feat(mge/imperative): simulates scalar

GitOrigin-RevId: e81630e25647688d2c2385e9faec0ee3b8c8174c

imperative/python/megengine/core/_trace_option.py

@@ -26,4 +26,6 @@ def set_symbolic_shape(option: bool):
     """ Sets whether tensor.shape returns a tensor instead of a tuple
     """
     global _use_symbolic_shape
+    _org = _use_symbolic_shape
     _use_symbolic_shape = option
+    return _org

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

@@ -14,7 +14,7 @@ from .._trace_option import use_symbolic_shape
 from ..ops import builtin
 from ..ops.special import Const
 from .core import TensorBase, TensorWrapperBase, apply
-from .utils import astensor1d, make_shape_tuple
+from .utils import astensor1d, isscalar, make_shape_tuple
 
 
 def remove_ellipsis(tensor, tuple_val):
@@ -89,9 +89,13 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
     if not isinstance(tuple_val, tuple):
         tuple_val = (tuple_val,)
     ndim_indexed = 0
+    ndim_indexed_scalar = 0
     for i in tuple_val:
         if not i is Ellipsis:
             ndim_indexed += 1 if not hasattr(i, "ndim") else i.ndim
+            if isscalar(i):
+                ndim_indexed_scalar += 1
+
     if ndim_indexed > inp.ndim:
         raise IndexError(
             "too many indices for tensor: tensor is {}-dimensional, but {} were indexed".format(
@@ -103,15 +107,6 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
     use_subtensor = True
     inp, tuple_val = check_bool_index(inp, tuple_val)
 
-    def is_scalar(d):
-        if isinstance(i, int):
-            return True
-        if type(d).__module__ == np.__name__:
-            return np.isscalar(d)
-        # if isinstance(d, (TensorBase, TensorWrapperBase)):
-        #     return d.shape == (1,)
-        return False
-
     new_axes = []
     tensors = []
     items = []
@@ -134,7 +129,7 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
             continue
 
         if (
-            not is_scalar(i)
+            not isscalar(i)
             and not i is np.newaxis
             and not i is Ellipsis
             and not isinstance(i, slice)
@@ -191,7 +186,7 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
         items.append(item)
     if new_axes:
         raise IndexError("newaxis is not allowed here")
-    return inp, tensors, items, use_subtensor
+    return inp, tensors, items, use_subtensor, ndim_indexed_scalar == inp.ndim
 
 
 def try_condtake(tensor, index):
@@ -217,11 +212,11 @@ def getitem(tensor, index):
     try_result = try_condtake(tensor, index)
     if len(try_result) == 2:
         return try_result[0]
-    tensor, tensors, items, use_subtensor = unpack_getitem(tensor, index)
+    tensor, tensors, items, use_subtensor, ret_scalar = unpack_getitem(tensor, index)
     for v in tensors:
         if isinstance(v.shape, v.__class__):
             break
-        if v.shape[0] == 0:
+        if len(v.shape) > 0 and v.shape[0] == 0:
             (empty_tensor,) = Const([], dtype=tensor.dtype, device=tensor.device)(
                 tensor
             )
@@ -231,6 +226,8 @@ def getitem(tensor, index):
     else:
         op = builtin.IndexingMultiAxisVec(items=items)
     (result,) = apply(op, tensor, *tensors)
+    if ret_scalar:
+        result.__wrapped__._data._isscalar = True
     return result
 
 
@@ -245,9 +242,9 @@ def setitem(tensor, index, value):
     if not isinstance(value, (TensorBase, TensorWrapperBase)):
         op = Const(value, dtype=tensor.dtype, device=tensor.device)
         (value,) = op(tensor)
-    tensor, tensors, items, use_subtensor = unpack_getitem(tensor, index)
+    tensor, tensors, items, use_subtensor, _ = unpack_getitem(tensor, index)
     for v in tensors:
-        if v.shape[0] == 0:
+        if len(v.shape) > 0 and v.shape[0] == 0:
             return tensor
     if use_subtensor:
         op = builtin.Subtensor(items=items)

