fix c class models on OrangePi

mindtorch/_apis/gpu.py

@@ -848,7 +848,7 @@ def addcmul(input, tensor1, tensor2, value=1.0):
     return legacy.addcmul(input, tensor1, tensor2, mindspore.Tensor(value))
 
 def addmm(input, mat1, mat2, alpha=1.0, beta=1.0):
-    return add(mul(input, beta), mul(bmm(mat1, mat2), alpha))
+    return add(mul(input, beta), mul(matmul(mat1, mat2), alpha))
 
 def im2col(input, kernel_size, dilation=1, padding=0, stride=1):
     out = legacy.im2_col(input, kernel_size, stride, dilation, padding)

mindtorch/_apis/npu.py

@@ -1,4 +1,5 @@
 import mindspore
+import mindtorch
 from mindspore._c_expression import _empty_instance
 from ..configs import use_pyboost, ON_A1, ON_ORANGE_PI
 from .._op_prim.ascend import legacy, pyboost
@@ -824,9 +825,11 @@ def argmax(input, axis, keepdims):
     return legacy.argmax(input, axis, keepdims)
 
 def argmin(input, axis, keepdims):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.argmin_ext_op(input, axis, keepdims)
-    return legacy.argmin(input, axis, keepdims)
+    if axis is None:
+        axis = -1
+    return legacy.arg_min_with_value(input, axis, keepdims)[0]
 
 
 def bmm(input, other):
@@ -1136,7 +1139,7 @@ def masked_scatter(input, mask, value):
     return legacy.masked_scatter(input, mask, value)
 
 def neg(input):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.neg_op(input)
     return legacy.neg(input)
 
@@ -1557,7 +1560,7 @@ def inplace_exponential(self, lambd, generator):
     return legacy.expo(self, lambd, generator)
 
 def im2col(input, kernel_size, dilation=1, padding=0, stride=1):
-    if use_pyboost() and not ON_A1:
+    if use_pyboost() and not ON_A1 and not ON_ORANGE_PI:
         return pyboost.im2col_ext_op(input, kernel_size, dilation, padding, stride)
     out = legacy.im2_col(input, kernel_size, stride, dilation, padding)
     out_shape = out.shape[:1] + (-1,) + out.shape[-1:]
@@ -1570,9 +1573,10 @@ def upsample_nearest2d(input, output_size, scale_factors):
     return legacy.upsample_nearest2d(input, scale_factor, align_corners)
 
 def addmm(input, mat1, mat2, alpha=1.0, beta=1.0):
-    if use_pyboost():
+    if use_pyboost() and not ON_ORANGE_PI:
         return pyboost.addmm_op(input, mat1, mat2, alpha, beta)
-    return legacy.addmm(input, mat1, mat2, alpha, beta)
+    return add(mul(input, beta), mul(matmul(mat1, mat2), alpha))
+
 
 def meshgrid(input, lambd):
     if use_pyboost():

mindtorch/ops/array.py

@@ -622,6 +622,9 @@ def _process_multi_dim_index(self, indexes, remain_indexes, indexed_dims):
     self_viewed = self
     self_viewed_shape = list(self.shape)
     dim = 0
+    if ON_ORANGE_PI:
+        if all([isinstance(index, slice) for index in indexes]):
+            return getitem(self_viewed, tuple(indexes)), remain_indexes
     for i, index in enumerate(indexes):
         if isinstance(index, (list, tuple, np.ndarray)):
             index_np = np.array(index) if isinstance(index, (list, tuple)) else index
@@ -634,7 +637,6 @@ def _process_multi_dim_index(self, indexes, remain_indexes, indexed_dims):
                 raise TypeError(f"Index {index} contain unsupported elements")
         self_viewed, dim, remain_indexes, self_viewed_shape = _process_dim_in_multi_dim_index(
             self_viewed, self, index, dim, indexed_dims, i, remain_indexes, self_viewed_shape)
-
     return self_viewed, remain_indexes
 
 

tests/run_test.py

@@ -6,7 +6,8 @@
 import mindnlp
 from mindnlp import transformers
 
-mindspore.set_context(pynative_synchronize=True)
+# mindspore.set_context(pynative_synchronize=True)
+mindspore.runtime.launch_blocking()
 
 def run_tests():
     """