[Feature] Support Paddle-OCR (#4396)

* init * update code * fix code style & disable thinking * adapt for common_engine.update_mm_requests_chunk_size * use 3d rope * use flash_attn_unpadded * opt siglip * update to be compatible with the latest codebase * fix typo * optim OCR performance * fix bug * fix bug * fix bug * fix bug * normlize name * modify xpu rope * revert logger * fix bug * fix bug * fix bug * support default_v1 * optim performance * fix bug --------- Co-authored-by: root <root@szzj-acg-tge1-fdda9.szzj.baidu.com> Co-authored-by: zhangyue66 <zhangyue66@baidu.com>
2025-12-24 13:28:13 +08:00 · 2025-10-24 23:34:30 +08:00
parent 822dea8d5f
commit e4e3cede7f
21 changed files with 2869 additions and 175 deletions
--- a/fastdeploy/model_executor/models/paddleocr_vl/init.py
+++ b/fastdeploy/model_executor/models/paddleocr_vl/init.py
@@ -0,0 +1,15 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
--- a/fastdeploy/model_executor/models/paddleocr_vl/config.py
+++ b/fastdeploy/model_executor/models/paddleocr_vl/config.py
@@ -0,0 +1,167 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from paddleformers.transformers.configuration_utils import PretrainedConfig
+
+
+class PPOCRVisionConfig(PretrainedConfig):
+    model_type = "paddleocr_vl"
+    base_config_key = "vision_config"
+
+    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=14,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=2,
+        **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
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+
+
+class PaddleOCRConfig(PretrainedConfig):
+    model_type = "paddleocr_vl"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    sub_configs = {"vision_config": PPOCRVisionConfig}
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=11008,
+        max_position_embeddings=32768,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        image_token_id=101304,
+        video_token_id=101305,
+        vision_start_token_id=101306,
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        use_flash_attention=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        head_dim=128,
+        hidden_act="silu",
+        use_bias=False,
+        rope_theta=10000,
+        weight_share_add_bias=True,
+        ignored_index=-100,
+        attention_probs_dropout_prob=0.0,
+        hidden_dropout_prob=0.0,
+        compression_ratio: float = 1.0,
+        num_key_value_heads=None,
+        max_sequence_length=None,
+        tie_word_embeddings=False,
+        vision_config=None,
+        **kwargs,
+    ):
+        # Set default for tied embeddings if not specified.
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.max_position_embeddings = max_position_embeddings
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.use_flash_attention = use_flash_attention
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.head_dim = head_dim
+        if hidden_act != "silu":
+            raise NotImplementedError
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.use_bias = use_bias
+        self.weight_share_add_bias = weight_share_add_bias
+        self.rope_theta = rope_theta
+        self.ignored_index = ignored_index
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.compression_ratio = compression_ratio
+        self.num_key_value_heads = num_key_value_heads
+        self.max_sequence_length = max_sequence_length
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+        # Currently, these configuration items are hard-coded
+        self.fuse_rms_norm = True
+        self.use_sparse_flash_attn = True
+        self.use_var_len_flash_attn = False
+        self.scale_qk_coeff = 1.0
+        self.fuse_softmax_mask = False
+        self.use_sparse_head_and_loss_fn = False
+        self.use_recompute_loss_fn = False
+        self.use_fused_head_and_loss_fn = False
+        self.fuse_linear = False
+        self.token_balance_seqlen = False
+        self.use_rmsnorm = True
+        self.fuse_ln = False
+        self.cachekv_quant = False
+        self.fuse_swiglu = False
--- a/fastdeploy/model_executor/models/paddleocr_vl/paddleocr_vl.py
+++ b/fastdeploy/model_executor/models/paddleocr_vl/paddleocr_vl.py
@@ -0,0 +1,455 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import re
+from functools import partial
+from typing import Dict, Optional, Union
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+from paddleformers.transformers import PretrainedModel
+from paddleformers.transformers.configuration_utils import PretrainedConfig
+from paddleformers.utils.log import logger
+
+from fastdeploy import envs
+from fastdeploy.config import FDConfig
+from fastdeploy.model_executor.forward_meta import ForwardMeta
+from fastdeploy.model_executor.graph_optimization.decorator import (
+    support_graph_optimization,
+)
+from fastdeploy.model_executor.layers.attention.attention import Attention
+from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
+from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
+from fastdeploy.model_executor.layers.normalization import RMSNorm
+from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_DecoderLayer
+from fastdeploy.model_executor.models.model_base import (
+    ModelCategory,
+    ModelForCasualLM,
+    ModelRegistry,
+)
+from fastdeploy.model_executor.utils import (
+    default_weight_loader,
+    process_weights_after_loading,
+)
+
+from .projector import Projector
+from .siglip import SiglipVisionModel
+
+
+@support_graph_optimization
+class PaddleOCRVLModel(nn.Layer):
+    def __init__(
+        self,
+        fd_config: FDConfig = None,
+    ):
+        super().__init__()
+
+        self.config = fd_config.model_config
+        self.num_layers = fd_config.model_config.num_hidden_layers
+        fd_config.model_config.pretrained_config.prefix_name = "model"
+        self._dtype = fd_config.model_config.torch_dtype
+
+        self.embed_tokens = VocabParallelEmbedding(
+            fd_config=fd_config,
+            num_embeddings=fd_config.model_config.vocab_size,
+            embedding_dim=fd_config.model_config.hidden_size,
+            params_dtype=self._dtype,
+            prefix=(f"{fd_config.model_config.pretrained_config.prefix_name}.embed_tokens"),
+        )
+
+        self.layers = nn.LayerList(
+            [
+                Ernie4_5_DecoderLayer(
+                    fd_config=fd_config,
+                    prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}",
+                )
+                for i in range(self.num_layers)
+            ]
+        )
+        for i, layer in enumerate(self.layers):
+            layer.self_attn.attn = Attention(
+                fd_config=fd_config,
+                layer_id=i,
+                prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}.self_attn",
+                use_neox_rotary_style=True,
+            )
+
+        self.norm = RMSNorm(
+            fd_config,
+            hidden_size=fd_config.model_config.hidden_size,
+            eps=fd_config.model_config.rms_norm_eps,
+            prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.norm",
+        )
+
+    def load_state_dict(self, state_dict):
+        """
+        Load model parameters from a given state dictionary.
