fix(mge/optimizer): allow lr to be 0

GitOrigin-RevId: dabd1fcc3390787988553abded8c572bfccf1957

imperative/python/megengine/optimizer/adadelta.py

@@ -63,7 +63,7 @@ class Adadelta(Optimizer):
         eps = param_group["eps"]
 
         def make_scalar(val):
-            return tensor(val)
+            return tensor(val, dtype="float32")
 
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor

imperative/python/megengine/optimizer/adagrad.py

@@ -62,7 +62,7 @@ class Adagrad(Optimizer):
         eps = param_group["eps"]
 
         def make_scalar(val):
-            return tensor(val)
+            return tensor(val, dtype="float32")
 
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor

imperative/python/megengine/optimizer/adam.py

@@ -61,7 +61,7 @@ class Adam(Optimizer):
         beta0, beta1 = param_group["betas"]
 
         def make_scalar(val):
-            return tensor(val)
+            return tensor(val, dtype="float32")
 
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor

imperative/python/megengine/optimizer/adamw.py

@@ -61,7 +61,7 @@ class AdamW(Optimizer):
         beta0, beta1 = param_group["betas"]
 
         def make_scalar(val):
-            return tensor(val)
+            return tensor(val, dtype="float32")
 
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor

imperative/python/megengine/optimizer/sgd.py

@@ -62,13 +62,13 @@ class SGD(Optimizer):
         # since `conver_inputs` is disabled for param updates,
         # scalar should be explicitly tansforred to tensor
 
-        _lr = tensor(lr)
-        _weight_decay = tensor(weight_decay)
-        _momentum = tensor(momentum)
+        _lr = tensor(lr, dtype="float32")
+        _weight_decay = tensor(weight_decay, dtype="float32")
+        _momentum = tensor(momentum, dtype="float32")
 
         inplace_mode = int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0"))
         if inplace_mode:
-            _neg_lr = tensor(-lr)
+            _neg_lr = tensor(-lr, dtype="float32")
             c1 = tensor([1.0])
 
         for param in param_group["params"]:

imperative/python/test/integration/test_trace_dump.py

@@ -133,12 +133,6 @@ def test_xornet_trace_dump():
 
     data = tensor(test_data.astype(np.float32))
     out = pred_fun(data)
-    pred_output = out.numpy()
-    pred_label = np.argmax(pred_output, 1)
-
-    with np.printoptions(precision=4, suppress=True):
-        print("Predicated probability:")
-        print(pred_output)
 
     with mkstemp() as out:
         pred_fun.dump(out, arg_names=["data"], output_names=["label"])