issue/263 fix T2-1-4

include/infinicore_infer/models/qwen3vl.h

@@ -26,7 +26,7 @@ typedef struct {
     qwen3vl_load_layer_fn load_attn_k_norm;
     qwen3vl_load_layer_fn load_attn_qkv_proj;
     qwen3vl_load_layer_fn load_attn_o_proj;
-    
+
     // MLP
     qwen3vl_load_layer_fn load_mlp_norm;
     qwen3vl_load_layer_fn load_mlp_gate_up;
@@ -46,19 +46,19 @@ typedef struct {
     qwen3vl_load_layer_fn load_attn_qkv_weight;
     qwen3vl_load_layer_fn load_attn_qkv_bias;
 
-    //block mlp
+    // block mlp
     qwen3vl_load_layer_fn load_mlp_linear_fc1_weight;
     qwen3vl_load_layer_fn load_mlp_linear_fc1_bias;
     qwen3vl_load_layer_fn load_mlp_linear_fc2_weight;
     qwen3vl_load_layer_fn load_mlp_linear_fc2_bias;
 
-    //block norm
-    qwen3vl_load_layer_fn  load_norm1_weight;
-    qwen3vl_load_layer_fn  load_norm1_bias;
-    qwen3vl_load_layer_fn  load_norm2_weight;
-    qwen3vl_load_layer_fn  load_norm2_bias;
+    // block norm
+    qwen3vl_load_layer_fn load_norm1_weight;
+    qwen3vl_load_layer_fn load_norm1_bias;
+    qwen3vl_load_layer_fn load_norm2_weight;
+    qwen3vl_load_layer_fn load_norm2_bias;
 
-    //deepstack_merger
+    // deepstack_merger
     qwen3vl_load_layer_fn load_deepstack_merger_linear_fc1_weight;
     qwen3vl_load_layer_fn load_deepstack_merger_linear_fc1_bias;
     qwen3vl_load_layer_fn load_deepstack_merger_linear_fc2_weight;
@@ -66,7 +66,7 @@ typedef struct {
     qwen3vl_load_layer_fn load_deepstack_merger_norm_weight;
     qwen3vl_load_layer_fn load_deepstack_merger_norm_bias;
 
-    //merger
+    // merger
     qwen3vl_load_global_fn load_merger_linear_fc1_weight;
     qwen3vl_load_global_fn load_merger_linear_fc1_bias;
     qwen3vl_load_global_fn load_merger_linear_fc2_weight;
@@ -76,7 +76,7 @@ typedef struct {
 
 } Qwen3vlVisWeightLoader;
 
-typedef struct { 
+typedef struct {
     Qwen3vlLangWeightLoader lang_loader;
     Qwen3vlVisWeightLoader vis_loader;
 } Qwen3vlWeightLoader;
@@ -116,7 +116,7 @@ typedef struct {
 } Qwen3vlVisMeta;
 
 typedef struct {
-    infiniDtype_t dtype; //INFINI_DTYPE_BF16
+    infiniDtype_t dtype; // INFINI_DTYPE_BF16
 
     Qwen3vlTextMeta text_meta;
     Qwen3vlVisMeta vis_meta;
@@ -132,29 +132,29 @@ typedef struct {
 /// @param device 协处理器种类
 /// @param ndev 协处理器数量
 /// @param dev_ids 协处理器编号，长度为 ndev
-__C __export struct Qwen3vlModel *
+__INFINI_C __export struct Qwen3vlModel *
 createQwen3vlModel(const Qwen3vlMeta *,
-                      const Qwen3vlWeights *);
+                   const Qwen3vlWeights *);
 
-__C Qwen3vlWeights *
+__INFINI_C Qwen3vlWeights *
 createQwen3vlWeights(const Qwen3vlMeta *meta,
-                        infiniDevice_t device,
-                        int ndev,
-                        const int *dev_ids,
-                        bool transpose_weight);
+                     infiniDevice_t device,
+                     int ndev,
+                     const int *dev_ids,
+                     bool transpose_weight);
 
