384 lines
16 KiB
Python
384 lines
16 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functions and classes related to optimization (weight updates)."""
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import re
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import collections
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import tensorflow as tf
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import tensorflow_addons.optimizers as tfa_optimizers
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.training import training_ops
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from utils import log
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class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
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"""Applys a warmup schedule on a given learning rate decay schedule."""
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def __init__(self, initial_learning_rate, decay_schedule_fn, warmup_steps, power=1.0, name=None):
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super().__init__()
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self.initial_learning_rate = initial_learning_rate
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self.warmup_steps = warmup_steps
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self.power = power
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self.decay_schedule_fn = decay_schedule_fn
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self.name = name
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def __call__(self, step):
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with tf.name_scope(self.name or "WarmUp") as name:
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# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
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# learning rate will be `global_step/num_warmup_steps * init_lr`.
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global_step_float = tf.cast(step, tf.float32)
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warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
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warmup_percent_done = global_step_float / warmup_steps_float
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warmup_learning_rate = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power)
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return tf.cond(
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global_step_float < warmup_steps_float,
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lambda: warmup_learning_rate,
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lambda: self.decay_schedule_fn(step - self.warmup_steps),
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name=name,
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)
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def get_config(self):
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return {
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"initial_learning_rate": self.initial_learning_rate,
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"decay_schedule_fn": self.decay_schedule_fn,
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"warmup_steps": self.warmup_steps,
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"power": self.power,
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"name": self.name,
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}
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def create_optimizer(init_lr, num_train_steps, num_warmup_steps, weight_decay_rate=0.01,
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layerwise_lr_decay=-1, n_transformer_layers=None, clip_norm=1.0,
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optimizer="adam", skip_adaptive=False, power=1.0, beta_1=0.9, beta_2=0.999, end_lr=0.0):
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"""Creates an optimizer with learning rate schedule."""
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# Implements linear decay of the learning rate.
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learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
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initial_learning_rate=init_lr, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=end_lr, power=power
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)
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if num_warmup_steps:
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learning_rate_fn = WarmUp(
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initial_learning_rate=init_lr, decay_schedule_fn=learning_rate_fn, warmup_steps=num_warmup_steps
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)
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layer_decay = None
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if layerwise_lr_decay > 0 and n_transformer_layers is not None:
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layer_decay = _get_layer_decay(layerwise_lr_decay, n_transformer_layers)
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if optimizer == "adam":
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optimizer = AdamWeightDecay(
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learning_rate=learning_rate_fn,
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weight_decay_rate=weight_decay_rate,
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layer_decay=layer_decay,
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beta_1=beta_1,
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beta_2=beta_2,
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epsilon=1e-6,
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exclude_from_weight_decay=["layer_norm", "bias", "LayerNorm"],
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clip_norm=clip_norm,
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)
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else:
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if skip_adaptive:
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skip_list = ["layer_norm", "bias", "LayerNorm"]
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else:
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skip_list = ["None"]
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log("Skip list for LAMB {}".format(skip_list))
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optimizer = tfa_optimizers.LAMB(
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learning_rate=learning_rate_fn,
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weight_decay_rate=weight_decay_rate,
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beta_1=beta_1,
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beta_2=beta_2,
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epsilon=1e-6,
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exclude_from_weight_decay=["layer_norm", "bias", "LayerNorm"],
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exclude_from_layer_adaptation=skip_list,
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)
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return optimizer
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class AdamWeightDecay(tf.keras.optimizers.Adam):
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"""Adam enables L2 weight decay and clip_by_global_norm on gradients.
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Just adding the square of the weights to the loss function is *not* the
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correct way of using L2 regularization/weight decay with Adam, since that will
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interact with the m and v parameters in strange ways.
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Instead we want ot decay the weights in a manner that doesn't interact with
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the m/v parameters. This is equivalent to adding the square of the weights to
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the loss with plain (non-momentum) SGD.
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"""
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def __init__(
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self,
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learning_rate=0.001,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-7,
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amsgrad=False,
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weight_decay_rate=0.0,
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include_in_weight_decay=None,
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exclude_from_weight_decay=None,
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layer_decay=None,
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clip_norm=1.0,
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name="AdamWeightDecay",
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**kwargs
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):
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super().__init__(learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
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self.weight_decay_rate = weight_decay_rate
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self._include_in_weight_decay = include_in_weight_decay
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self._exclude_from_weight_decay = exclude_from_weight_decay
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self.layer_decay = layer_decay
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self.clip_norm = clip_norm
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@classmethod
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def from_config(cls, config):
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"""Creates an optimizer from its config with WarmUp custom object."""
