Support new versions of tensorflow and numpy

Author: hankcs

hanlp/components/parsers/biaffine_parser_tf.py

@@ -200,11 +200,12 @@ def build_optimizer(self, optimizer='adam', lr=2e-3, mu=.9, nu=.9, epsilon=1e-12
             scheduler = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=lr,
                                                                        decay_steps=decay_steps,
                                                                        decay_rate=decay)
-            optimizer = tf.keras.optimizers.Adam(learning_rate=scheduler,
-                                                 beta_1=mu,
-                                                 beta_2=nu,
-                                                 epsilon=epsilon,
-                                                 clipnorm=clip)
+            from hanlp.optimizers.adamw.optimization import AdamTF
+            optimizer = AdamTF(learning_rate=scheduler,
+                               beta_1=mu,
+                               beta_2=nu,
+                               epsilon=epsilon,
+                               clipnorm=clip)
             return optimizer
         return super().build_optimizer(optimizer, **kwargs)
 

hanlp/optimizers/adamw/optimization.py

@@ -95,7 +95,13 @@ def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
     return optimizer
 
 
-class AdamWeightDecay(tf.keras.optimizers.Adam):
+try:
+    AdamTF = tf.keras.optimizers.legacy.Adam  # avoid slowdown when using v2.11+ Keras optimizers on M1/M2 Macs
+except:
+    AdamTF = tf.keras.optimizers.Adam
+
+
+class AdamWeightDecay(AdamTF):
     """Adam enables L2 weight decay and clip_by_global_norm on gradients.
     
       Just adding the square of the weights to the loss function is *not* the

hanlp/transform/conll_tf.py

@@ -736,7 +736,7 @@ def batched_inputs_to_batches(self, corpus, indices, shuffle):
         raw_batch = [[], [], [], []] if use_pos else [[], [], []]
         max_len = len(max([corpus[i] for i in indices], key=len))
         for idx in indices:
-            arc = np.zeros((max_len, max_len), dtype=np.bool)
+            arc = np.zeros((max_len, max_len), dtype=bool)
             rel = np.zeros((max_len, max_len), dtype=np.int64)
             for b in raw_batch[:2]:
                 b.append([])

hanlp/version.py

@@ -2,7 +2,7 @@
 # Author: hankcs
 # Date: 2019-12-28 19:26
 
-__version__ = '2.1.0-beta.59'
+__version__ = '2.1.0-beta.60'
 """HanLP version"""