[Model][LoRA]LoRA support added for MiniCPMV2.6 (#8943)

Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/idefics2_vision_model.py

@@ -65,11 +65,10 @@ class Idefics2VisionEmbeddings(nn.Module):
         self.position_embedding = nn.Embedding(self.num_positions,
                                                self.embed_dim)
 
-    def forward(
+    def forward(self,
-        self,
                 pixel_values: torch.FloatTensor,
                 patch_attention_mask: torch.BoolTensor,
-    ) -> torch.Tensor:
+                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
         batch_size, _, max_im_h, max_im_w = pixel_values.shape
         patch_embeds = self.patch_embedding(pixel_values)
         embeddings = patch_embeds.flatten(2).transpose(1, 2)
@@ -84,6 +83,11 @@ class Idefics2VisionEmbeddings(nn.Module):
                                   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)
@@ -287,10 +291,12 @@ class Idefics2VisionTransformer(nn.Module):
         self,
         pixel_values,
         patch_attention_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.tensor:
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ) -> torch.Tensor:
         hidden_states = self.embeddings(
             pixel_values=pixel_values,
-            patch_attention_mask=patch_attention_mask)
+            patch_attention_mask=patch_attention_mask,
+            tgt_sizes=tgt_sizes)
         encoder_outputs = self.encoder(hidden_states)
         last_hidden_state = self.post_layernorm(encoder_outputs)
         return last_hidden_state

vllm/model_executor/models/minicpmv.py

@@ -31,17 +31,15 @@ import torch
 import torch.types
 from PIL import Image
 from torch import nn
-from torch.nn.init import trunc_normal_
 from transformers import PretrainedConfig
 from typing_extensions import NotRequired
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
-from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.layers.resampler import (Resampler2,
+from vllm.model_executor.layers.resampler import (BaseResampler, Resampler2,
                                                   get_2d_sincos_pos_embed)
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
@@ -106,58 +104,6 @@ class MiniCPMVImagePixelInputs(TypedDict):
 DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
 
 
-class BaseResampler(nn.Module):
-    """
-    A 2D perceiver-resampler network with one cross attention layers by
-        (grid_size**2) learnable queries and 2d sincos pos_emb
-    Outputs:
-        A tensor with the shape of (grid_size**2, embed_dim)
-    """
-
-    def __init__(
-        self,
-        num_queries: int,
-        embed_dim: int,
-        num_heads: int,
-        kv_dim: Optional[int] = None,
-        norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
-    ) -> None:
-        super().__init__()
-
-        self.num_queries = num_queries
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-
-        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
-        trunc_normal_(self.query, std=0.02)
-        if kv_dim is not None and kv_dim != embed_dim:
-            self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
-        else:
-            # Maintain the same return value with ReplicatedLinear.forward
-            self.kv_proj = lambda *args, **kwargs: (
-                nn.Identity()(*args, **kwargs),
-                None,
-            )
-        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
-        self.ln_q = norm_layer(embed_dim)
-        self.ln_kv = norm_layer(embed_dim)
-        self.ln_post = norm_layer(embed_dim)
-        self.proj = nn.Parameter(
-            (embed_dim**-0.5) * torch.randn(embed_dim, embed_dim))
-
-    def _init_weights(self, m: nn.Module) -> None:
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def _repeat(self, query, N: int):
-        return query.unsqueeze(1).repeat(1, N, 1)
-
-
 class Resampler2_5(BaseResampler):
 
     def __init__(
@@ -869,7 +815,35 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
         return "resampler" in name
 
 
-class MiniCPMV2_6(MiniCPMVBaseModel):
+class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+    # LoRA specific attributes
+    supported_lora_modules = [
+        # vision encoder
+        "fc1",
+        "fc2",
+        "out_proj",
+        # language model
+        "qkv_proj",  # same name with vision encoder
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        # resampler
+        "kv_proj",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
 
     def __init__(
         self,
@@ -894,15 +868,8 @@ class MiniCPMV2_6(MiniCPMVBaseModel):
                           name="model")
 
     def init_vision_module(self) -> nn.Module:
-        # A custom version of SiglipVisionTransformer, won't work with TP
-        from vllm.model_executor.models.na_vit import SiglipVisionTransformer
 