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

@@ -102,8 +102,9 @@ class Graph(_imperative_rt.ComputingGraph):
 
 
 class VarNode(TensorBase):
-    def __init__(self, node: _imperative_rt.VarNode):
+    def __init__(self, node: _imperative_rt.VarNode, isscalar=False):
         self._node = node
+        self._isscalar = isscalar
         if hasattr(self.graph, "_var_cache"):
             self.graph._var_cache[node] = self
 

imperative/python/megengine/core/tensor/raw_tensor/__init__.py

@@ -33,8 +33,9 @@ class RawTensor(TensorBase):
     _del_cb = None
     _handle = None
 
-    def __init__(self, handle=None):
+    def __init__(self, handle=None, isscalar=False):
         self._handle = handle
+        self._isscalar = isscalar
         if handle is not None:
             if self._init_cb:
                 self._init_cb()
@@ -49,10 +50,15 @@ class RawTensor(TensorBase):
 
     @property
     def shape(self):
+        if self._isscalar:
+            return ()
         return get_shape(self._handle)
 
     def numpy(self):
-        return get_value(self._handle)
+        ret = get_value(self._handle)
+        if self._isscalar:
+            ret = ret.squeeze()
+        return ret
 
     def _dev_tensor(self):
         return _get_dev_tensor(self._handle)
@@ -102,7 +108,7 @@ def _(array: np.ndarray, dtype=None, device=None):
     device = None if device is None else as_device(device).to_c()
     if 0 in array.strides:
         array = array.squeeze().reshape(array.shape)
-    return RawTensor(put(array, dtype=dtype, device=device))
+    return RawTensor(put(array, dtype=dtype, device=device), isscalar=(array.ndim == 0))
 
 
 @as_raw_tensor.register(RawTensor)

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

@@ -21,7 +21,9 @@ from .indexing import getitem as _getitem
 from .indexing import setitem as _setitem
 from .raw_tensor import RawTensor, as_raw_tensor
 from .tensor import Tensor
+from .utils import isscalar
 from .utils import make_shape_tuple as _make_shape_tuple
+from .utils import setscalar
 
 _ElwMod = Elemwise.Mode
 
@@ -39,6 +41,13 @@ def _elwise(*args, mode):
         )
     args = utils.convert_inputs(*args)
     (result,) = apply(op, *args)
+    _isscalar = True
+    for i in args:
+        if isscalar(i) == False:
+            _isscalar = False
+            break
+    if _isscalar:
+        setscalar(result)
     return result
 
 
@@ -153,6 +162,8 @@ def _remove_axis(inp: Tensor, axis) -> Tensor:
     param = Param(*map(builtin.AxisAddRemove.AxisDesc.make_remove, axis))
     op = builtin.AxisAddRemove(param=param)
     (result,) = apply(op, inp)
+    if len(axis) == inp.ndim:
+        setscalar(result)
     return result
 
 
@@ -189,6 +200,8 @@ def _reduce(mode):
         if self.dtype == np.bool_:
             if mode in ["MIN", "MAX"]:
                 result = result.astype("bool")
+        if axis is None or self.ndim == 1:
+            setscalar(result)
         return result
 
     return f
@@ -321,9 +334,7 @@ class ArrayMethodMixin(abc.ABC):
     __complex__ = lambda self: complex(self.item())
 
     def __len__(self):
-        shape = self.shape
-        if use_symbolic_shape():
-            shape = shape.numpy()
+        shape = self.__wrapped__.shape
         if shape:
             return int(shape[0])
         raise TypeError("ndim is 0")
@@ -344,18 +355,17 @@ class ArrayMethodMixin(abc.ABC):
 
     @property
     def ndim(self):
-        shape = self.shape
-        if isinstance(shape, self.__class__):
-            # XXX: assume ndim is not changed during trace
-            ndim = shape.__wrapped__.shape[0]
-            return ndim
+        shape = self.__wrapped__.shape
+        if shape is None:
+            raise ValueError("unkown ndim")
         return len(shape)
 
     @property
     def size(self):
-        if use_symbolic_shape():
-            return self.shape.prod()
-        return np.prod(self.shape).item()
+        shape = self.shape
+        if shape.__class__ is tuple:
+            return np.prod(self.shape).item()
+        return shape.prod()
 
     @property
     def T(self):
@@ -416,8 +426,8 @@ class ArrayMethodMixin(abc.ABC):
 
         .. testoutput::
 
-            [2]
-            [10.]
+            2
+            10.
 
