529 lines
23 KiB
Python
529 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team.
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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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from typing import Callable, Optional, Union
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import torch
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import torch.nn as nn
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from ...configuration_utils import PretrainedConfig
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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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 auto_docstring, logging
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from ..clip.modeling_clip import (
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CLIPMLP,
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CLIPAttention,
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CLIPEncoder,
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CLIPEncoderLayer,
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CLIPVisionEmbeddings,
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CLIPVisionModel,
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CLIPVisionTransformer,
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)
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from ..llama.modeling_llama import eager_attention_forward
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from ..qwen2_vl.modeling_qwen2_vl import VisionRotaryEmbedding, apply_rotary_pos_emb_vision
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logger = logging.get_logger(__name__)
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class MLCDVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MLCDVisionModel`]. It is used to instantiate a MLCD
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vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the vision encoder of the MLCD
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[DeepGlint-AI/mlcd-vit-bigG-patch14-336](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 1664):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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projection_dim (`int`, *optional*, defaults to 1024):
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Dimensionality of text and vision projection layers.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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image_size (`int`, *optional*, defaults to 336):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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Example:
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```python
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>>> from transformers import MLCDVisionConfig, MLCDVisionModel
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>>> # Initializing a MLCDVisionConfig with DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
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>>> configuration = MLCDVisionConfig()
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>>> # Initializing a MLCDVisionModel (with random weights) from the DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
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>>> model = MLCDVisionModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mlcd_vision_model"
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base_config_key = "vision_config"
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def __init__(
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self,
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hidden_size=1664,
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intermediate_size=8192,
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num_hidden_layers=48,
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num_attention_heads=16,
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num_key_value_groups=1,
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num_channels=3,
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image_size=336,
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patch_size=14,
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hidden_act="gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_groups = num_key_value_groups
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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class MLCDMLP(CLIPMLP):
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pass
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class MLCDRotaryEmbedding(VisionRotaryEmbedding):
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def forward(self, num_patches_height: int, num_patches_width: int) -> torch.Tensor:
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"""
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Calculate the Rotary Position Embedding (RoPE) for MLCDVisionModel based on the grid size.
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Args:
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num_patches_height (int): Number of patches in the height dimension.
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num_patches_width (int): Number of patches in the width dimension.
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Returns:
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torch.Tensor: Rotary positional embeddings for the given grid size.
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"""
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# Generate position IDs for height and width dimensions
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hpos_ids = (
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torch.arange(num_patches_height, device=self.inv_freq.device).unsqueeze(1).expand(-1, num_patches_width)
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)
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wpos_ids = (
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torch.arange(num_patches_width, device=self.inv_freq.device).unsqueeze(0).expand(num_patches_height, -1)
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)
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# Flatten and stack the position IDs
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pos_ids = torch.stack([hpos_ids.flatten(), wpos_ids.flatten()], dim=-1)
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# Generate the full rotary positional embeddings for the maximum grid size
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max_grid_size = max(num_patches_height, num_patches_width)
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seq = torch.arange(max_grid_size, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
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rotary_pos_emb_full = torch.outer(seq, self.inv_freq)
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# Select and flatten the embeddings based on the position IDs
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rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
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return rotary_pos_emb
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class MLCDVisionEmbeddings(CLIPVisionEmbeddings):
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def __init__(self, config: MLCDVisionConfig):
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super().__init__(config)
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del self.position_embedding
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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target_dtype = self.patch_embedding.weight.dtype
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# patch_embeds -> shape = [batch, width, grid, grid]
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patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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return embeddings
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class MLCDAttention(CLIPAttention):
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"""Multi-headed attention with RoPE. Refer to papers:
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- Attention is all you need:
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https://huggingface.co/papers/1706.03762
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- RoFormer: Enhanced Transformer with Rotary Position Embedding:
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https://huggingface.co/papers/2104.09864
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"""
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def __init__(self, config: MLCDVisionConfig):
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super().__init__(config)
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self.num_key_value_groups = config.num_key_value_groups
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self.is_causal = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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batch_size, seq_length = hidden_states.shape[:-1]
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# Each of shape: [batch_size, seq_length, num_heads, head_dim]
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query_states = self.q_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
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key_states = self.k_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
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value_states = self.v_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
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# Apply positional embeddings
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cos = position_embeddings[0].unsqueeze(0).float()
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sin = position_embeddings[1].unsqueeze(0).float()
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query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
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# Each of shape: [batch_size, num_heads, seq_length, head_dim]
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query_states = query_states.permute(0, 2, 1, 3).contiguous()
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key_states = key_states.permute(0, 2, 1, 3).contiguous()
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value_states = value_states.permute(0, 2, 1, 3).contiguous()
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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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.dropout,
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scaling=self.scale,
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is_causal=self.is_causal,
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**kwargs,
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)
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attn_output = attn_output.permute(1, 0, 2, 3).contiguous() # [seq_length, batch_size, num_heads, head_dim]
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attn_output = attn_output.view(seq_length, batch_size, -1) # [seq_length, batch_size, embedding_dim]
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attn_output = self.out_proj(attn_output)
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attn_output = attn_output.permute(1, 0, 2).contiguous() # [batch_size, seq_length, embedding_dim]
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return attn_output, attn_weights
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class MLCDEncoderLayer(CLIPEncoderLayer):
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def __init__(self, config: MLCDVisionConfig):
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super().__init__(config)
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self.self_attn = MLCDAttention(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> tuple[torch.FloatTensor]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`):
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Input to the layer of shape `(batch, seq_len, embed_dim)`.
