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640 lines
25 KiB
640 lines
25 KiB
# Copyright 2025 HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections.abc
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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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from ... import initialization as init
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, torch_int
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from ...utils.generic import check_model_inputs
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from ..clip.modeling_clip import CLIPMLP
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from ..janus.modeling_janus import JanusVisionAttention
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from ..llama.modeling_llama import LlamaRMSNorm
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from ..llava.modeling_llava import (
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LlavaCausalLMOutputWithPast,
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LlavaForConditionalGeneration,
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LlavaModel,
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LlavaModelOutputWithPast,
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LlavaPreTrainedModel,
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)
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from .configuration_internvl import InternVLConfig, InternVLVisionConfig
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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key_states = key
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value_states = value
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# No upcasting of the attention weights to float32 in this implementation
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class InternVLVisionRMSNorm(LlamaRMSNorm):
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pass
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class InternVLVisionAttention(JanusVisionAttention):
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def __init__(self, config: InternVLVisionConfig):
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super().__init__(config)
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del self.num_key_value_groups
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# Needed for flash attention
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self.is_causal = False
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qk_norm = config.use_qk_norm
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self.q_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
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self.k_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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):
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batch_size, seq_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scale,
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is_causal=False,
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**kwargs,
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)
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attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)
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output = self.projection_layer(attn_output)
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output = self.projection_dropout(output)
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return output, attn_weights
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@dataclass
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@auto_docstring(
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custom_intro="""
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Class for outputs of [`InternVLVisionModel`].
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"""
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)
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class InternVLVisionModelOutputWithPooling(BaseModelOutputWithPooling):
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r"""
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
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*config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
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will be returned.
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"""
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class InternVLVisionPatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.patch_shape = patch_shape
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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embeddings = self.projection(pixel_values.to(self.projection.weight.dtype))
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embeddings = embeddings.flatten(2).transpose(1, 2)
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return embeddings
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# Based on timm implementation, which can be found here:
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# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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class InternVLVisionEmbeddings(nn.Module):
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"""
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Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
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"""
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def __init__(self, config: InternVLVisionConfig) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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if config.use_mask_token:
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self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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else:
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self.mask_token = None
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self.patch_embeddings = InternVLVisionPatchEmbeddings(config)
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self.patch_size = config.patch_size
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self.image_size = (
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config.image_size
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if isinstance(config.image_size, collections.abc.Iterable)
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else (config.image_size, config.image_size)
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)
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num_patches = self.patch_embeddings.num_patches
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if config.use_absolute_position_embeddings:
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self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
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else:
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self.position_embeddings = None
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
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"""
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This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
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images. This method is also adapted to support torch.jit tracing.
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Adapted from:
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- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
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- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
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"""
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num_patches = embeddings.shape[1] - 1
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num_positions = self.position_embeddings.shape[1] - 1
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# always interpolate when tracing to ensure the exported model works for dynamic input shapes
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if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
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return self.position_embeddings
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class_pos_embed = self.position_embeddings[:, :1]
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patch_pos_embed = self.position_embeddings[:, 1:]
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dim = embeddings.shape[-1]
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new_height = height // self.patch_size[0]
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new_width = width // self.patch_size[1]
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sqrt_num_positions = torch_int(num_positions**0.5)
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patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
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patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed,
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size=(new_height, new_width),
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mode="bicubic",
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align_corners=False,
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)
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
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def forward(
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self,
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pixel_values: torch.Tensor,
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bool_masked_pos: torch.BoolTensor | None = None,
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) -> torch.Tensor:
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_, _, height, width = pixel_values.shape
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embeddings = self.patch_embeddings(pixel_values)
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batch_size, seq_len, _ = embeddings.size()
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if bool_masked_pos is not None:
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mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
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# replace the masked visual tokens by mask_tokens
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w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
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embeddings = embeddings * (1 - w) + mask_tokens * w
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings), dim=1)
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if self.position_embeddings is not None:
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embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
