feat(mge/tensor): support non-Tensor value in `_reset` and remove depreciated tests

GitOrigin-RevId: faf6c78aa8f6d7c43c95dc174261cc5c5d9edac1

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

@@ -16,7 +16,6 @@ from .._imperative_rt.common import CompNode
 from .._imperative_rt.core2 import Tensor, apply
 from ..ops import builtin
 from ..ops.builtin import Elemwise, GetVarShape
-from ..ops.special import Const
 from . import utils
 from .indexing import getitem as _getitem
 from .indexing import setitem as _setitem
@@ -373,7 +372,7 @@ class ArrayMethodMixin(abc.ABC):
     @property
     def size(self):
         r"""
-        Returns the size of the self :class:`~.Tensor`. 
+        Returns the size of the self :class:`~.Tensor`.
         The returned value is a subclass of :class:`tuple`.
         """
         shape = self.shape
@@ -390,7 +389,7 @@ class ArrayMethodMixin(abc.ABC):
 
     def item(self, *args):
         r"""
-        Returns the value of this :class:`~.Tensor` as a standard Python :class:`numbers.Number`. 
+        Returns the value of this :class:`~.Tensor` as a standard Python :class:`numbers.Number`.
         This only works for tensors with one element. For other cases, see :meth:`~.tolist`.
         """
         if not args:
@@ -401,8 +400,8 @@ class ArrayMethodMixin(abc.ABC):
 
     def tolist(self):
         r"""
-        Returns the tensor as a (nested) list. 
-        For scalars, a standard Python number is returned, just like with :meth:`~.item`. 
+        Returns the tensor as a (nested) list.
+        For scalars, a standard Python number is returned, just like with :meth:`~.item`.
         Tensors are automatically moved to the CPU first if necessary.
 
         This operation is not differentiable.
@@ -450,7 +449,7 @@ class ArrayMethodMixin(abc.ABC):
     def sum(self, axis=None, keepdims: bool = False):
         r"""
         Returns the sum of each row of the input tensor in the given dimension ``axis``.
-        
+
         If ``axis`` is a list of axises, reduce over all of them.
         If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
         except in the dimension(s) ``axis`` where it is of size 1.
@@ -483,7 +482,7 @@ class ArrayMethodMixin(abc.ABC):
     def prod(self, axis=None, keepdims: bool = False):
         r"""
         Returns the product of each row of the input tensor in the given dimension ``axis``.
-        
+
         If ``axis`` is a list of axises, reduce over all of them.
         If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
         except in the dimension(s) ``axis`` where it is of size 1.
@@ -516,7 +515,7 @@ class ArrayMethodMixin(abc.ABC):
     def min(self, axis=None, keepdims: bool = False):
         r"""
         Returns the min value of each row of the input tensor in the given dimension ``axis``.
-        
+
         If ``axis`` is a list of axises, reduce over all of them.
         If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
         except in the dimension(s) ``axis`` where it is of size 1.
@@ -549,7 +548,7 @@ class ArrayMethodMixin(abc.ABC):
     def max(self, axis=None, keepdims: bool = False):
         r"""
         Returns the max value of each row of the input tensor in the given dimension ``axis``.
-        
+
         If ``axis`` is a list of axises, reduce over all of them.
         If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
         except in the dimension(s) ``axis`` where it is of size 1.
@@ -582,7 +581,7 @@ class ArrayMethodMixin(abc.ABC):
     def mean(self, axis=None, keepdims: bool = False):
         r"""
         Returns the mean value of each row of the input tensor in the given dimension ``axis``.
-        
+
         If ``axis`` is a list of axises, reduce over all of them.
         If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor,
         except in the dimension(s) ``axis`` where it is of size 1.

imperative/python/megengine/tensor.py

@@ -119,6 +119,8 @@ class Tensor(_Tensor, ArrayMethodMixin):
         return super().detach()
 
     def _reset(self, other):
+        if not isinstance(other, _Tensor):
+            other = Tensor(other, dtype=self.dtype, device=self.device)
         super()._reset(other)
 
     def __repr__(self):
@@ -141,8 +143,6 @@ class Tensor(_Tensor, ArrayMethodMixin):
 
