feat(functional): let advance indexing support empty tensor and add more tests

GitOrigin-RevId: 49e1492934813caf4e491a901610b95439bac236

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

@@ -176,6 +176,8 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
         def is_bool_list(x):
             if not isinstance(x, list):
                 return False
+            if len(x) == 0:
+                return False
             for i in x:
                 if not isinstance(i, bool):
                     return False
@@ -246,17 +248,6 @@ def getitem(tensor, index):
     if len(try_result) == 2:
         return try_result[0]
     tensor, tensors, items, use_subtensor, ret_scalar = unpack_getitem(tensor, index)
-    for v in tensors:
-        if v.shape is None:
-            break
-        if isinstance(v.shape, v.__class__):
-            break
-        if len(v.shape) > 0 and v.shape[0] == 0:
-            (empty_tensor,) = Const([], dtype=tensor.dtype, device=tensor.device)(
-                tensor
-            )
-            return empty_tensor
-
     if use_subtensor:
         op = builtin.Subtensor(items=items)
     else:

imperative/python/test/unit/core/test_indexing_op.py

@@ -610,6 +610,25 @@ def test_subtensor_on_empty_tensor(symbolic):
     run_test(lambda x: x[100:200, 300:400, 500:600])
 
 
+@pytest.mark.parametrize("symbolic", [True, False, None])
+def test_indexingMultiAxisVec_on_empty_tensor(symbolic):
+    np_x = np.array([], dtype=np.float32).reshape(10, 10, 0)
+    mge_x = megengine.tensor(np_x)
+
+    def run_test(fn):
+        out_ref = fn(np_x)
+        if symbolic is not None:
+            fn = jit.trace(symbolic=symbolic)(fn)
+        for i in range(3):
+            out = fn(mge_x)
+            np.testing.assert_equal(out.numpy(), out_ref)
+
+    run_test(lambda x: x[[1, 2, 3]])
+    run_test(lambda x: x[[1, 2, 3], [4, 5, 6]])
+    run_test(lambda x: x[[]])
+    run_test(lambda x: x[[], [], []])
+
+
 @pytest.mark.parametrize("symbolic", [True, False, None])
 def test_setsubtensor_on_empty_tensor(symbolic):
     def run_test(inp_shp, fn):
@@ -655,3 +674,39 @@ def test_setsubtensor_on_empty_tensor(symbolic):
     run_test((10, 10, 10), test4)
     run_test((10, 10, 10), test5)
     run_test((10, 10, 10), test6)
+
+
+@pytest.mark.parametrize("symbolic", [True, False, None])
+def test_indexingSetMultiAxisVec_on_empty_tensor(symbolic):
+    def run_test(inp_shp, fn):
+        np_x = np.random.randn(*inp_shp).astype(np.float32)
+        mge_x = megengine.tensor(np_x)
+        out_ref = fn(np_x)
+        if symbolic is not None:
+            fn = jit.trace(symbolic=symbolic)(fn)
+        for i in range(3):
+            out = fn(mge_x)
+            np.testing.assert_equal(out.numpy(), out_ref)
+
+    def test1(x):
+        x[[1, 2, 3]] = x[[1, 2, 3]]
+        return x
+
+    def test2(x):
+        x[[1, 2, 3], [1, 2, 3]] = x[[1, 2, 3], [1, 2, 3]]
+        return x
+
+    def test3(x):
+        x[[]] = x[[]]
+        return x
+
+    def test4(x):
+        x[[], [], []] = x[[], [], []]
+        return x
+
+    run_test((10, 10, 0), test1)
+    run_test((10, 10, 0), test2)
+    run_test((10, 10, 0), test3)
+    run_test((10, 10, 0), test4)
+    run_test((10, 10, 10), test3)
+    run_test((10, 10, 10), test4)

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

@@ -860,8 +860,8 @@ def test_condtake():
     np.testing.assert_equal(idx.numpy(), np.where(y.reshape(-1))[0])
 
 
-# @pytest.mark.parametrize("is_symbolic", [None, False, True])
-def test_condtake(is_symbolic=None):
+@pytest.mark.parametrize("is_symbolic", [None, False, True])
+def test_condtake(is_symbolic):
     shapes = [
         (3, 3, 3),
         (0,),

src/opr/impl/indexing.cpp

@@ -292,8 +292,6 @@ cg::OperatorNodeBase::NodeProp*
 IndexingMultiAxisVecBase<Opr>::do_make_node_prop() const {
     auto prop = Super::do_make_node_prop();
     using DT = NodeProp::DepType;
-    // TODO: should also allow input shape is empty if any
-    // indexer's shape is empty
     prop->add_dep_type_existing_var(input(0), DT::VALUE_ALLOW_EMPTY);
     for (auto i: m_input2idxonly_axis_indexer) {
         if (i) {