refactor(mge/functional): support tensor shape in interpolate and split

GitOrigin-RevId: 6430b64f010ea5d0ecb1caa59b6da0a1547552ae

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

@@ -31,7 +31,9 @@ def dtype_promotion(raw_inputs):
     ]
     inputs = [i for i in raw_inputs if hasattr(i, "dtype")]
     assert len(scalar_inputs + inputs) > 0
-    dtype = np.result_type(*inputs)
+    dtype = None
+    if len(inputs) > 0:
+        dtype = np.result_type(*inputs)
     dtype_all = np.result_type(*(inputs + scalar_inputs))
     assert (
         dtype != np.float64 and dtype != np.int64

imperative/python/megengine/functional/elemwise.py

@@ -10,8 +10,9 @@
 import functools
 
 from ..core.ops import builtin
-from ..core.tensor import utils
+from ..core.tensor import megbrain_graph, utils
 from ..core.tensor.core import apply
+from ..device import get_default_device
 from ..tensor import Tensor
 
 __all__ = [
@@ -76,11 +77,17 @@ __all__ = [
 
 def _elwise(*args, mode):
     op = builtin.Elemwise(mode=mode)
+    tensor_args = list(
+        filter(lambda x: isinstance(x, (Tensor, megbrain_graph.VarNode)), args)
+    )
+    if len(tensor_args) == 0:
+        dtype = utils.dtype_promotion(args)
+        first_arg = Tensor(args[0], dtype=dtype, device=get_default_device())
+        args = utils.convert_inputs(first_arg, *args[1:])
+    else:
+        args = utils.convert_inputs(*args)
     if mode in ("true_div", "exp", "pow", "log", "expm1", "log1p"):
-        args = tuple(
-            map(lambda x: x.astype("float32") if hasattr(x, "dtype") else x, args)
-        )
-    args = utils.convert_inputs(*args)
+        args = tuple(map(lambda x: x.astype("float32"), args))
     (result,) = apply(op, *args)
     return result
 

imperative/python/megengine/functional/nn.py

@@ -1126,11 +1126,8 @@ def interpolate(
     if mode == "LINEAR":
         inp = add_axis(inp, 3)
 
-    if not isinstance(inp.shape, inp.__class__):
-        if len(inp.shape) != 4:
-            raise ValueError(
-                "shape of input tensor must correspond to the operartion mode"
-            )
+    if inp.ndim != 4:
+        raise ValueError("shape of input tensor must correspond to the operartion mode")
 
     if size is None:
         if scale_factor is None:

imperative/python/megengine/functional/tensor.py

@@ -317,7 +317,7 @@ def split(inp, nsplits_or_sections, axis=0):
     def swapaxis(inp, src, dst):
         if src == dst:
             return inp
-        shape = [i for i in range(len(inp.shape))]
+        shape = [i for i in range(inp.ndim)]
         shape[src] = dst
         shape[dst] = src
         return inp.transpose(shape)
@@ -325,9 +325,11 @@ def split(inp, nsplits_or_sections, axis=0):
     inp = swapaxis(inp, 0, axis)
 
     if isinstance(nsplits_or_sections, int):
-        incr_step = math.ceil(inp.shape[0] / nsplits_or_sections)
-        while incr_step < inp.shape[0]:
-            sections.append(incr_step)
+        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

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

@@ -19,13 +19,13 @@ def test_abs():
         np.abs(np.array([-3.0, -4.0, -5.0], dtype=np.float32)),
     )
 
-    # assertTensorClose(F.abs(-3.0), np.abs(np.float32(-3.0)))
+    assertTensorClose(F.abs(-3.0).numpy(), np.abs(np.float32(-3.0)))
 
 
 def test_multiply():
-    # assertTensorClose(
-    #     F.mul(-3.0, -4.0), np.multiply(np.float32(-3.0), np.float32(-4.0))
-    # )
+    assertTensorClose(
+        F.mul(-3.0, -4.0).numpy(), np.multiply(np.float32(-3.0), np.float32(-4.0))
+    )
 
     assertTensorClose(
         F.mul(tensor([3.0, 4.0]), 4.0).numpy(),

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

@@ -194,9 +194,6 @@ def test_matmul():
 
 
 def test_interpolate():
-    if use_tensor_shape():  # XXX: please fix me
-        return
-
     def linear_interpolate():
         inp = tensor(np.arange(1, 3, dtype=np.float32).reshape(1, 1, 2))
 

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

@@ -125,8 +125,6 @@ def test_stack():
 
 
 def test_split():
-    if use_tensor_shape():  # XXX: please fix me
-        return
     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)