chore(mge/functional): remove duplicated code

GitOrigin-RevId: f9efea46cb996b1583d8b8d038a9edf21d7ac83c

imperative/python/megengine/core/tensor/tensor_wrapper.py

@@ -210,7 +210,8 @@ def _todo(*_):
 def _expand_args(args):
     if len(args) == 1:
         if isinstance(
-            args[0], (collections.abc.Sequence, TensorBase, TensorWrapperBase)
+            args[0],
+            (collections.abc.Sequence, TensorBase, TensorWrapperBase, np.ndarray),
         ):
             args = args[0]
     return args

imperative/python/megengine/functional/elemwise.py

@@ -88,13 +88,6 @@ def _elwise(*args, mode):
     return result
 
 
-def _logical(*args, mode):
-    op = builtin.CondExecPredLogical(mode=mode)
-    args = utils.convert_inputs(*args)
-    (result,) = apply(op, *args)
-    return result
-
-
 def _elemwise_multi_type(*args, mode, **kwargs):
     op = builtin.ElemwiseMultiType(mode=mode, **kwargs)
     args = utils.convert_inputs(*args)

imperative/python/megengine/functional/tensor.py

@@ -19,6 +19,7 @@ from ..core.ops import builtin
 from ..core.ops._internal import param_defs as P
 from ..core.ops.special import Const
 from ..core.tensor.core import TensorBase, TensorWrapperBase, apply
+from ..core.tensor.tensor_wrapper import _remove_axis
 from ..core.tensor.utils import (
     astensor1d,
     convert_inputs,
@@ -231,9 +232,7 @@ def broadcast(inp: Tensor, shape: Union[int, Iterable[int]]) -> Tensor:
           [3. 4. 5.]]]
 
     """
-    shape = astensor1d(shape, inp, dtype="int32", device=inp.device)
-    (result,) = apply(builtin.Broadcast(), inp, shape)
-    return result
+    return inp.broadcast(shape)
 
 
 def concat(inps: Iterable[Tensor], axis: int = 0, device=None) -> Tensor:
@@ -730,10 +729,7 @@ def transpose(inp: Tensor, pattern: Iterable[int]) -> Tensor:
          [1 0]]
 
     """
-    op = builtin.Dimshuffle(pattern)
-    (inp,) = convert_inputs(inp)
-    (result,) = apply(op, inp)
-    return result
+    return inp.transpose(pattern)
 
 
 dimshuffle = transpose
@@ -773,26 +769,7 @@ def reshape(inp: Tensor, target_shape: Iterable[int]) -> Tensor:
           [10 11]]]
 
     """
-    if isinstance(target_shape, (TensorBase, TensorWrapperBase)):
-        target_shape = target_shape.numpy()
-    target_shape = tuple(map(int, target_shape))
-    unspec_axis = None
-    for i, s in enumerate(target_shape):
-        if s < 0:
-            if s != -1:
-                raise ValueError("expect shape[{}] >= -1, got {}".format(i, s))
-            if unspec_axis is not None:
-                raise ValueError("multiple -1 in shape: {} & {}".format(unspec_axis, i))
-            unspec_axis = i
-
-    # TODO: device should be None (cpu)
-    (target_shape,) = Const(target_shape, dtype="int32", device=inp.device)(inp)
-    if unspec_axis is None:
-        op = builtin.Reshape()
-    else:
-        op = builtin.Reshape(unspec_axis=unspec_axis)
-    (x,) = apply(op, inp, target_shape)
-    return x
+    return inp.reshape(target_shape)
 
 
 AxisAddRemove = builtin.AxisAddRemove
@@ -915,25 +892,7 @@ def remove_axis(
         (1, 1, 2)
 
     """
-    Param = builtin.AxisAddRemove.Param
-
-    def get_axes():
-        if axis is None:
-            return [i for i, s in enumerate(inp.shape) if s == 1]
-        try:
-            return [int(axis)]
-        except (TypeError, ValueError):
-            pass
-        return list(map(int, axis))
-
-    axis = get_axes()
-    axis = sorted(i + inp.ndim if i < 0 else i for i in axis)
-    axis = [a - i for i, a in enumerate(axis)]
-
-    param = Param(*map(builtin.AxisAddRemove.AxisDesc.make_remove, axis))
-    op = builtin.AxisAddRemove(param=param)
-    (result,) = apply(op, inp)
-    return result
+    return _remove_axis(inp, axis)
 
 
 def linspace(