fix apis for s-z class (#2159)

Author: lvyufeng

mindnlp/core/_prims/numpy.py

@@ -388,6 +388,13 @@ def inplace_fill_scalar(input, value):
 
 __all__.append('inplace_fill_scalar')
 
+def inplace_fill_tensor(input, value):
+    out = np.full_like(input.numpy(), value)
+    numpy_to_tensor_overwrite(out, input)
+    return input
+
+__all__.append('inplace_fill_tensor')
+
 def inplace_normal(input, mean, std, generator_):
     out = np.random.normal(mean, std, input.shape).astype(core.dtype2np[input.dtype])
     numpy_to_tensor_overwrite(out, input)
@@ -600,6 +607,8 @@ def randn(size, seed, offset, dtype):
 
 def erfinv(input):
     out = scipy.special.erfinv(input)
+    if not isinstance(out, np.ndarray):
+        out = np.array(out)
     return core.Tensor.from_numpy(out)
 
 __all__.append('erfinv')
@@ -910,6 +919,8 @@ def maximum(input, other):
 
 def prod_ext(input, dim, keepdim, dtype):
     out = np.prod(input.numpy(), axis=dim, keepdims=keepdim)
+    if not isinstance(out, np.ndarray):
+        out = np.array(out)
     return core.Tensor.from_numpy(out)
 
 __all__.append('prod_ext')
@@ -1136,3 +1147,9 @@ def sign(input):
     return core.Tensor.from_numpy(out)
 
 __all__.append('sign')
+
+def log2(input):
+    out = np.log2(input.numpy())
+    return core.Tensor.from_numpy(out)
+
+__all__.append('log2')

mindnlp/core/_tensor.py

@@ -348,6 +348,8 @@ def __rsub__(self, other):
         return ops.sub(other, self)
 
     def __eq__(self, other):
+        if other is None:
+            return False
         return ops.eq(self, other)
 
     def __gt__(self, other):
@@ -1891,7 +1893,8 @@ def repeat_interleave(self, repeats, dim=None, output_size=None):
         return ops.repeat_interleave(self, repeats, dim, output_size=output_size)
 
     # Tensor.reshape
-    def reshape(self, *shape):
+    def reshape(self, *shape, **kwargs):
+        shape = kwargs.pop('shape', shape)
         return ops.reshape(self, *shape)
 
     # Tensor.reshape_as
@@ -1956,13 +1959,13 @@ def scatter_add(self, dim, index, src):
 
 
     # Tensor.scatter_reduce_
-    def scatter_reduce_(self, dim, index, src):
+    def scatter_reduce_(self, dim, index, src, reduce, *, include_self=True):
         return self.copy_(ops.scatter_reduce(self, dim, index, src))
 
 
     # Tensor.scatter_reduce
-    def scatter_reduce(self, dim, index, src):
-        return ops.scatter_reduce(self, dim, index, src)
+    def scatter_reduce(self, dim, index, src, reduce, *, include_self=True):
+        return ops.scatter_reduce(self, dim, index, src, reduce)
 
 
     # Tensor.select
@@ -2436,7 +2439,8 @@ def unsqueeze_(self, dim):
 
 
     # Tensor.var
-    def var(self, dim=None, *, correction=1, keepdim=False):
+    def var(self, dim=None, *, correction=1, keepdim=False, **kwargs):
+        correction = int(kwargs.pop('unbiased', correction))
         return ops.var(self, dim, correction=correction, keepdim=keepdim)
 
     # Tensor.vdot

mindnlp/core/nn/functional.py

@@ -56,7 +56,7 @@ def softplus(input, beta=1, threshold=20):
     return execute('softplus_ext', input, beta, threshold)
 
 def logsigmoid(input):
-    return execute('logsigmoid', input)
+    return execute('logsigmoid', input)[0]
 
 def leaky_relu(input, alpha=0.2):
     return execute('leaky_relu_ext', input, alpha)

mindnlp/core/ops/array.py

@@ -271,6 +271,11 @@ def scatter_add(input, dim, index, src):
 
 
 # scatter_reduce
+def scatter_reduce(input, dim, index, src, reduce, *, include_self=True):
+    if reduce == 'sum':
+        return scatter_add(input, dim, index, src)
+    else:
+        raise ValueError(f'do not support reduce: {reduce}')
 
 
 # split
@@ -1005,7 +1010,7 @@ def setitem_np(input, slice, value):
     # select_scatter
     # slice_scatter
     "scatter_add",
-    # scatter_reduce
+    "scatter_reduce",
     "split",
     "squeeze",
     "stack",

mindnlp/core/ops/random.py

@@ -9,7 +9,7 @@
 
 
 # bernoulli
-def bernoulli(input, *, generator=None, out=None):
+def bernoulli(input, *, generator=None, out=None, **kwargs):
     if generator is None:
         generator = default_generator
     output = execute("bernoulli_ext", input, generator)