         """
         return _reduce("SUM")(self, axis, keepdims)
@@ -444,10 +454,10 @@ class GenericTensorWrapper(ArrayMethodMixin, TensorWrapperBase):
 
     @property
     def shape(self):
-        if use_symbolic_shape():
-            return apply(GetVarShape(), self)[0]
-        else:
-            return self.__wrapped__.shape
+        shape = self.__wrapped__.shape
+        if shape == () or not use_symbolic_shape():
+            return shape
+        return apply(GetVarShape(), self)[0]
 
     @property
     def device(self):

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

@@ -133,7 +133,9 @@ def concatenate(inputs, axis=0, *, device=None):
 def astype(x, dtype):
     dtype = np.dtype(dtype)
     if not is_equal(x.dtype, dtype):
+        isscalar = x.__wrapped__._data._isscalar
         (x,) = apply(builtin.TypeCvt(param=dtype), x)
+        x.__wrapped__._data._isscalar = isscalar
     return x
 
 
@@ -176,13 +178,29 @@ def result_type(*args):
 
 
 def isscalar(x):
-    try:
-        return x.ndim == 0
-    except:
-        pass
+    if isinstance(x, TensorWrapperBase):
+        x = x.__wrapped__
+
+    if hasattr(x, "_isscalar"):
+        return x._isscalar
+    if isinstance(x, TensorBase):
+        return x._data._isscalar
+
     return np.isscalar(x)
 
 
+def setscalar(x):
+    if isinstance(x, TensorWrapperBase):
+        x = x.__wrapped__
+
+    if hasattr(x, "_isscalar"):
+        x._isscalar = True
+    elif isinstance(x, TensorBase):
+        x._data._isscalar = True
+    else:
+        raise NotImplementedError("Unsupport type {}".format(type(x)))
+
+
 def astensor1d(x, *reference, dtype=None, device=None):
     """
     Convert something to 1D tensor. Support following types
@@ -195,8 +213,8 @@ def astensor1d(x, *reference, dtype=None, device=None):
     except AttributeError:
         pass
     else:
-        if ndim != 1:
-            raise ValueError("ndim != 1: %d" % ndim)
+        if ndim != 0 and ndim != 1:
+            raise ValueError("ndim != 1 or 0, get : %d" % ndim)
         if not isinstance(x, (TensorBase, TensorWrapperBase)):
             (x,) = Const(x, dtype=dtype, device=device)(*reference)
         return x
@@ -216,7 +234,11 @@ def astensor1d(x, *reference, dtype=None, device=None):
 
 def _expand_int(s, i):
     if isinstance(i, (TensorBase, TensorWrapperBase)):
-        s += list(i.numpy())
+        i_np = i.numpy()
+        if i_np.ndim == 0:
+            s.append(int(i_np))
+        else:
+            s += list(i_np)
         return
     if isinstance(i, Iterable):
         for ii in i:

imperative/python/megengine/distributed/helper.py

@@ -63,8 +63,12 @@ def param_pack_split(inp: Tensor, offsets: list, shapes: list):
     """
     op = ParamPackSplit()
     op.offsets = offsets
-    op.shapes = shapes
-    return apply(op, inp)
+    op.shapes = [s or (1,) for s in shapes]
+    outputs = apply(op, inp)
+    for s, x in zip(shapes, outputs):
+        if not s:
+            x._isscalar = True
+    return outputs
 
 
 def param_pack_concat(inps: list, offsets: Tensor, offsets_val: list):

imperative/python/megengine/functional/elemwise.py

@@ -13,6 +13,7 @@ from ..core.ops import builtin
 from ..core.ops.builtin import Elemwise
 from ..core.tensor import megbrain_graph, utils
 from ..core.tensor.core import apply
+from ..core.tensor.utils import isscalar, setscalar
 from ..device import get_default_device
 from ..jit.tracing import is_tracing
 from ..tensor import Tensor
@@ -105,7 +106,14 @@ def _elwise(*args, mode):
         args = utils.convert_inputs(*args)
     if mode in ("true_div", "exp", "pow", "log", "expm1", "log1p"):
         args = tuple(map(lambda x: x.astype("float32"), args))
+    _isscalar = True
+    for i in args:
+        if isscalar(i) == False:
+            _isscalar = False
+            break
     (result,) = apply(op, *args)
+    if _isscalar:
+        setscalar(result)
     return result
 