+
+        Args:
+            state_dict (dict[str, np.ndarray | paddle.Tensor]):
+                A dictionary containing model parameters, where keys are parameter names
+                and values are NumPy arrays or PaddlePaddle tensors.
+        """
+        self.embed_tokens.load_state_dict(state_dict)
+        self.norm.load_state_dict(state_dict)
+        for i in range(self.num_layers):
+            logger.info(f"Start load layer {i}")
+            self.layers[i].load_state_dict(state_dict)
+
+    def get_input_embeddings(self, ids_remove_padding: paddle.Tensor) -> paddle.Tensor:
+        return self.embed_tokens(ids_remove_padding=ids_remove_padding)
+
+    def forward(
+        self,
+        input_embeddings: paddle.Tensor,
+        forward_meta: ForwardMeta,
+    ):
+        hidden_states = input_embeddings
+        residual = None
+        for i in range(self.num_layers):
+            hidden_states, residual = self.layers[i](forward_meta, hidden_states, residual)
+
+        hidden_states = hidden_states + residual
+
+        out = self.norm(hidden_states)
+
+        return out
+
+
+@ModelRegistry.register_model_class(
+    architecture="PaddleOCRVLForConditionalGeneration",
+    module_name="paddleocr_vl.paddleocr_vl",
+    category=ModelCategory.MULTIMODAL,
+    primary_use=ModelCategory.MULTIMODAL,
+)
+class PaddleOCRVLForConditionalGeneration(ModelForCasualLM):
+    def __init__(self, fd_config):
+        super().__init__(fd_config)
+
+        config = fd_config.model_config
+        self.config = config
+        self.mlp_AR = Projector(config, config.vision_config, prefix="mlp_AR")
+        self.visual = SiglipVisionModel(config.vision_config, prefix="visual")
+        self.model = PaddleOCRVLModel(fd_config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = ParallelLMHead(
+            fd_config=fd_config,
+            embedding_dim=fd_config.model_config.hidden_size,
+            num_embeddings=fd_config.model_config.vocab_size,
+            prefix="lm_head",
+        )
+
+        # Persistent buffers for CUDA graphs.
+        if envs.FD_ENABLE_MAX_PREFILL:
+            max_length = fd_config.scheduler_config.max_num_seqs * fd_config.model_config.max_model_len
+        else:
+            max_length = fd_config.model_config.max_model_len
+        self._input_embeddings = paddle.zeros(
+            [max_length, fd_config.model_config.hidden_size],
+            dtype=fd_config.model_config.dtype,
+        )
+
+    @paddle.no_grad()
+    def load_weights(self, weights_iterator) -> None:
+        """
+        Load model parameters from a given weights_iterator object.
+
+        Args:
+            weights_iterator (Iterator): An iterator yielding (name, weight) pairs.
+        """
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("up_gate_proj", "gate_proj", "gate"),
+            ("up_gate_proj", "up_proj", "up"),
+            ("embed_tokens.embeddings", "embed_tokens", None),
+            ("lm_head.linear", "lm_head", None),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        process_weights_after_loading_fn = process_weights_after_loading(dict(self.named_sublayers()))
+        for loaded_weight_name, loaded_weight in weights_iterator:
+            loaded_weight_name = (
+                self.process_weights_before_loading_fn(loaded_weight_name)
+                if getattr(self, "process_weights_before_loading_fn", None)
+                else loaded_weight_name
+            )
+            if loaded_weight_name is None:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in loaded_weight_name:
+                    continue
+                model_param_name = loaded_weight_name.replace(weight_name, param_name)
+                if model_param_name not in params_dict:
+                    continue
+                param = params_dict[model_param_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                model_param_name = loaded_weight_name
+                if model_param_name not in params_dict:
+                    continue
+                param = params_dict[model_param_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
+                weight_loader(param, loaded_weight)
+            model_sublayer_name = re.sub(r"\.(weight)$", "", model_param_name)
+            process_weights_after_loading_fn(model_sublayer_name, param)
+
+    @paddle.no_grad()
+    def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray, paddle.Tensor]]):
+        """
+        Load model parameters from a given state dictionary.
+
+        Args:
+            state_dict (dict[str, np.ndarray | paddle.Tensor]):
+                A dictionary containing model parameters, where keys are parameter names
+                and values are NumPy arrays or PaddlePaddle tensors.
