test_learning_rate.py

# Tensorflow version 1.15.2
import re
import six
from six.moves import zip
import tensorflow.compat.v1 as tf
import pdb

def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps,
                        optimizer="adamw", poly_power=1.0, start_warmup_step=0):
    """Creates an optimizer training op."""
    global_step = tf.train.get_or_create_global_step()

    learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)

    # Implements linear decay of the learning rate.
    learning_rate = tf.train.polynomial_decay(
        learning_rate,
        global_step,
        num_train_steps,
        end_learning_rate=0.0,
        power=poly_power,
        cycle=False)

    # Implements linear warmup. I.e., if global_step - start_warmup_step <
    # num_warmup_steps, the learning rate will be
    # `(global_step - start_warmup_step)/num_warmup_steps * init_lr`.
    if num_warmup_steps:
        tf.logging.info("++++++ warmup starts at step " + str(start_warmup_step)
                        + ", for " + str(num_warmup_steps) + " steps ++++++")
        global_steps_int = tf.cast(global_step, tf.int32)
        start_warm_int = tf.constant(start_warmup_step, dtype=tf.int32)
        global_steps_int = global_steps_int - start_warm_int
        warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)

        global_steps_float = tf.cast(global_steps_int, tf.float32)
        warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)

        warmup_percent_done = global_steps_float / warmup_steps_float
        warmup_learning_rate = init_lr * warmup_percent_done

        is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
        learning_rate = (
            (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)

    # It is OK that you use this optimizer for finetuning, since this
    # is how the model was trained (note that the Adam m/v variables are NOT
    # loaded from init_checkpoint.)
    # It is OK to use AdamW in the finetuning even the model is trained by LAMB.
    # As report in the Bert pulic github, the learning rate for SQuAD 1.1 finetune
    # is 3e-5, 4e-5 or 5e-5. For LAMB, the users can use 3e-4, 4e-4,or 5e-4 for a
    # batch size of 64 in the finetune.
    if optimizer == "adamw":
        tf.logging.info("using adamw")
        optimizer = AdamWeightDecayOptimizer(
            learning_rate=learning_rate,
            weight_decay_rate=0.01,
            beta_1=0.9,
            beta_2=0.999,
            epsilon=1e-6,
            exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
    else:
        raise ValueError("Not supported optimizer: ", optimizer)

    tvars = tf.trainable_variables()
    grads = tf.gradients(loss, tvars)

    # This is how the model was pre-trained.
    (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)

    train_op = optimizer.apply_gradients(
        list(zip(grads, tvars)), global_step=global_step)

    # Normally the global step update is done inside of `apply_gradients`.
    # However, neither `AdamWeightDecayOptimizer` nor `LAMBOptimizer` do this.
    # But if you use a different optimizer, you should probably take this line
    # out.
    new_global_step = global_step + 1
    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
    return train_op, learning_rate

class AdamWeightDecayOptimizer(tf.train.Optimizer):
  """A basic Adam optimizer that includes "correct" L2 weight decay."""

  def __init__(self,
               learning_rate,
               weight_decay_rate=0.0,
               beta_1=0.9,
               beta_2=0.999,
               epsilon=1e-6,
               exclude_from_weight_decay=None,
               name="AdamWeightDecayOptimizer"):
    """Constructs a AdamWeightDecayOptimizer."""
    super(AdamWeightDecayOptimizer, self).__init__(False, name)

    self.learning_rate = learning_rate
    self.weight_decay_rate = weight_decay_rate
    self.beta_1 = beta_1
    self.beta_2 = beta_2
    self.epsilon = epsilon
    self.exclude_from_weight_decay = exclude_from_weight_decay

  def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    """See base class."""
    assignments = []
    for (grad, param) in grads_and_vars:
      if grad is None or param is None:
        continue

      param_name = self._get_variable_name(param.name)

      m = tf.get_variable(
          name=six.ensure_str(param_name) + "/adam_m",
          shape=param.shape.as_list(),
          dtype=tf.float32,
          trainable=False,
          initializer=tf.zeros_initializer())
      v = tf.get_variable(
          name=six.ensure_str(param_name) + "/adam_v",
          shape=param.shape.as_list(),
          dtype=tf.float32,
          trainable=False,
          initializer=tf.zeros_initializer())

      # Standard Adam update.
      next_m = (
          tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
      next_v = (
          tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
                                                    tf.square(grad)))

      update = next_m / (tf.sqrt(next_v) + self.epsilon)

      # Just adding the square of the weights to the loss function is *not*
      # the correct way of using L2 regularization/weight decay with Adam,
      # since that will interact with the m and v parameters in strange ways.
      #
      # Instead we want ot decay the weights in a manner that doesn't interact
      # with the m/v parameters. This is equivalent to adding the square
      # of the weights to the loss with plain (non-momentum) SGD.
      if self._do_use_weight_decay(param_name):
        update += self.weight_decay_rate * param

      update_with_lr = self.learning_rate * update

      next_param = param - update_with_lr

      assignments.extend(
          [param.assign(next_param),
           m.assign(next_m),
           v.assign(next_v)])
    return tf.group(*assignments, name=name)

  def _do_use_weight_decay(self, param_name):
    """Whether to use L2 weight decay for `param_name`."""
    if not self.weight_decay_rate:
      return False
    if self.exclude_from_weight_decay:
      for r in self.exclude_from_weight_decay:
        if re.search(r, param_name) is not None:
          return False
    return True

  def _get_variable_name(self, param_name):
    """Get the variable name from the tensor name."""
    m = re.match("^(.*):\\d+$", six.ensure_str(param_name))
    if m is not None:
      param_name = m.group(1)
    return param_name

# Test the optimizer and plot the learning rate per step
import matplotlib.pyplot as plt

# Set up the optimizer
num_train_steps = 10000
num_warmup_steps = 200
init_lr = 2.0e-5

loss = tf.Variable(0.0, name='dummy_loss')

# Create the optimizer
train_op, learning_rate_tensor = create_optimizer(loss=loss, init_lr=init_lr, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, optimizer="adamw", poly_power=1.0, start_warmup_step=0)
# Create a session
sess = tf.Session()

# Initialize variables
sess.run(tf.global_variables_initializer())

# Run the optimizer for each step and track the learning rate
lrs = []
for i in range(num_train_steps):
    _, lr = sess.run([train_op, learning_rate_tensor])
    lrs.append(lr)

# save lrs to file
with open(f'lrs_init_lr_{init_lr}_num_train_steps_{num_train_steps}_num_warmup_steps_{num_warmup_steps}.txt', 'w') as f:
    for item in lrs:
        f.write("%s\n" % item)

# Plot the learning rate
plt.plot(lrs)
plt.xlabel("Step")
plt.ylabel("Learning rate")
plt.title(f"Learning rate schedule. \n Init LR: {init_lr}, Num train steps: {num_train_steps}, Num warmup steps: {num_warmup_steps}")
# grid lines
plt.grid(True)

# save plot to file
plt.savefig(f'plots/learning_rate_schedule_init_lr_{init_lr}_num_train_steps_{num_train_steps}_num_warmup_steps_{num_warmup_steps}.png', bbox_inches='tight', pad_inches=0.1, format='png', dpi=300)
plt.show()