 

imperative/python/megengine/functional/loss.py

@@ -63,7 +63,7 @@ def l1_loss(pred: Tensor, label: Tensor) -> Tensor:
 
     .. testoutput::
 
-        [2.75]
+        2.75
 
     """
     diff = pred - label
@@ -115,7 +115,7 @@ def square_loss(pred: Tensor, label: Tensor) -> Tensor:
 
     .. testoutput::
 
-        [9.75]
+        9.75
 
     """
     diff = pred - label
@@ -170,7 +170,7 @@ def cross_entropy(
 
     .. testoutput::
 
-        [0.6931]
+        0.6931
 
     """
     n0 = pred.ndim
@@ -226,7 +226,7 @@ def binary_cross_entropy(
 
     .. testoutput::
 
-        [0.6931]
+        0.6931
 
     """
     if not with_logits:
@@ -265,7 +265,7 @@ def hinge_loss(pred: Tensor, label: Tensor, norm: str = "L1") -> Tensor:
 
     .. testoutput::
 
-        [1.5]
+        1.5
 
     """
     assert norm in ["L1", "L2"], "norm must be L1 or L2"

imperative/python/megengine/functional/math.py

@@ -155,7 +155,7 @@ def sum(
 
     .. testoutput::
 
-        [21]
+        21
 
     """
     return inp.sum(axis=axis, keepdims=keepdims)
@@ -189,7 +189,7 @@ def prod(
 
     .. testoutput::
 
-        [720]
+        720
 
     """
     return inp.prod(axis=axis, keepdims=keepdims)
@@ -226,7 +226,7 @@ def mean(
 
     .. testoutput::
 
-        [3.5]
+        3.5
 
     """
     return inp.mean(axis=axis, keepdims=keepdims)
@@ -263,7 +263,7 @@ def var(
 
     .. testoutput::
 
-        [2.9167]
+        2.9167
     """
     if axis is None:
         m = mean(inp, axis=axis, keepdims=False)
@@ -340,7 +340,7 @@ def min(
 
     .. testoutput::
 
-        [1]
+        1
 
     """
     return inp.min(axis=axis, keepdims=keepdims)
@@ -377,7 +377,7 @@ def max(
 
     .. testoutput::
 
-        [6]
+        6
 
     """
     return inp.max(axis=axis, keepdims=keepdims)
@@ -412,7 +412,7 @@ def norm(
 
     .. testoutput::
 
-        [4.3589]
+        4.3589
 
     """
     if axis is None:
@@ -460,7 +460,7 @@ def argmin(
 
     .. testoutput::
 
-        [0]
+        0
 
     """
     if isinstance(axis, collections.abc.Iterable):
@@ -519,7 +519,7 @@ def argmax(
 
     .. testoutput::
 
-        [5]
+        5
 
     """
     if isinstance(axis, collections.abc.Iterable):

imperative/python/megengine/functional/tensor.py

@@ -111,6 +111,8 @@ def full(shape, value, dtype="float32", device=None):
     (x,) = Const(value, dtype=dtype, device=device)(
         Tensor(value, dtype=dtype, device=device)
     )
+    if len(shape) == 0:  # scalar
+        return x
     return broadcast_to(x, shape)
 
 

imperative/python/megengine/functional/utils.py

@@ -53,7 +53,7 @@ def topk_accuracy(
 
     .. testoutput::
 
-        [0.] [0.375]
+        0.0 0.375
     """
     if isinstance(topk, int):
         topk = (topk,)

imperative/python/megengine/jit/tracing.py

@@ -168,8 +168,6 @@ class trace:
         self._output_bindings = None
         self._output_names = None
 