+        """
+        self.model.load_state_dict(state_dict)
+        self.visual.load_state_dict(state_dict)
+        self.projector.load_state_dict(state_dict)
+        self.lm_head.load_state_dict(state_dict)
+
+    @property
+    def projector(self):
+        return self.mlp_AR
+
+    @classmethod
+    def name(self):
+        return "PaddleOCRVLForConditionalGeneration"
+
+    def compute_logits(self, hidden_states: paddle.Tensor):
+        logits = self.lm_head(hidden_states)
+        logits = paddle.cast(logits, paddle.float32)
+        logits[:, self.vocab_size :] = -float("inf")
+
+        return logits
+
+    def get_input_embeddings(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        image_features: Optional[paddle.Tensor] = None,
+    ) -> paddle.Tensor:
+        input_embeddings = self.model.get_input_embeddings(ids_remove_padding=ids_remove_padding)
+        image_mask = ids_remove_padding == self.model.config.image_token_id
+        image_token_num = image_mask.sum()
+
+        if image_token_num > 0:
+            input_embeddings[image_mask] = image_features.cast(self._dtype)
+        return input_embeddings
+
+    def forward(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        image_features: Optional[paddle.Tensor],
+        forward_meta: ForwardMeta,
+    ):
+        input_embeddings = self.get_input_embeddings(
+            ids_remove_padding=ids_remove_padding, image_features=image_features
+        )
+        self._input_embeddings.copy_(input_embeddings, False)
+
+        hidden_states = self.model(
+            input_embeddings=self._input_embeddings,
+            forward_meta=forward_meta,
+        )
+
+        return hidden_states
+
+
+class PaddleOCRVLPretrainedModel(PretrainedModel):
+
+    config_class = FDConfig
+
+    def _init_weight(self, layer):
+        """
+        _init_weight
+        """
+        return None
+
+    @classmethod
+    def arch_name(self):
+        return "PaddleOCRVLForConditionalGeneration"
+
+    from fastdeploy.model_executor.models.tp_utils import TensorSplitMode as tsm
+    from fastdeploy.model_executor.models.utils import LayerIdPlaceholder as layerid
+    from fastdeploy.model_executor.models.utils import WeightMeta
+
+    weight_infos = [
+        WeightMeta(
+            f".layers.{{{layerid.LAYER_ID}}}.self_attn.qkv_proj.weight",
+            True,
+            tsm.GQA,
+        ),
+        WeightMeta(f".layers.{{{layerid.LAYER_ID}}}.self_attn.o_proj.weight", False),
+        WeightMeta(
+            f".layers.{{{layerid.FFN_LAYER_ID}}}.mlp.up_gate_proj.weight",
+            True,
+            tsm.PairFused,
+        ),
+        WeightMeta(f".layers.{{{layerid.FFN_LAYER_ID}}}.mlp.down_proj.weight", False),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.experts.{{{layerid.TEXT_EXPERT_ID}}}.up_gate_proj.weight",
+            True,
+            tsm.PairFused,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.experts.{{{layerid.TEXT_EXPERT_ID}}}.down_proj.weight",
+            False,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.experts.{{{layerid.IMG_EXPERT_ID}}}.up_gate_proj.weight",
+            True,
+            tsm.PairFused,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.experts.{{{layerid.IMG_EXPERT_ID}}}.down_proj.weight",
+            False,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.shared_experts.up_gate_proj.weight",
+            True,
+            tsm.PairFused,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.shared_experts.down_proj.weight",
+            False,
+        ),
+        WeightMeta(
+            f".layers.{{{layerid.MOE_LAYER_ID}}}.mlp.shared_experts.down_proj.weight",
+            False,
+        ),
+        WeightMeta(".embed_tokens.weight", False),
+        WeightMeta("lm_head.weight", True),
+    ]
+
+    weight_vison = [
+        # resampler_model
+        WeightMeta("ernie.resampler_model.spatial_linear.0.weight", False),
+        WeightMeta("resampler_model.spatial_linear.0.weight", False),
+        # vision
+        WeightMeta(
+            f"vision_model.blocks.{{{layerid.LAYER_ID}}}.attn.proj.weight",
+            False,
+        ),
+        WeightMeta(f"vision_model.blocks.{{{layerid.LAYER_ID}}}.mlp.fc2.weight", False),
+        WeightMeta(f"vision_model.blocks.{{{layerid.LAYER_ID}}}.mlp.fc1.weight", True),
+        WeightMeta(f"vision_model.blocks.{{{layerid.LAYER_ID}}}.mlp.fc1.bias", True),
+        WeightMeta(
+            f"vision_model.blocks.{{{layerid.LAYER_ID}}}.attn.qkv.weight",
+            True,
+            tsm.GQA,
+        ),
+        WeightMeta(
+            f"vision_model.blocks.{{{layerid.LAYER_ID}}}.attn.qkv.bias",
+            True,
+            tsm.GQA,
+        ),
+    ]
+
+    @classmethod
+    def _get_tensor_parallel_mappings(cls, config: PretrainedConfig, is_split=True):
+        """
+        get_tensor_parallel_mappings
+        """
+        from fastdeploy.model_executor.models.tp_utils import (
+            build_expanded_keys,
+            has_prefix,
+            split_or_merge_func_v1,
+        )
+
+        fn = split_or_merge_func_v1(
+            is_split=is_split,
+            tensor_parallel_degree=config.tensor_parallel_degree,
+            tensor_parallel_rank=config.tensor_parallel_rank,
+            num_attention_heads=config.num_attention_heads,
+            num_key_value_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+        )
+        vision_fn = split_or_merge_func_v1(
+            is_split=is_split,
+            tensor_parallel_degree=config.tensor_parallel_degree,
+            tensor_parallel_rank=config.tensor_parallel_rank,
+            num_attention_heads=config.vision_config.get("num_heads"),
+            num_key_value_heads=config.vision_config.get("num_heads"),
+            head_dim=config.vision_config.get("hidden_size") // config.vision_config.get("num_heads"),
+        )
+
+        def get_tensor_parallel_split_mappings(
+            num_layers: int,
+            moe_num_experts: list[int],
+            moe_layer_start_index: int,
+            prefix_name: str,
+        ):
+            base_actions = {}
+            for weight_name, is_column, extra in cls.weight_infos:
+                params = {
+                    "is_column": is_column,
+                    **({extra.value: True} if extra else {}),
+                }
+
+                if "lm_head.weight" in weight_name or weight_name == "":
