Upload ZEN/optimization.py

ZEN/optimization.py

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+# coding=utf-8
+# This file is derived from the code at
+# https://github.com/huggingface/transformers/blob/master/transformers/optimization.py
+#
+# Original copyright notice:
+#
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch optimization for BERT model."""
+
+import math
+import torch
+from torch.optim import Optimizer
+from torch.optim.optimizer import required
+from torch.nn.utils import clip_grad_norm_
+import logging
+import abc
+import sys
+
+logger = logging.getLogger(__name__)
+
+if sys.version_info >= (3, 4):
+    ABC = abc.ABC
+else:
+    ABC = abc.ABCMeta('ABC', (), {})
+
+
+class _LRSchedule(ABC):
+    """ Parent of all LRSchedules here. """
+    warn_t_total = False  # is set to True for schedules where progressing beyond t_total steps doesn't make sense
+
+    def __init__(self, warmup=0.002, t_total=-1, **kw):
+        """
+        :param warmup:  what fraction of t_total steps will be used for linear warmup
+        :param t_total: how many training steps (updates) are planned
+        :param kw:
+        """
+        super(_LRSchedule, self).__init__(**kw)
+        if t_total < 0:
+            logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
+        if not 0.0 <= warmup < 1.0 and not warmup == -1:
+            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
+        warmup = max(warmup, 0.)
+        self.warmup, self.t_total = float(warmup), float(t_total)
+        self.warned_for_t_total_at_progress = -1
+
+    def get_lr(self, step, nowarn=False):
+        """
+        :param step:    which of t_total steps we're on
+        :param nowarn:  set to True to suppress warning regarding training beyond specified 't_total' steps
+        :return:        learning rate multiplier for current update
+        """
+        if self.t_total < 0:
+            return 1.
+        progress = float(step) / self.t_total
+        ret = self.get_lr_(progress)
+        # warning for exceeding t_total (only active with warmup_linear
+        if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
+            logger.warning(
+                "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
+                    .format(ret, self.__class__.__name__))
+            self.warned_for_t_total_at_progress = progress
+        # end warning
+        return ret
+
+    @abc.abstractmethod
+    def get_lr_(self, progress):
+        """
+        :param progress:    value between 0 and 1 (unless going beyond t_total steps) specifying training progress
+        :return:            learning rate multiplier for current update
+        """
+        return 1.
+
+
+class ConstantLR(_LRSchedule):
+    def get_lr_(self, progress):
+        return 1.
+
+
+class WarmupCosineSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
+    If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
+    """
+    warn_t_total = True
+
+    def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
+        """
+        :param warmup:      see LRSchedule
+        :param t_total:     see LRSchedule
+        :param cycles:      number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
+        :param kw:
+        """
+        super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
+        self.cycles = cycles
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)  # progress after warmup
+            return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
+
+
+class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
+    learning rate (with hard restarts).
+    """
+
+    def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+        super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
+        assert (cycles >= 1.)
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)  # progress after warmup
+            ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
+            return ret
+
+
+class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
+    """
+    All training progress is divided in `cycles` (default=1.) parts of equal length.
+    Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
+    followed by a learning rate decreasing from 1. to 0. following a cosine curve.
+    """
+
+    def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+        assert (warmup * cycles < 1.)
+        warmup = warmup * cycles if warmup >= 0 else warmup
+        super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles,
+                                                                     **kw)
+
+    def get_lr_(self, progress):
+        progress = progress * self.cycles % 1.
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)  # progress after warmup
+            ret = 0.5 * (1. + math.cos(math.pi * progress))
+            return ret
+
+
+class WarmupConstantSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Keeps learning rate equal to 1. after warmup.
+    """
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        return 1.
+
+
+class WarmupLinearSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
+    """
+    warn_t_total = True
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        return max((progress - 1.) / (self.warmup - 1.), 0.)
+
+
+SCHEDULES = {
+    None: ConstantLR,
+    "none": ConstantLR,
+    "warmup_cosine": WarmupCosineSchedule,
+    "warmup_constant": WarmupConstantSchedule,
+    "warmup_linear": WarmupLinearSchedule
+}
+
+
+class BertAdam(Optimizer):
+    """Implements BERT version of Adam algorithm with weight decay fix.
+    Params:
+        lr: learning rate
+        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
+        t_total: total number of training steps for the learning
+            rate schedule, -1  means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
+        schedule: schedule to use for the warmup (see above).
+            Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
+            If `None` or `'none'`, learning rate is always kept constant.
+            Default : `'warmup_linear'`
+        b1: Adams b1. Default: 0.9
+        b2: Adams b2. Default: 0.999
+        e: Adams epsilon. Default: 1e-6
+        weight_decay: Weight decay. Default: 0.01
+        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
+    """
+
+    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
+                 b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
+        if lr is not required and lr < 0.0:
+            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
+        if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
+            raise ValueError("Invalid schedule parameter: {}".format(schedule))
+        if not 0.0 <= b1 < 1.0:
+            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
+        if not 0.0 <= b2 < 1.0:
+            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
+        if not e >= 0.0:
+            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
+        # initialize schedule object
+        if not isinstance(schedule, _LRSchedule):
+            schedule_type = SCHEDULES[schedule]
+            schedule = schedule_type(warmup=warmup, t_total=t_total)
+        else:
+            if warmup != -1 or t_total != -1:
+                logger.warning(
+                    "warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
+                    "Please specify custom warmup and t_total in _LRSchedule object.")
+        defaults = dict(lr=lr, schedule=schedule,
+                        b1=b1, b2=b2, e=e, weight_decay=weight_decay,
+                        max_grad_norm=max_grad_norm)
+        super(BertAdam, self).__init__(params, defaults)
+
+    def get_lr(self):
+        lr = []
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                if len(state) == 0:
+                    return [0]
+                lr_scheduled = group['lr']
+                lr_scheduled *= group['schedule'].get_lr(state['step'])
+                lr.append(lr_scheduled)
+        return lr
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['next_m'] = torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['next_v'] = torch.zeros_like(p.data)
+
+                next_m, next_v = state['next_m'], state['next_v']
+                beta1, beta2 = group['b1'], group['b2']
+
+                # Add grad clipping
+                if group['max_grad_norm'] > 0:
+                    clip_grad_norm_(p, group['max_grad_norm'])
+
+                # Decay the first and second moment running average coefficient
+                # In-place operations to update the averages at the same time
+                next_m.mul_(beta1).add_(1 - beta1, grad)
+                next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                update = next_m / (next_v.sqrt() + group['e'])
+
+                # 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 to 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 group['weight_decay'] > 0.0:
+                    update += group['weight_decay'] * p.data
+
+                lr_scheduled = group['lr']
+                lr_scheduled *= group['schedule'].get_lr(state['step'])
+
+                update_with_lr = lr_scheduled * update
+                p.data.add_(-update_with_lr)
+
+                state['step'] += 1
+
+                # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+                # No bias correction
+                # bias_correction1 = 1 - beta1 ** state['step']
+                # bias_correction2 = 1 - beta2 ** state['step']
+
+        return loss