-__C __export Qwen3vlWeightLoader *
+__INFINI_C __export Qwen3vlWeightLoader *
 createQwen3vlWeightLoader();
 
 /// @brief 销毁模型
-__C __export void destroyQwen3vlModel(struct Qwen3vlModel *);
+__INFINI_C __export void destroyQwen3vlModel(struct Qwen3vlModel *);
 
-__C __export struct Qwen3vlCache *
+__INFINI_C __export struct Qwen3vlCache *
 createQwen3vlCache(const struct Qwen3vlModel *);
 
-__C __export void
+__INFINI_C __export void
 dropQwen3vlCache(const struct Qwen3vlModel *,
-                    struct Qwen3vlCache *);
+                 struct Qwen3vlCache *);
 
 /// @brief 批次推理一轮，并采样出新的 token
 /// @param tokens 输入 token 地址
@@ -167,18 +167,18 @@ dropQwen3vlCache(const struct Qwen3vlModel *,
 /// @param topk 采样 topk（1 表示贪心采样）
 /// @param topp 采样 topp
 /// @param output 输出 token 数组，每个请求一个输出，长度至少为nreq
-__C __export void
+__INFINI_C __export void
 inferBatchQwen3vl(struct Qwen3vlModel *,
-                    const uint32_t *tokens, uint32_t ntok,
-                    void *pixel_values, uint32_t total_patches,
-                    uint32_t *image_grid_thw, uint32_t num_images,
-                    void *pixel_values_videos, uint32_t total_patches_videos,
-                    uint32_t *video_grid_thw, uint32_t num_videos,
-                    uint32_t patch_features,
-                    const uint32_t *req_lens, uint32_t nreq, const uint32_t *req_pos,
-                    struct Qwen3vlCache **caches,
-                    const float *temperature, const uint32_t *topk, const float *topp,
-                    uint32_t *output);
+                  const uint32_t *tokens, uint32_t ntok,
+                  void *pixel_values, uint32_t total_patches,
+                  uint32_t *image_grid_thw, uint32_t num_images,
+                  void *pixel_values_videos, uint32_t total_patches_videos,
+                  uint32_t *video_grid_thw, uint32_t num_videos,
+                  uint32_t patch_features,
+                  const uint32_t *req_lens, uint32_t nreq, const uint32_t *req_pos,
+                  struct Qwen3vlCache **caches,
+                  const float *temperature, const uint32_t *topk, const float *topp,
+                  uint32_t *output);
 
 /// @brief 批次推理一轮，输出 output embedding 后的 logits
 /// @param tokens 输入 token 地址
@@ -188,7 +188,7 @@ inferBatchQwen3vl(struct Qwen3vlModel *,
 /// @param req_pos 每个请求的起始位置
 /// @param kv_caches 每个请求的 KV Cache
 /// @param logits 输出 token 数组，每个请求一个输出，长度至少为nreq
-__C __export void
+__INFINI_C __export void
 forwardBatchQwen3vl(struct Qwen3vlModel *,
                     const uint32_t *tokens, uint32_t ntok,
                     void *pixel_values, uint32_t total_patches,

scripts/libinfinicore_infer/__init__.py

@@ -6,6 +6,7 @@ from .deepseek_v3 import (
     DeepSeekV3MetaCStruct,
     DeepSeekV3WeightsCStruct,
     DeepSeekV3WeightLoaderCStruct,
+    DeepSeekV3CacheCStruct,
 )
 from .qwen3vl import (
     Qwen3vlModel,
@@ -33,6 +34,7 @@ __all__ = [
     "DeepSeekV3MetaCStruct",
     "DeepSeekV3WeightsCStruct",
     "DeepSeekV3WeightLoaderCStruct",
+    "DeepSeekV3CacheCStruct",
     "Qwen3vlModel",
     "Qwen3vlMetaCStruct",
     "TextMetaCStruct",