+                    key = weight_name
+                elif not has_prefix(prefix_name, weight_name):
+                    key = f"{prefix_name}{weight_name}"
+                else:
+                    key = weight_name
+                base_actions[key] = partial(fn, **params)
+            final_actions = {}
+            final_actions = build_expanded_keys(
+                base_actions,
+                num_layers,
+                (moe_layer_start_index if moe_layer_start_index > 0 else num_layers),
+                text_num_experts=moe_num_experts[0],
+                img_num_experts=moe_num_experts[1],
+            )
+            return final_actions
+
+        def get_vison_parallel_split_mappings(num_layers: int):
+            base_actions = {}
+            for weight_name, is_column, extra in cls.weight_vison:
+                params = {
+                    "is_column": is_column,
+                    **({extra.value: True} if extra else {}),
+                }
+                base_actions[weight_name] = partial(vision_fn, **params)
+            final_actions = {}
+            final_actions = build_expanded_keys(
+                base_actions,
+                num_layers,
+            )
+            return final_actions
+
+        moe_layer_start_index = -1
+        if isinstance(config.moe_layer_start_index, list):
+            moe_layer_start_index = min(config.moe_layer_start_index)
+        elif isinstance(config.moe_layer_start_index, int):
+            moe_layer_start_index = config.moe_layer_start_index
+
+        mappings = get_tensor_parallel_split_mappings(
+            config.num_hidden_layers,
+            config.moe_num_experts,
+            moe_layer_start_index,
+            config.prefix_name,
+        )
+        vision_mappings = get_vison_parallel_split_mappings(config.vision_config.get("depth"))
+
+        return {**mappings, **vision_mappings}
--- a/fastdeploy/model_executor/models/paddleocr_vl/projector.py
+++ b/fastdeploy/model_executor/models/paddleocr_vl/projector.py
@@ -0,0 +1,107 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import math
+
+import paddle
+import paddle.nn as nn
+
+from fastdeploy.model_executor.layers.utils import get_tensor
+
+
+class GELUActivation(nn.Layer):
+    """
+    Original Implementation of the GELU activation function in Google BERT repo when initially created. For
+    information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
+    torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
+    Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
+    """
+
+    def __init__(self, use_gelu_python: bool = False):
+        super().__init__()
+        if use_gelu_python:
+            self.act = self._gelu_python
+        else:
+            self.act = nn.functional.gelu
+
+    def _gelu_python(self, input):
+        return input * 0.5 * (1.0 + paddle.erf(input / math.sqrt(2.0)))
+
+    def forward(self, input):
+        return self.act(input)
+
+
+class Projector(nn.Layer):
+
+    def __init__(self, text_config, vision_config, prefix=""):
+        super().__init__()
+        self.prefix_name = prefix
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.merge_kernel_size = (2, 2)
+
+        self.hidden_size = self.vision_config.hidden_size * self.merge_kernel_size[0] * self.merge_kernel_size[1]
+
+        self.pre_norm = nn.LayerNorm(self.vision_config.hidden_size, epsilon=1e-05)
+        self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size)
+        self.act = GELUActivation()
+        self.linear_2 = nn.Linear(self.hidden_size, self.text_config.hidden_size)
+
+    def forward(self, image_features, image_grid_thw):
+        m1, m2 = self.merge_kernel_size
+        if isinstance(image_features, (list, tuple)):
+            processed_features = list()
+            for image_feature, image_grid in zip(image_features, image_grid_thw):
+                image_feature = self.pre_norm(image_feature)  # shape: (T*H*W, D)
+                t, h, w = image_grid
+                from einops import rearrange
+
+                image_feature = rearrange(
+                    image_feature,
+                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
+                    t=int(t),
+                    h=int(h // m1),
+                    p1=int(m1),
+                    w=int(w // m2),
+                    p2=int(m2),
+                )
+                hidden_states = self.linear_1(image_feature)
+                hidden_states = self.act(hidden_states)
+                hidden_states = self.linear_2(hidden_states)
+                processed_features.append(hidden_states)
+
+            return processed_features
+
+        dim = image_features.shape[-1]
+        image_features = paddle.reshape(image_features, [-1, dim])
+        hidden_states = self.pre_norm(image_features)
+        hidden_states = paddle.reshape(hidden_states, [-1, self.hidden_size])
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+    def load_state_dict(self, state_dict):
+        params_dict = dict(self.named_parameters())
+        for param_name, param in params_dict.items():
+            state_dict_key = f"{self.prefix_name}.{param_name}"
+            if state_dict_key not in state_dict:
+                raise ValueError(f"The key {state_dict_key} does not exist in state_dict. ")
+            tensor = get_tensor(state_dict.pop(state_dict_key))
+            if param.shape != tensor.shape:
+                raise ValueError(f"{state_dict_key} param.shape={param.shape} tensor.shape={tensor.shape}")
+            else:
+                param.copy_(tensor, False)
--- a/fastdeploy/model_executor/models/paddleocr_vl/siglip.py
+++ b/fastdeploy/model_executor/models/paddleocr_vl/siglip.py
@@ -0,0 +1,740 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.functional.flash_attention import flash_attn_unpadded
+from paddleformers.transformers.activations import ACT2FN
+from paddleformers.transformers.model_utils import PretrainedModel
+
+from fastdeploy.model_executor.layers.utils import get_tensor
+from fastdeploy.model_executor.utils import slice_fn
+
+try:
+    from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
+except:
+    flash_attention_v3_varlen = None
+
+from .config import PPOCRVisionConfig
+
+
+def rotate_half(x):
+    Dh = x.shape[-1]
+    x1 = x[..., : Dh // 2]
+    x2 = x[..., Dh // 2 :]
