fix_bug_in_check_lamb_warmup_step

mindspore/nn/optim/lamb.py

@@ -111,7 +111,6 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
 def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
                        end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
     """Check the type of inputs."""
-    _ = warmup_steps
     validator.check_float_positive('start_learning_rate', start_learning_rate, prim_name)
     validator.check_float_legal_value('start_learning_rate', start_learning_rate, prim_name)
     validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
@@ -119,7 +118,7 @@ def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
     validator.check_float_positive('power', power, prim_name)
     validator.check_float_legal_value('power', power, prim_name)
     validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
-    validator.check_integer('warmup_steps', decay_steps, 0, Rel.GT, prim_name)
+    validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GE, prim_name)
     validator.check_value_type("beta1", beta1, [float], prim_name)
     validator.check_value_type("beta2", beta2, [float], prim_name)
     validator.check_value_type("eps", eps, [float], prim_name)

tests/ut/python/nn/optim/test_lamb.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test lamb """
 import numpy as np
+import pytest
 
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
@@ -50,29 +51,27 @@ class NetWithoutWeight(nn.Cell):
         return x
 
 
-def test_lamb_1():
+def test_lamb_compile():
-    """ test_Lamb_1 """
+    """ test_Lamb_compile """
     inputs = Tensor(np.ones([1, 64]).astype(np.float32))
     label = Tensor(np.zeros([1, 10]).astype(np.float32))
     net = Net()
     net.set_train()
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = Lamb(net.trainable_params(), decay_steps=10, warmup_steps=5)
+    optimizer = Lamb(net.trainable_params(), decay_steps=10)
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
     _executor.compile(train_network, inputs, label)
 
 
-def test_lamb_2():
+def test_lamb_error():
-    """ test_Lamb_2 """
-    inputs = Tensor(np.ones([1, 64]).astype(np.float32))
-    label = Tensor(np.zeros([1, 10]).astype(np.float32))
     net = Net()
-    net.set_train()
+    with pytest.raises(TypeError):
-    loss = nn.SoftmaxCrossEntropyWithLogits()
+        Lamb(net.get_parameters(), decay_steps=6, warmup_steps=5.0)
-    optimizer = Lamb(net.trainable_params(), decay_steps=10, warmup_steps=0)
 
-    net_with_loss = WithLossCell(net, loss)
+    with pytest.raises(TypeError):
-    train_network = TrainOneStepCell(net_with_loss, optimizer)
+        Lamb(net.get_parameters(), decay_steps=1.0)
-    _executor.compile(train_network, inputs, label)
+
+    with pytest.raises(ValueError):
+        Lamb(net.get_parameters(), decay_steps=0)