scripts/libinfinicore_infer/qwen3vl.py

@@ -183,15 +183,15 @@ class Qwen3vlModel(BaseModel):
             POINTER(Qwen3vlModelCStruct),
             POINTER(c_uint),
             c_uint,
-            c_void_p,          # pixel_values,
-            c_uint,            # total_patches,
-            POINTER(c_uint),   # image_grid_thw,
-            c_uint,            # num_images,
-            c_void_p,          # pixel_values_videos,
-            c_uint,            # total_patches_videos,
-            POINTER(c_uint),   # video_grid_thw,
-            c_uint,            # num_videos,
-            c_uint,            # patch_features,
+            c_void_p,  # pixel_values,
+            c_uint,  # total_patches,
+            POINTER(c_uint),  # image_grid_thw,
+            c_uint,  # num_images,
+            c_void_p,  # pixel_values_videos,
+            c_uint,  # total_patches_videos,
+            POINTER(c_uint),  # video_grid_thw,
+            c_uint,  # num_videos,
+            c_uint,  # patch_features,
             POINTER(c_uint),
             c_uint,
             POINTER(c_uint),
@@ -206,15 +206,15 @@ class Qwen3vlModel(BaseModel):
             POINTER(Qwen3vlModelCStruct),
             POINTER(c_uint),
             c_uint,
-            c_void_p,          # pixel_values,
-            c_uint,            # total_patches,
-            POINTER(c_uint),   # image_grid_thw,
-            c_uint,            # num_images,
-            c_void_p,          # pixel_values_videos,
-            c_uint,            # total_patches_videos,
-            POINTER(c_uint),   # video_grid_thw,
-            c_uint,            # num_videos,
-            c_uint,            # patch_features,
+            c_void_p,  # pixel_values,
+            c_uint,  # total_patches,
+            POINTER(c_uint),  # image_grid_thw,
+            c_uint,  # num_images,
+            c_void_p,  # pixel_values_videos,
+            c_uint,  # total_patches_videos,
+            POINTER(c_uint),  # video_grid_thw,
+            c_uint,  # num_videos,
+            c_uint,  # patch_features,
             POINTER(c_uint),
             c_uint,
             POINTER(c_uint),
@@ -226,7 +226,9 @@ class Qwen3vlModel(BaseModel):
         return self.lib.createQwen3vlWeightLoader()
 
     def create_weights(self, meta, device_type, ndev, dev_ids, transpose_weight):
-        return self.lib.createQwen3vlWeights(meta, device_type, ndev, dev_ids, transpose_weight)
+        return self.lib.createQwen3vlWeights(
+            meta, device_type, ndev, dev_ids, transpose_weight
+        )
 
     def create_model(self, meta, weights):
         return self.lib.createQwen3vlModel(meta, weights)
@@ -324,4 +326,4 @@ class Qwen3vlModel(BaseModel):
             req_pos,
             caches,
             logits,
-        )
+        )
\ No newline at end of file

src/allocator.hpp

@@ -16,7 +16,7 @@ public:
 
 class MemoryPool : public AllocatorBase {
 public:
-    static constexpr size_t DEFAULT_ALIGNMENT = 256;
+    static constexpr size_t DEFAULT_ALIGNMENT = 512;
 
     explicit MemoryPool(size_t initialSize = 0, size_t alignment = DEFAULT_ALIGNMENT);
     ~MemoryPool();

src/models/inference_context.cpp

@@ -34,24 +34,24 @@ void InferenceContext::add(std::shared_ptr<Tensor> c,
 }
 
 void InferenceContext::conv(std::shared_ptr<Tensor> y,
-                             std::shared_ptr<Tensor> x,
-                             std::shared_ptr<Tensor> w,
-                             std::shared_ptr<Tensor> bias,
-                             void *pads,
-                             void *strides,
-                             void *dilations,
-                             size_t n) {
+                            std::shared_ptr<Tensor> x,
+                            std::shared_ptr<Tensor> w,
+                            std::shared_ptr<Tensor> bias,
+                            void *pads,
+                            void *strides,
+                            void *dilations,
+                            size_t n) {
     size_t key = CacheManager::createDescriptorKey(y, x, w, bias);
     // Combine additional parameters into the key for unique identification
-    hash_combine(key, std::hash<void*>()(pads));
-    hash_combine(key, std::hash<void*>()(strides));
-    hash_combine(key, std::hash<void*>()(dilations));
+    hash_combine(key, std::hash<void *>()(pads));
+    hash_combine(key, std::hash<void *>()(strides));
+    hash_combine(key, std::hash<void *>()(dilations));
     hash_combine(key, std::hash<size_t>()(n));
 
