[Model] Support DeepSeek-OCR-2 (#33165)
Signed-off-by: liuli <ll407707@alibaba-inc.com> Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Co-authored-by: liuli <ll407707@alibaba-inc.com> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
This commit is contained in:
RED
@@ -79,6 +79,7 @@ class ImageEncoderViT(nn.Module):
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rel_pos_zero_init: bool = True,
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window_size: int = 0,
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global_attn_indexes: tuple[int, ...] = (),
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last_conv_output: int = 1024,
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) -> None:
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"""
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Args:
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@@ -155,7 +156,7 @@ class ImageEncoderViT(nn.Module):
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256, 512, kernel_size=3, stride=2, padding=1, bias=False
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)
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self.net_3 = Conv2dLayer(
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512, 1024, kernel_size=3, stride=2, padding=1, bias=False
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512, last_conv_output, kernel_size=3, stride=2, padding=1, bias=False
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)
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def get_abs_pos(self, abs_pos: torch.Tensor, tgt_size: int):
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283
vllm/model_executor/models/deepencoder2.py
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283
vllm/model_executor/models/deepencoder2.py
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@@ -0,0 +1,283 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# adapted from
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# https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/DeepSeek-OCR2-master/DeepSeek-OCR2-vllm/deepencoderv2/qwen2_d2e.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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import transformers
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class CustomQwen2Decoder(nn.Module):
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"""
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Qwen2 visual encoder
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non-causal attention + causal attention
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token_type_ids ：0=non-causal, 1=causal
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"""
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def __init__(
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self,
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decoder_layer: int = 24,
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max_position_embeddings: int = 131072,
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hidden_dimension: int = 896,
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num_attention_heads: int = 14,
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num_key_value_heads: int = 2,
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intermediate_size: int = 4864,
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vocab_size: int = 151936,
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attn_implementation: str = "sdpa", # ⭐
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rms_norm_eps: float = 1e-06,
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rope_theta: float = 1000000.0,
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attention_dropout: float = 0.0,
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hidden_act: str = "silu",
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initializer_range: float = 0.02,
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):
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super().__init__()
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# load
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Qwen2Model = transformers.models.qwen2.modeling_qwen2.Qwen2Model
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Qwen2Config = transformers.Qwen2Config
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# config
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config = Qwen2Config(
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hidden_size=hidden_dimension,
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num_hidden_layers=decoder_layer,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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intermediate_size=intermediate_size,
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max_position_embeddings=max_position_embeddings,
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vocab_size=vocab_size,
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rms_norm_eps=rms_norm_eps,
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rope_theta=rope_theta,
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attention_dropout=attention_dropout,
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hidden_act=hidden_act,
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initializer_range=initializer_range,
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_attn_implementation=attn_implementation, # ⭐
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)
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#
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self.model = self._create_custom_model(Qwen2Model, config)
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del self.model.embed_tokens
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def _create_custom_model(self, Qwen2Model, config):
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"""Qwen2Model"""
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class CustomQwen2ModelInner(Qwen2Model):
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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past_key_values=None,
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inputs_embeds=None,
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token_type_ids=None, # ⭐
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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cache_position=None,
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):
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# token_type_ids
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self._current_token_type_ids = token_type_ids
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causal_mask_mapping = {
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"full_attention": self._update_causal_mask(
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attention_mask,
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inputs_embeds,
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cache_position,
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past_key_values,
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output_attentions,
