[Feature] Optim PaddleOCR-VL (#4873)

* [Feature] Optim PaddleOCR-VL

* fix bug

custom_ops/gpu_ops/gelu_tanh.cu

@@ -0,0 +1,106 @@
+// 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.
+
+#include "helper.h"
+#include "paddle/extension.h"
+
+__forceinline__ __device__ float tanh_ptx(float x) {
+  float y;
+  asm volatile("tanh.approx.f32 %0, %1;" : "=f"(y) : "f"(x));
+  return y;
+}
+
+__device__ __forceinline__ float gelu_tanh_func(const float& val) {
+  const float cdf =
+      0.5f * (1.0f + tanh_ptx((0.7978845608028654f *
+                               (val + 0.044715f * val * val * val))));
+  return val * cdf;
+}
+
+template <typename T>
+__global__ void gelu_tanh_kernel(T* __restrict__ out,
+                                 const T* __restrict__ input,
+                                 const int d) {
+  constexpr uint32_t kVecSize = 16 / sizeof(T);
+  const int64_t token_idx = blockIdx.x;
+  const int64_t thread_idx = threadIdx.x;
+  const int64_t stride = blockDim.x;
+  const int64_t offset = token_idx * d;
+  using vec_t = AlignedVector<T, kVecSize>;
+#if (__CUDACC_VER_MAJOR__ >= 12 && defined(__CUDA_ARCH__) && \
+     (__CUDA_ARCH__ >= 900))
+  asm volatile("griddepcontrol.wait;");
+#endif
+
+#pragma unroll 1
+  for (uint32_t idx = thread_idx; idx < d / kVecSize; idx += stride) {
+    vec_t x_vec;
+    Load(input + offset + idx * kVecSize, &x_vec);
+#pragma unroll
+    for (uint32_t i = 0; i < kVecSize; ++i) {
+      x_vec[i] = static_cast<T>(gelu_tanh_func(static_cast<float>(x_vec[i])));
+    }
+    Store(x_vec, out + token_idx * d + idx * kVecSize);
+  }
+
+  const int64_t remaining_offset = d - d % (stride * kVecSize);
+  // process the remaining elements
+#pragma unroll 1
+  for (int64_t idx = thread_idx; idx < d % (stride * kVecSize); idx += stride) {
+    float x = static_cast<float>(input[offset + remaining_offset + idx]);
+    out[token_idx * d + remaining_offset + idx] =
+        static_cast<T>(gelu_tanh_func(x));
+  }
+
+#if (__CUDACC_VER_MAJOR__ >= 12 && defined(__CUDA_ARCH__) && \
+     (__CUDA_ARCH__ >= 900))
+  asm volatile("griddepcontrol.launch_dependents;");
+#endif
+}
+
+std::vector<paddle::Tensor> GeluTanh(paddle::Tensor& input) {
+  int d = input.dims()[1];
+  int64_t num_tokens = input.dims()[0];
+  cudaStream_t stream = input.stream();
+
+  paddle::Tensor output =
+      GetEmptyTensor(input.dims(), input.dtype(), input.place());
+
+  DISPATCH_FLOAT_FP6_DTYPE(input.dtype(), scalar_t, {
+    uint32_t vec_size = 16 / sizeof(scalar_t);
+    cudaLaunchConfig_t config;
+    config.gridDim = num_tokens;
+    config.blockDim = std::min(d / vec_size, 1024U);
+    config.dynamicSmemBytes = 0;
+    config.stream = stream;
+    cudaLaunchAttribute attrs[1];
+    attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
+    attrs[0].val.programmaticStreamSerializationAllowed = false;
+    config.numAttrs = 1;
+    config.attrs = attrs;
+
+    cudaLaunchKernelEx(&config,
+                       gelu_tanh_kernel<scalar_t>,
+                       output.data<scalar_t>(),
+                       input.data<scalar_t>(),
+                       d);
+  });
+
+  return {output};
+}
+
+PD_BUILD_STATIC_OP(gelu_tanh)
+    .Inputs({"input"})
+    .Outputs({"output"})
+    .SetKernelFn(PD_KERNEL(GeluTanh));