     infiniopConvDescriptor_t desc;
     if (!cache_manager->getConvDescriptor(key, desc)) {
         RUN_INFINI(infiniopCreateConvDescriptor(
-            op_handle, &desc, y->desc(), x->desc(), w->desc(), 
+            op_handle, &desc, y->desc(), x->desc(), w->desc(),
             bias ? bias->desc() : nullptr, pads, strides, dilations, n));
         cache_manager->putConvDescriptor(key, desc);
     }
@@ -63,7 +63,7 @@ void InferenceContext::conv(std::shared_ptr<Tensor> y,
 
     RUN_INFINI(infiniopConv(
         desc, workspace, workspace_size,
-        y->data(), x->data(), w->data(), 
+        y->data(), x->data(), w->data(),
         bias ? bias->data() : nullptr, stream));
 }
 

src/models/inference_context.hpp

@@ -92,7 +92,7 @@ inline void add(std::shared_ptr<Tensor> c, std::shared_ptr<Tensor> a, std::share
 }
 
 inline void conv(std::shared_ptr<Tensor> y, std::shared_ptr<Tensor> x, std::shared_ptr<Tensor> w, std::shared_ptr<Tensor> bias,
-                void *pads, void *strides, void *dilations, size_t n) {
+                 void *pads, void *strides, void *dilations, size_t n) {
     getInferenceContext().conv(y, x, w, bias, pads, strides, dilations, n);
 }
 

src/models/qwen3vl/qwen3vl_cache.cpp

 #include "qwen3vl_impl.hpp"
 
-__C struct Qwen3vlCache *
+__INFINI_C struct Qwen3vlCache *
 createQwen3vlCache(const struct Qwen3vlModel *model) {
     Qwen3vlCache *cache = new Qwen3vlCache();
     auto ndev = model->dev_resources.size();
@@ -27,9 +27,9 @@ createQwen3vlCache(const struct Qwen3vlModel *model) {
 
 //////还有visual deepstack需要cache？
 
-__C void
+__INFINI_C void
 dropQwen3vlCache(const struct Qwen3vlModel *model,
-                    struct Qwen3vlCache *cache) {
+                 struct Qwen3vlCache *cache) {
     auto ndev = model->dev_resources.size();
     auto nlayer = model->meta.text_meta.num_hidden_layers;
     for (size_t idev = 0; idev < ndev; idev++) {
@@ -40,4 +40,4 @@ dropQwen3vlCache(const struct Qwen3vlModel *model,
         }
     }
     delete cache;
-}
+}
\ No newline at end of file

src/models/qwen3vl/qwen3vl_impl.hpp

@@ -45,7 +45,6 @@ struct MergerWeight {
     std::shared_ptr<Tensor> norm_weight, norm_bias;
 };
 
-
 struct Qwen3vlVisualEncoderWeight {
     std::shared_ptr<Tensor> patch_embed_weight, patch_embed_bias, pos_embed_weight;
     std::vector<Qwen3vlVisBlockWeight> blocks;
@@ -53,9 +52,8 @@ struct Qwen3vlVisualEncoderWeight {
     std::shared_ptr<MergerWeight> merger;
 };
 
-
 struct Qwen3vlDeviceWeights {
-    std::shared_ptr<Tensor> sin_table,cos_table;
+    std::shared_ptr<Tensor> sin_table, cos_table;
     std::shared_ptr<Qwen3vlLanguageModelWeight> w_lang;
     std::shared_ptr<Qwen3vlVisualEncoderWeight> w_vis;
     infiniDevice_t device;
@@ -69,10 +67,10 @@ struct Qwen3vlWeights {
     std::vector<std::shared_ptr<Qwen3vlDeviceWeights>> device_weights;
 