-        set_symbolic_shape(self._symbolic_shape)
-
     def _new_handle(self):
         handle = len(self._tinfo)
         info = TensorInfo()
@@ -368,6 +366,7 @@ class trace:
         interrupted = False
 
         def do_enter():
+            self._save_symbolic_shape = set_symbolic_shape(self._symbolic_shape)
             self._set_active(True)
             if self._untraced:
                 self._init_trace(self._symbolic)
@@ -423,6 +422,8 @@ class trace:
             apply.disable(apply_compiled_mode)
             apply.disable(apply_const_compiled_mode)
             self._set_active(False)
+            # Restore global variable
+            set_symbolic_shape(self._save_symbolic_shape)
 
         def do_exit():
             if not self._untraced and self._pc != len(self._seq):
@@ -498,7 +499,7 @@ class trace:
                 opnode = info.data_setter = G.InputNode(
                     device=info.device,
                     dtype=info.dtype,
-                    shape=info.shape,
+                    shape=info.shape or (1,),
                     graph=graph,
                     use_static_shape=_input_node_use_static_shape(),
                 )
@@ -544,7 +545,7 @@ class trace:
                             *links,
                             device=info.device,
                             dtype=info.dtype,
-                            shape=info.shape,
+                            shape=info.shape or (1,),
                             graph=graph,
                             use_static_shape=_input_node_use_static_shape(),
                         )
@@ -719,13 +720,13 @@ class trace:
             h2v[h] = graph.make_h2d(
                 dtype=info.dtype,
                 device=dumped_device,
-                shape=info.shape,
+                shape=info.shape or (1,),
                 name=arg_names[i] if arg_names else None,
             )
         for k, h in self._kwarg_bindings.items():
             info = self._tinfo[h]
             h2v[h] = graph.make_h2d(
-                dtype=info.dtype, device=dumped_device, shape=info.shape, name=k
+                dtype=info.dtype, device=dumped_device, shape=info.shape or (1,), name=k
             )
 
         for op, ihandles, ohandles in self._seq:
@@ -919,6 +920,7 @@ class CompiledTensorProxy(RawTensor):
 
     def __init__(self, handle):
         self.__handle = handle
+        self._isscalar = False
         self.__info = active_trace._tinfo[handle]
         self.__shape = None
         self.__data = None
@@ -934,6 +936,8 @@ class CompiledTensorProxy(RawTensor):
 
     @property
     def shape(self):
+        if self._isscalar:
+            return ()
         if self.__shape is None:
             if self.__info.shape_read:
                 self.__shape = self.__info.shape_reader.get_value().shape
@@ -951,6 +955,8 @@ class CompiledTensorProxy(RawTensor):
                 self.__value = self._dev_tensor().numpy()
             else:
                 raise TraceMismatchError("value of this tensor is not read in trace")
+            if self._isscalar:
+                self.__value = self.__value.squeeze()
         return self.__value
 
     def _dev_tensor(self):
@@ -970,9 +976,10 @@ class CompiledTensorProxy(RawTensor):
 
 
 class LazyEvalTensor(RawTensor):
-    def __init__(self, varnode):
-        super(LazyEvalTensor, self).__init__()
+    def __init__(self, varnode, isscalar=False):
+        super().__init__()
         self.__varnode = varnode
+        self._isscalar = isscalar
 
     @property
     def dtype(self):
@@ -984,10 +991,15 @@ class LazyEvalTensor(RawTensor):
 
     @property
     def shape(self):
+        if self._isscalar:
+            return ()
         return self.__varnode.shape
 
     def numpy(self):
-        return self.__varnode.value
+        ret = self.__varnode.value
+        if self._isscalar:
+            ret = ret.squeeze()
+        return ret
 
     def _dev_tensor(self):
         raise RuntimeError("cannot access data during symbolic tracing")
@@ -1041,10 +1053,12 @@ class TracedLazyTensor(TraceMixin, LazyEvalTensor):
 
 def assign_raw_tensor(lhs, rhs):
     handle = rhs._handle
+    # Keep isscalar of lhs
+    isscalar = lhs._isscalar
     rhs.__dict__.clear()
     lhs.__dict__.clear()
     lhs.__class__ = RawTensor
-    lhs.__init__(handle)
+    lhs.__init__(handle, isscalar=isscalar)
 
