fix(mge/functional): fix tensor split

GitOrigin-RevId: 0a112ab0bd

imperative/python/megengine/functional/elemwise.py

@@ -158,7 +158,7 @@ def div(x, y):
 
 def floor_div(x, y):
     """Element-wise `floor(x / y)`."""
-    return _elwise(x, y, mode=Elemwise.Mode.FLOOR_DIVIDE)
+    return _elwise(x, y, mode=Elemwise.Mode.FLOOR_DIV)
 
 
 def neg(x):

imperative/python/megengine/functional/tensor.py

@@ -28,7 +28,7 @@ from ..core.tensor.utils import (
 )
 from ..device import get_default_device
 from ..tensor import Tensor
-from .elemwise import ceil
+from .elemwise import ceil, floor_div
 
 __all__ = [
     "arange",
@@ -324,52 +324,73 @@ def split(inp, nsplits_or_sections, axis=0):
 
     .. testcode::
 
+        import os
         import numpy as np
         from megengine import tensor
         import megengine.functional as F
 
-        x = tensor(np.random.random((2,3,4,5)), dtype=np.float32)
-        out = F.split(x, 2, axis=3)
-        print(out[0].numpy().shape, out[1].numpy().shape)
+        x = tensor(np.random.random((10, 20)), dtype=np.float32)
+        y = F.split(x, 3)
+        z = F.split(x, [6, 17], axis=1)
+        
+        if os.environ.get("MEGENGINE_USE_SYMBOLIC_SHAPE"):
+            print([tuple(i.shape.numpy().tolist()) for i in y])
+            print([tuple(i.shape.numpy().tolist()) for i in z])
+        else:
+            print([i.shape for i in y])
+            print([i.shape for i in z])
 
     Outputs:
 
     .. testoutput::
 
-        (2, 3, 4, 3) (2, 3, 4, 2)
+        [(4, 20), (3, 20), (3, 20)]
+        [(10, 6), (10, 11), (10, 3)]
 
     """
-    sub_tensors = []
-    sections = []
-
-    def swapaxis(inp, src, dst):
-        if src == dst:
-            return inp
-        shape = [i for i in range(inp.ndim)]
-        shape[src] = dst
-        shape[dst] = src
-        return inp.transpose(shape)
-
-    inp = swapaxis(inp, 0, axis)
-
-    if isinstance(nsplits_or_sections, int):
-        incr_step = ceil(inp.shape[0] / nsplits_or_sections)
-        nsplits = nsplits_or_sections
-        while nsplits > 0:
-            nsplits -= 1
-            sections.append(incr_step.astype("int32"))
-            incr_step += nsplits_or_sections
-    else:
-        sections = nsplits_or_sections
+    ndim = len(inp.shape)
+    if axis >= ndim:
+        raise ValueError("Invalid axis {}".format(axis))
 
-    st = 0
-    for se in sections:
-        sub_tensors.append(swapaxis(inp[st:se], axis, 0))
-        st = se
+    Ntotal = inp.shape[axis]
 
-    if st < inp.shape[0]:
-        sub_tensors.append(swapaxis(inp[st:], axis, 0))
+    try:
+        Nsections = len(nsplits_or_sections) + 1
+        is_array = True
+    except TypeError:
+        Nsections = int(nsplits_or_sections)
+        is_array = False
+
+    if is_array:
+        div_points = [0] + list(nsplits_or_sections) + [Ntotal]
+        for i in range(1, len(div_points)):
+            if div_points[i - 1] >= div_points[i]:
+                raise ValueError(
+                    "Invalid nsplits_or_secions: {}".format(nsplits_or_sections)
+                )
+    else:  # scalar
+        if Nsections <= 0:
+            raise ValueError("Number sections must be larger than 0")
+        if Nsections > Ntotal:
+            raise ValueError(
+                "The size {} at dim {} cannot be split into {} sections".format(
+                    Ntotal, axis, Nsections
+                )
+            )
+        div_points = [0] + [
+            floor_div(Ntotal + Nsections - i - 1, Nsections) for i in range(Nsections)
+        ]
+        for i in range(2, Nsections + 1):
+            div_points[i] = div_points[i - 1] + div_points[i]
 
+    sub_tensors = []
+    for i in range(Nsections):
+        l = div_points[i]
+        r = div_points[i + 1]
+        slices = tuple(
+            [slice(None)] * axis + [slice(l, r)] + [slice(None)] * (ndim - axis - 1)
+        )
+        sub_tensors.append(inp[slices])
     return sub_tensors
 
 

imperative/python/test/unit/functional/test_tensor.py

@@ -77,14 +77,34 @@ def test_stack():
 
 def test_split():
     data = np.random.random((2, 3, 4, 5)).astype(np.float32)
-    mge_out1 = F.split(tensor(data), 2, axis=3)
-    mge_out2 = F.split(tensor(data), [3, 5], axis=3)
+    inp = tensor(data)
+
+    mge_out0 = F.split(inp, 2, axis=3)
+    mge_out1 = F.split(inp, [3], axis=3)
 
     np_out = np.split(data, [3, 5], axis=3)
 
-    np.testing.assert_equal(mge_out1[0].numpy(), mge_out2[0].numpy())
+    assert len(mge_out0) == 2
+    assert len(mge_out1) == 2
+
+    np.testing.assert_equal(mge_out0[0].numpy(), np_out[0])
     np.testing.assert_equal(mge_out1[0].numpy(), np_out[0])
 
+    np.testing.assert_equal(mge_out0[1].numpy(), np_out[1])
+    np.testing.assert_equal(mge_out1[1].numpy(), np_out[1])
+
+    try:
+        F.split(inp, 4)
+        assert False
+    except ValueError as e:
+        pass
+
+    try:
+        F.split(inp, [3, 3, 5], axis=3)
+        assert False
+    except ValueError as e:
+        assert str(e) == "Invalid nsplits_or_secions: [3, 3, 5]"
+
 
 def test_reshape():
     x = np.arange(6, dtype="float32")