fix c class ut

mindnlp/core/__init__.py

@@ -47,7 +47,7 @@
 from .amp import autocast, GradScaler
 
 from . import profiler, cuda, optim, amp, compiler, jit, version, __future__, overrides, \
-    return_types, linalg, fx, backends, testing, nn, fft
+    return_types, linalg, fx, backends, testing, nn, fft, _jit_internal, utils
 
 from ._lowrank import svd_lowrank
 from .random import get_rng_state, initial_seed, manual_seed, seed, set_rng_state

mindnlp/core/_dynamo/utils.py

@@ -0,0 +1,3 @@
+def is_compile_supported(device_type):
+    return False
+

mindnlp/core/_jit_internal.py

@@ -1,3 +1,18 @@
+from typing import (  # noqa: UP035, F401  # (Dict, List, Tuple) imported by torch.jit.annotations
+    Any,
+    Callable,
+    Dict,
+    Final,
+    ForwardRef,
+    get_args,
+    get_origin,
+    List,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
 class FunctionModifiers:
     """
     Used to denote the behavior of a function in TorchScript. See export() and

mindnlp/core/_tensor.py

@@ -463,6 +463,11 @@ def clamp_min(self, value):
     Tensor.unsqueeze_ = ops.inplace_unsqueeze
     StubTensor.unsqueeze_ = ops.inplace_unsqueeze
 
+    def pin_memory(self, *args, **kwargs):
+        return self
+
+    Tensor.pin_memory = pin_memory
+    StubTensor.pin_memory = pin_memory
 
 def _rebuild_from_type_v2(func, new_type, args, state):
     ret = func(*args)

mindnlp/core/jit/__init__.py

@@ -9,7 +9,7 @@
     # _overload,
     # _overload_method,
     # export,
-    # Final,
+    Final,
     # Future,
     # ignore,
     # is_scripting,

mindnlp/core/jit/annotations.py

@@ -1,4 +1,4 @@
-from core._jit_internal import (  # type: ignore[attr-defined]
+from .._jit_internal import (  # type: ignore[attr-defined]
     # _Await,
     # _qualified_name,
     # Any,
@@ -15,7 +15,7 @@
     # is_optional,
     # is_tuple,
     # is_union,
-    # List,
+    List,
     # Optional,
     # Tuple,
     # Union,

mindnlp/core/linalg/__init__.py

@@ -1,5 +1,5 @@
 from collections import namedtuple
-from mindspore import ops
+from mindspore import ops, mint
 from mindspore.ops._primitive_cache import _get_cache_prim
 
 from mindnlp import core
@@ -21,3 +21,5 @@ def cholesky_ex(A, *, upper=False, check_errors=False, out=None):
     return linalg_cholesky_ex(out, info)
 
 
+def norm(A, ord=None, dim=None, keepdim=False, *, out=None, dtype=None):
+    return mint.norm(A, ord, dim, keepdim, dtype=dtype)

mindnlp/core/nn/functional.py

@@ -162,6 +162,11 @@ def avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, coun
 
     return ops.avg_pool2d(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)
 
+def adaptive_avg_pool1d(input, output_size):
+    if use_pyboost():
+        return mint.nn.functional.adaptive_avg_pool1d(input, output_size)
+    return ops.adaptive_avg_pool1d(input, output_size)
+
 def adaptive_avg_pool2d(input, output_size):
     if use_pyboost():
         return mint.nn.functional.adaptive_avg_pool2d(input, output_size)
@@ -1206,7 +1211,7 @@ def _none_or_dtype(input: Optional[core.Tensor]) -> Optional[int]:
     raise RuntimeError("input to _none_or_dtype() must be None or core.Tensor")
 
 def unfold(input, kernel_size, dilation=1, padding=0, stride=1):
-    if use_pyboost():
+    if use_pyboost() and not ON_A1:
         return mint.nn.functional.unfold(input, kernel_size, dilation, padding, stride)
     return ops.unfold(input, kernel_size, dilation, padding, stride)
 

mindnlp/core/nn/modules/__init__.py

@@ -8,7 +8,7 @@
 from .activation import *
 from .conv import Conv3d, Conv2d, Conv1d, ConvTranspose2d, ConvTranspose1d
 from .padding import ZeroPad2d, ConstantPad2d, ConstantPad1d, ConstantPad3d
-from .batchnorm import BatchNorm2d, BatchNorm1d
+from .batchnorm import BatchNorm2d, BatchNorm1d, SyncBatchNorm
 from .pooling import AdaptiveAvgPool2d, AvgPool1d, MaxPool2d, MaxPool1d, AdaptiveAvgPool1d, AvgPool2d
 from .flatten import Unflatten, Flatten
 from .rnn_cell import RNNCell, GRUCell, LSTMCell