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custom_objects = {"WarmUp": WarmUp}
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return super().from_config(config, custom_objects=custom_objects)
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super()._prepare_local(var_device, var_dtype, apply_state)
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apply_state["weight_decay_rate"] = tf.constant(self.weight_decay_rate, name="adam_weight_decay_rate")
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def _decay_weights_op(self, var, learning_rate, apply_state):
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do_decay = self._do_use_weight_decay(var.name)
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if do_decay:
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return var.assign_sub(
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learning_rate * var * apply_state["weight_decay_rate"], use_locking=self._use_locking
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)
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return tf.no_op()
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def apply_gradients(self, grads_and_vars, name=None, experimental_aggregate_gradients=True):
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grads, tvars = list(zip(*grads_and_vars))
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# Being done in train_step
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##(grads, _) = tf.clip_by_global_norm(grads, clip_norm=self.clip_norm)
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return super().apply_gradients(zip(grads, tvars), name=name,
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experimental_aggregate_gradients=experimental_aggregate_gradients)
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def _get_lr(self, var, apply_state):
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"""Retrieves the learning rate with the given state."""
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# if apply_state is None:
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# return self._decayed_lr_t[var_dtype], {}
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var_name, var_device, var_dtype = var.name, var.device, var.dtype.base_dtype
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apply_state = apply_state or {}
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coefficients = apply_state.get((var_device, var_dtype))
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if coefficients is None:
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coefficients = self._fallback_apply_state(var_device, var_dtype)
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apply_state[(var_device, var_dtype)] = coefficients
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lr_t = coefficients["lr_t"]
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lr = coefficients["lr"]
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if self.layer_decay is not None:
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update_for_var = False
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for key in self.layer_decay:
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if key in var_name:
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update_for_var = True
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lr_t *= self.layer_decay[key]
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lr *= self.layer_decay[key]
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break
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if not update_for_var:
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raise ValueError("No learning rate specified for variable", var)
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return lr_t, lr, coefficients, dict(apply_state=apply_state)
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def _resource_apply_dense(self, grad, var, apply_state=None):
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# print("Dense: {} {} {}".format(var.name, var.device, var.dtype.base_dtype))
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lr_t, _, coefficients, kwargs = self._get_lr(var, apply_state)
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decay = self._decay_weights_op(var, lr_t, apply_state)
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with tf.control_dependencies([decay]):
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m = self.get_slot(var, 'm')
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v = self.get_slot(var, 'v')
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if not self.amsgrad:
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return training_ops.resource_apply_adam(
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var.handle,
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m.handle,
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v.handle,
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coefficients['beta_1_power'],
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coefficients['beta_2_power'],
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lr_t,
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coefficients['beta_1_t'],
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coefficients['beta_2_t'],
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coefficients['epsilon'],
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grad,
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use_locking=self._use_locking)
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else:
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vhat = self.get_slot(var, 'vhat')
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return training_ops.resource_apply_adam_with_amsgrad(
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var.handle,
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m.handle,
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v.handle,
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vhat.handle,
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coefficients['beta_1_power'],
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coefficients['beta_2_power'],
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lr_t,
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coefficients['beta_1_t'],
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coefficients['beta_2_t'],
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coefficients['epsilon'],
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grad,
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use_locking=self._use_locking)
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def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
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# print("Sparse: {} {} {}".format(var.name, var.device, var.dtype.base_dtype))
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lr_t, lr, coefficients, kwargs = self._get_lr(var, apply_state)
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decay = self._decay_weights_op(var, lr_t, apply_state)
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with tf.control_dependencies([decay]):
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# m_t = beta1 * m + (1 - beta1) * g_t
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m = self.get_slot(var, 'm')
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m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
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m_t = state_ops.assign(m, m * coefficients['beta_1_t'],
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use_locking=self._use_locking)
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with tf.control_dependencies([m_t]):
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m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
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# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
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v = self.get_slot(var, 'v')
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v_scaled_g_values = (grad * grad) * coefficients['one_minus_beta_2_t']
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v_t = state_ops.assign(v, v * coefficients['beta_2_t'],
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use_locking=self._use_locking)
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with tf.control_dependencies([v_t]):
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v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
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if not self.amsgrad:
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v_sqrt = math_ops.sqrt(v_t)
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var_update = state_ops.assign_sub(
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var, lr * m_t / (v_sqrt + coefficients['epsilon']),
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use_locking=self._use_locking)