     @deprecated(version="1.0", reason="no need to reuse an existing tensor since 1.0")
     def set_value(self, value):
-        if not isinstance(value, _Tensor):
-            value = Tensor(value, dtype=self.dtype, device=self.device)
         self._reset(value)
 
     @deprecated(version="1.0", reason="use *= 0 instead")

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

@@ -50,6 +50,14 @@ def test_reduce():
     test_x(np.array([True, False, True]))
 
 
+def test_set_value():
+    v0 = np.random.random((2, 3)).astype(np.float32)
+    param = Tensor(v0)
+    v1 = np.random.random((2, 3)).astype(np.float32)
+    param[...] = v1
+    np.testing.assert_allclose(param.numpy(), v1, atol=5e-6)
+
+
 def test_set_subtensor():
     x = Tensor([1, 2, 3])
     x[:] = [1, 1, 1]

imperative/python/test/unit/module/test_module_tensor.py

-# -*- coding: utf-8 -*-
-# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
-#
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-import copy
-
-import numpy as np
-import pytest
-
-import megengine as mge
-import megengine.functional as F
-from megengine import Parameter, Tensor
-from megengine.module import Conv2d
-
-
-# TODO: delete this test after deleting set_value
-def test_set_value():
-    v0 = np.random.random((2, 3)).astype(np.float32)
-    param = Parameter(v0)
-    v1 = np.random.random((2, 3)).astype(np.float32)
-    param.set_value(v1)
-    np.testing.assert_allclose(param.numpy(), v1, atol=5e-6)
-    v2 = np.random.random((3, 3)).astype(np.float32)
-    # TODO: add this
-    # with pytest.raises(ValueError):
-    #     param.set_value(v2)
-    np.testing.assert_allclose(param.numpy(), v1, atol=5e-6)
-
-
-@pytest.mark.skip(reason="fill unsupported")
-def test_fill():
-    a = Tensor(np.zeros((2, 3), dtype=np.float32))
-    a.fill(3)
-    np.testing.assert_allclose(a.numpy(), np.full((2, 3), 3, dtype=np.float32))
-    a.fill(124.568)
-    np.testing.assert_allclose(a.numpy(), np.full((2, 3), 124.568, dtype=np.float32))
-
-
-# TODO: remove or rewrite following test
-# def test_attach():
-#     p_ = np.random.random((2, 3)).astype(np.float32)
-
-#     with Graph() as g:
-#         g.set_option('eager_evaluation', False)
-#         p = Parameter(p_)
-#         v = p * 2
-#         f = compile(v, None)
-
-#     out, = f()
-#     np.testing.assert_allclose(out, p_ * 2)
-
-#     F.add_update(p, p)
-#     out, = f()
-#     np.testing.assert_allclose(out, p_ * 4)
-
-
-# TODO: remove or rewrite following test
-# def test_module_attach():
-#     v = np.random.random((1, 3, 64, 64)).astype(np.float32)
-#     net = Conv2d(3, 16, 3)
-
-#     with Graph() as g:
-#         g.set_option('eager_evaluation', False)
-
-#         data0 = Input("data")
-#         f = compile(net(data0), None)
-
-#     out0, = f(data=v)
-
-#     data1 = Input("data", value=v)
-#     out1 = net(data1)
-
-#     np.testing.assert_allclose(out0, out1.numpy())
-
-
-# def test_shape_warning():
-#     with Graph() as cg:
-#         cg.set_option("eager_evaluation", False)
-#         b = Tensor(np.ones((2, 3)).astype(np.float32))
-#         with pytest.warns(None) as record:
-#             print(b.shape)
-#         if len(record) != 0:
-#             raise ValueError(
-#                 "Getting the shape of a constant Tensor should throw no Warning"
-#             )