941 lines
41 KiB
Python
941 lines
41 KiB
Python
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# coding=utf-8
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# Copyright 2024 Google AI and The HuggingFace 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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""" PyTorch Siglip model. """
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# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
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import math
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import os
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import warnings
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from dataclasses import dataclass
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn.init import _calculate_fan_in_and_fan_out
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from transformers.activations import ACT2FN
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from transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
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from transformers.modeling_outputs import BaseModelOutput
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from transformers.modeling_outputs import BaseModelOutputWithPooling
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import add_start_docstrings
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from transformers.utils import add_start_docstrings_to_model_forward
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from transformers.utils import is_flash_attn_2_available
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from transformers.utils import logging
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from transformers.utils import ModelOutput
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from transformers.utils import replace_return_docstrings
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logger = logging.get_logger(__name__)
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class SiglipVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
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Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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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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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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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-06):
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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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Example:
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```python
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>>> from transformers import SiglipVisionConfig, SiglipVisionModel
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>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = SiglipVisionConfig()
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>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = SiglipVisionModel(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 = "siglip_vision_model"
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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image_size=224,
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patch_size=16,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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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_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.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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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from SiglipConfig
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if config_dict.get("model_type") == "siglip":
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config_dict = config_dict["vision_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
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SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"google/siglip-base-patch16-224",
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# See all SigLIP models at https://huggingface.co/models?filter=siglip
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]
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func
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from flash_attn import flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis # noqa
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from flash_attn.bert_padding import pad_input
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from flash_attn.bert_padding import unpad_input
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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def _trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
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"The distribution of values may be incorrect.",
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stacklevel=2,
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)
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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if tensor.dtype in [torch.float16, torch.bfloat16]:
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# The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
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og_dtype = tensor.dtype
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tensor = tensor.to(torch.float32)
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tensor.erfinv_()
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tensor = tensor.to(og_dtype)
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else:
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.0))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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if tensor.dtype == torch.float16:
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# The `clamp_` op is not (yet?) defined in float16+cpu
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tensor = tensor.to(torch.float32)
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tensor.clamp_(min=a, max=b)
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tensor = tensor.to(torch.float16)
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else:
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tensor.clamp_(min=a, max=b)
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def trunc_normal_tf_(
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tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
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) -> torch.Tensor:
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"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \\leq \text{mean} \\leq b`.
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NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
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bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
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and the result is subsquently scaled and shifted by the mean and std args.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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"""
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with torch.no_grad():
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_trunc_normal_(tensor, 0, 1.0, a, b)
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tensor.mul_(std).add_(mean)
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def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
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fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
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if mode == "fan_in":
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denom = fan_in
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elif mode == "fan_out":
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denom = fan_out
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elif mode == "fan_avg":
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denom = (fan_in + fan_out) / 2
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variance = scale / denom
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if distribution == "truncated_normal":
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# constant is stddev of standard normal truncated to (-2, 2)
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trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
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elif distribution == "normal":
