fix(mge/imperative): support broadcast with None

GitOrigin-RevId: dd330a2a1dc603ea52a655b350ee1a421015e7d7

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

@@ -99,7 +99,39 @@ def _transpose(data, axes):
 
 
 def _broadcast(inp, shape):
-    shape = astensor1d(shape, inp, dtype="int32", device=inp.device)
+    auto_infer = False
+    if isinstance(shape, (list, tuple)):
+        shape_tuple = list(shape)
+        for i, s in enumerate(shape_tuple):
+            if isinstance(s, type(None)):
+                if s is None:
+                    right = i - len(shape_tuple)
+                    inp_shape = inp._tuple_shape
+                    if len(inp_shape) + right >= 0:
+                        shape_tuple[right] = list(inp_shape)[right]
+                        auto_infer = True
+                        continue
+                    else:
+                        raise ValueError("invalided Broadcast shape")
+                else:
+                    raise ValueError(
+                        "expect shape[{}] >= 0 or use `None` or 'x' and 'X' to auto infer, got {}".format(
+                            i, s
+                        )
+                    )
+            if s < 0:
+                raise ValueError(
+                    "expect shape[{}] >= 0 or use `None` or 'x' and 'X' to auto infer, got {}".format(
+                        i, s
+                    )
+                )
+        if auto_infer:
+            shape = tuple(shape_tuple)
+    try:
+        shape_tuple = make_shape_tuple(shape)
+    except ValueError:
+        shape_tuple = shape
+    shape = astensor1d(shape_tuple, inp, dtype="int32", device=inp.device)
     (result,) = apply(builtin.Broadcast(), inp, shape)
     return result
 

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

@@ -223,6 +223,34 @@ def test_reshape(is_varnode):
         np.testing.assert_equal(yy.numpy(), y)
 
 
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_broadcast_auto_infer(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    x = np.random.random((1, 2, 3)).astype(np.float32)
+    xx = make_tensor(x, network)
+
+    for shape in [
+        (1, 2, 3),
+        (1, None, 3),
+    ]:
+        yy = F.broadcast_to(xx, shape)
+        np.testing.assert_equal(yy.numpy(), x)
+
+    with pytest.raises(ValueError):
+        F.broadcast_to(xx, (1, -1, 3))
+
+    with pytest.raises(ValueError):
+        F.broadcast_to(xx, (None, 1, 2, 3))
+
+    F.broadcast_to(xx, (1, None, 2, 3))
+    t = tensor(2, dtype=np.int32)
+    F.broadcast_to(xx, (t, None, 2, 3))
+
+
 @pytest.mark.parametrize("is_trace", [True, False])
 def test_reshape_on_empty_tensor(is_trace):
     input1_shape = (100, 0, 1)