@@ -2130,2 +2130,4 @@
     # ms.ops.prelu do not use tensor function of weight, so without cast_to_ms_tensor(weight), not effect.
     # weight = cast_to_ms_tensor(weight)
+    if is_under_ascend_context() and input.ndim < 2:
+        input = _expand(input, 2)

@@ -289,3 +289,3 @@
         if target.dtype == ms.int64:
             target = target.astype(ms.int32)
-        out = self.multilabel_margin_loss(input, target)
+        out, _ = self.multilabel_margin_loss(input, target)

@@ -183,1 +183,4 @@
 
+def from_numpy(np_data):
+    # return cast_to_adapter_tensor(ms.Tensor.from_numpy(np_data))
+    return cast_to_adapter_tensor(ms.Tensor(np_data))

@@ -183,2 +183,4 @@
 
+def from_numpy(np_data):
+    return cast_to_adapter_tensor(ms.Tensor.from_numpy(np_data))
 

@@ -1001,4 +1001,3 @@
 - 不支持七维及以上
 - Ascend上对float64类型的输入支持有限，部分接口无法处理float64类型入参，需转换为float32或float16类型之后输入
-- 对于备注中存在”输入参数有限制“标注的接口，请查看[限制条件清单](ConstraintList.md),获取详细信息

@@ -15,3 +15,3 @@
 from mindspore.ops._primitive_cache import _get_cache_prim
 
-from msadapter.pytorch.tensor import tensor, cast_to_ms_tensor, cast_to_adapter_tensor
+from msadapter.pytorch.tensor import tensor, cast_to_ms_tensor, cast_to_adapter_tensor, from_numpy

@@ -904,3 +906,3 @@
 
         output = ms.ops.prod(input, axis, keepdim)
-        return cast_to_adapter_tensor(output)
+        return cast_to_adapter_tensor(output)ƒ

@@ -1555,0 +1557,4 @@
+            if isinstance(p, ms.Tensor):
+                p = p.numpy()
+            np_output = np.random.binomial(1, p, size=self.shape)
+            return from_numpy(np_output).to(dtype=self.dtype)

@@ -1000,4 +1001,4 @@
 - 不支持layout, device, requires_grad, memory_format
 - 不支持七维及以上
 - Ascend上对float64类型的输入支持有限，部分接口无法处理float64类型入参，需转换为float32或float16类型之后输入

@@ -1568,0 +1567,4 @@
+            if isinstance(p, ms.Tensor):
+                p = p.numpy()
+            np_output = np.random.binomial(1, p, size=self.shape)
+            return ms.Tensor.from_numpy(np_output).to(dtype=self.dtype)