     Qwen3vlWeights(const Qwen3vlMeta *meta,
-                      infiniDevice_t device,
-                      int ndev,
-                      const int *dev_ids,
-                      bool transpose_weight);
+                   infiniDevice_t device,
+                   int ndev,
+                   const int *dev_ids,
+                   bool transpose_weight);
 };
 
 struct Qwen3vlDeviceResource {
@@ -137,7 +135,7 @@ struct Qwen3vlModel {
 };
 
 struct Qwen3vlCache {
-    std::vector<std::vector<std::shared_ptr<Tensor>>> k_rot, v; 
+    std::vector<std::vector<std::shared_ptr<Tensor>>> k_rot, v;
 };
 
-#endif
+#endif
\ No newline at end of file

src/models/qwen3vl/qwen3vl_weight.cpp

@@ -23,7 +23,7 @@ inline std::shared_ptr<Tensor> getOutEmbd(
 }
 
 inline void getLayerWeight(
-    const Qwen3vlMeta *meta, Qwen3vlLayerWeight& layer, int ndev) {
+    const Qwen3vlMeta *meta, Qwen3vlLayerWeight &layer, int ndev) {
     auto nkvh = meta->text_meta.num_key_value_heads;
     auto nh = meta->text_meta.num_attention_heads;
     auto dh = meta->text_meta.head_dim;
@@ -39,7 +39,7 @@ inline void getLayerWeight(
     layer.attn_qkv_proj = Tensor::weight(nullptr, meta->dtype, qkv_proj_shape);
     auto o_proj_shape = std::vector<size_t>({d, nh / ndev * dh});
     layer.attn_o_proj = Tensor::weight(nullptr, meta->dtype, o_proj_shape);
-    
+
     layer.mlp_norm = Tensor::weight(nullptr, meta->dtype, dh_shape);
     auto up_shape = std::vector<size_t>({2 * di / ndev, d});
     layer.mlp_gate_up = Tensor::weight(nullptr, meta->dtype, up_shape);
@@ -47,11 +47,10 @@ inline void getLayerWeight(
     layer.mlp_down = Tensor::weight(nullptr, meta->dtype, down_shape);
 }
 
