit import MLP2
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from sglang.srt.utils import add_prefix
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KIMIV_VT_INFER_MAX_PATCH_NUM = 16328
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logger = logging.getLogger(__name__)
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def apply_rope(
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xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, x_shape=None
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Args: (The leading dimensions of all inputs should be the same)
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xq: query, tensor of shape (..., num_heads, head_dim)
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xk: key, tensor of shape (..., num_heads, head_dim)
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freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid.
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Returns:
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xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
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"""
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freqs_cis = freqs_cis.unsqueeze(-2) # ..., 1, head_dim/2
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# ..., num_heads, head_dim/2
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xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
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xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
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xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim
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xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim
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return xq_out.type_as(xq), xk_out.type_as(xk)
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def tpool_patch_merger(
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x: torch.Tensor,
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grid_thws: torch.Tensor,
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merge_kernel_size: tuple[int, int] = (2, 2),
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) -> list[torch.Tensor]:
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d_model = x.size(-1)
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outputs = []
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pre_sum = 0
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for t, h, w in grid_thws.tolist():
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# Get the current sequence
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seq = x[pre_sum : pre_sum + t * h * w]
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# Reshape along self.merge_kernel_size and concat to the last dimension
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kernel_height, kernel_width = merge_kernel_size
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new_height, new_width = h // kernel_height, w // kernel_width
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reshaped_seq = seq.view(
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t, new_height, kernel_height, new_width, kernel_width, d_model
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)
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reshaped_seq = (
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reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean(dim=0)
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) # temporal pooling
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padded_seq = reshaped_seq.view(
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new_height * new_width, kernel_height * kernel_width, -1
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)
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outputs.append(padded_seq)
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pre_sum += t * h * w
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return outputs
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class MoonViTEncoderLayer(nn.Module):
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def __init__(
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self,
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num_heads: int,
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hidden_dim: int,
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mlp_dim: int,
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*,
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activation=F.gelu,
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attn_bias: bool = False,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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use_data_parallel: bool = False,
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):
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super().__init__()
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self.num_heads = num_heads
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self.hidden_dim = hidden_dim
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self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
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self.norm0 = nn.LayerNorm(hidden_dim)
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self.norm1 = nn.LayerNorm(hidden_dim)
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self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation)
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self.attn = VisionAttention(
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embed_dim=hidden_dim,
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num_heads=num_heads,
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projection_size=hidden_dim,
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use_qkv_parallel=True,
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qkv_bias=attn_bias,
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proj_bias=attn_bias,
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flatten_batch=True,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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use_data_parallel=use_data_parallel,
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customized_position_embedding_applier=apply_rope,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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cu_seqlens: torch.Tensor,
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max_seqlen: int,
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rope_freqs_cis: torch.Tensor | None = None,
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):
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residual = hidden_states
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hidden_states = self.norm0(hidden_states)
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hidden_states = self.attn(
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hidden_states,
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cu_seqlens=cu_seqlens,
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position_embeddings=rope_freqs_cis,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.norm1(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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def get_rope_shape_decorate(func):
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_get_rope_shape_first_call_flag = set()
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def wrapper(org, interpolation_mode, shape):
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key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
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if key not in _get_rope_shape_first_call_flag:
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_get_rope_shape_first_call_flag.add(key)
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_ = func(org, interpolation_mode, shape=(64, 64))
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return func(org, interpolation_mode, shape)
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return wrapper
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def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
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"""
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From:
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https://github.com/OpenGVLab/InternVideo/blob/421f6d2361fc8f61a3394244571f2601a4e99e29/InternVideo2/multi_modality/models/backbones/internvideo2/pos_embed.py#L86
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embed_dim: output dimension for each position
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pos: a list of positions to be encoded: size (M,)
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out: (M, D)
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"""
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assert embed_dim % 2 == 0
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omega = np.arange(embed_dim // 2, dtype=np.float32)
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omega /= embed_dim / 2.0
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omega = 1.0 / 10000**omega # (D/2,)
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pos = pos.reshape(-1) # (M,)
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out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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return emb
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@get_rope_shape_decorate
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@torch.compile(dynamic=True)
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def get_rope_shape(org, interpolation_mode, shape):
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return (
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F.interpolate(
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org.permute((2, 0, 1)).unsqueeze(0),
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size=shape,
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mode=interpolation_mode,
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)
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.squeeze(0)
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.permute((1, 2, 0))
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.flatten(end_dim=1)
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)
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def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
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"""
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t_size: int of the temporal size
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return:
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pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token)
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"""
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grid_t = np.arange(t_size, dtype=np.float32)
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pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
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if cls_token:
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pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
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return pos_embed
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class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
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def __init__(
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self,
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height: int,
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width: int,
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num_frames: int,
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dim: int,
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interpolation_mode: str = "bicubic",
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) -> None:
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super().__init__()
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self.height = height
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self.width = width
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self.num_frames = num_frames
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self.dim = dim
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self.interpolation_mode = interpolation_mode
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self.weight = nn.Parameter(torch.empty(height, width, dim))
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self.register_buffer(
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"time_weight",
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torch.from_numpy(get_1d_sincos_pos_embed(self.dim, self.num_frames))
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.float()
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.unsqueeze(1),
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persistent=False,
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)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.normal_(self.weight)
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def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
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pos_embs = []
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for t, h, w in grid_thws.tolist():
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assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
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if (h, w) == self.weight.shape[:-1]:
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pos_emb_2d = self.weight.flatten(end_dim=1)
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else:
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pos_emb_2d = get_rope_shape(
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self.weight,
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interpolation_mode=self.interpolation_mode,
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shape=(h, w),
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)
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if t == 1:
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pos_emb_3d = pos_emb_2d
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else:
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pos_emb_3d = (
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pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
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)
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pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
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out = x + torch.cat(pos_embs)
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return out
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class Rope2DPosEmbRepeated(nn.Module):
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"""2D rotary position embedding with multi-resolution support.