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)
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}
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outputs = super().forward(
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input_ids=input_ids,
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attention_mask=causal_mask_mapping,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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return outputs
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def _update_causal_mask(
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self,
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attention_mask,
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input_tensor,
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cache_position,
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past_key_values,
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output_attentions,
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):
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dtype, device = input_tensor.dtype, input_tensor.device
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min_dtype = torch.finfo(dtype).min
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batch_size, sequence_length = (
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input_tensor.shape[0],
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input_tensor.shape[1],
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)
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token_type_ids = self._current_token_type_ids
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# attention mask
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causal_mask = self._create_custom_4d_mask(
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sequence_length=sequence_length,
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dtype=dtype,
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device=device,
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batch_size=batch_size,
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token_type_ids=token_type_ids,
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)
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# padding mask
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if attention_mask is not None and attention_mask.dim() == 2:
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padding_mask = attention_mask[:, None, None, :].to(dtype=dtype)
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padding_mask = (1.0 - padding_mask) * min_dtype
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causal_mask = causal_mask + padding_mask
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return causal_mask
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def _create_custom_4d_mask(
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self,
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sequence_length,
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dtype,
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device,
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batch_size,
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token_type_ids,
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):
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min_dtype = torch.finfo(dtype).min
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masks = []
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for b in range(batch_size):
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mask = torch.full(
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(sequence_length, sequence_length),
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fill_value=min_dtype,
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dtype=dtype,
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device=device,
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)
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type_ids = token_type_ids[b]
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image_positions = (type_ids == 0).nonzero(as_tuple=True)[0]
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text_positions = (type_ids == 1).nonzero(as_tuple=True)[0]
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# non-casual
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if len(image_positions) > 0:
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mask[image_positions[:, None], image_positions] = 0.0
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# causal
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for i, text_pos in enumerate(text_positions):
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if len(image_positions) > 0:
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mask[text_pos, image_positions] = 0.0
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mask[text_pos, text_positions[: i + 1]] = 0.0
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masks.append(mask)
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mask = torch.stack(masks, dim=0).unsqueeze(1)
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return mask
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return CustomQwen2ModelInner(config)
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def forward(
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self,
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inputs_embeds: torch.Tensor,
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token_type_ids: torch.Tensor,
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attention_mask: torch.Tensor = None,
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**kwargs,
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):
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"""
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Args:
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inputs_embeds: [batch_size, seq_len, hidden_dim]
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token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal
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attention_mask: [batch_size, seq_len], optional
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"""
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return self.model(
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inputs_embeds=inputs_embeds,
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token_type_ids=token_type_ids,
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attention_mask=attention_mask,
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**kwargs,
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)
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class Qwen2Decoder2Encoder(nn.Module):
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"""
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Decoder based on Multilingual BART
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Set the initial weights and configuration with a pretrained multilingual BART model,
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and modify the detailed configurations as a Nougat decoder
