feat(cann): GGML_OP_CPY optimization

Signed-off-by: noemotiovon <[email protected]>

Author: noemotiovon

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -751,71 +751,46 @@ static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src,
 }
 
 void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
-    ggml_tensor* src0 = dst->src[0];
-
-    aclTensor* acl_src = ggml_cann_create_tensor(src0);
-    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
-    if (ggml_are_same_shape(src0, dst)) {
-        if (dst->type == src0->type) {
-            cann_copy(ctx, acl_src, acl_dst);
-        } else {
-            aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
+     ggml_tensor* src0 = dst->src[0];
+    void* src_trans_buffer = src0->data;
+    ggml_cann_pool_alloc src_buffer_allocator;
+    if (!ggml_is_contiguous(src0)) {
+        aclTensor* acl_src = ggml_cann_create_tensor(src0);
+        src_buffer_allocator.alloc(ctx.pool(),
+            ggml_nelements(src0) * ggml_type_size(src0->type));
+        src_trans_buffer = src_buffer_allocator.get();
+        size_t src_trans_nb[GGML_MAX_DIMS];
+        src_trans_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
         }
-    } else {
-        if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
-            if (dst->type == src0->type) {
-                size_t cpy_size = ggml_nbytes(dst);
-                ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size,
-                    ACL_MEMCPY_DEVICE_TO_DEVICE);
-                return;
-            } else {
-                ggml_cann_pool_alloc src_buffer_allocator(
-                    ctx.pool(),
-                    ggml_nelements(dst) * ggml_type_size(dst->type));
-                void* src_trans_buffer = src_buffer_allocator.get();
-                size_t src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = ggml_type_size(dst->type);
-                for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
-                }
-                aclTensor* src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                    ggml_type_size(dst->type), src0->ne, src_trans_nb,
-                    GGML_MAX_DIMS);
-
-                aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-                size_t cpy_size = ggml_nbytes(dst);
-                ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
-                    ACL_MEMCPY_DEVICE_TO_DEVICE);
-                ggml_cann_release_resources(ctx, src_trans_tensor);
-                return;
-            }
-        } else if (ggml_is_contiguous(dst)) {
-            ggml_cann_pool_alloc src_buffer_allocator(
-                ctx.pool(), ggml_nelements(dst) * ggml_type_size(dst->type));
-            void* src_trans_buffer = src_buffer_allocator.get();
-            size_t src_trans_nb[GGML_MAX_DIMS];
-            src_trans_nb[0] = ggml_type_size(dst->type);
-            for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
-            }
-            aclTensor* src_trans_tensor = ggml_cann_create_tensor(
-                src_trans_buffer, ggml_cann_type_mapping(dst->type),
-                ggml_type_size(dst->type), src0->ne, src_trans_nb,
-                GGML_MAX_DIMS);
+        aclTensor* src_trans_tensor = ggml_cann_create_tensor(
+            src_trans_buffer, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), src0->ne, src_trans_nb,
+            GGML_MAX_DIMS);
+        cann_copy(ctx, acl_src, src_trans_tensor);
+        ggml_cann_release_resources(ctx, acl_src, src_trans_tensor);
+    }
 
-            aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
+    size_t src_reshape_nb[GGML_MAX_DIMS];
+    src_reshape_nb[0] = ggml_type_size(src0->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1];
+    }
+    
+    aclTensor* trans_acl_src = ggml_cann_create_tensor(src_trans_buffer, 
+        ggml_cann_type_mapping(src0->type),ggml_type_size(src0->type), 
+        dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
 
-            size_t cpy_size = ggml_nbytes(dst);
-            ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
-                ACL_MEMCPY_DEVICE_TO_DEVICE);
-            ggml_cann_release_resources(ctx, src_trans_tensor);
-            return;
-        } else {
-            GGML_ABORT("Unsupport dst is not tontiguous.");
-        }
+    if (dst->type == src0->type) {
+        cann_copy(ctx, trans_acl_src, acl_dst);
+    } else {
+        aclnn_cast(ctx, trans_acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
     }
-    ggml_cann_release_resources(ctx, acl_src, acl_dst);
+
+    ggml_cann_release_resources(ctx, trans_acl_src, acl_dst);
+    return;
 }
 
 /**