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with torch.no_grad():
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tensor.normal_(std=math.sqrt(variance))
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elif distribution == "uniform":
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bound = math.sqrt(3 * variance)
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with torch.no_grad():
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tensor.uniform_(-bound, bound)
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else:
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raise ValueError(f"invalid distribution {distribution}")
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def lecun_normal_(tensor):
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variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
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def default_flax_embed_init(tensor):
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variance_scaling_(tensor, mode="fan_in", distribution="normal")
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@dataclass
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# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
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class SiglipVisionModelOutput(ModelOutput):
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"""
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Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
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Args:
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image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The image embeddings obtained by applying the projection layer to the pooler_output.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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image_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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attentions: Optional[Tuple[torch.FloatTensor]] = None
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class SiglipVisionEmbeddings(nn.Module):
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def __init__(self, config: SiglipVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.patch_embedding = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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padding="valid",
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)
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self.num_patches_per_side = self.image_size // self.patch_size
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self.num_patches = self.num_patches_per_side**2
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self.num_positions = self.num_patches
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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def forward(
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self,
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pixel_values: torch.FloatTensor,
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patch_attention_mask: torch.BoolTensor,
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tgt_sizes: Optional[torch.IntTensor] = None,
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) -> torch.Tensor:
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batch_size = pixel_values.size(0)
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patch_embeds = self.patch_embedding(pixel_values)
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embeddings = patch_embeds.flatten(2).transpose(1, 2)
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max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
|
||
|
|
max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
|
||
|
|
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
|
||
|
|
position_ids = torch.full(
|
||
|
|
size=(
|
||
|
|
batch_size,
|
||
|
|
max_nb_patches_h * max_nb_patches_w,
|
||
|
|
),
|
||
|
|
fill_value=0,
|
||
|
|
)
|
||
|
|
|
||
|
|
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
|
||
|
|
if tgt_sizes is not None:
|
||
|
|
nb_patches_h = tgt_sizes[batch_idx][0]
|
||
|
|
nb_patches_w = tgt_sizes[batch_idx][1]
|
||
|
|
else:
|
||
|
|
nb_patches_h = p_attn_mask[:, 0].sum()
|
||
|
|
nb_patches_w = p_attn_mask[0].sum()
|
||
|
|
|
||
|
|
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
|
||
|
|
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
|
||
|
|
|
||
|
|
bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
|
||
|
|
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
|
||
|
|
|
||
|
|
pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
|
||
|
|
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
|
||
|
|
|
||
|
|
position_ids = position_ids.to(self.position_embedding.weight.device)
|
||
|
|
|
||
|
|
embeddings = embeddings + self.position_embedding(position_ids)
|
||
|
|
return embeddings
|
||
|
|
|
||
|
|
|
||
|
|
class SiglipAttention(nn.Module):
|
||
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||
|
|
|
||
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.embed_dim = config.hidden_size
|
||
|
|
self.num_heads = config.num_attention_heads
|
||
|
|
self.head_dim = self.embed_dim // self.num_heads
|
||
|
|
if self.head_dim * self.num_heads != self.embed_dim:
|
||
|
|
raise ValueError(
|
||
|
|
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
||
|
|
f" {self.num_heads})."
|
||
|
|
)
|
||
|
|
self.scale = self.head_dim**-0.5
|
||
|
|
self.dropout = config.attention_dropout
|
||
|
|
|
||
|
|
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||
|
|
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||
|
|
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||
|
|
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
output_attentions: Optional[bool] = False,
|
||
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
|
|
"""Input shape: Batch x Time x Channel"""
|
||
|
|
|
||
|
|
batch_size, q_len, _ = hidden_states.size()
|
||
|
|
|
||
|
|
query_states = self.q_proj(hidden_states)
|
||
|
|
key_states = self.k_proj(hidden_states)
|
||
|
|
value_states = self.v_proj(hidden_states)
|
||
|
|
|
||
|
|
query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
|
||
|
|
k_v_seq_len = key_states.shape[-2]
|
||
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
|
||
|
|
|
||
|
|
if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
|
||
|
|
raise ValueError(
|
||
|
|
f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
|
||
|
|
f" {attn_weights.size()}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if attention_mask is not None:
|
||
|
|
if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
|
||
|
|
raise ValueError(
|
||
|
|
f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
|
||
|
|
)
|
||
|
|
attn_weights = attn_weights + attention_mask
|
||
|
|
|
||
|
|
# upcast attention to fp32
|
||
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||
|
|
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
|
||
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
||
|
|
|
||
|
|
if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
|
||
|
|
raise ValueError(
|
||
|
|
f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is"
|
||
|
|
f" {attn_output.size()}"
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
|
|
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
|
||
|
|
|
||
|
|
attn_output = self.out_proj(attn_output)
|
||
|
|
|
||
|
|
return attn_output, attn_weights
|
||
|
|
|
||
|
|
|
||
|
|
class SiglipFlashAttention2(SiglipAttention):
|
||
|
|
"""
|
||
|
|
Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
|
||
|
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
||
|
|
flash attention and deal with padding tokens in case the input contains any of them.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
self.is_causal = False # Hack to make sure we don't use a causal mask
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
||
|
|
position_ids: Optional[torch.LongTensor] = None,
|
||
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||
|
|
output_attentions: bool = False,
|
||
|
|
use_cache: bool = False,
|
||
|
|
**kwargs,
|
||
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||
|
|
output_attentions = False
|
||
|
|
|
||
|
|
bsz, q_len, _ = hidden_states.size()
|
||
|
|
|
||
|
|
query_states = self.q_proj(hidden_states)
|
||
|
|
key_states = self.k_proj(hidden_states)
|
||
|
|
value_states = self.v_proj(hidden_states)
|
||
|
|
|
||
|
|
# Flash attention requires the input to have the shape
|
||
|
|
# batch_size x seq_length x head_dim x hidden_dim
|
||
|
|
# therefore we just need to keep the original shape
|
||
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
|
|
|
||
|
|
kv_seq_len = key_states.shape[-2]
|
||
|
|
if past_key_value is not None:
|
||
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||
|
|
# cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||
|
|
# query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||
|
|
|
||
|
|
# if past_key_value is not None:
|
||
|
|
# cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||
|
|
# key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
|
|
||
|
|
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||
|
|
# to be able to avoid many of these transpose/reshape/view.
|
||
|
|
query_states = query_states.transpose(1, 2)
|
||
|
|
key_states = key_states.transpose(1, 2)
|
||
|
|
value_states = value_states.transpose(1, 2)
|
||
|
|
|
||
|
|
dropout_rate = self.dropout if self.training else 0.0
|
||
|
|
|
||
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||
|
|
# cast them back in the correct dtype just to be sure everything works as expected.
|
||
|
|
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||
|
|
# in fp32. (LlamaRMSNorm handles it correctly)
|
||
|
|
|
||
|
|
input_dtype = query_states.dtype
|
||
|
|
if input_dtype == torch.float32:
|
||
|
|
if torch.is_autocast_enabled():
|
||
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
||
|
|
# Handle the case where the model is quantized
|
||
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
||
|
|
target_dtype = self.config._pre_quantization_dtype
|
||
|
|
else:
|
||
|
|
target_dtype = self.q_proj.weight.dtype
|
||
|
|
|
||
|
|
logger.warning_once(
|
||
|
|
"The input hidden states seems to be silently casted in float32, this might be related to the fact"
|
||
|
|
" you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||
|
|
f" {target_dtype}."
|
||
|
|
)
|
||
|
|
|
||
|
|
query_states = query_states.to(target_dtype)
|
||
|
|
key_states = key_states.to(target_dtype)
|
||
|
|
value_states = value_states.to(target_dtype)
|
||
|
|
|
||
|
|
attn_output = self._flash_attention_forward(
|
||
|
|
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
|
||
|
|
attn_output = self.out_proj(attn_output)
|
||
|
|
|
||
|
|
if not output_attentions:
|
||
|
|
attn_weights = None
|
||
|
|
|
||
|
|
return attn_output, attn_weights
|
||
|
|
|
||
|
|
def _flash_attention_forward(
|
||
|
|
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||
|
|
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||
|
|
Args:
|
||
|
|
query_states (`torch.Tensor`):
|
||
|
|
Input query states to be passed to Flash Attention API
|
||
|
|
key_states (`torch.Tensor`):
|
||
|
|
Input key states to be passed to Flash Attention API
|
||
|
|
value_states (`torch.Tensor`):
|
||
|
|
Input value states to be passed to Flash Attention API
|
||
|
|
attention_mask (`torch.Tensor`):
|
||
|
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||
|
|
position of padding tokens and 1 for the position of non-padding tokens.