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"""
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def __init__(
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self,
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decoder_layer: int,
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hidden_dimension: int,
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num_attention_heads: int,
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num_key_value_heads: int,
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intermediate_size: int,
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):
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super().__init__()
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self.model = CustomQwen2Decoder(
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decoder_layer=decoder_layer,
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hidden_dimension=hidden_dimension,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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intermediate_size=intermediate_size,
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attn_implementation="sdpa",
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)
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self.query_768 = nn.Embedding(144, hidden_dimension)
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self.query_1024 = nn.Embedding(256, hidden_dimension)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = x.flatten(2).transpose(1, 2)
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bs, n_query, _ = x.shape
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if n_query == 144:
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param_img = self.query_768.weight
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elif n_query == 256:
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param_img = self.query_1024.weight
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batch_query_imgs = param_img.unsqueeze(0).expand(
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bs, -1, -1
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) # (batch_size, num_queries, hidden_size)
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x_combined = torch.cat([x, batch_query_imgs], dim=1)
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token_type_ids = torch.cat(
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[
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torch.zeros(bs, n_query, dtype=torch.long),
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torch.ones(bs, n_query, dtype=torch.long),
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],
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dim=1,
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)
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y = self.model(x_combined, token_type_ids)[0]
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y = y[:, n_query:, :] # causal flow query
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return y
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def build_qwen2_decoder_as_encoder(
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decoder_layer=24,
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hidden_dimension=896,
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num_attention_heads=14,
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num_key_value_heads=2,
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intermediate_size=4864,
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):
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decoder_as_encoder = Qwen2Decoder2Encoder(
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decoder_layer=decoder_layer,
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hidden_dimension=hidden_dimension,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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intermediate_size=intermediate_size,
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)
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return decoder_as_encoder
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444
vllm/model_executor/models/deepseek_ocr2.py
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444
vllm/model_executor/models/deepseek_ocr2.py
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@@ -0,0 +1,444 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Deepseek-OCR model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from functools import partial
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import torch
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import torch.nn as nn
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from transformers import BatchFeature
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.model_executor.models.interfaces import (
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MultiModalEmbeddings,
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SupportsLoRA,
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SupportsMultiModal,
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SupportsPP,
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)
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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WeightsMapper,
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init_vllm_registered_model,
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maybe_prefix,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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NestedTensors,
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)
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from vllm.multimodal.parse import (
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ImageEmbeddingItems,
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ImageProcessorItems,
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ImageSize,
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MultiModalDataItems,
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)
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from vllm.multimodal.processing import (
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BaseDummyInputsBuilder,
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.sequence import IntermediateTensors
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from vllm.tokenizers import cached_tokenizer_from_config
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from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
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from vllm.transformers_utils.processors.deepseek_ocr2 import (
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BASE_SIZE,
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CROP_MODE,
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IMAGE_SIZE,
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DeepseekOCR2Processor,
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)
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from ...transformers_utils.processors.deepseek_ocr import count_tiles