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return control_flow_ops.group(*[var_update, m_t, v_t])
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else:
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v_hat = self.get_slot(var, 'vhat')
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v_hat_t = math_ops.maximum(v_hat, v_t)
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with tf.control_dependencies([v_hat_t]):
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v_hat_t = state_ops.assign(
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v_hat, v_hat_t, use_locking=self._use_locking)
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v_hat_sqrt = math_ops.sqrt(v_hat_t)
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var_update = state_ops.assign_sub(
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var,
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lr * m_t / (v_hat_sqrt + coefficients['epsilon']),
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use_locking=self._use_locking)
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return control_flow_ops.group(*[var_update, m_t, v_t, v_hat_t])
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def get_config(self):
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config = super().get_config()
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config.update({"weight_decay_rate": self.weight_decay_rate})
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return config
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def _do_use_weight_decay(self, param_name):
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"""Whether to use L2 weight decay for `param_name`."""
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if self.weight_decay_rate == 0:
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return False
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if self._include_in_weight_decay:
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for r in self._include_in_weight_decay:
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if re.search(r, param_name) is not None:
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return True
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if self._exclude_from_weight_decay:
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for r in self._exclude_from_weight_decay:
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if re.search(r, param_name) is not None:
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return False
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return True
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# Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
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class GradientAccumulator(object):
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"""Distribution strategies-aware gradient accumulation utility."""
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def __init__(self):
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"""Initializes the accumulator."""
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self._gradients = []
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self._accum_steps = tf.Variable(
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initial_value=0, dtype=tf.int64, trainable=False, aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA
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)
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@property
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def step(self):
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"""Number of accumulated steps."""
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return self._accum_steps.value()
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@property
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def gradients(self):
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"""The accumulated gradients."""
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return list(
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gradient.value() if gradient is not None else gradient for gradient in self._get_replica_gradients()
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)
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def __call__(self, gradients):
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"""Accumulates :obj:`gradients`."""
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if not self._gradients:
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self._gradients.extend(
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[
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tf.Variable(tf.zeros_like(gradient), trainable=False) if gradient is not None else gradient
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for gradient in gradients
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]
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)
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if len(gradients) != len(self._gradients):
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raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients)))
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for accum_gradient, gradient in zip(self._get_replica_gradients(), gradients):
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if accum_gradient is not None and gradient is not None:
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accum_gradient.assign_add(gradient)
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self._accum_steps.assign_add(1)
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def reset(self):
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"""Resets the accumulated gradients."""
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if self._gradients:
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self._accum_steps.assign(0)
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for gradient in self._get_replica_gradients():
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if gradient is not None:
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gradient.assign(tf.zeros_like(gradient))
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def _get_replica_gradients(self):
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if tf.distribute.has_strategy():
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# In a replica context, we want to accumulate gradients on each replica
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# without synchronization, so we directly assign the value of the
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# current replica.
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replica_context = tf.distribute.get_replica_context()
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if replica_context is None or tf.distribute.get_strategy().num_replicas_in_sync == 1:
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return self._gradients
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return (
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gradient.device_map.select_for_current_replica(gradient.values, replica_context)
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for gradient in self._gradients
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if gradient is not None
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)
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else:
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return self._gradients
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def _get_layer_decay(layer_decay, n_layers):
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"""Have lower learning rates for layers closer to the input."""
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key_to_depths = collections.OrderedDict({
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"/embeddings/": 0,
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"/embeddings_project/": 0,
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"/start_logits/": n_layers + 2,
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"/end_logits/": n_layers + 2,
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"/answer_class/": n_layers + 2,
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"/qa_outputs/": n_layers + 2,
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})
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for layer in range(n_layers):
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key_to_depths["encoder/layer_._" + str(layer) + "/"] = layer + 1
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return {
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key: layer_decay ** (n_layers + 2 - depth)
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for key, depth in key_to_depths.items()
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}
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