|
||
|
|
dropout (`int`, *optional*):
|
||
|
|
Attention dropout
|
||
|
|
softmax_scale (`float`, *optional*):
|
||
|
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||
|
|
"""
|
||
|
|
|
||
|
|
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
||
|
|
causal = self.is_causal and query_length != 1
|
||
|
|
|
||
|
|
# Contains at least one padding token in the sequence
|
||
|
|
if attention_mask is not None:
|
||
|
|
batch_size = query_states.shape[0]
|
||
|
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||
|
|
query_states, key_states, value_states, attention_mask, query_length
|
||
|
|
)
|
||
|
|
|
||
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||
|
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||
|
|
|
||
|
|
attn_output_unpad = flash_attn_varlen_func(
|
||
|
|
query_states,
|
||
|
|
key_states,
|
||
|
|
value_states,
|
||
|
|
cu_seqlens_q=cu_seqlens_q,
|
||
|
|
cu_seqlens_k=cu_seqlens_k,
|
||
|
|
max_seqlen_q=max_seqlen_in_batch_q,
|
||
|
|
max_seqlen_k=max_seqlen_in_batch_k,
|
||
|
|
dropout_p=dropout,
|
||
|
|
softmax_scale=softmax_scale,
|
||
|
|
causal=causal,
|
||
|
|
)
|
||
|
|
|
||
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||
|
|
else:
|
||
|
|
attn_output = flash_attn_func(
|
||
|
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||
|
|
)
|
||
|
|
|
||
|
|
return attn_output
|
||
|
|
|
||
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||
|
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||
|
|
|
||
|
|
key_layer = index_first_axis(
|
||
|
|
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
value_layer = index_first_axis(
|
||
|
|
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
if query_length == kv_seq_len:
|
||
|
|
query_layer = index_first_axis(
|
||
|
|
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||
|
|
)
|
||
|
|
cu_seqlens_q = cu_seqlens_k
|
||
|
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||
|
|
indices_q = indices_k
|
||
|
|
elif query_length == 1:
|
||
|
|
max_seqlen_in_batch_q = 1
|
||
|
|
cu_seqlens_q = torch.arange(
|
||
|
|
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||
|
|
) # There is a memcpy here, that is very bad.
|
||
|
|
indices_q = cu_seqlens_q[:-1]
|
||
|
|
query_layer = query_layer.squeeze(1)
|
||
|
|
else:
|
||
|
|
# The -q_len: slice assumes left padding.
|
||
|
|
attention_mask = attention_mask[:, -query_length:]
|
||
|
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||
|
|
|
||
|
|
return (
|
||
|
|
query_layer,
|
||
|
|
key_layer,
|
||
|
|
value_layer,
|
||
|
|
indices_q,
|
||
|
|
(cu_seqlens_q, cu_seqlens_k),
|
||
|
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
|
||
|
|
class SiglipMLP(nn.Module):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.activation_fn = ACT2FN[config.hidden_act]
|
||
|
|
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
||
|
|
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
||
|
|
|
||
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
|
|
hidden_states = self.fc1(hidden_states)
|
||
|
|
hidden_states = self.activation_fn(hidden_states)
|
||
|
|
hidden_states = self.fc2(hidden_states)
|
||
|
|
return hidden_states
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
|
||
|
|
class SiglipEncoderLayer(nn.Module):
|
||
|
|
def __init__(self, config: SiglipVisionConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.embed_dim = config.hidden_size
|
||
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||
|
|
self.self_attn = SiglipAttention(config) if not self._use_flash_attention_2 else SiglipFlashAttention2(config)
|
||
|
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||
|
|
self.mlp = SiglipMLP(config)
|
||
|
|
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
attention_mask: torch.Tensor,
|
||
|
|
output_attentions: Optional[bool] = False,
|
||
|
|
) -> Tuple[torch.FloatTensor]:
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
hidden_states (`torch.FloatTensor`):
|
||
|
|
Input to the layer of shape `(batch, seq_len, embed_dim)`.
|
||
|
|
attention_mask (`torch.FloatTensor`):
|
||
|
|
Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
|
||
|
|
output_attentions (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
|
|
returned tensors for more detail.
|
||
|
|
"""
|
||
|
|
residual = hidden_states
|
||
|
|
|
||
|
|
hidden_states = self.layer_norm1(hidden_states)
|
||
|
|
hidden_states, attn_weights = self.self_attn(
|
||
|
|
hidden_states=hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
)
|
||
|
|
hidden_states = residual + hidden_states
|
||
|
|
|
||
|
|
residual = hidden_states
|
||
|
|
hidden_states = self.layer_norm2(hidden_states)
|
||
|
|
hidden_states = self.mlp(hidden_states)
|
||
|
|
hidden_states = residual + hidden_states
|
||
|
|
|
||
|
|
outputs = (hidden_states,)
|
||
|
|
|
||
|
|
if output_attentions:
|
||
|
|
outputs += (attn_weights,)
|
||
|
|
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class SiglipPreTrainedModel(PreTrainedModel):
|
||
|
|
"""
|
||
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
|
|
models.