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This class is intended to be used in the following way:
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1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis.
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2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration.
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3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation.
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The rope is shared across all attention layers and all heads.
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Refs:
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- RoFormer: https://arxiv.org/abs/2104.09864
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- VisionLLaMA: https://arxiv.org/abs/2403.00522
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- https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py
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Args:
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dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed)
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max_height (int): the maximum height of the 2D grid
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max_width (int): the maximum width of the 2D grid
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theta_base (float): the base of the theta
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"""
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def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000):
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super().__init__()
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self.dim = dim
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assert self.dim % 4 == 0, "dim must be divisible by 4"
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self.max_height = max_height
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self.max_width = max_width
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self.theta_base = theta_base
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def extra_repr(self):
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return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}"
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def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
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"""Calculate the cis(freqs) for each position in the 2D grid.
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Return: complex tensor of shape (max_height, max_width, dim//2) and value:
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height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim))
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weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim)) with (i in [0, dim//4))
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note: `cis` is a mathematical notation defined by cis x = cos x + i sin x,
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"""
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N = self.max_height * self.max_width
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flat_pos = torch.arange(0, N).float().to(device)
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x_pos = flat_pos % self.max_width
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y_pos = flat_pos // self.max_width
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dim_range = (
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torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device)
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) # C/4
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freqs = 1.0 / (self.theta_base ** (dim_range / self.dim))
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x_freqs = torch.outer(x_pos, freqs).float() # N, C/4
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y_freqs = torch.outer(y_pos, freqs).float() # N, C/4
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x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs) # N, C/4
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y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs) # N, C/4
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# N, C/4, 2
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freqs_cis = torch.cat(
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[x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1
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)
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# max_height, max_width, C/2
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freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
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return freqs_cis
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def get_freqs_cis(
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self, grid_thws: torch.Tensor, device: torch.device
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) -> torch.Tensor:
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"""
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Args:
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grid_thws (torch.Tensor): grid time, height and width
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Returns:
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freqs_cis: tensor of shape (sum(t * height * width), dim//2)
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"""
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if not hasattr(self, "freqs_cis"):
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self.register_buffer(
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"freqs_cis", self._precompute_freqs_cis(device), persistent=False
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)
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shapes = grid_thws.tolist()
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assert all(
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1 <= h <= self.max_height and 1 <= w <= self.max_width for t, h, w in shapes
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), (
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shapes,
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self.max_height,
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self.max_width,
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)
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freqs_cis = torch.cat(
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[
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self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
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for t, h, w in shapes
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],
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dim=0,
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)
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return freqs_cis
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class MoonVision3dPatchEmbed(nn.Module):
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def __init__(
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self,
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out_dim: int,
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in_dim: int = 3,
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patch_size: int | tuple[int, int] = (14, 14),
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pos_emb_height: int = 14,
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pos_emb_width: int = 14,
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pos_emb_time: int = 4,
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pos_emb_type: str = "divided_fixed",
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):
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super().__init__()
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assert isinstance(
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patch_size, int | Sequence
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), f"Invalid patch_size type: {type(patch_size)}"
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if isinstance(patch_size, int):
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patch_size = (patch_size, patch_size)
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assert (
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len(patch_size) == 2
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), f"Expected patch_size to be a tuple of 2, got {patch_size}"
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self.patch_size = patch_size
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self.proj = nn.Conv2d(
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in_dim, out_dim, kernel_size=patch_size, stride=patch_size
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)
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if pos_emb_type == "divided_fixed":
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self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
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height=pos_emb_height,
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width=pos_emb_width,
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num_frames=pos_emb_time,
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dim=out_dim,
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)
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else:
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raise NotImplementedError(f"Not support pos_emb_type: {pos_emb_type}")
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def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x (L, Channels): input tensor
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grid_hws (N, 3): temporal, height and width
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Returns:
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(L, Cout) tensor
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"""
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x = self.proj(x).view(x.size(0), -1)
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# apply positional embedding
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x = self.pos_emb(x, grid_thws)
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return x
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class MoonViT3dEncoder(nn.Module):
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def __init__(
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self,
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hidden_dim: int,
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num_layers: int,
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block_cfg: dict,
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video_attn_type: str = "spatial_temporal",
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) -> None:
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super().__init__()
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assert (
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video_attn_type == "spatial_temporal"
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), f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
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self.video_attn_type = video_attn_type
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self.rope_2d = Rope2DPosEmbRepeated(
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block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512
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)
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self.blocks = nn.ModuleList(
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[MoonViTEncoderLayer(**block_cfg) for _ in range(num_layers)]
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)
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self.final_layernorm = nn.LayerNorm(hidden_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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grid_thws: torch.Tensor,
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) -> torch.Tensor:
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rope_freqs_cis = self.rope_2d.get_freqs_cis(
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grid_thws=grid_thws, device=hidden_states.device
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)
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lengths = torch.cat(
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(
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torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
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grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
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)
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)
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max_seqlen = lengths.max()
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cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
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for block in self.blocks:
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hidden_states = block(
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hidden_states, cu_seqlens, max_seqlen, rope_freqs_cis=rope_freqs_cis
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)
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hidden_states = self.final_layernorm(hidden_states)
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|
|
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]
|