fix a class models on OrangePi

mindtorch/_apis/npu.py

@@ -909,10 +909,11 @@ def narrow(input, dim, start, length):
     return legacy.slice(input, begin, size)
 
 def std(input, dim, correction, keepdim):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.std_op(input, dim, correction, keepdim)
-    return legacy.reduce_std(input, dim, keepdim)
-
+    if dim is None:
+        dim = ()
+    return legacy.reduce_std(input, dim, bool(correction), keepdim)[0]
 
 def log(input):
     if use_pyboost():
@@ -1083,9 +1084,9 @@ def sin(input):
     return legacy.sin(input)
 
 def batch_norm(input, weight, bias, running_mean=None, runnning_var=None, training=False, momentum=0.1, epsilon=1e-5):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.batch_norm_ext_op(input, weight, bias, running_mean, runnning_var, training, momentum, epsilon)
-    return legacy.batch_norm(input, weight, bias, running_mean, runnning_var, training, momentum, epsilon, 'NHWC')
+    return legacy.batch_norm(input, weight, bias, running_mean, runnning_var, training, epsilon, momentum, 'NCHW')
 
 def silu(input):
     if use_pyboost():
@@ -1448,9 +1449,11 @@ def reciprocal(input):
     return legacy.reciprocal(input)
 
 def index_add_ext(input, dim, index, source, alpha):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.index_add_ext_op(input, dim, index, source, alpha)
-    return legacy.index_add(input, dim, index, source, alpha)
+    if alpha != 1:
+        source = mul(alpha, source)
+    return legacy.index_add(input, cast(index, mindspore.int32), source, dim, True, True)
 
 def polar(abs, angle):
     if use_pyboost():

mindtorch/nn/modules/activation.py

@@ -1347,11 +1347,7 @@ def forward(
             check_other=False,
         )
 
-        is_fastpath_enabled = mindtorch.backends.mha.get_fastpath_enabled()
-
-        if not is_fastpath_enabled:
-            why_not_fast_path = "mindtorch.backends.mha.get_fastpath_enabled() was not True"
-        elif not is_batched:
+        if not is_batched:
             why_not_fast_path = (
                 f"input not batched; expected query.dim() of 3 but got {query.dim()}"
             )

mindtorch/ops/random.py

@@ -3,7 +3,7 @@
 from mindtorch._C import default_generator
 from mindtorch.executor import execute
 from .._bind import get_default_dtype, get_device_in_context
-from ..configs import ON_A1
+from ..configs import ON_A1, ON_ORANGE_PI
 
 generator_step_ = 12
 
@@ -26,7 +26,7 @@ def multinomial(input, num_samples, replacement=False, *, generator=None, out=No
         num_samples = num_samples.item()
     if generator is None:
         generator = default_generator
-    if input.device.type == 'npu':
+    if input.device.type == 'npu' and not ON_ORANGE_PI:
         output = execute("multinomial", input, num_samples, replacement, generator)
 
     else: