docs(mge): fix doctest

GitOrigin-RevId: 131fed8733

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

@@ -31,7 +31,7 @@ class Function:
                 self.y = y
                 return y
 
-            def backward(self. output_grads):
+            def backward(self, output_grads):
                 y = self.y
                 return output_grads * y * (1-y)
 

imperative/python/megengine/data/transform/vision/transform.py

@@ -194,9 +194,9 @@ class Compose(VisionTransform):
         will be random shuffled, the 2nd and 4th transform will also be shuffled.
     :param order: The same with :class:`VisionTransform`
 
-    Example:
+    Examples:
 
-    ..testcode::
+    .. testcode::
 
         from megengine.data.transform import RandomHorizontalFlip, RandomVerticalFlip, CenterCrop, ToMode, Compose
 

imperative/python/megengine/functional/elemwise.py

@@ -197,8 +197,8 @@ def sqrt(inp: Tensor) -> Tensor:
 
     .. testoutput::
 
-        [[0.      1.     1.4142]
-         [1.7321  2.     2.2361 ]]
+        [[0.     1.     1.4142]
+         [1.7321 2.     2.2361]]
 
     """
     return inp ** 0.5
@@ -227,8 +227,8 @@ def square(inp: Tensor) -> Tensor:
 
     .. testoutput::
 
-        [[0.      1.     4.]
-         [9.      16.    25.]]
+        [[ 0.  1.  4.]
+         [ 9. 16. 25.]]
 
     """
     return inp ** 2
@@ -437,7 +437,7 @@ def clamp(inp: Tensor, lower=None, upper=None) -> Tensor:
     :param lower: lower-bound of the range to be clamped to
     :param upper: upper-bound of the range to be clamped to
 
-    Example:
+    Examples:
 
     .. testcode::
 
@@ -452,6 +452,8 @@ def clamp(inp: Tensor, lower=None, upper=None) -> Tensor:
 
         print(F.clamp(a, upper=3).numpy())
 
+    Outputs:
+
     .. testoutput::
 
         [2 2 2 3 4]

imperative/python/megengine/functional/math.py

@@ -58,6 +58,8 @@ def isnan(inp: Tensor) -> Tensor:
 
         print(F.isnan(x).numpy())
 
+    Outputs:
+
     .. testoutput::
 
         [False  True False]
@@ -83,6 +85,8 @@ def isinf(inp: Tensor) -> Tensor:
 
         print(F.isinf(x).numpy())
 
+    Outputs:
+    
     .. testoutput::
 
         [False  True False]
@@ -141,7 +145,9 @@ def sum(
         data = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3))
         out = F.sum(data)
         print(out.numpy())
-
+    
+    Outputs:
+    
     .. testoutput::
 
         [21]
@@ -208,6 +214,8 @@ def mean(
         out = F.mean(data)
         print(out.numpy())
 
+    Outputs:
+
     .. testoutput::
 
         [3.5]
@@ -250,9 +258,11 @@ def var(
         out = F.var(data)
         print(out.numpy())
 
+    Outputs:
+
     .. testoutput::
 
-        [2.9166667]
+        [2.9167]
     """
     if axis is None:
         m = mean(inp, axis=axis, keepdims=False)
@@ -288,9 +298,11 @@ def std(
         out = F.std(data, axis=1)
         print(out.numpy())
 
+    Outputs:
+
     .. testoutput::
 
-        [0.8164966 0.8164966]
+        [0.8165 0.8165]
     """
     return var(inp, axis=axis, keepdims=keepdims) ** 0.5
 
@@ -354,6 +366,8 @@ def max(
         y = F.max(x)
         print(y.numpy())
 
+    Outputs:
+
     .. testoutput::
 
         [6]
@@ -388,9 +402,11 @@ def norm(
         y = F.norm(x)
         print(y.numpy())
 
+    Outputs:
+
     .. testoutput::
 
-        [4.358899]
+        [4.3589]
 