 
 # this hook turns RawTensor into LazyEvalTensor
@@ -1060,7 +1074,7 @@ def apply_symbolic_mode(op: OpDef, *args: RawTensor):
             data_setter = G.InputNode(
                 device=x.device,
                 dtype=x.dtype,
-                shape=x.shape,
+                shape=x.shape or (1,),
                 graph=graph,
                 use_static_shape=True,
             )
@@ -1091,7 +1105,9 @@ apply.disable(apply_symbolic_mode)
 @apply.register()
 def apply_const_symbolic_mode(op: Const, *args: RawTensor):
     graph = active_trace._lazy_eval_graph
-    ret = LazyEvalTensor(graph.make_const(op.value, dtype=op.dtype, device=op.device))
+    ret = LazyEvalTensor(
+        graph.make_const(op.value, dtype=op.dtype, device=op.device), isscalar=True
+    )
     active_trace._lazy_eval_tensors.add(ret)
     return (ret,)
 

imperative/python/megengine/quantization/observer.py

@@ -46,9 +46,9 @@ class Observer(Module):
 
     def get_dtype(self):
         q_dict = self.get_qparams()
-        numpy_scale = None if "scale" not in q_dict else q_dict["scale"].numpy()[0]
+        numpy_scale = None if "scale" not in q_dict else q_dict["scale"].numpy()
         numpy_zero_point = (
-            None if "zero_point" not in q_dict else q_dict["zero_point"].numpy()[0]
+            None if "zero_point" not in q_dict else q_dict["zero_point"].numpy()
         )
         return get_quantized_dtype(self.dtype, numpy_scale, numpy_zero_point)
 

imperative/python/test/integration/test_advance_indexing.py

@@ -18,7 +18,7 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.0, dtype=np.float32)
+        self.a = Parameter([1.0], dtype=np.float32)
 
     def forward(self, x, y):
         x = x[y] * self.a
@@ -28,7 +28,7 @@ class Simple(Module):
 class Simple2(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.0, dtype=np.float32)
+        self.a = Parameter([1.0], dtype=np.float32)
 
     def forward(self, x):
         x = x[1, ..., :, 0:4:2, 0:2] * self.a

imperative/python/test/integration/test_ai.py

@@ -18,7 +18,7 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.0, dtype=np.float32)
+        self.a = Parameter([1.0], dtype=np.float32)
 
     def forward(self, x):
         x = x[:, 0] * self.a

imperative/python/test/integration/test_detach.py

@@ -18,8 +18,8 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.0, dtype=np.float32)
-        self.b = Parameter(1.0, dtype=np.float32)
+        self.a = Parameter([1.0], dtype=np.float32)
+        self.b = Parameter([1.0], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_hello_world.py

@@ -21,7 +21,7 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.23, dtype=np.float32)
+        self.a = Parameter([1.23], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_lr_scheduler.py

@@ -18,7 +18,7 @@ from megengine.optimizer import SGD, MultiStepLR
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.23, dtype=np.float32)
+        self.a = Parameter([1.23], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_optimizer.py

@@ -32,7 +32,7 @@ class MLP(Module):
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.23, dtype=np.float32)
+        self.a = Parameter([1.23], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_save_load.py

@@ -11,7 +11,7 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.23, dtype=np.float32)
+        self.a = Parameter([1.23], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_sgd_momentum.py

@@ -19,7 +19,7 @@ from megengine.module import Module
 class Simple(Module):
     def __init__(self):
         super().__init__()
-        self.a = Parameter(1.23, dtype=np.float32)
+        self.a = Parameter([1.23], dtype=np.float32)
 
     def forward(self, x):
         x = x * self.a

imperative/python/test/integration/test_trace_dump.py

@@ -107,7 +107,7 @@ def test_xornet_trace_dump():
         if step % 50 == 0:
             minibatch = next(val_dataset)
             _, loss = val_fun(data, label)
-            loss = loss.numpy()[0]
+            loss = loss.numpy()
             val_loss.append((step, loss))
             print("Step: {} loss={}".format(step, loss))
         opt.step()

imperative/python/test/unit/core/test_indexing_op.py

@@ -449,7 +449,7 @@ def test_advance_indexing_high_level():
     y = np.array([1, 2])
     yy = Tensor(y)
     np.testing.assert_equal(x[:, y[0]], xx[:, y[0]].numpy())
-    # np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy()) # FIXME
+    np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy())
     np.testing.assert_equal(x[:, y], xx[:, y].numpy())
     np.testing.assert_equal(x[:, y], xx[:, yy].numpy())
 