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embeddings = self.dropout(embeddings)
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return embeddings
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class InternVLVisionMLP(CLIPMLP):
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pass
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NORM2FN = {"layer_norm": nn.LayerNorm, "rms_norm": InternVLVisionRMSNorm}
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class InternVLVisionLayer(GradientCheckpointingLayer):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: InternVLVisionConfig) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = InternVLVisionAttention(config)
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self.mlp = InternVLVisionMLP(config)
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# InternVL uses different layernorm implementations for different models
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self.layernorm_before = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
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init_values = config.layer_scale_init_value
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self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
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self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> tuple[torch.Tensor] | tuple[torch.Tensor, torch.Tensor]:
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attention_output, _ = self.attention(
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self.layernorm_before(hidden_states), # in InternVLVision, layernorm is applied before self-attention
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)
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attention_output = self.lambda_1 * attention_output
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# first residual connection
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hidden_states = attention_output + hidden_states
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# in InternVLVision, layernorm is also applied after self-attention
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.mlp(layer_output)
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layer_output = self.dropout(layer_output)
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if self.lambda_2 is not None:
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layer_output = self.lambda_2 * layer_output
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# second residual connection
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layer_output = layer_output + hidden_states
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return layer_output
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class InternVLVisionEncoder(nn.Module):
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def __init__(self, config: InternVLVisionConfig) -> None:
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([InternVLVisionLayer(config) for i in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> tuple | BaseModelOutput:
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for layer_module in self.layer:
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hidden_states = layer_module(hidden_states)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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)
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@auto_docstring
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class InternVLVisionPreTrainedModel(PreTrainedModel):
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config: InternVLVisionConfig
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base_model_prefix = "internvl_vision"
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main_input_name = "pixel_values"
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input_modalities = ("image", "video")
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supports_gradient_checkpointing = True
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_no_split_modules = ["InternVLVisionLayer"]
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_supports_sdpa = True
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_supports_flash_attn = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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_can_record_outputs = {
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"hidden_states": InternVLVisionLayer,
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"attentions": InternVLVisionAttention,
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}
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@torch.no_grad()
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def _init_weights(self, module):
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"""Initialize the weights"""
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super()._init_weights(module)
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if isinstance(module, InternVLVisionEmbeddings):
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init.zeros_(module.cls_token)
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if module.mask_token is not None:
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init.zeros_(module.mask_token)
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if module.position_embeddings is not None:
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init.zeros_(module.position_embeddings)
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elif isinstance(module, InternVLVisionLayer):
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init.constant_(module.lambda_1, self.config.layer_scale_init_value)
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init.constant_(module.lambda_2, self.config.layer_scale_init_value)
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@auto_docstring
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class InternVLVisionModel(InternVLVisionPreTrainedModel):
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def __init__(self, config: InternVLVisionConfig) -> None:
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super().__init__(config)
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self.config = config
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self.embeddings = InternVLVisionEmbeddings(config)
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self.encoder = InternVLVisionEncoder(config)
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self.layernorm = (
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nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.embeddings.patch_embeddings
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@check_model_inputs(tie_last_hidden_states=False)
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@auto_docstring
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def forward(
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self, pixel_values: torch.Tensor, bool_masked_pos: torch.BoolTensor | None = None, **kwargs
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) -> tuple | InternVLVisionModelOutputWithPooling:
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r"""
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bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
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Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
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"""
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embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
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encoder_outputs = self.encoder(embedding_output)
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sequence_output = encoder_outputs[0]
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sequence_output = self.layernorm(sequence_output)
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return InternVLVisionModelOutputWithPooling(
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last_hidden_state=sequence_output,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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)
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class InternVLPreTrainedModel(LlavaPreTrainedModel):
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input_modalities = ("image", "text", "video")
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INTERNVL_INPUTS_DOCSTRING = None
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class InternVLMultiModalProjector(nn.Module):
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def __init__(self, config: InternVLConfig):
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super().__init__()
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self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2)
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self.linear_1 = nn.Linear(
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config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2, config.text_config.hidden_size
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)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size)
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def forward(self, image_features):
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hidden_states = self.layer_norm(image_features)
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hidden_states = self.linear_1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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class InternVLModelOutputWithPast(LlavaModelOutputWithPast):
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pass
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class InternVLModel(LlavaModel):
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def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5):
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"""Perform pixel shuffle downsampling on vision features.
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Args:
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vision_features (`torch.Tensor`):
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Input tensor of shape (batch_size, width, height, channels).
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scale_factor (`float`, *optional*, defaults to `0.5`):
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Factor by which to downsample. Default is 0.5, which halves the dimensions.
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Returns:
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vision_features (`torch.Tensor`):
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Downsampled tensor of shape (batch_size, height*scale_factor, width*scale_factor, channels/(scale_factor^2)).