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from .deepencoder import ImageEncoderViT
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from .deepencoder2 import build_qwen2_decoder_as_encoder
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from .deepseek_ocr import DeepseekOCRImagePixelInputs
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from .deepseek_vl2 import MlpProjector
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# The image token id may be various
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_IMAGE_TOKEN = "<image>"
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class DeepseekOCR2ProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(DeepseekVLV2Config)
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(DeepseekOCR2Processor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": None}
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def get_num_image_tokens(
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self, *, image_width: int, image_height: int, cropping: bool = True
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) -> int:
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image_size = IMAGE_SIZE
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base_size = BASE_SIZE
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patch_size = 16
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downsample_ratio = 4
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if CROP_MODE:
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if image_width <= 768 and image_height <= 768:
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crop_ratio = [1, 1]
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else:
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# find the closest aspect ratio to the target
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crop_ratio = count_tiles(
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image_width, image_height, image_size=IMAGE_SIZE
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)
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num_width_tiles, num_height_tiles = crop_ratio
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else:
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num_width_tiles = num_height_tiles = 1
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h = w = math.ceil((base_size // patch_size) / downsample_ratio)
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h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio)
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global_views_tokens = h * w
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if num_width_tiles > 1 or num_height_tiles > 1:
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local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2)
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else:
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local_views_tokens = 0
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return global_views_tokens + local_views_tokens + 1
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def get_image_size_with_most_features(self) -> ImageSize:
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if IMAGE_SIZE == 1024 and BASE_SIZE == 1280:
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return ImageSize(width=1024 * 2, height=1024 * 2)
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return ImageSize(width=768 * 2, height=768 * 2)
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class DeepseekOCR2DummyInputsBuilder(
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BaseDummyInputsBuilder[DeepseekOCR2ProcessingInfo]
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):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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return image_token * num_images
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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mm_options: Mapping[str, BaseDummyOptions] | None = None,
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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max_image_size = self.info.get_image_size_with_most_features()
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return {
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"image": self._get_dummy_images(
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width=max_image_size.width,
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height=max_image_size.height,
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num_images=num_images,
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)
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}
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class DeepseekOCR2MultiModalProcessor(
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BaseMultiModalProcessor[DeepseekOCR2ProcessingInfo]
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):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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if mm_data:
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processed_outputs = self.info.ctx.call_hf_processor(
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self.info.get_hf_processor(**mm_kwargs),
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dict(prompt=prompt, **mm_data),
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mm_kwargs,
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)
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else:
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tokenizer = self.info.get_tokenizer()
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processed_outputs = tokenizer(
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prompt, add_special_tokens=True, return_tensors="pt"
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)
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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images_spatial_crop = hf_inputs.get("images_spatial_crop", torch.empty((0, 2)))
|
||||
is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
|
||||
patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
|
||||
return dict(
|
||||
pixel_values=MultiModalFieldConfig.batched("image"),
|
||||
images_spatial_crop=MultiModalFieldConfig.batched("image"),
|
||||
images_crop=MultiModalFieldConfig.flat_from_sizes(
|
||||
"image", patches_per_image
|
||||
),
|
||||
)
|
||||
|
||||
def _get_prompt_updates(
|
||||
self,
|
||||
mm_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
out_mm_kwargs: MultiModalKwargsItems,
|
||||
) -> Sequence[PromptUpdate]:
|
||||
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
||||
|
||||
image_token_id = hf_processor.image_token_id
|
||||
assert isinstance(image_token_id, int)
|
||||
|
||||
def get_replacement_deepseek_vl2(item_idx: int):
|
||||
images = mm_items.get_items(
|
||||
"image", (ImageEmbeddingItems, ImageProcessorItems)
|
||||
)
|
||||
|
||||
if isinstance(images, ImageEmbeddingItems):
|
||||
num_image_tokens = images.get_feature_size(item_idx)
|
||||
else:
|
||||
size = images.get_image_size(item_idx)
|
||||