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Represents the hidden states from the previous layer or the input embeddings.
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position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
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A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
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Represents absolute positional embeddings for the query and key in the attention mechanism.
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attention_mask (`torch.FloatTensor`):
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Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
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output_attentions (`bool`, *optional*, defaults to `False`):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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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.layer_norm2(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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outputs = (hidden_states,)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs
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class MLCDEncoder(CLIPEncoder):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
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[`MLCDEncoderLayer`].
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Args:
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config: MLCDVisionConfig
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"""
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def __init__(self, config: MLCDVisionConfig):
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"""Overwrite dummy `MLCDConfig` to `MLCDVisionConfig`."""
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super().__init__(config)
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def forward(
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self,
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inputs_embeds: torch.FloatTensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[tuple, BaseModelOutput]:
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r"""
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Args:
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors
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than the model's internal embedding lookup matrix.
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position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
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A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
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Represents absolute positional embeddings for the query and key in the attention mechanism.
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
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for more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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encoder_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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hidden_states = inputs_embeds
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for idx, encoder_layer in enumerate(self.layers):
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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layer_outputs = encoder_layer(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=encoder_states,
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attentions=all_attentions,
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)
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class MLCDVisionTransformer(CLIPVisionTransformer):
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def __init__(self, config: MLCDVisionConfig):
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super().__init__(config)
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self.vision_rotary_embedding = MLCDRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2)
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self.class_pos_emb = nn.Parameter(torch.randn(1, config.hidden_size // config.num_attention_heads // 2))
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@auto_docstring
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def forward(
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self,
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pixel_values: Optional[torch.FloatTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[tuple, BaseModelOutputWithPooling]:
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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if pixel_values is None:
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raise ValueError("You have to specify pixel_values")
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num_patches_height = pixel_values.shape[-2] // self.config.patch_size
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num_patches_width = pixel_values.shape[-1] // self.config.patch_size
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rotary_pos_emb = self.vision_rotary_embedding(num_patches_height, num_patches_width)
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rotary_pos_emb = rotary_pos_emb.to(self.class_pos_emb.device)
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rotary_pos_emb = torch.cat([self.class_pos_emb, rotary_pos_emb], dim=0)
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emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
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position_embeddings = (emb.cos(), emb.sin())
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hidden_states = self.embeddings(pixel_values)
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hidden_states = self.pre_layrnorm(hidden_states)
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encoder_outputs = self.encoder(
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inputs_embeds=hidden_states,
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position_embeddings=position_embeddings,
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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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)
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last_hidden_state = encoder_outputs[0]
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pooled_output = last_hidden_state[:, 0, :]
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pooled_output = self.post_layernorm(pooled_output)
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if not return_dict:
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return (last_hidden_state, pooled_output) + encoder_outputs[1:]
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return BaseModelOutputWithPooling(
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last_hidden_state=last_hidden_state,
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pooler_output=pooled_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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@auto_docstring
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class MLCDPreTrainedModel(PreTrainedModel):
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config: MLCDVisionConfig
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base_model_prefix = "mlcd"
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supports_gradient_checkpointing = True
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_supports_flash_attn = True
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_supports_sdpa = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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factor = self.config.initializer_factor
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if isinstance(module, MLCDVisionEmbeddings):
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factor = self.config.initializer_factor
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nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
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nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
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elif isinstance(module, MLCDAttention):
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factor = self.config.initializer_factor
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in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
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out_proj_std = (module.embed_dim**-0.5) * factor
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nn.init.normal_(module.q_proj.weight, std=in_proj_std)
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nn.init.normal_(module.k_proj.weight, std=in_proj_std)
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nn.init.normal_(module.v_proj.weight, std=in_proj_std)
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nn.init.normal_(module.out_proj.weight, std=out_proj_std)
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elif isinstance(module, MLCDMLP):
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factor = self.config.initializer_factor
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in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
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fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
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nn.init.normal_(module.fc1.weight, std=fc_std)
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nn.init.normal_(module.fc2.weight, std=in_proj_std)
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elif isinstance(module, MLCDVisionTransformer):
|
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factor = self.config.initializer_factor
|
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pos_emb_std = (module.config.hidden_size // module.config.num_attention_heads // 2) ** -0.5 * factor
|
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nn.init.normal_(module.class_pos_emb, mean=0.0, std=pos_emb_std)
|
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elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
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module.weight.data.fill_(1.0)
|
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elif isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
class MLCDVisionModel(CLIPVisionModel):
|
|
@auto_docstring
|
|
def forward(
|
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self,
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|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
>>> from transformers import AutoProcessor, MLCDVisionModel
|
|
>>> model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
|
|
>>> processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs, output_attentions=True)
|
|
|
|
>>> features = outputs.last_hidden_state
|
|
>>> print(f"Extracted features shape: {features.shape}")
|
|
>>> print(f"Number of attention layers: {len(outputs.attentions)}")
|
|
>>> print(f"Attention shape: {outputs.attentions[0].shape}")
|
|
```"""
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
|
|
return self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"MLCDVisionConfig",
|
|
"MLCDPreTrainedModel",
|
|
"MLCDVisionModel",
|
|
]
|