     """
     if p == 0:
@@ -426,6 +442,8 @@ def argmin(
         y = F.argmin(x)
         print(y.numpy())
 
+    Outputs:
+
     .. testoutput::
 
         [0]
@@ -479,6 +497,8 @@ def argmax(
         x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2,3))
         y = F.argmax(x)
         print(y.numpy())
+ 
+    Outputs:
 
     .. testoutput::
 

imperative/python/megengine/functional/nn.py

@@ -372,10 +372,12 @@ def softplus(inp: Tensor) -> Tensor:
         x = tensor(np.arange(-3, 3, dtype=np.float32))
         y = F.softplus(x)
         print(y.numpy())
+    
+    Outputs:
+    
+    .. testoutput::
 
-    .. output::
-
-        [0.04858735 0.126928   0.3132617  0.6931472  1.3132617  2.126928  ]
+        [0.0486 0.1269 0.3133 0.6931 1.3133 2.1269]
 
     """
     return log1p(exp(-abs(inp))) + relu(inp)
@@ -411,10 +413,12 @@ def log_softmax(inp: Tensor, axis: Union[int, Sequence[int]]) -> Tensor:
         y = F.log_softmax(x, axis=1)
         print(y.numpy())
 
-    .. output::
+    Outputs:
+    
+    .. testoutput::
 
-        [[-4.4519143 -3.4519143 -2.4519143 -1.4519144 -0.4519144]
-         [-4.4519143 -3.4519143 -2.4519143 -1.4519144 -0.4519144]]
+        [[-4.4519 -3.4519 -2.4519 -1.4519 -0.4519]
+         [-4.4519 -3.4519 -2.4519 -1.4519 -0.4519]]
 
     """
     return inp - logsumexp(inp, axis, keepdims=True)
@@ -432,6 +436,7 @@ def logsigmoid(inp: Tensor) -> Tensor:
     :param inp: The input tensor
 
     Examples:
+
     .. testcode::
 
         import numpy as np
@@ -442,9 +447,12 @@ def logsigmoid(inp: Tensor) -> Tensor:
         y = F.logsigmoid(x)
         print(y.numpy())
 
-    .. output::
+    Outputs:
+
+    .. testoutput::
 
-        [-5.0067153  -4.01815    -3.0485873  -2.126928   -1.3132617  -0.6931472  -0.3132617  -0.126928   -0.04858735 -0.01814993]
+        [-5.0067 -4.0181 -3.0486 -2.1269 -1.3133 -0.6931 -0.3133 -0.1269 -0.0486
+         -0.0181]
 
     """
     return -softplus(-inp)
@@ -478,6 +486,7 @@ def logsumexp(
     :param keepdims: whether to retain :attr:`axis` or not for the output tensor.
 
     Examples:
+    
     .. testcode::
 
         import numpy as np
@@ -488,9 +497,11 @@ def logsumexp(
         y = F.logsumexp(x, axis=1, keepdims=False)
         print(y.numpy())
 
-    .. output::
+    Outputs:
+
+    .. testoutput::
 
-        [-0.5480856  4.4519143]
+        [-0.5481  4.4519]
 
     """
     max_value = max(inp, axis, keepdims=True)
@@ -577,8 +588,9 @@ def softmax(inp: Tensor, axis: Optional[int] = None) -> Tensor:
     Outputs:
 
     .. testoutput::
-        [[0.01165623 0.03168492 0.08612854 0.23412167 0.6364086 ]
-         [0.01165623 0.03168492 0.08612854 0.23412167 0.6364086 ]]
+
+        [[0.0117 0.0317 0.0861 0.2341 0.6364]
+         [0.0117 0.0317 0.0861 0.2341 0.6364]]
 
     """
     if axis is None:
@@ -1026,7 +1038,7 @@ def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
 
     Examples:
 
-    .. teestcode::
+    .. testcode::
 
         import numpy as np
         from megengine import tensor
@@ -1039,9 +1051,10 @@ def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
 
     Outputs:
 
+    .. testoutput::
+
         [55.]
 