|
||
|
|
"""
|
||
|
|
|
||
|
|
config_class = SiglipVisionConfig
|
||
|
|
base_model_prefix = "siglip"
|
||
|
|
supports_gradient_checkpointing = True
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
"""Initialize the weights"""
|
||
|
|
|
||
|
|
if isinstance(module, SiglipVisionEmbeddings):
|
||
|
|
width = self.config.hidden_size
|
||
|
|
nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
|
||
|
|
elif isinstance(module, nn.Embedding):
|
||
|
|
default_flax_embed_init(module.weight)
|
||
|
|
elif isinstance(module, SiglipAttention):
|
||
|
|
nn.init.normal_(module.q_proj.weight)
|
||
|
|
nn.init.normal_(module.k_proj.weight)
|
||
|
|
nn.init.normal_(module.v_proj.weight)
|
||
|
|
nn.init.normal_(module.out_proj.weight)
|
||
|
|
nn.init.zeros_(module.q_proj.bias)
|
||
|
|
nn.init.zeros_(module.k_proj.bias)
|
||
|
|
nn.init.zeros_(module.v_proj.bias)
|
||
|
|
nn.init.zeros_(module.out_proj.bias)
|
||
|
|
elif isinstance(module, SiglipMLP):
|
||
|
|
nn.init.normal_(module.fc1.weight)
|
||
|
|
nn.init.normal_(module.fc2.weight)
|
||
|
|
nn.init.normal_(module.fc1.bias, std=1e-6)
|
||
|
|
nn.init.normal_(module.fc2.bias, std=1e-6)
|
||
|
|
elif isinstance(module, (nn.Linear, nn.Conv2d)):
|
||
|
|
lecun_normal_(module.weight)
|
||
|
|
if module.bias is not None:
|
||
|
|
nn.init.zeros_(module.bias)
|
||
|
|
elif isinstance(module, nn.LayerNorm):
|
||
|
|
module.bias.data.zero_()
|
||
|
|
module.weight.data.fill_(1.0)
|
||
|
|
|
||
|
|
|
||
|
|
SIGLIP_START_DOCSTRING = r"""
|
||
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||
|
|
etc.)
|
||
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||
|
|
and behavior.
|
||
|
|
Parameters:
|
||
|
|
config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model.
|
||
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
||
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
SIGLIP_VISION_INPUTS_DOCSTRING = r"""
|
||
|
|
Args:
|
||
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
||
|
|
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
|
||
|
|
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
|
||
|
|
output_attentions (`bool`, *optional*):
|
||
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||
|
|
tensors for more detail.
|
||
|
|
output_hidden_states (`bool`, *optional*):
|
||
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||
|
|
more detail.
|
||
|
|
return_dict (`bool`, *optional*):
|
||
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
|
||
|
|
class SiglipEncoder(nn.Module):
|
||
|
|
"""
|
||
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
||
|
|
[`SiglipEncoderLayer`].
|
||
|
|
Args:
|
||
|
|
config: SiglipConfig
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, config: SiglipVisionConfig):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||
|
|
self.gradient_checkpointing = False
|
||
|
|
|
||
|
|
# Ignore copy
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
inputs_embeds,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
) -> Union[Tuple, BaseModelOutput]:
|
||
|
|
r"""
|
||
|
|
Args:
|
||
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
||
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
||
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
||
|
|
than the model's internal embedding lookup matrix.
|
||
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||
|
|
- 1 for tokens that are **not masked**,
|
||
|
|
- 0 for tokens that are **masked**.
|
||
|
|
[What are attention masks?](../glossary#attention-mask)
|
||
|
|
output_attentions (`bool`, *optional*):
|
||
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
|
|
returned tensors for more detail.
|
||
|
|
output_hidden_states (`bool`, *optional*):
|
||
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
||
|
|
for more detail.