+    return paddle.concat([-x2, x1], axis=-1)
+
+
+def _ensure_cos_sin_dim(cos, sin, dim_needed):
+    last = cos.shape[-1]
+    if last == dim_needed:
+        return cos, sin
+    elif last * 2 == dim_needed:
+        cos = paddle.concat([cos, cos], axis=-1)
+        sin = paddle.concat([sin, sin], axis=-1)
+        return cos, sin
+    else:
+        raise ValueError(f"Unexpected cos/sin last-dim: {last}, expected {dim_needed} or {dim_needed//2}")
+
+
+def apply_rotary_pos_emb_vision(x, cos, sin):
+    orig_dtype = x.dtype
+    x = x.astype("float32")
+    x_embed = (x * cos) + (rotate_half(x) * sin)
+    return x_embed.astype(orig_dtype)
+
+
+class QKVLinear(nn.Linear):
+    def __init__(self, config, in_features, out_features, weight_attr=None, bias_attr=None):
+        super().__init__(in_features, out_features, weight_attr, bias_attr)
+        self.config = config
+        self.in_features = in_features
+        self.out_features = out_features
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        assert self.head_dim * self.num_heads == self.embed_dim
+        self.weight.weight_loader = self.weight_loader
+        self.bias.weight_loader = self.weight_loader
+
+    def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
+        # Tensor parallelism splits the weight along the output_dim
+        loaded_weight = get_tensor(loaded_weight)
+        if not param._is_initialized():
+            param.initialize()
+        if loaded_shard_id == "q":
+            param_shard_offset = 0
+            param_shard_size = self.num_heads * self.head_dim
+        elif loaded_shard_id == "k":
+            param_shard_offset = self.num_heads * self.head_dim
+            param_shard_size = self.num_heads * self.head_dim
+        else:
+            # loaded_shard_id == "v"
+            param_shard_offset = self.num_heads * self.head_dim * 2
+            param_shard_size = self.num_heads * self.head_dim
+
+        param = slice_fn(param, self.out_features, start=param_shard_offset, end=param_shard_offset + param_shard_size)
+        assert param.shape == loaded_weight.shape, (
+            f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
+        )
+        # Ensure loaded weight dtype matches model param dtype
+        if loaded_weight.dtype != param.dtype:
+            if loaded_weight.dtype == paddle.int8 and param.dtype == paddle.float8_e4m3fn:
+                loaded_weight = loaded_weight.view(param.dtype)
+            else:
+                loaded_weight = loaded_weight.cast(param.dtype)
+        param.copy_(loaded_weight, False)
+
+
+class SiglipAttention(nn.Layer):
+    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
+        assert self.head_dim * self.num_heads == self.embed_dim
+        self.scale = self.head_dim**-0.5
+
+        self.qkv_proj = QKVLinear(config, self.embed_dim, self.embed_dim * 3, bias_attr=True)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+        prop = paddle.device.cuda.get_device_properties()
+        cc = prop.major * 10 + prop.minor
+        is_current_sm_supported = cc >= 90
+        is_paddle_supported = any(num >= 90 for num in paddle.version.cuda_archs())
+        if is_current_sm_supported and is_paddle_supported:
+            self.flash_attn_func = flash_attention_v3_varlen
+            self.flash_attn_kwargs = {}
+        else:
+            self.flash_attn_func = flash_attn_unpadded
+            self.flash_attn_kwargs = {"scale": self.scale, "training": False}
+
+    def forward(
+        self,
+        hidden_states: paddle.Tensor,  # [B, L, D]
+        attention_mask: Optional[paddle.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        cu_seqlens: Optional[List[paddle.Tensor]] = None,
+        max_seqlen: Optional[paddle.Tensor] = None,
+        rope_emb: Optional[Tuple[paddle.Tensor, paddle.Tensor]] = None,  # (cos, sin)
+    ):
+        B, seq_length, D = hidden_states.shape
+
+        qkv = (
+            self.qkv_proj(hidden_states)
+            .reshape(
+                [
+                    seq_length,
+                    3,
+                    self.num_heads,
+                    -1,
+                ]
+            )
+            .transpose(perm=[1, 0, 2, 3])
+        )
+        q, k, v = qkv.unbind(axis=0)
+        cos, sin = rope_emb
+
+        # --------
+        q = apply_rotary_pos_emb_vision(q, cos, sin)
+        k = apply_rotary_pos_emb_vision(k, cos, sin)
+
+        attn_output = self.flash_attn_func(
+            q,
+            k,
+            v,
+            cu_seqlens,
+            cu_seqlens,
+            max_seqlen,
+            max_seqlen,
+            causal=False,
+            **self.flash_attn_kwargs,
+        )[0]
+        # --------
+
+        attn_output = attn_output.reshape(seq_length, -1)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class SiglipVisionEmbeddings(nn.Layer):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size  # 1152
+        self.image_size = config.image_size  # 384
+        self.patch_size = config.patch_size  # 14
+
+        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 = (self.image_size // self.patch_size) ** 2  # 729
+        self.num_positions = self.num_patches
+        self.cache_position_embedding = dict()
+        self.cache_position_count = dict()
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)
+
+        self.register_buffer(
+            "position_ids",
+            paddle.arange(self.num_positions).unsqueeze(0),
+            persistable=False,
+        )
+
+    def interpolate_pos_encoding(self, embeddings, height: int, width: int, is_after_patchify: bool = False):
+
+        num_positions = self.position_embedding.weight.shape[0]
+
+        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+        dim = embeddings.shape[-1]
+
+        if is_after_patchify:
+            new_height = height
+            new_width = width
+        else:
+            new_height = height // self.patch_size
+            new_width = width // self.patch_size
+
+        sqrt_num_positions = paddle.to_tensor(num_positions**0.5, dtype=paddle.int64)
+        patch_pos_embed = patch_pos_embed.reshape((1, sqrt_num_positions, sqrt_num_positions, dim))
+        patch_pos_embed = patch_pos_embed.transpose((0, 3, 1, 2))
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.transpose((0, 2, 3, 1)).reshape((1, -1, dim))
+        return patch_pos_embed
+
+    @staticmethod