-        if self.config._attn_implementation == "flash_attention_2":
+        model = Idefics2VisionTransformer(self.config.vision_config)
-            self.config.vision_config._attn_implementation = "flash_attention_2"
-        else:
-            # not support sdpa
-            self.config.vision_config._attn_implementation = "eager"
-        model = SiglipVisionTransformer(self.config.vision_config)
         if self.config.drop_vision_last_layer:
             model.encoder.layers = model.encoder.layers[:-1]
         return model
@@ -928,7 +895,7 @@ class MiniCPMV2_6(MiniCPMVBaseModel):
             pixel_values,
             patch_attention_mask=patch_attn_mask,
             tgt_sizes=tgt_sizes,
-        ).last_hidden_state
+        )
         return vision_embedding
 
     def get_vision_hidden_states(
@@ -960,12 +927,12 @@ class MiniCPMV2_6(MiniCPMVBaseModel):
             all_pixel_values.type(dtype),
             patch_attention_mask=patch_attn_mask,
             tgt_sizes=tgt_sizes,
-        ).last_hidden_state
+        )
 
         return self.resampler(vision_embedding, tgt_sizes)
 
     def is_default_weight_loading(self, name: str) -> bool:
-        return "resampler" in name or "vpm" in name
+        return "resampler" in name
 