|
||
|
|
return_dict (`bool`, *optional*):
|
||
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||
|
|
"""
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
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
|
||
|
|
|
||
|
|
encoder_states = () if output_hidden_states else None
|
||
|
|
all_attentions = () if output_attentions else None
|
||
|
|
|
||
|
|
hidden_states = inputs_embeds
|
||
|
|
for encoder_layer in self.layers:
|
||
|
|
if output_hidden_states:
|
||
|
|
encoder_states = encoder_states + (hidden_states,)
|
||
|
|
if self.gradient_checkpointing and self.training:
|
||
|
|
layer_outputs = self._gradient_checkpointing_func(
|
||
|
|
encoder_layer.__call__,
|
||
|
|
hidden_states,
|
||
|
|
attention_mask,
|
||
|
|
output_attentions,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
layer_outputs = encoder_layer(
|
||
|
|
hidden_states,
|
||
|
|
attention_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = layer_outputs[0]
|
||
|
|
|
||
|
|
if output_attentions:
|
||
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
||
|
|
|
||
|
|
if output_hidden_states:
|
||
|
|
encoder_states = encoder_states + (hidden_states,)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
||
|
|
return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings("""The vision model from SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING)
|
||
|
|
class SiglipVisionTransformer(SiglipPreTrainedModel):
|
||
|
|
config_class = SiglipVisionConfig
|
||
|
|
main_input_name = "pixel_values"
|
||
|
|
_supports_flash_attn_2 = True
|
||
|
|
_no_split_modules = []
|
||
|
|
|
||
|
|
def __init__(self, config: SiglipVisionConfig):
|
||
|
|
super().__init__(config)
|
||
|
|
self.config = config
|
||
|
|
embed_dim = config.hidden_size
|
||
|
|
|
||
|
|
self.embeddings = SiglipVisionEmbeddings(config)
|
||
|
|
self.encoder = SiglipEncoder(config)
|
||
|
|
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
||
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def get_input_embeddings(self) -> nn.Module:
|
||
|
|
return self.embeddings.patch_embedding
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
|
||
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
pixel_values,
|
||
|
|
patch_attention_mask: Optional[torch.BoolTensor] = None,
|
||
|
|
tgt_sizes: Optional[torch.IntTensor] = None,
|
||
|
|
output_attentions: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
||
|
|
r"""
|
||
|
|
Returns:
|
||
|
|
"""
|
||
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
|
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
|
||
|
|
|
||
|
|
batch_size = pixel_values.size(0)
|
||
|
|
if patch_attention_mask is None:
|
||
|
|
patch_attention_mask = torch.ones(
|
||
|
|
size=(
|
||
|
|
batch_size,
|
||
|
|
pixel_values.size(2) // self.config.patch_size,
|
||
|
|
pixel_values.size(3) // self.config.patch_size,
|
||
|
|
),
|
||
|
|
dtype=torch.bool,
|
||
|
|
device=pixel_values.device,
|
||
|
|
)
|
||
|
|
|
||
|
|
hidden_states = self.embeddings(
|
||
|
|
pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, tgt_sizes=tgt_sizes
|
||
|
|
)
|
||
|
|
|
||
|
|
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
|
||
|
|
# The call to `_upad_input` in `_flash_attention_forward` is expensive
|
||
|
|
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
|
||
|
|
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
|
||
|
|
if not torch.any(~patch_attention_mask):
|
||
|
|
attention_mask = None
|
||
|
|
else:
|
||
|
|
attention_mask = (
|
||
|
|
_prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
|
||
|
|
if not self._use_flash_attention_2
|
||
|
|
else patch_attention_mask
|
||
|
|
)
|
||
|
|
|
||
|
|
encoder_outputs = self.encoder(
|
||
|
|
inputs_embeds=hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
output_attentions=output_attentions,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
|
||
|
|
last_hidden_state = encoder_outputs[0]
|
||
|
|
last_hidden_state = self.post_layernorm(last_hidden_state)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (last_hidden_state, None) + encoder_outputs[1:]
|
||
|
|
|
||
|
|
return BaseModelOutputWithPooling(
|
||
|
|
last_hidden_state=last_hidden_state,
|
||
|
|
pooler_output=None,
|
||
|
|
hidden_states=encoder_outputs.hidden_states,
|
||
|
|
attentions=encoder_outputs.attentions,
|
||
|
|
)
|