+    def flatten_list(image_grid_thw):
+        tmp_image_grid_thw = list()
+        for image_grid in image_grid_thw:
+            if isinstance(image_grid, list):
+                tmp_image_grid_thw.extend(image_grid)
+            else:
+                tmp_image_grid_thw.append(image_grid)
+        return tmp_image_grid_thw
+
+    def fetch_position_embedding_lfu_cache(self, embeddings, h, w, max_cache=20):
+        grid = (h, w)
+        if grid in self.cache_position_embedding:
+            self.cache_position_count[grid] += 1
+            return self.cache_position_embedding[grid]
+
+        if len(self.cache_position_embedding) >= max_cache:
+            min_hit_grid = min(self.cache_position_count, key=self.cache_position_count.get)
+            self.cache_position_count.pop(min_hit_grid)
+            self.cache_position_embedding.pop(min_hit_grid)
+
+        position_embedding = self.interpolate_pos_encoding(embeddings, h, w, True)
+        self.cache_position_count[grid] = 1
+        self.cache_position_embedding[grid] = position_embedding
+        return position_embedding
+
+    def forward(
+        self,
+        pixel_values: paddle.Tensor,  # [B, L, C, H, W]
+        position_ids: Optional[paddle.Tensor] = None,  # [B or 1, S]
+        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
+        interpolate_pos_encoding: bool = False,
+    ) -> paddle.Tensor:
+        if pixel_values.dim() == 4:
+            pixel_values = pixel_values.unsqueeze(0)
+        if pixel_values.dim() == 5:
+            assert position_ids is not None
+            from einops import rearrange
+
+            batch_size, squence_len, channel, height, width = pixel_values.shape
+            target_dtype = self.patch_embedding.weight.dtype
+            pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
+            patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
+            embeddings = patch_embeds.flatten(-2).squeeze(-1)
+            embeddings = rearrange(embeddings, "(b l) d -> b l d", b=batch_size, l=squence_len)
+            # todo: not debug
+            if interpolate_pos_encoding and image_grid_thw is not None:
+                flatten_image_grid_thw = self.flatten_list(image_grid_thw)
+                flatten_image_grid_thw = np.array(flatten_image_grid_thw)
+                assert batch_size == 1
+                start = 0
+
+                assert sum([np.prod(x) for x in flatten_image_grid_thw]) == embeddings.shape[1], (
+                    flatten_image_grid_thw,
+                    embeddings.shape,
+                )
+                embeddings = embeddings.squeeze(0)
+                tmp_embeddings = list()
+                for image_grid in image_grid_thw:
+                    t, h, w = image_grid
+                    end = start + t * h * w
+                    image_embeddings = embeddings[int(start) : int(end), :]
+                    position_embedding = (
+                        self.interpolate_pos_encoding(image_embeddings, h, w, True).squeeze(0).tile((t, 1))
+                    ).astype(image_embeddings.dtype)
+                    image_embeddings = image_embeddings + position_embedding
+                    tmp_embeddings.append(image_embeddings)
+                    start = end
+                embeddings = paddle.concat(tmp_embeddings, axis=0).unsqueeze(0)
+            else:
+                embeddings = embeddings + self.packing_position_embedding(position_ids)
+            return embeddings
+        else:
+            raise NotImplementedError(str(pixel_values.shape))
+
+
+class SiglipMLP(nn.Layer):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        if config.hidden_act == "gelu_pytorch_tanh":
+            config.hidden_act = "silu"
+        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: paddle.Tensor) -> paddle.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class SiglipEncoderLayer(paddle.nn.Layer):
+    def __init__(self, config):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = paddle.nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
+        self.self_attn = SiglipAttention(config)
+        self.layer_norm2 = paddle.nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+
+    # @paddle.jit.to_static
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_attentions=False,
+        cu_seqlens=None,
+        max_seqlen=None,
+        rope_emb=None,
+    ):
+
+        residual = hidden_states
+        ############################
+        ln1_out = self.layer_norm1(hidden_states)
+
+        x = self.self_attn(
+            hidden_states=ln1_out,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            rope_emb=rope_emb,
+        )
+
+        hs_post_attn = residual + x
+
+        residual = hs_post_attn
+        ln2_out = self.layer_norm2(residual)
+
+        mlp_out = self.mlp(ln2_out)
+
+        hidden_states_out = residual + mlp_out
+
+        outputs = (hidden_states_out,)
+
+        return outputs
+
+
+class SigLIPRotaryEmbedding(nn.Layer):
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.rope_init()
+
+    def rope_init(self):
+        arange = paddle.arange(0, self.dim, 2, dtype="float32")
+        inv_freq = 1.0 / (self.theta ** (arange / self.dim))
+        self.register_buffer("inv_freq", inv_freq.astype(paddle.get_default_dtype()), persistable=False)
+
+    def forward(self, seqlen: int) -> paddle.Tensor:
+        seq = paddle.arange(seqlen, dtype=self.inv_freq.dtype)
+        freqs = paddle.outer(seq, self.inv_freq)
+        return freqs
+
+
+class SiglipEncoder(nn.Layer):
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+        embed_dim = config.hidden_size
+        num_heads = config.num_attention_heads
+        head_dim = embed_dim // num_heads
+        self.layers = nn.LayerList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)
+        self.gradient_checkpointing = False
+
+    @staticmethod
+    def flatten_list(image_grid_thw):
+        tmp_image_grid_thw = list()
+        for image_grid in image_grid_thw:
+            if isinstance(image_grid, list):
+                tmp_image_grid_thw.extend(image_grid)
+            else:
+                tmp_image_grid_thw.append(image_grid)
+        return tmp_image_grid_thw
+
+    def build_window_index(self, image_grid, window_size):
+        """
+        返回：
+          window_indices: int64 [sum(t*h*w_valid)]