|
||||
num_image_tokens = self.info.get_num_image_tokens(
|
||||
image_width=size.width,
|
||||
image_height=size.height,
|
||||
cropping=CROP_MODE,
|
||||
)
|
||||
return [image_token_id] * num_image_tokens
|
||||
|
||||
return [
|
||||
PromptReplacement(
|
||||
modality="image",
|
||||
target=[image_token_id],
|
||||
replacement=get_replacement_deepseek_vl2,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
DeepseekOCR2MultiModalProcessor,
|
||||
info=DeepseekOCR2ProcessingInfo,
|
||||
dummy_inputs=DeepseekOCR2DummyInputsBuilder,
|
||||
)
|
||||
class DeepseekOCR2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
|
||||
hf_to_vllm_mapper = WeightsMapper(
|
||||
orig_to_new_prefix={
|
||||
# map prefix for language backbone
|
||||
"model.embed_tokens.": "language_model.model.embed_tokens.",
|
||||
"model.layers.": "language_model.model.layers.",
|
||||
"model.norm.": "language_model.model.norm.",
|
||||
"lm_head.": "language_model.lm_head.",
|
||||
# remove "model." prefix for other components
|
||||
"model.": "",
|
||||
}
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return "<image>"
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
config: DeepseekVLV2Config = vllm_config.model_config.hf_config
|
||||
multimodal_config = vllm_config.model_config.multimodal_config
|
||||
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
self.vision_config = config.vision_config
|
||||
self.projector_config = config.projector_config
|
||||
self.text_config = config.text_config
|
||||
model_config = vllm_config.model_config
|
||||
tokenizer = cached_tokenizer_from_config(model_config)
|
||||
self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
|
||||
|
||||
with self._mark_tower_model(vllm_config, "image"):
|
||||
self.sam_model = ImageEncoderViT(
|
||||
depth=12,
|
||||
embed_dim=768,
|
||||
img_size=1024,
|
||||
mlp_ratio=4,
|
||||
norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
|
||||
num_heads=12,
|
||||
patch_size=16,
|
||||
qkv_bias=True,
|
||||
use_rel_pos=True,
|
||||
global_attn_indexes=[2, 5, 8, 11],
|
||||
window_size=14,
|
||||
out_chans=256,
|
||||
last_conv_output=896,
|
||||
)
|
||||
self.qwen2_model = build_qwen2_decoder_as_encoder()
|
||||
|
||||
self.projector = MlpProjector(self.projector_config)
|
||||
self.tile_tag = config.tile_tag
|
||||
self.global_view_pos = config.global_view_pos
|
||||
|
||||
# special token for image token sequence format
|
||||
n_embed = self.projector_config.n_embed
|
||||
embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
|
||||
if self.tile_tag == "2D":
|
||||
# This is a typo in original implementation
|
||||
self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
|
||||
)
|
||||
|
||||
with self._mark_language_model(vllm_config):
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=self.text_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"),
|
||||
)
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> DeepseekOCRImagePixelInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
images_spatial_crop = kwargs.pop("images_spatial_crop", None)
|
||||
images_crop = kwargs.pop("images_crop", None)
|
||||
|
||||
if pixel_values is None or torch.sum(pixel_values).item() == 0:
|
||||
return None
|
||||
|
||||
base_size = self.vision_config.image_size
|
||||
return DeepseekOCRImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=pixel_values,
|
||||
images_crop=images_crop,
|
||||
images_spatial_crop=images_spatial_crop,
|
||||
resolve_bindings={
|
||||
"base_size": base_size,
|
||||
},
|
||||
)
|
||||
|
||||
def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
|
||||
global_features_1 = self.sam_model(image_tensor)
|
||||
global_features_2 = self.qwen2_model(global_features_1)
|
||||
|
||||
features = self.projector(global_features_2)
|
||||
|
||||
_, hw, dim = features.shape
|
||||
|
||||
return features.view(-1, dim)
|
||||
|
||||
def _encode_local_features(self, patches: torch.Tensor) -> torch.Tensor | None:
|
||||
if torch.sum(patches).item() == 0:
|
||||
return None
|
||||
|
||||
local_features = self.sam_model(patches)
|
||||
local_features = self.qwen2_model(local_features)
|
||||
|
||||
features = self.projector(local_features)
|
||||
|
||||
_, _, dim = features.shape
|
||||
|
||||
return features.view(-1, dim)
|
||||
|
||||
def _pixel_values_to_embedding(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
images_crop: torch.Tensor,
|
||||
images_spatial_crop: torch.Tensor,
|
||||
) -> NestedTensors:
|
||||
images_in_this_batch = []
|
||||
|
||||
is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
|
||||
patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
|
||||
images_crop = images_crop.split(patches_per_image.tolist())
|
||||
for jdx in range(images_spatial_crop.size(0)):
|
||||
patches = images_crop[jdx]
|
||||
image_ori = pixel_values[[jdx]]
|
||||
|
||||
global_features = self._encode_global_features(image_ori)
|
||||
local_features = self._encode_local_features(patches)
|
||||
|
||||
if local_features is not None:
|
||||
combined = torch.cat(
|
||||
[local_features, global_features, self.view_seperator[None, :]],
|
||||
dim=0,
|
||||
)
|
||||
else:
|
||||
combined = torch.cat(
|
||||
[global_features, self.view_seperator[None, :]], dim=0
|
||||
)
|
||||
|
||||
images_in_this_batch.append(combined)
|
||||
|
||||
return images_in_this_batch
|
||||
|
||||
def _process_image_input(
|
||||
self, image_input: DeepseekOCRImagePixelInputs
|
||||
) -> torch.Tensor:
|
||||
pixel_values = image_input.data
|
||||
images_crop = image_input.images_crop
|
||||
images_spatial_crop = image_input.images_spatial_crop.to(dtype=torch.long)
|
||||
|
||||
vision_features = self._pixel_values_to_embedding(
|
||||
pixel_values=pixel_values,
|
||||
images_crop=images_crop,
|
||||
images_spatial_crop=images_spatial_crop,
|
||||
)
|
||||
|
||||
return vision_features
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return None
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
return vision_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
):
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = None
|
||||
|
||||
hidden_states = self.language_model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.language_model.compute_logits(hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
autoloaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
||||
return autoloaded_weights
|
||||
|
||||
def get_mm_mapping(self) -> MultiModelKeys:
|
||||
"""
|
||||
Get the module prefix in multimodal models
|
||||
"""
|
||||
return MultiModelKeys.from_string_field(
|
||||
language_model="language_model",
|
||||
connector="projector",
|
||||
tower_model=["sam_model", "qwen2_model"],
|
||||
)
|
||||
@@ -308,6 +308,7 @@ _MULTIMODAL_MODELS = {
|
||||
),
|
||||
"DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
|
||||
"DeepseekOCRForCausalLM": ("deepseek_ocr", "DeepseekOCRForCausalLM"),
|
||||
"DeepseekOCR2ForCausalLM": ("deepseek_ocr2", "DeepseekOCR2ForCausalLM"),
|
||||
"DotsOCRForCausalLM": ("dots_ocr", "DotsOCRForCausalLM"),
|
||||
"Eagle2_5_VLForConditionalGeneration": (
|
||||
"eagle2_5_vl",
|
||||
|
||||
Reference in New Issue
Block a user