fix transformers r class ut

mindnlp/core/_tensor.py

@@ -312,8 +312,10 @@ def __setitem__(self, slices, value):
             value = tensor(value, dtype=self.dtype)
         else:
             value = value.to(self.dtype)
+
         if 1 in value.shape and self[slices].ndim != value.ndim:
             value = value.squeeze()
+
         return origin_setitem(self, slices, value)
 
     Tensor.__setitem__ = __setitem__
@@ -658,6 +660,21 @@ def __contains__(self, item):
     Tensor.exponential_ = ops.inplace_exponential
     StubTensor.exponential_ = ops.inplace_exponential
 
+    Tensor.log_ = ops.inplace_log
+    StubTensor.log_ = ops.inplace_log
+
+    Tensor.mul_ = ops.inplace_mul
+    StubTensor.mul_ = ops.inplace_mul
+
+    Tensor.neg_ = ops.inplace_neg
+    StubTensor.neg_ = ops.inplace_neg
+
+    Tensor.exp_ = ops.inplace_exp
+    StubTensor.exp_ = ops.inplace_exp
+
+    Tensor.sub_ = ops.inplace_sub
+    StubTensor.sub_ = ops.inplace_sub
+
 def _rebuild_from_type_v2(func, new_type, args, state):
     ret = func(*args)
     return ret

mindnlp/core/ops/inplace.py

@@ -1,8 +1,10 @@
+import numbers
 import mindspore
 from mindspore import ops
 from mindspore.ops._primitive_cache import _get_cache_prim
 from mindspore.common.generator import default_generator
-from mindspore.ops.auto_generate.gen_ops_prim import inplace_normal_op, inplace_scatter_value_op, inplace_scatter_src_reduce_op
+from mindspore.ops.auto_generate.gen_ops_prim import inplace_normal_op, inplace_scatter_value_op, inplace_scatter_src_reduce_op, \
+    inplace_scatter_src_op
 
 from mindnlp import core
 from ..configs import use_pyboost
@@ -85,6 +87,8 @@ def inplace_add(input, other, alpha):
     return input
 
 def inplace_scatter(input, dim, index, src):
+    if not isinstance(src, numbers.Number):
+        return inplace_scatter_src_op(input, dim, index, src)
     return inplace_scatter_value_op(input, dim, index, src)
 
 def inplace_index_copy(input, dim, index, tensor):
@@ -157,6 +161,26 @@ def inplace_exponential(tensor, lambd=1.0):
 
     return tensor
 
+def inplace_log(self):
+    self.data = core.log(self)
+    return self
+
+def inplace_mul(self, other):
+    self.data = core.mul(self, other)
+    return self
+
+def inplace_neg(self):
+    self.data = core.neg(self)
+    return self
+
+def inplace_exp(self):
+    self.data = core.exp(self)
+    return self
+
+def inplace_sub(self, other):
+    self.data = core.sub(self, other)
+    return self
+
 __all__ = [
     'inplace_copy',
     'inplace_zero',
@@ -173,5 +197,10 @@ def inplace_exponential(tensor, lambd=1.0):
     'inplace_triu',
     'inplace_round',
     'inplace_scatter_reduce',
-    'inplace_exponential'
+    'inplace_exponential',
+    'inplace_log',
+    'inplace_mul',
+    'inplace_neg',
+    'inplace_exp',
+    'inplace_sub'
 ]

mindnlp/core/ops/other.py

@@ -178,12 +178,10 @@ def cumsum(input, dim=None, dtype=None, out=None, **kwargs):
 
 # diag
 has_diag = hasattr(mindspore.mint, "diag")
-
-
 def diag(input, diagonal=0):
     if use_pyboost() and has_diag:
         return mindspore.mint.diag(input, diagonal)
-    return ops.diag(input)
+    return mindspore.numpy.diag(input, diagonal)
 
 
 # diag_embed
@@ -806,6 +804,8 @@ def searchsorted(
     sorter=None,
 ):
     if use_pyboost() and has_searchsorted:
+        if not isinstance(values, core.Tensor):
+            values = core.tensor(values)
         return call_ms_func(
             mindspore.mint.searchsorted,
             sorted_sequence,
@@ -1030,12 +1030,26 @@ def unfold(input, dimension, size, step):
     return output
 
 
+def cartesian_prod(*tensors):
+    """
+    手动实现 torch.cartesian_prod
+    :param tensors: 一个或多个一维张量
+    :return: 笛卡尔积结果的二维张量 (每行一个组合)
+    """
+    # 生成网格坐标
+    grids = core.meshgrid(*tensors, indexing='ij')
+
+    # 展平每个网格张量并堆叠
+    return core.stack([g.reshape(-1) for g in grids], dim=1)
+
+
 __all__ = [
     "bincount",
     "broadcast_shapes",
     "broadcast_tensors",
     "broadcast_to",
     "bucketize",
+    "cartesian_prod",
     "cdist",
     "clone",
     "contains",