+          cu_seqlens_within_windows: int32 [num_windows_total*t]，首位补 0 的前缀和
+        """
+        from einops import rearrange
+
+        window_indices = list()
+        pad_values = -100
+        start_window_index = 0
+        cu_seqlens_within_windows = list()
+
+        for t, h, w in map(int, image_grid):
+            window_index = paddle.arange(t * h * w).reshape((t, h, w))
+            pad_h = (-h) % window_size
+            pad_w = (-w) % window_size
+            assert pad_h >= 0 and pad_w >= 0, (pad_h, pad_w)
+            window_index = F.pad(window_index, (0, pad_w, 0, pad_h), value=pad_values)
+            window_index = rearrange(
+                window_index,
+                "t (h p1) (w p2) -> t (h w) (p1 p2)",
+                p1=window_size,
+                p2=window_size,
+            )
+            window_seqlens = (window_index != pad_values).long().sum(-1).reshape(-1)
+            window_index = window_index.reshape(-1)
+            window_index = window_index[window_index != pad_values]
+            window_indices.append(window_index + start_window_index)
+            cu_seqlens_within_windows.append(window_seqlens.cumsum(0) + start_window_index)
+            start_window_index += t * h * w
+        window_indices = paddle.concat(window_indices, axis=0)
+        cu_seqlens_within_windows = paddle.concat(cu_seqlens_within_windows, axis=0)
+        cu_seqlens_within_windows = F.pad(cu_seqlens_within_windows, (1, 0), value=0).astype("int32")
+        return window_indices, cu_seqlens_within_windows
+
+    def forward(
+        self,
+        inputs_embeds: paddle.Tensor,
+        attention_mask: Optional[paddle.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cu_seqlens: Optional[paddle.Tensor] = None,
+        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
+        height_position_ids: Optional[paddle.Tensor] = None,
+        width_position_ids: Optional[paddle.Tensor] = None,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[int] = -1,
+        vision_or_text: str = "vision",
+    ):
+        assert vision_or_text in ["vision", "text"]
+        use_window_attn = window_size > 0 and vision_or_text == "vision"
+        use_rope = (use_rope is True) and (vision_or_text == "vision")
+        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
+        )
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        hidden_states = inputs_embeds
+        attention_mask = attention_mask.to(inputs_embeds.dtype) if attention_mask is not None else None
+
+        if use_rope is True:
+            flatten_image_grid_thw = self.flatten_list(image_grid_thw)
+            flatten_image_grid_thw = np.array(flatten_image_grid_thw)
+            assert sum([np.prod(x) for x in flatten_image_grid_thw]) == hidden_states.shape[1], (
+                flatten_image_grid_thw,
+                hidden_states.shape,
+            )
+
+            if width_position_ids is None or height_position_ids is None:
+                split_hids = list()
+                split_wids = list()
+                for t, h, w in flatten_image_grid_thw:
+                    t, h, w = map(int, (t, h, w))
+                    image_pids = paddle.arange(t * h * w) % (h * w)
+                    sample_hids = image_pids // w
+                    sample_wids = image_pids % w
+                    split_hids.append(sample_hids)
+                    split_wids.append(sample_wids)
+                width_position_ids = paddle.concat(split_wids, axis=0)
+                height_position_ids = paddle.concat(split_hids, axis=0)
+
+            window_indices, cu_seqlens_within_windows = None, None
+
+            if use_window_attn:
+                window_indices, cu_seqlens_within_windows = self.build_window_index(
+                    flatten_image_grid_thw, window_size
+                )
+                reversed_window_indices = window_indices.argsort()
+                height_position_ids = height_position_ids[window_indices]
+                width_position_ids = width_position_ids[window_indices]
+
+            pids = paddle.stack([height_position_ids, width_position_ids], axis=-1).astype(paddle.int64)
+            max_grid_size = pids.max() + 1
+            rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
+
+            rope_emb = rope_emb_max_grid[pids].flatten(1)
+            rope_emb = rope_emb.tile((1, 2))
+            cos = rope_emb.cos().astype("float32")
+            sin = rope_emb.sin().astype("float32")
+            cos = cos.unsqueeze(-2)
+            sin = sin.unsqueeze(-2)
+            rope_emb = (cos, sin)
+        else:
+            rope_emb = None
+
+            window_indices, cu_seqlens_within_windows = None, None
+
+            if use_window_attn:
+                flatten_image_grid_thw = self.flatten_list(image_grid_thw)
+                assert (
+                    sum([np.prod(x.astype("float32").cpu().numpy()) for x in flatten_image_grid_thw])
+                    == hidden_states.shape[1]
+                ), (flatten_image_grid_thw, hidden_states.shape)
+
+                window_indices, cu_seqlens_within_windows = self.build_window_index(
+                    flatten_image_grid_thw, window_size
+                )
+                reversed_window_indices = window_indices.argsort()
+
+        if use_window_attn:
+            assert cu_seqlens_within_windows is not None
+            attn_cu_seqlens = cu_seqlens_within_windows
+            hidden_states = hidden_states[:, window_indices, :]
+        else:
+            attn_cu_seqlens = cu_seqlens
+
+        max_seqlen = (attn_cu_seqlens[1:] - attn_cu_seqlens[:-1]).max().item()
+
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                encoder_states = encoder_states + (
+                    (hidden_states[:, reversed_window_indices, :],) if use_window_attn else (hidden_states,)
+                )
+
+            layer_outputs = encoder_layer(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                cu_seqlens=attn_cu_seqlens,
+                max_seqlen=max_seqlen,
+                rope_emb=rope_emb,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if use_window_attn:
+            hidden_states = hidden_states[:, reversed_window_indices, :]
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        return hidden_states
+
+
+class SiglipMultiheadAttentionPoolingHead(nn.Layer):
+    """Multihead Attention Pooling."""
