fix: 解决一些warning，并把sync操作从算子内部移除

Author: Chamberlain0w0

src/04kernel/src/kernels/batch_normalization/cnnl_kernel.cc

@@ -128,8 +128,8 @@ namespace refactor::kernel {
             auto y = outputs[0];
 
             void *xTrans = workspace;
-            void *yTrans = xTrans + xTransSize;
-            void *cursor = yTrans + xTransSize;
+            void *yTrans = reinterpret_cast<uint8_t *>(xTrans) + xTransSize;
+            void *cursor = reinterpret_cast<uint8_t *>(yTrans) + xTransSize;
 
             // transpose NCHW input to NHWC
             CNNL_ASSERT(cnnlTranspose_v2(handle, d->NCHW2NHWC, d->inDesc, x,
@@ -147,7 +147,6 @@ namespace refactor::kernel {
             CNNL_ASSERT(cnnlTranspose_v2(handle, d->NHWC2NCHW, d->inDescTrans, yTrans,
                                          d->inDesc, y, cursor, workspaceSize));
 
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
 
         return {std::move(routine), totalWorkspaceSize};

src/04kernel/src/kernels/cast/cnnl_kernel.cc

@@ -65,7 +65,6 @@ namespace refactor::kernel {
         return [d = std::move(d)](Resources &res, void *workspace, void const *const *inputs, void *const *outputs) {
             CNNL_ASSERT(cnnlCastDataType(res.fetchOrStore<CnnlContext>()->handle,
                                          d->inDesc, inputs[0], d->cast, d->outDesc, outputs[0]));
-            // BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
     }
 

src/04kernel/src/kernels/clip/cnnl_kernel.cc

@@ -57,7 +57,6 @@ namespace refactor::kernel {
                                     CNNL_POINTER_MODE_DEVICE, d->t,
                                     inputs[0], inputs[1], hasMax ? inputs[2] : nullptr,
                                     d->t, outputs[0]));
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
     }
 

src/04kernel/src/kernels/concat/cnnl_kernel.cc

@@ -52,7 +52,7 @@ namespace refactor::kernel {
             }
             ~Descriptors() noexcept(false) {
                 CNNL_ASSERT(cnnlDestroyTensorDescriptor(in));
-                for (auto i = 0; i < out.size(); i++) {
+                for (size_t i = 0; i < out.size(); i++) {
                     CNNL_ASSERT(cnnlDestroyTensorDescriptor(out[i]));
                 }
             }
@@ -62,7 +62,7 @@ namespace refactor::kernel {
         };
         auto d = std::make_shared<Descriptors>(info.num, info.dataType != DT::F64);
         setCnnlTensor(d->in, info.dataType, slice(info.inDim.data(), info.inDim.size()));
-        for (auto i = 0; i < info.outDims.size(); i++) {
+        for (size_t i = 0; i < info.outDims.size(); i++) {
             setCnnlTensor(d->out[i], info.dataType, slice(info.outDims[i].data(), info.outDims[i].size()));
         }
 

src/04kernel/src/kernels/conv/cnnl_kernel.cc

@@ -209,9 +209,9 @@ namespace refactor::kernel {
             // }
 
             void *xTrans = workspace;
-            void *wTrans = xTrans + xTransSize;
-            void *yTrans = wTrans + wTransSize;
-            void *opWorkspace = yTrans + yTransSize;
+            void *wTrans = reinterpret_cast<uint8_t *>(xTrans) + xTransSize;
+            void *yTrans = reinterpret_cast<uint8_t *>(wTrans) + wTransSize;
+            void *opWorkspace = reinterpret_cast<uint8_t *>(yTrans) + yTransSize;
 
             // transpose NCHW input to NHWC
             CNNL_ASSERT(cnnlTranspose_v2(handle, d->NCHW2NHWC, d->x, x,

src/04kernel/src/kernels/expand/cnnl_kernel.cc

@@ -60,7 +60,6 @@ namespace refactor::kernel {
         return [d = std::move(d)](Resources &res, void *workspace, void const *const *inputs, void *const *outputs) {
             CNNL_ASSERT(cnnlExpand(res.fetchOrStore<CnnlContext>()->handle,
                                    d->inDesc, inputs[0], d->outDesc, outputs[0]));
-            // BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
     }
 #endif

src/04kernel/src/kernels/gather/cnnl_kernel.cc

@@ -79,7 +79,6 @@ namespace refactor::kernel {
                                      d->inDesc, inputs[0], reinterpret_cast<const int *>(workspace),
                                      d->indexDesc, reinterpret_cast<const int *>(inputs[1]),
                                      d->outDesc, outputs[0]));
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
        };
 
         return {std::move(routine), workspaceSize};

src/04kernel/src/kernels/mat_mul/cnnl_kernel.cc

@@ -141,7 +141,6 @@ namespace refactor::kernel {
                     workspace, algoWorkspaceSize));
             }
 
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
 
         return {std::move(routine), algoWorkspaceSize};

src/04kernel/src/kernels/pool/cnnl_kernel.cc

@@ -130,7 +130,7 @@ namespace refactor::kernel {
             auto handle = res.fetchOrStore<CnnlContext>()->handle;
 
             void *extraInputDev = workspace;
-            void *poolWorkSpace = workspace + extraInputSize;
+            void *poolWorkSpace = reinterpret_cast<uint8_t *>(workspace) + extraInputSize;
 
             void *extraInputHost = malloc(extraInputSize);
             CNNL_ASSERT(cnnlInitPoolingExtraInput(handle, d->pooling, d->x, d->y, extraInputHost));
@@ -145,7 +145,7 @@ namespace refactor::kernel {
                 &b, extraInputDev, d->y, outputs[0],
                 poolWorkSpace, workspaceSize));
 
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
+            res.fetchOrStore<CnnlContext>()->queueSync();
 
             free(extraInputHost);
         };

src/04kernel/src/kernels/simple_binary/binary_cnnl.cc

@@ -180,7 +180,6 @@ namespace refactor::kernel {
                                          workspace, workspaceSize));
             }
 
-            BANG_ASSERT(cnrtQueueSync(res.fetchOrStore<CnnlContext>()->queue));
         };
 
         return {std::move(routine), workspaceSize};