-
 inline void getVisualWeight(
     const Qwen3vlMeta *meta, std::shared_ptr<Qwen3vlVisualEncoderWeight> w_vis) {
     Qwen3vlVisMeta vis_meta = meta->vis_meta;
-    auto patch_embed_shape = std::vector<size_t>({vis_meta.hidden_size , vis_meta.in_channels, vis_meta.temporal_patch_size, vis_meta.patch_size, vis_meta.patch_size});
+    auto patch_embed_shape = std::vector<size_t>({vis_meta.hidden_size, vis_meta.in_channels, vis_meta.temporal_patch_size, vis_meta.patch_size, vis_meta.patch_size});
     w_vis->patch_embed_weight = Tensor::weight(nullptr, meta->dtype, patch_embed_shape);
     w_vis->patch_embed_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size});
     w_vis->pos_embed_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.num_position_embeddings, vis_meta.hidden_size});
@@ -63,11 +62,11 @@ inline void getVisualWeight(
     w_vis->merger->norm_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size});
     w_vis->merger->norm_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size});
     w_vis->blocks = std::vector<Qwen3vlVisBlockWeight>(vis_meta.depth);
-    for (size_t i = 0; i < vis_meta.depth; i++) { 
-        w_vis->blocks[i].attn_proj_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size,vis_meta.hidden_size});
+    for (size_t i = 0; i < vis_meta.depth; i++) {
+        w_vis->blocks[i].attn_proj_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size, vis_meta.hidden_size});
         w_vis->blocks[i].attn_proj_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size});
-        w_vis->blocks[i].attn_qkv_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.in_channels*vis_meta.hidden_size,vis_meta.hidden_size});
-        w_vis->blocks[i].attn_qkv_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.in_channels*vis_meta.hidden_size});
+        w_vis->blocks[i].attn_qkv_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.in_channels * vis_meta.hidden_size, vis_meta.hidden_size});
+        w_vis->blocks[i].attn_qkv_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.in_channels * vis_meta.hidden_size});
         w_vis->blocks[i].mlp_linear_fc1_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size, vis_meta.hidden_size});
         w_vis->blocks[i].mlp_linear_fc1_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size});
         w_vis->blocks[i].mlp_linear_fc2_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size, vis_meta.intermediate_size});
@@ -78,18 +77,16 @@ inline void getVisualWeight(
         w_vis->blocks[i].norm2_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.hidden_size});
     }
     w_vis->deepstack_mergers = std::vector<DeepstackMergerWeight>(3);
-    for (size_t i = 0; i < 3; i++){
-        w_vis->deepstack_mergers[i].linear_fc1_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size,vis_meta.intermediate_size});
-        w_vis->deepstack_mergers[i].linear_fc2_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.out_hidden_size,vis_meta.intermediate_size});
+    for (size_t i = 0; i < 3; i++) {
+        w_vis->deepstack_mergers[i].linear_fc1_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size, vis_meta.intermediate_size});
+        w_vis->deepstack_mergers[i].linear_fc2_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.out_hidden_size, vis_meta.intermediate_size});
         w_vis->deepstack_mergers[i].linear_fc1_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size});
         w_vis->deepstack_mergers[i].linear_fc2_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.out_hidden_size});
         w_vis->deepstack_mergers[i].norm_weight = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size});
         w_vis->deepstack_mergers[i].norm_bias = Tensor::weight(nullptr, meta->dtype, {vis_meta.intermediate_size});
     }
-    
 }
 
-
 inline std::shared_ptr<Tensor> getSinTable(const Qwen3vlMeta *meta) {
     auto half_dh = meta->text_meta.head_dim / 2;
     auto unit = dsize(meta->dtype);
@@ -172,7 +169,6 @@ Qwen3vlWeights::Qwen3vlWeights(
         }
 