-    .. testoutputs::
     """
     op = builtin.Dot()
     inp1, inp2 = utils.convert_inputs(inp1, inp2)
@@ -1058,7 +1071,7 @@ def svd(inp: Tensor, full_matrices=False, compute_uv=True) -> Tensor:
 
     Examples:
 
-    .. teestcode::
+    .. testcode::
 
         import numpy as np
         from megengine import tensor
@@ -1070,7 +1083,9 @@ def svd(inp: Tensor, full_matrices=False, compute_uv=True) -> Tensor:
 
     Outputs:
 
-        [7.348, 1.]
+    .. testoutput::
+    
+        [7.3485 1.    ]
 
     """
     op = builtin.SVD(full_matrices=full_matrices, compute_uv=compute_uv)
@@ -1445,6 +1460,8 @@ def indexing_one_hot(
         val = F.indexing_one_hot(src, index)
         print(val.numpy())
 
+    Outputs:
+    
     .. testoutput::
 
         [1.]

imperative/python/megengine/functional/tensor.py

@@ -60,7 +60,7 @@ __all__ = [
 ]
 
 
-def eye(n: int, *, dtype=None, device: Optional[CompNode] = None) -> Tensor:
+def eye(n: int, *, dtype="float32", device: Optional[CompNode] = None) -> Tensor:
     """
     Returns a 2D tensor with ones on the diagonal and zeros elsewhere.
 
@@ -80,7 +80,7 @@ def eye(n: int, *, dtype=None, device: Optional[CompNode] = None) -> Tensor:
 
         data_shape = (4, 6)
         n, m = data_shape
-        out = F.eye(n, m, dtype=np.float32)
+        out = F.eye([n, m], dtype=np.float32)
         print(out.numpy())
 
     Outputs:
@@ -135,6 +135,8 @@ def zeros_like(inp: Tensor) -> Tensor:
         out = F.zeros_like(inp)
         print(out.numpy())
 
+    Outputs:
+    
     .. testoutput::
 
         [[0 0 0]
@@ -638,7 +640,7 @@ def cond_take(mask: Tensor, x: Tensor) -> Tensor:
 
     .. testoutput::
 
-        Tensor([1. 4.]) Tensor([0 3], dtype=int32)
+        [1. 4.] [0 3]
 
     """
     if not isinstance(x, (TensorWrapperBase, TensorBase)):
@@ -888,6 +890,8 @@ def linspace(
         a = F.linspace(3,10,5)
         print(a.numpy())
 
+    Outputs:
+    
     .. testoutput::
 
         [ 3.    4.75  6.5   8.25 10.  ]
@@ -930,6 +934,8 @@ def arange(
 
         a = F.arange(5)
         print(a.numpy())
+    
+    Outputs:
 
     .. testoutput::
 
@@ -977,7 +983,9 @@ def param_pack_split(inp: Tensor, offsets: List, shapes: List) -> Tensor:
         b, c = F.param_pack_split(a, [0, 1, 1, 10], [(1,), (3, 3)])
         print(b.numpy())
         print(c.numpy())
-
+    
+    Outputs:
+    
     .. testoutput::
 
         [1]
@@ -1000,7 +1008,7 @@ def param_pack_concat(inps: List, offsets: Tensor, offsets_val: List) -> Tensor:
     :param offsets: device value of offsets
     :param offsets_val: offsets of inputs, length of 2 * n,
             format [begin0, end0, begin1, end1].
-    :return: split tensors
+    :return: concat tensors
 
     Examples:
 