@@ -469,10 +469,9 @@ def test_advance_indexing_high_level():
     y = np.array([1])
     yy = Tensor(y)
     np.testing.assert_equal(x[:, y[0]], xx[:, y[0]].numpy())
-    # np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy()) # FIXME
+    np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy())
     np.testing.assert_equal(x[:, y], xx[:, y].numpy())
 
-    # XXX: no way to tell whether yy is scalar or ndim=1 array
     np.testing.assert_equal(x[:, y], xx[:, yy].numpy())
 
     x = np.arange(9).reshape(3, 3).astype("int32")

imperative/python/test/unit/test_tracing.py

@@ -21,6 +21,7 @@ from megengine.core.ops import builtin as ops
 from megengine.core.ops.builtin import Elemwise
 from megengine.core.tensor.core import apply
 from megengine.core.tensor.raw_tensor import as_raw_tensor
+from megengine.core.tensor.utils import isscalar
 from megengine.functional import exp, log
 from megengine.jit import exclude_from_trace, trace
 from megengine.random import normal, uniform
@@ -263,20 +264,21 @@ def test_optimize_for_inference_broadcast():
 
 
 def test_trace_cvt_bool():
-    set_symbolic_shape(True)
     x = tensor([0], dtype=np.int32)
 
     @trace(symbolic=True)
     def f(x):
-        return x.shape[0] == 0
+        a = x.shape
+        b = a[0]
+        assert isscalar(b)
+        return b == 0
 
     for i in range(3):
-        np.testing.assert_equal(f(x).numpy()[0], False)
+        np.testing.assert_equal(f(x).numpy(), False)
 
 
 def test_trace_reshape():
     for symbolic in [False, True]:
-        set_symbolic_shape(True)
         x1 = tensor(np.random.randn(2, 10, 10))
         x2 = tensor(np.random.randn(4, 10, 10))
         x3 = tensor(np.random.randn(8, 10, 10))
@@ -359,7 +361,6 @@ def test_raise_on_trace():
 
 def test_trace_broadcast():
     for symbolic in [False, True]:
-        set_symbolic_shape(True)
         x1 = tensor(np.random.randn(3, 1, 1))
         x2 = tensor(np.random.randn(1, 4, 1))
         x3 = tensor(np.random.randn(1, 1, 5))
@@ -397,7 +398,6 @@ def test_trace_nms():
 
 
 def test_trace_valid_broadcast():
-    set_symbolic_shape(True)
     x1 = tensor(np.random.randn(1, 1))
     x2 = tensor(np.random.randn(1, 2))
     shape = (tensor([2]), tensor([2]))

imperative/python/test/unit/test_zero_dim_tensor.py

+import numpy as np
+
+import megengine.functional as F
+from megengine import Tensor
+from megengine.core._trace_option import use_symbolic_shape
+
+
+def test_zero_dim():
+    a = Tensor(1)
+    a_np = np.array(1, dtype=np.int32)
+    np.testing.assert_equal(a, a_np)
+    if use_symbolic_shape():
+        np.testing.assert_equal(a.shape, np.array(a_np.shape))
+    else:
+        np.testing.assert_equal(a.shape, a_np.shape)
+
+
+def test_sum():
+    a = Tensor([1, 2])
+    a = a.reshape((1, 2))
+    assert a.sum().ndim == 0
+    assert a.sum(axis=1).ndim == 1
+
+
+def test_max():
+    a = Tensor([1, 2])
+    a = a.reshape((1, 2))
+    assert a.max().ndim == 0
+    assert a.max(axis=1).ndim == 1
+
+
+def test_reshape():
+    a = Tensor(1)
+    a = a.reshape((1, 1))
+
+
+def test_squeeze():
+    a = Tensor(1)
+    a = a.reshape((1, 1))
+    assert F.squeeze(a).ndim == 0
+
+
+def test_elemementwise():
+    a = Tensor(1.0)
+    assert F.exp(a).ndim == 0
+    assert (a + a).ndim == 0
+    assert (a + 1).ndim == 0
+
+
+def test_astype():
+    a = Tensor(1.0)
+    assert a.astype("int32").ndim == 0