+
+    def __init__(self, config: PPOCRVisionConfig):
+        super().__init__()
+
+        self.probe = self.create_parameter(
+            shape=(1, 1, config.hidden_size),
+            default_initializer=paddle.nn.initializer.Normal(),
+        )
+        self.attention = nn.MultiHeadAttention(config.hidden_size, config.num_attention_heads)
+        self.layernorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+
+    def forward(self, hidden_state, key_padding_mask=None):
+        batch_size = hidden_state.shape[0]
+        probe = self.probe.tile((batch_size, 1, 1))
+
+        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
+
+        residual = hidden_state
+        hidden_state = self.layernorm(hidden_state)
+        hidden_state = residual + self.mlp(hidden_state)
+
+        return hidden_state[:, 0]
+
+
+class SiglipVisionTransformer(nn.Layer):
+    def __init__(self, config: PPOCRVisionConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim, epsilon=config.layer_norm_eps)
+        self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head
+        if self.use_head:
+            self.head = SiglipMultiheadAttentionPoolingHead(config)
+
+    def forward(
+        self,
+        pixel_values,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = False,
+        attention_mask=None,
+        sample_indices=None,
+        image_indices=None,
+        position_ids=None,
+        height_position_ids=None,
+        width_position_ids=None,
+        cu_seqlens=None,
+        padding_mask=None,
+        vision_return_embed_list: Optional[bool] = False,
+        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
+        return_pooler_output: Optional[bool] = True,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[bool] = -1,
+    ):
+        hidden_states = self.embeddings(
+            pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            image_grid_thw=image_grid_thw,
+        )
+        last_hidden_state = self.encoder(
+            inputs_embeds=hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            attention_mask=attention_mask,
+            cu_seqlens=cu_seqlens,
+            image_grid_thw=image_grid_thw,
+            use_rope=use_rope,
+            height_position_ids=height_position_ids,
+            width_position_ids=width_position_ids,
+            window_size=window_size,
+            vision_or_text="vision",
+        )
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        sample_hidden_state = list()
+        assert cu_seqlens is not None
+        for i in range(cu_seqlens.shape[0] - 1):
+            start = cu_seqlens[i]
+            end = cu_seqlens[i + 1]
+            tensor = last_hidden_state[:, start:end, :].squeeze(0)
+            sample_hidden_state.append(tensor)
+
+        return sample_hidden_state
+
+
+class SiglipVisionModel(PretrainedModel):
+    config_class = PPOCRVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: PPOCRVisionConfig, prefix=""):
+        super().__init__(config)
+        self.prefix_name = prefix
+        self.vision_model = SiglipVisionTransformer(config)
+
+    def get_input_embeddings(self) -> nn.Layer:
+        return self.vision_model.embeddings.patch_embedding
+
+    def forward(
+        self,
+        pixel_values,
+        sample_indices=None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
+        position_ids=None,
+        vision_return_embed_list: Optional[bool] = False,
+        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
+        cu_seqlens=None,
+        return_pooler_output: Optional[bool] = True,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[bool] = -1,
+    ):
+        return self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            vision_return_embed_list=vision_return_embed_list,
+            image_grid_thw=image_grid_thw,
+            sample_indices=sample_indices,
+            cu_seqlens=cu_seqlens,
+            return_pooler_output=return_pooler_output,
+            use_rope=use_rope,
+            window_size=window_size,
+        )
+
+    def load_state_dict(self, state_dict):
+        params_dict = dict(self.named_parameters())
+        for param_name, param in params_dict.items():
+            state_dict_key = f"{self.prefix_name}.{param_name}"
+            if state_dict_key not in state_dict:
+                if "self_attn.qkv_proj.weight" in state_dict_key:
+                    q_weight_key = state_dict_key.replace("qkv_proj", "q_proj")
+                    k_weight_key = state_dict_key.replace("qkv_proj", "k_proj")
+                    v_weight_key = state_dict_key.replace("qkv_proj", "v_proj")
+                    q_tensor = get_tensor(state_dict.pop(q_weight_key))
+                    k_tensor = get_tensor(state_dict.pop(k_weight_key))
+                    v_tensor = get_tensor(state_dict.pop(v_weight_key))
+                    weight_tensor = paddle.concat([q_tensor, k_tensor, v_tensor], axis=-1).transpose([1, 0])
+                    tensor = paddle.transpose(weight_tensor, perm=[1, 0])
+                elif "self_attn.qkv_proj.bias" in state_dict_key:
+                    q_bias_key = state_dict_key.replace("qkv_proj", "q_proj")
+                    k_bias_key = state_dict_key.replace("qkv_proj", "k_proj")
+                    v_bias_key = state_dict_key.replace("qkv_proj", "v_proj")
+                    q_bias = get_tensor(state_dict.pop(q_bias_key))
+                    k_bias = get_tensor(state_dict.pop(k_bias_key))
+                    v_bias = get_tensor(state_dict.pop(v_bias_key))
+                    qkv_bias = paddle.concat([q_bias, k_bias, v_bias], axis=-1)
+                    tensor = qkv_bias
+                else:
+                    raise ValueError(f"The key {state_dict_key} does not exist in state_dict. ")
+            else:
+                tensor = get_tensor(state_dict.pop(state_dict_key))
+            if param.shape != tensor.shape:
+                raise ValueError(f"{state_dict_key} param.shape={param.shape} tensor.shape={tensor.shape}")
+            else:
+                param.copy_(tensor, False)