 
 _SUPPORT_VERSION = {

vllm/model_executor/models/na_vit.py

@@ -1,804 +0,0 @@
-import logging
-import math
-import os
-import warnings
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn.init import _calculate_fan_in_and_fan_out
-from transformers.activations import ACT2FN
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
-from transformers.modeling_outputs import (BaseModelOutput,
-                                           BaseModelOutputWithPooling)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (ModelOutput, is_flash_attn_2_available,
-                                replace_return_docstrings)
-
-logger = logging.getLogger("vllm")
-
-
-# For Siglip: copied from
-#   HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
-# Remove hints as there's little possibility to change these code.
-class SiglipVisionConfig(PretrainedConfig):
-
-    model_type = "siglip_vision_model"
-
-    def __init__(
-        self,
-        hidden_size=768,
-        intermediate_size=3072,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        num_channels=3,
-        image_size=224,
-        patch_size=16,
-        hidden_act="gelu_pytorch_tanh",
-        layer_norm_eps=1e-6,
-        attention_dropout=0.0,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_channels = num_channels
-        self.patch_size = patch_size
-        self.image_size = image_size
-        self.attention_dropout = attention_dropout
-        self.layer_norm_eps = layer_norm_eps
-        self.hidden_act = hidden_act
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
-                                                                  os.PathLike],
-                        **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(
-            pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from SiglipConfig
-        if config_dict.get("model_type") == "siglip":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(
-                cls,
-                "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                "You are using a model of type %s to "
-                "instantiate a model of type %s. "
-                "This is not supported for all configurations"
-                "of models and can yield errors.", config_dict['model_type'],
-                cls.model_type)
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
-
-SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "google/siglip-base-patch16-224",
-    # See all SigLIP models at https://huggingface.co/models?filter=siglip
-]
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import pad_input  # noqa
-    from flash_attn.bert_padding import index_first_axis, unpad_input
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(
-        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-def _trunc_normal_(tensor, mean, std, a, b):
-
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect.",
-            stacklevel=2,
-        )
-
-    # Values are generated by using a truncated uniform distribution and
-    # then using the inverse CDF for the normal distribution.
-    # Get upper and lower cdf values
-    l_ = norm_cdf((a - mean) / std)
-    u = norm_cdf((b - mean) / std)
-
-    # Uniformly fill tensor with values from [l, u], then translate to
-    # [2l-1, 2u-1].
-    tensor.uniform_(2 * l_ - 1, 2 * u - 1)
-
-    # Use inverse cdf transform for normal distribution to get truncated
-    # standard normal
-    if tensor.dtype in [torch.float16, torch.bfloat16]:
-        # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
-        og_dtype = tensor.dtype
-        tensor = tensor.to(torch.float32)
-        tensor.erfinv_()
-        tensor = tensor.to(og_dtype)
-    else:
-        tensor.erfinv_()
-
-    # Transform to proper mean, std
-    tensor.mul_(std * math.sqrt(2.0))
-    tensor.add_(mean)
-
-    # Clamp to ensure it's in the proper range
-    if tensor.dtype == torch.float16:
-        # The `clamp_` op is not (yet?) defined in float16+cpu
-        tensor = tensor.to(torch.float32)
-        tensor.clamp_(min=a, max=b)
-        tensor = tensor.to(torch.float16)
-    else:
-        tensor.clamp_(min=a, max=b)
-
-
-def trunc_normal_tf_(tensor: torch.Tensor,
-                     mean: float = 0.0,
-                     std: float = 1.0,
-                     a: float = -2.0,
-                     b: float = 2.0) -> torch.Tensor:
-    with torch.no_grad():
-        _trunc_normal_(tensor, 0, 1.0, a, b)
-        tensor.mul_(std).add_(mean)
-
-
-def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-    if mode == "fan_in":
-        denom = fan_in
-    elif mode == "fan_out":
-        denom = fan_out
-    elif mode == "fan_avg":
-        denom = (fan_in + fan_out) / 2
-
-    variance = scale / denom
-
-    if distribution == "truncated_normal":
-        # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
-    elif distribution == "normal":
-        with torch.no_grad():
-            tensor.normal_(std=math.sqrt(variance))
-    elif distribution == "uniform":
-        bound = math.sqrt(3 * variance)
-        with torch.no_grad():
-            tensor.uniform_(-bound, bound)
-    else:
-        raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
-    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
-
-
-def default_flax_embed_init(tensor):
-    variance_scaling_(tensor, mode="fan_in", distribution="normal")
-
-
-class SiglipVisionModelOutput(ModelOutput):
-    image_embeds: Optional[torch.FloatTensor] = None
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-class SiglipVisionEmbeddings(nn.Module):
-
-    def __init__(self, config: SiglipVisionConfig):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.image_size = config.image_size
-        self.patch_size = config.patch_size
-
-        self.patch_embedding = nn.Conv2d(
-            in_channels=config.num_channels,
-            out_channels=self.embed_dim,
-            kernel_size=self.patch_size,
-            stride=self.patch_size,
-            padding="valid",
-        )
-
-        self.num_patches_per_side = self.image_size // self.patch_size
-        self.num_patches = self.num_patches_per_side**2
-        self.num_positions = self.num_patches
-        self.position_embedding = nn.Embedding(self.num_positions,
-                                               self.embed_dim)
-
-    def forward(self,
-                pixel_values: torch.FloatTensor,
-                patch_attention_mask: torch.BoolTensor,
-                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
-        batch_size = pixel_values.size(0)
-
-        patch_embeds = self.patch_embedding(pixel_values)
-        embeddings = patch_embeds.flatten(2).transpose(1, 2)
-
-        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(
-                "embed_dim must be divisible by num_heads (got `embed_dim`: "
-                f"{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(
-                "Attention weights should be of size "
-                f"{(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(
-                    "Attention mask should be of size "
-                    f"{(batch_size, 1, q_len, k_v_seq_len)}",
-                    f"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(
-                "`attn_output` should be of size "
-                f"{(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):
-
-    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)
-
-        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)
-
-        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
-
-        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(
-                "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"
-                " %s.", 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):
-        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]:
-        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,
-        )