[Feature] Optim PaddleOCR-VL (#4873)

* [Feature] Optim PaddleOCR-VL

* fix bug

fastdeploy/model_executor/models/paddleocr_vl/siglip_ops.py

@@ -0,0 +1,74 @@
+"""
+# 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
+
+import paddle
+from paddleformers.transformers.activations import ACT2FN
+
+from fastdeploy.platforms import current_platform
+
+if current_platform.is_cuda():
+    from fastdeploy.model_executor.ops.gpu import fused_neox_rope_embedding, gelu_tanh
+
+
+def rotate_half(x):
+    Dh = x.shape[-1]
+    x1 = x[..., : Dh // 2]
+    x2 = x[..., Dh // 2 :]
+    return paddle.concat([-x2, x1], axis=-1)
+
+
+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)
+
+
+def native_neox_rope_embedding(qkv, cos, sin, num_heads):
+    B, seq_length, D = qkv.shape
+    qkv = qkv.reshape(
+        [
+            seq_length,
+            3,
+            num_heads,
+            -1,
+        ]
+    ).transpose(perm=[1, 0, 2, 3])
+    q, k, v = qkv.unbind(axis=0)
+    q = apply_rotary_pos_emb_vision(q, cos, sin)
+    k = apply_rotary_pos_emb_vision(k, cos, sin)
+    return q, k, v
+
+
+def neox_rope_embedding(
+    qkv: paddle.Tensor, cos_emb: paddle.Tensor, sin_emb: paddle.Tensor, num_heads: int, head_dim: int
+) -> List[paddle.Tensor]:
+    if current_platform.is_cuda():
+        return fused_neox_rope_embedding(qkv, cos_emb, sin_emb, num_heads, head_dim)
+    else:
+        return native_neox_rope_embedding(qkv, cos_emb, sin_emb, num_heads)
+
+
+def get_activation_fn(hidden_act: str):
+    if hidden_act == "gelu_pytorch_tanh":
+        if current_platform.is_cuda():
+            return gelu_tanh
+        else:
+            return ACT2FN["gelu_new"]
+    else:
+        return ACT2FN[hidden_act]