fix(imperative): fix inplace operation of optim

GitOrigin-RevId: 2aaa71eb66c1096d117ed70d2cadae3f85e32ab6

imperative/python/megengine/optimizer/optimizer.py

@@ -97,7 +97,7 @@ class Optimizer(metaclass=ABCMeta):
                     "optimizer can only optimize Parameters, but one of the params is "
                     + str(type(param))
                 )
-            param[...] = Tensor(param.numpy(), no_cache=True)
+            param[...] = Tensor(param, no_cache=True)
 
         for name, default in self._defaults.items():
             if default is required and name not in param_group:

imperative/python/test/unit/amp/test_convert_format.py

 import numpy as np
 import pytest
 
+import megengine as mge
+import megengine.autodiff as autodiff
 import megengine.functional as F
 import megengine.module as M
+import megengine.optimizer as optim
 from megengine import Parameter, Tensor, amp
 from megengine.core._config import set_auto_format_convert
 from megengine.core._trace_option import use_symbolic_shape
@@ -57,3 +60,42 @@ def test_convert_module(is_inplace):
             )
         else:
             assert param.shape == expected_shape[name], name
+
+
+class Module(M.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = M.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn = M.BatchNorm2d(16)
+
+    def forward(self, x):
+        out = F.relu(self.bn(self.conv(x)))
+        return out
+
+
+def test_format_remained():
+    m = Module()
+
+    m = amp.convert_module_format(m)
+
+    gm = autodiff.GradManager().attach(m.parameters())
+    opt = optim.SGD(m.parameters(), lr=0.01)
+    scaler = amp.GradScaler()
+
+    image = mge.tensor(np.random.normal(size=(1, 3, 224, 224)), dtype="float32")
+    label = mge.tensor(np.ones((1, 224, 224)), dtype="int32")
+
+    image = amp.convert_tensor_format(image)
+
+    @amp.autocast(enabled=True)
+    def train_step(image):
+        with gm:
+            logits = m(image)
+            loss = F.nn.cross_entropy(logits, label)
+            scaler.backward(gm, loss)
+        opt.step().clear_grad()
+        return logits
+
+    for _ in range(5):
+        res = train_step(image)
+        assert res.format == "nhwc"