fix diffusers pipeline h class ut (#2086)

Author: lvyufeng

mindnlp/core/_tensor.py

@@ -681,6 +681,8 @@ def __contains__(self, item):
     Tensor.bernoulli_ = ops.inplace_bernoulli
     StubTensor.bernoulli_ = ops.inplace_bernoulli
 
+    Tensor.scatter_reduce = ops.scatter_reduce
+    StubTensor.scatter_reduce = ops.scatter_reduce
 
 def _rebuild_from_type_v2(func, new_type, args, state):
     ret = func(*args)

mindnlp/core/ops/array.py

@@ -5,6 +5,7 @@
 from mindspore import ops
 from mindspore.ops._primitive_cache import _get_cache_prim
 from mindspore.ops.operations._grad_ops import StridedSliceGrad
+from mindspore.ops.auto_generate.gen_ops_prim import inplace_scatter_src_reduce_op
 
 from ..configs import use_pyboost, ON_ORANGE_PI
 from .other import broadcast_tensors, finfo
@@ -220,8 +221,15 @@ def scatter_add(input, dim, index, src):
         return mindspore.mint.scatter_add(input, dim, index, src)
     return ops.tensor_scatter_elements(input, index, src, dim, 'add')
 
+scatter_reduce_dict = {
+    'sum': 'add',
+    'amax': 'max',
+    'amin': 'min',
+    'mean': 'mean'
+}
 # scatter_reduce
-
+def scatter_reduce(input, dim, index, src, reduce, *, include_self=True):
+    return inplace_scatter_src_reduce_op(input.clone(), dim, index, src, scatter_reduce_dict[reduce])
 
 # scatter_nd_update
 def scatter_nd_update(input, indices, update):
@@ -799,7 +807,7 @@ def strided_slice_update(input, begin, end, strides, update, begin_mask=0, end_m
     # select_scatter
     # slice_scatter
     'scatter_add',
-    # scatter_reduce
+    'scatter_reduce',
     'scatter_nd_update',
     'scatter_update',
     'split',

mindnlp/core/ops/creation.py

@@ -53,12 +53,18 @@ def zeros(*size, dtype=None, device=None, requires_grad=False, **kwargs):
             size = ((),)
     if isinstance(size[0], (tuple, list)):
         size = size[0]
+
+    new_size = ()
+    for s in size:
+        if not isinstance(s, int):
+            s = s.item()
+        new_size += (s,)
     if use_pyboost() and has_zeros:
         # if device == 'cpu':
         #     return mindspore.Tensor(np.zeros(size), dtype=dtype)
-        return mindspore.mint.zeros(size, dtype=dtype)
+        return mindspore.mint.zeros(new_size, dtype=dtype)
     size = tuple(size)
-    return _zeros(size, dtype)
+    return _zeros(new_size, dtype)
 
 # zeros_like
 has_zeros_like = hasattr(mindspore.mint, 'zeros_like')