fix(mge/param_pack): fix param pack with just one param

GitOrigin-RevId: 0d28a12e594c43e42a7486857dace201da2a3dfc

python_module/megengine/module/parampack.py

@@ -82,7 +82,7 @@ class ParamPack(Module):
                 group = group[idx:]
                 if idx == 1:
                     # ignore param packs with only one item
-                    self._packed_params.append(params[0])
+                    self._packed_params.append(params[0]['tensor'])
                     self._grouped_params.append(params)
                     continue
 

python_module/test/integration/test_parampack.py

@@ -110,6 +110,42 @@ def test_static_graph_parampack():
     pred = infer(data).numpy()
     assert calculate_precision(data, pred) > 0.95, "Test precision must be high enough"
 
+@pytest.mark.slow
+def test_nopack_parampack():
+    net = XORNet()
+    net = ParamPack(net,
+                    max_size_per_group=0,
+                    max_nr_params_per_group=0)
+    opt = SGD(
+        net.parameters(requires_grad=True), lr=0.01, momentum=0.9, weight_decay=5e-4
+    )
+
+    @trace(symbolic=True)
+    def train(data, label):
+        pred = net(data)
+        opt.zero_grad()
+        loss = cross_entropy_with_softmax(pred, label)
+        opt.backward(loss)
+        return loss
+
+    @trace(symbolic=True)
+    def infer(data):
+        return net(data)
+
+    train_dataset = minibatch_generator()
+    losses = []
+
+    for data, label in itertools.islice(train_dataset, 2000):
+        loss = train(data, label)
+        loss = loss[0][0]
+        opt.step()
+        losses.append(loss.numpy())
+    assert np.mean(losses[-100:]) < 0.1, "Final training Loss must be low enough"
+
+    data, _ = next(train_dataset)
+    pred = infer(data).numpy()
+    assert calculate_precision(data, pred) > 0.95, "Test precision must be high enough"
+
 @pytest.mark.slow
 def test_dynamic_graph_parampack():
     net = XORNet()