feat(mge/functional): add arange in tensor.py

GitOrigin-RevId: ad88a4c18ecdfb8e4bb3a8da7497ac9f5a7e7343

python_module/megengine/functional/__init__.py

@@ -75,6 +75,7 @@ from .nn import (
 from .sort import argsort, sort, top_k
 from .tensor import (
     add_axis,
+    arange,
     broadcast_to,
     concat,
     dimshuffle,

python_module/megengine/functional/tensor.py

@@ -17,6 +17,7 @@ from megengine._internal import CompGraph, CompNode
 from ..core import zeros
 from ..core.graph import _use_default_if_none
 from ..core.tensor import Tensor, wrap_io_tensor
+from .elemwise import ceil
 from .utils import _decide_comp_node_and_comp_graph
 
 
@@ -553,6 +554,46 @@ def linspace(
     return ret.astype(dtype)
 
 
+def arange(
+    start: Union[int, float, Tensor],
+    end: Union[int, float, Tensor],
+    step: Union[int, float, Tensor] = 1,
+    dtype=np.float32,
+    device: Optional[CompNode] = None,
+    comp_graph: Optional[CompGraph] = None,
+) -> Tensor:
+    r"""
+    Returns a Tensor with values from `start` to `end` with adjacent interval `step`
+
+    :param start: starting value of the squence, shoule be scalar
+    :param end: ending value of the squence, shoule be scalar
+    :param step: the gap between each pair of adjacent values. Default 1
+    :param dtype: result data type
+    :return: The generated tensor
+
+    Examples:
+
+    .. testcode::
+
+        import numpy as np
+        import megengine.functional as F
+
+        a = F.arange(1, 5, 1)
+        print(a.numpy())
+
+    .. testoutput::
+
+        [1. 2. 3. 4.]
+
+    """
+    if dtype is not np.float32:
+        raise ValueError("arange is only implemented for float32")
+    num = ceil((end - start) / step)
+    stop = start + step * (num - 1)
+    ret = linspace(start, stop, num, device=device, comp_graph=comp_graph)
+    return ret
+
+
 def zeros_like(inp: Tensor) -> Tensor:
     r"""
     Returns a zero tensor with the same shape as input tensor

python_module/test/unit/functional/test_functional.py

@@ -157,6 +157,38 @@ def test_broadcast_to():
     opr_test(cases, F.broadcast_to, compare_fn=compare_fn)
 
 
+def test_arange():
+    cases = [
+        {"input": [1, 9, 1]},
+        {"input": [2, 10, 2]},
+    ]
+    opr_test(
+        cases,
+        F.arange,
+        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+    )
+
+    cases = [
+        {"input": [9, 1, -1]},
+        {"input": [10, 2, -2]},
+    ]
+    opr_test(
+        cases,
+        F.arange,
+        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+    )
+
+    cases = [
+        {"input": [9.3, 1.2, -0.5]},
+        {"input": [10.3, 2.1, -1.7]},
+    ]
+    opr_test(
+        cases,
+        F.arange,
+        ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+    )
+
+
 def test_add_update():
     shape = (2, 3)
     v = np.random.random(shape).astype(np.float32)