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https://git.datalinker.icu/vllm-project/vllm.git
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805 lines
31 KiB
Python
805 lines
31 KiB
Python
import logging
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import math
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import os
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import warnings
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from typing import Optional, Tuple, 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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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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BaseModelOutputWithPooling)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (ModelOutput, is_flash_attn_2_available,
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replace_return_docstrings)
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logger = logging.getLogger("vllm")
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# For Siglip: copied from
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# HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
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# Remove hints as there's little possibility to change these code.
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class SiglipVisionConfig(PretrainedConfig):
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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,
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os.PathLike],
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**kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(
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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(
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cls,
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"model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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"You are using a model of type %s to "
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"instantiate a model of type %s. "
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"This is not supported for all configurations"
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"of models and can yield errors.", config_dict['model_type'],
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cls.model_type)
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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, flash_attn_varlen_func
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from flash_attn.bert_padding import pad_input # noqa
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from flash_attn.bert_padding import index_first_axis, 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(
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torch.cumsum(seqlens_in_batch, dim=0, dtype=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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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_(tensor: torch.Tensor,
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mean: float = 0.0,
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std: float = 1.0,
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a: float = -2.0,
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b: float = 2.0) -> torch.Tensor:
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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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class SiglipVisionModelOutput(ModelOutput):
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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,
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self.embed_dim)
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def forward(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) -> 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)
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max_nb_patches_h, max_nb_patches_w = (max_im_h // self.patch_size,
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max_im_w // self.patch_size)
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boundaries = torch.arange(1 / self.num_patches_per_side, 1.0,
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1 / self.num_patches_per_side)
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position_ids = torch.full(
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size=(
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batch_size,
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max_nb_patches_h * max_nb_patches_w,
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),
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fill_value=0,
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)
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for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
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if tgt_sizes is not None:
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nb_patches_h = tgt_sizes[batch_idx][0]
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nb_patches_w = tgt_sizes[batch_idx][1]
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else:
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nb_patches_h = p_attn_mask[:, 0].sum()
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nb_patches_w = p_attn_mask[0].sum()
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fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
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fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
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bucket_coords_h = torch.bucketize(fractional_coords_h,
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boundaries,
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right=True)
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bucket_coords_w = torch.bucketize(fractional_coords_w,
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boundaries,
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right=True)
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pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side +
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bucket_coords_w).flatten()
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position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
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position_ids = position_ids.to(self.position_embedding.weight.device)
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embeddings = embeddings + self.position_embedding(position_ids)
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return embeddings
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class SiglipAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
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def __init__(self, config):
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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.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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"embed_dim must be divisible by num_heads (got `embed_dim`: "
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f"{self.embed_dim} and `num_heads`:"
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f" {self.num_heads}).")
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor],
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Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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batch_size, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(batch_size, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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key_states = key_states.view(batch_size, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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value_states = value_states.view(batch_size, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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k_v_seq_len = key_states.shape[-2]
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attn_weights = torch.matmul(query_states, key_states.transpose(
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2, 3)) * self.scale
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if attn_weights.size() != (batch_size, self.num_heads, q_len,
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k_v_seq_len):
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raise ValueError(
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"Attention weights should be of size "
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f"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
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f" {attn_weights.size()}")
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if attention_mask is not None:
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if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
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raise ValueError(
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"Attention mask should be of size "
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f"{(batch_size, 1, q_len, k_v_seq_len)}",
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f"but is {attention_mask.size()}")
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights,
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dim=-1,
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dtype=torch.float32).to(
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query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights,
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p=self.dropout,
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training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (batch_size, self.num_heads, q_len,
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self.head_dim):
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raise ValueError(
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"`attn_output` should be of size "
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f"{(batch_size, self.num_heads, q_len, self.head_dim)}, "
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"but is"
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f" {attn_output.size()}")
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights
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class SiglipFlashAttention2(SiglipAttention):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.is_causal = False # Hack to make sure we don't use a causal mask
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor],
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Optional[Tuple[torch.Tensor]]]:
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output_attentions = False
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_heads,
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self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value.get_usable_length(
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kv_seq_len, self.layer_idx)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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dropout_rate = self.dropout if self.training else 0.0
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning(
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"The input hidden states seems to be "
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"silently casted in float32, "
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"this might be related to the fact "
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"you have upcasted embedding or layer norm layers in float32. "
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"We will cast back the input in"
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" %s.", target_dtype)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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attn_output = self._flash_attention_forward(query_states,
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key_states,
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value_states,
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attention_mask,
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q_len,
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dropout=dropout_rate)
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attn_output = attn_output.reshape(bsz, q_len,
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self.embed_dim).contiguous()
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attn_output = self.out_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights
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def _flash_attention_forward(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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query_length,
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dropout=0.0,
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softmax_scale=None):
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causal = self.is_causal and query_length != 1
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# Contains at least one padding token in the sequence
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if attention_mask is not None:
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batch_size = query_states.shape[0]
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(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]:
|
|
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):
|
|
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)
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder
|
|
# with CLIP->Siglip
|
|
class SiglipEncoder(nn.Module):
|
|
|
|
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]:
|
|
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)
|
|
|
|
|
|
class SiglipVisionTransformer(SiglipPreTrainedModel):
|
|
config_class = SiglipVisionConfig
|
|
main_input_name = "pixel_values"
|
|
_supports_flash_attn_2 = True
|
|
|
|
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
|
|
|
|
@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,
|
|
)
|