@@ -1013,10 +1021,12 @@ def param_pack_concat(inps: List, offsets: Tensor, offsets_val: List) -> Tensor:
         a = tensor(np.ones((1,), np.int32))
         b = tensor(np.ones((3, 3), np.int32))
         offsets_val = [0, 1, 1, 10]
-        offsets = tensor(offsets, np.int32)
+        offsets = tensor(offsets_val, np.int32)
         c = F.param_pack_concat([a, b], offsets, offsets_val)
         print(c.numpy())
-
+    
+    Outputs:
+    
     .. testoutput::
 
         [1 1 1 1 1 1 1 1 1 1]

imperative/python/megengine/functional/utils.py

@@ -63,19 +63,6 @@ def accuracy(
     return accs
 
 
-def zero_grad(inp: Tensor) -> Tensor:
-    r"""
-    Returns a tensor which is treated as constant during backward gradient calcuation,
-    i.e. its gradient is zero.
-
-    :param inp: Input tensor.
-
-    See implementation of :func:`~.softmax` for example.
-    """
-    print("zero_grad is obsoleted, please use detach instead")
-    raise NotImplementedError
-
-
 def copy(inp, cn):
     r"""
     Copy tensor to another device.

imperative/python/megengine/module/activation.py

@@ -219,7 +219,7 @@ class LeakyReLU(Module):
 
     .. testoutput::
 
-        [-0.08   -0.12  6.   10.  ]
+        [-0.08 -0.12  6.   10.  ]
 
     """
 

imperative/python/megengine/module/batchnorm.py

@@ -267,15 +267,17 @@ class BatchNorm2d(_BatchNorm):
         m = M.BatchNorm2d(4)
         inp = mge.tensor(np.random.rand(1, 4, 3, 3).astype("float32"))
         oup = m(inp)
-        print(m.weight, m.bias)
+        print(m.weight.numpy(), m.bias.numpy())
         # Without Learnable Parameters
         m = M.BatchNorm2d(4, affine=False)
         oup = m(inp)
         print(m.weight, m.bias)
-
+    
+    Outputs:
+    
     .. testoutput::
 
-        Tensor([1. 1. 1. 1.]) Tensor([0. 0. 0. 0.])
+        [1. 1. 1. 1.] [0. 0. 0. 0.]
         None None
     """
 

imperative/python/megengine/module/sequential.py

@@ -17,23 +17,25 @@ class Sequential(Module):
     Alternatively, an ordered dict of modules can also be passed in.
 
     To make it easier to understand, here is a small example:
-
+    
+    Examples:
+    
     .. testcode::
 
         import numpy as np
-        import megengine.nn as nn
-        import megengine.nn.functional as F
+        from megengine import tensor
+        import megengine.functional as F
 
         batch_size = 64
-        data = nn.Input("data", shape=(batch_size, 1, 28, 28), dtype=np.float32, value=np.zeros((batch_size, 1, 28, 28)))
-        label = nn.Input("label", shape=(batch_size,), dtype=np.int32, value=np.zeros(batch_size,))
+        data = tensor(np.zeros((batch_size, 1, 28, 28)), dtype=np.float32)
+        label = tensor(np.zeros(batch_size,), dtype=np.int32)
 
         data = data.reshape(batch_size, -1)
-        net = nn.Sequential(
-                nn.Linear(28 * 28, 320),
-                nn.Linear(320, 500),
-                nn.Linear(500, 320),
-                nn.Linear(320, 10)
+        net = M.Sequential(
+                M.Linear(28 * 28, 320),
+                M.Linear(320, 500),
+                M.Linear(500, 320),
+                M.Linear(320, 10)
             )
         pred = net(data)
 

imperative/python/megengine/random/distribution.py

@@ -37,7 +37,9 @@ def normal(
 
         x = rand.normal(mean=0, std=1, size=(2, 2))
         print(x.numpy())
-
+    
+    Outputs:
+    
     .. testoutput::
         :options: +SKIP
 
@@ -73,7 +75,9 @@ def uniform(
 
         x = rand.uniform(size=(2, 2))
         print(x.numpy())
-
+    
+    Outputs:
+    
     .. testoutput::
         :options: +SKIP