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"""
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batch_size, width, height, channels = vision_features.size()
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if height % scale_factor != 0 or width % scale_factor != 0:
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raise ValueError("Height and width must be divisible by scale_factor for proper downsampling.")
|
|
|
|
# Reshape to allow downsampling
|
|
vision_features = vision_features.view(
|
|
batch_size, width, int(height * scale_factor), int(channels / scale_factor)
|
|
)
|
|
# Permute dimensions to align downsampled axis correctly
|
|
vision_features = vision_features.permute(0, 2, 1, 3).contiguous()
|
|
|
|
# Reshape to achieve final downsampled dimensions
|
|
vision_features = vision_features.view(
|
|
batch_size, int(height * scale_factor), int(width * scale_factor), int(channels / (scale_factor**2))
|
|
)
|
|
|
|
# Swap height and width back for proper orientation
|
|
vision_features = vision_features.permute(0, 2, 1, 3).contiguous()
|
|
|
|
return vision_features
|
|
|
|
@check_model_inputs(tie_last_hidden_states=False)
|
|
@auto_docstring(
|
|
custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
|
|
)
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
vision_feature_layer: int | list[int] | None = None,
|
|
vision_feature_select_strategy: str | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | BaseModelOutputWithPooling:
|
|
r"""
|
|
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
|
|
The tensors corresponding to the input images.
|
|
vision_feature_layer (`int` or `list[int]`):
|
|
Layer index or list of layer indices to extract features from.
|
|
"""
|
|
pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility
|
|
|
|
downsample_ratio = self.config.downsample_ratio
|
|
if vision_feature_layer != -1:
|
|
kwargs["output_hidden_states"] = True
|
|
vision_outputs = self.vision_tower(pixel_values=pixel_values, return_dict=True, **kwargs)
|
|
if vision_feature_layer == -1:
|
|
vision_features = vision_outputs.last_hidden_state
|
|
else:
|
|
vision_features = vision_outputs.hidden_states[vision_feature_layer]
|
|
if vision_feature_select_strategy == "default":
|
|
vision_features = vision_features[:, 1:, :]
|
|
|
|
# Calculate dimensions based on vision features
|
|
channels = vision_features.shape[1]
|
|
feature_size = int(channels**0.5)
|
|
batch_size = vision_features.shape[0]
|
|
|
|
# Reshape tensor to spatial dimensions
|
|
vision_features = vision_features.reshape(batch_size, feature_size, feature_size, -1)
|
|
|
|
# Apply downsampling using pixel shuffle
|
|
vision_features = self.pixel_shuffle(vision_features, scale_factor=downsample_ratio)
|
|
|
|
# Reshape tensor to prepare for projection
|
|
vision_features = vision_features.reshape(batch_size, -1, vision_features.shape[-1])
|
|
|
|
# Project features through multi-modal projector
|
|
vision_features = self.multi_modal_projector(vision_features)
|
|
vision_outputs.pooler_output = vision_features
|
|
|
|
return vision_outputs
|
|
|
|
@check_model_inputs(tie_last_hidden_states=False)
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
vision_feature_layer: int | list[int] | None = None,
|
|
vision_feature_select_strategy: str | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | InternVLModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if pixel_values is not None:
|
|
image_features = self.get_image_features(
|
|
pixel_values=pixel_values,
|
|
vision_feature_layer=vision_feature_layer,
|
|
vision_feature_select_strategy=vision_feature_select_strategy,
|
|
return_dict=True,
|
|
).pooler_output
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
special_image_mask = self.get_placeholder_mask(
|
|
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
|
|
)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
|
|
|
outputs = self.language_model(
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return InternVLModelOutputWithPast(
|
|
last_hidden_state=outputs.last_hidden_state,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
|
)
|
|
|
|
|
|
class InternVLCausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
|
|
pass
|
|
|
|
|
|
class InternVLForConditionalGeneration(LlavaForConditionalGeneration):
|
|
def forward(**super_kwargs):
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
|
|
|
|
>>> torch_device = "cuda"
|
|
>>> processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL3-1B-hf")
|
|
>>> model = AutoModelForImageTextToText.from_pretrained(
|
|
... "OpenGVLab/InternVL3-1B-hf", dtype=torch.bfloat16, device_map=torch_device
|
|
... )
|
|
|
|
>>> messages = [
|
|
... {
|
|
... "role": "user",
|
|
... "content": [
|
|
... {
|
|
... "type": "image",
|
|
... "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
|
|
... },
|
|
... {
|
|
... "type": "image",
|
|
... "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
|
|
... },
|
|
... {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
|
|
... ],
|
|
... },
|
|
... ]
|
|
|
|
>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device)
|
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=200)
|
|
>>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
|
|
The images depict the Statue of Liberty and the Golden Gate Bridge.
|
|
```"""
|
|
super().forward(**super_kwargs)
|
|
|
|
|
|
__all__ = [
|
|
"InternVLVisionPreTrainedModel",
|
|
"InternVLVisionModel",
|
|
"InternVLPreTrainedModel",
|
|
"InternVLModel",
|
|
"InternVLForConditionalGeneration",
|
|
]
|