         getVisualWeight(meta, device_weights[dev]->w_vis);
-
     }
 }
 
@@ -201,8 +197,8 @@ void load_output_embd(Qwen3vlWeights *weights, void *cpu_ptr) {
         auto weight = weights->device_weights[dev];
         RUN_INFINI(infinirtSetDevice(weight->device, weight->dev_id));
         weight->w_lang->out_embd->load(cpu_ptr, weight->load_stream);
-        if(weights->transpose_weight) {
-            weight->w_lang->out_embd->permute({1,0}); //[d,voc]
+        if (weights->transpose_weight) {
+            weight->w_lang->out_embd->permute({1, 0}); //[d,voc]
         }
     }
 }
@@ -239,9 +235,8 @@ void load_attn_qkv_proj(Qwen3vlWeights *weights, void *cpu_ptr, size_t layer) {
         size_t offset = idev * ((nkvh * 2 + nh) / ndev * dh) * d * dsize(weights->meta->dtype);
         RUN_INFINI(infinirtSetDevice(weight->device, weight->dev_id));
         weight->w_lang->layers[layer].attn_qkv_proj->load((char *)cpu_ptr + offset, weight->load_stream);
-        if(weights->transpose_weight) {
-            weight->w_lang->layers[layer].attn_qkv_proj = 
-                weight->w_lang->layers[layer].attn_qkv_proj->permute({1,0}); //[d, (nh+2*nkvh)*dh]
+        if (weights->transpose_weight) {
+            weight->w_lang->layers[layer].attn_qkv_proj = weight->w_lang->layers[layer].attn_qkv_proj->permute({1, 0}); //[d, (nh+2*nkvh)*dh]
         }
     }
 }
@@ -267,9 +262,8 @@ void load_attn_o_proj(Qwen3vlWeights *weights, void *cpu_ptr, size_t layer) {
         size_t offset = idev * d * (nh / ndev * dh) * dsize(weights->meta->dtype);
         RUN_INFINI(infinirtSetDevice(weight->device, weight->dev_id));
         weight->w_lang->layers[layer].attn_o_proj->load((char *)cpu_ptr + offset, weight->load_stream);
-        if(weights->transpose_weight) {
-            weight->w_lang->layers[layer].attn_o_proj = 
-                weight->w_lang->layers[layer].attn_o_proj->permute({1,0}); //[nh/ndev*dh, d]
+        if (weights->transpose_weight) {
+            weight->w_lang->layers[layer].attn_o_proj = weight->w_lang->layers[layer].attn_o_proj->permute({1, 0}); //[nh/ndev*dh, d]
         }
     }
 }
@@ -295,9 +289,8 @@ void load_mlp_gate_up(Qwen3vlWeights *weights, void *cpu_ptr, size_t layer) {
         size_t offset = idev * (2 * di / ndev) * d * dsize(weights->meta->dtype);
         RUN_INFINI(infinirtSetDevice(weight->device, weight->dev_id));
         weight->w_lang->layers[layer].mlp_gate_up->load((char *)cpu_ptr + offset, weight->load_stream);
-        if(weights->transpose_weight) {
-            weight->w_lang->layers[layer].mlp_gate_up = 
-                weight->w_lang->layers[layer].mlp_gate_up->permute({1,0}); //[d, 2*di/ndev]
+        if (weights->transpose_weight) {
+            weight->w_lang->layers[layer].mlp_gate_up = weight->w_lang->layers[layer].mlp_gate_up->permute({1, 0}); //[d, 2*di/ndev]
         }
     }
 }
@@ -313,10 +306,9 @@ void load_mlp_down(Qwen3vlWeights *weights, void *cpu_ptr, size_t layer) {
         size_t offset = idev * d * (di / ndev) * dsize(weights->meta->dtype);
         RUN_INFINI(infinirtSetDevice(weight->device, weight->dev_id));
         weight->w_lang->layers[layer].mlp_down->load((char *)cpu_ptr + offset, weight->load_stream);
-        if(weights->transpose_weight) {
-            weight->w_lang->layers[layer].mlp_down = 
-                weight->w_lang->layers[layer].mlp_down->permute({1,0}); //[di/ndev, d]
-        } 
+        if (weights->transpose_weight) {
+            weight->w_lang->layers[layer].mlp_down = weight->w_lang->layers[layer].mlp_down->permute({1, 0}); //[di/ndev, d]
+        }
     }
 }
 
@@ -569,7 +561,6 @@ void load_merger_norm_bias(Qwen3vlWeights *weights, void *cpu_ptr) {
     }
 }
 
-
 static Qwen3vlWeightLoader weight_loader = {
     // Language model loaders
     .lang_loader = {
@@ -614,15 +605,14 @@ static Qwen3vlWeightLoader weight_loader = {
         .load_merger_linear_fc2_bias = load_merger_linear_fc2_bias,
         .load_merger_norm_weight = load_merger_norm_weight,
         .load_merger_norm_bias = load_merger_norm_bias,
-    }
-};
+    }};
 
-__C Qwen3vlWeights *
+__INFINI_C Qwen3vlWeights *
 createQwen3vlWeights(const Qwen3vlMeta *meta,
-                        infiniDevice_t device,
-                        int ndev,
-                        const int *dev_ids,
-                        bool transpose_weight) {
+                     infiniDevice_t device,
+                     int ndev,
+                     const int *dev_ids,
+                     bool transpose_weight) {
 
     printf("=== C++ createQwen3vlWeights ===\n");
     printf("sizeof(Qwen3vlTextMeta): %zu\n", sizeof(Qwen3vlTextMeta));
@@ -640,7 +630,7 @@ createQwen3vlWeights(const Qwen3vlMeta *meta,
     return weights;
 };
 
-__C Qwen3vlWeightLoader *
+__INFINI_C Qwen3vlWeightLoader *
 createQwen3vlWeightLoader() {
     return &weight_loader;
-}
+}
\ No newline at end of file