mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-12-11 06:55:27 +00:00
856 lines
32 KiB
Python
856 lines
32 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey)
|
|
#
|
|
# See ../LICENSE for clarification regarding multiple authors
|
|
#
|
|
# 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.
|
|
|
|
from collections import defaultdict
|
|
from typing import List, Optional, Union, Tuple, List
|
|
from lhotse.utils import fix_random_seed
|
|
import torch
|
|
from scaling import ActivationBalancer
|
|
import random
|
|
from torch import Tensor
|
|
from torch.optim import Optimizer
|
|
import logging
|
|
|
|
|
|
|
|
|
|
class ScaledAdam(Optimizer):
|
|
"""
|
|
Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
|
|
proportional to the norm of that parameter; and also learn the scale of the parameter,
|
|
in log space, subject to upper and lower limits (as if we had factored each parameter as
|
|
param = underlying_param * log_scale.exp())
|
|
|
|
|
|
Args:
|
|
params: The parameters or param_groups to optimize (like other Optimizer subclasses)
|
|
lr: The learning rate. We will typically use a learning rate schedule that starts
|
|
at 0.03 and decreases over time, i.e. much higher than other common
|
|
optimizers.
|
|
clipping_scale: (e.g. 2.0)
|
|
A scale for gradient-clipping: if specified, the normalized gradients
|
|
over the whole model will be clipped to have 2-norm equal to
|
|
`clipping_scale` times the median 2-norm over the most recent period
|
|
of `clipping_update_period` minibatches. By "normalized gradients",
|
|
we mean after multiplying by the rms parameter value for this tensor
|
|
[for non-scalars]; this is appropriate because our update is scaled
|
|
by this quantity.
|
|
betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
|
|
Must satisfy 0 < beta <= beta2 < 1.
|
|
scalar_lr_scale: A scaling factor on the learning rate, that we use to update the
|
|
scale of each parameter tensor and scalar parameters of the mode..
|
|
If each parameter were decomposed
|
|
as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
|
|
would be a the scaling factor on the learning rate of p_scale.
|
|
eps: A general-purpose epsilon to prevent division by zero
|
|
param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
|
|
learning the scale on the parameters (we'll constrain the rms of each non-scalar
|
|
parameter tensor to be >= this value)
|
|
param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
|
|
learning the scale on the parameters (we'll constrain the rms of each non-scalar
|
|
parameter tensor to be <= this value)
|
|
scalar_max: Maximum absolute value for scalar parameters (applicable if your
|
|
model has any parameters with numel() == 1).
|
|
size_update_period: The periodicity, in steps, with which we update the size (scale)
|
|
of the parameter tensor. This is provided to save a little time
|
|
in the update.
|
|
clipping_update_period: if clipping_scale is specified, this is the period
|
|
"""
|
|
def __init__(
|
|
self,
|
|
params,
|
|
lr=3e-02,
|
|
clipping_scale=None,
|
|
betas=(0.9, 0.98),
|
|
scalar_lr_scale=0.1,
|
|
eps=1.0e-08,
|
|
param_min_rms=1.0e-05,
|
|
param_max_rms=3.0,
|
|
scalar_max=2.0,
|
|
size_update_period=4,
|
|
clipping_update_period=100,
|
|
):
|
|
|
|
|
|
|
|
defaults = dict(
|
|
lr=lr,
|
|
clipping_scale=clipping_scale,
|
|
betas=betas,
|
|
scalar_lr_scale=scalar_lr_scale,
|
|
eps=eps,
|
|
param_min_rms=param_min_rms,
|
|
param_max_rms=param_max_rms,
|
|
scalar_max=scalar_max,
|
|
size_update_period=size_update_period,
|
|
clipping_update_period=clipping_update_period,
|
|
)
|
|
|
|
super(ScaledAdam, self).__init__(params, defaults)
|
|
|
|
def __setstate__(self, state):
|
|
super(ScaledAdam, self).__setstate__(state)
|
|
|
|
|
|
@torch.no_grad()
|
|
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:
|
|
with torch.enable_grad():
|
|
loss = closure()
|
|
|
|
batch = True
|
|
for group in self.param_groups:
|
|
|
|
|
|
for i,p in enumerate(group["params"]):
|
|
state = self.state[p]
|
|
|
|
# Perform optimization step
|
|
grad = p.grad
|
|
if grad.is_sparse:
|
|
raise RuntimeError(
|
|
"ScaledAdam optimizer does not support sparse gradients"
|
|
)
|
|
# State initialization
|
|
if len(state) == 0:
|
|
self._init_state(group, p, state)
|
|
|
|
if i == 0:
|
|
clipping_scale = self._get_clipping_scale(group, p, state)
|
|
|
|
self._step_one_batch(group, p, state, clipping_scale)
|
|
|
|
|
|
return loss
|
|
|
|
|
|
def _init_state(self,
|
|
group: dict,
|
|
p: Tensor,
|
|
state: dict):
|
|
"""
|
|
Initializes state dict for parameter 'p'.
|
|
|
|
Args:
|
|
group: Dict to look up configuration values.
|
|
p: The parameter that we are initializing the state for
|
|
state: Dict from string to whatever state we are initializing
|
|
"""
|
|
eps = group["eps"]
|
|
size_update_period = group["size_update_period"]
|
|
|
|
state["step"] = 0
|
|
|
|
kwargs = {'device':p.device, 'dtype':p.dtype}
|
|
|
|
# 'delta' implements conventional momentum. There are
|
|
# several different kinds of update going on, so rather than
|
|
# compute "exp_avg" like in Adam, we store and decay a
|
|
# parameter-change "delta", which combines all forms of
|
|
# update. this is equivalent to how it's done in Adam,
|
|
# except for the first few steps.
|
|
state["delta"] = torch.zeros_like(
|
|
p, memory_format=torch.preserve_format
|
|
)
|
|
|
|
numel = p.numel()
|
|
|
|
if numel > 1:
|
|
# "param_rms" just periodically records the scalar root-mean-square value of
|
|
# the parameter tensor.
|
|
param_rms = (p**2).mean().sqrt() + eps
|
|
state["param_rms"] = param_rms
|
|
|
|
state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
|
|
state["scale_grads"] = torch.zeros(size_update_period, **kwargs)
|
|
|
|
|
|
# exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
|
|
state["exp_avg_sq"] = torch.zeros_like(
|
|
p, memory_format=torch.preserve_format
|
|
)
|
|
|
|
def _get_clipping_scale(self,
|
|
group: dict,
|
|
p: Tensor,
|
|
state: dict) -> float:
|
|
"""
|
|
Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
|
|
by this amount before applying the rest of the update.
|
|
This function is only to be called for the first parameter in the group.
|
|
"""
|
|
clipping_scale = group["clipping_scale"]
|
|
step = state["step"]
|
|
if clipping_scale is None or step == 0:
|
|
# no clipping. return early on step == 0 because the other
|
|
# parameters' state won't have been initialize yet.
|
|
return 1.0
|
|
clipping_update_period = group["clipping_update_period"]
|
|
|
|
tot_sumsq = torch.tensor(0.0, device=p.device)
|
|
for p in group["params"]:
|
|
state = self.state[p]
|
|
grad = p.grad
|
|
if grad.is_sparse:
|
|
raise RuntimeError(
|
|
"ScaledAdam optimizer does not support sparse gradients"
|
|
)
|
|
if p.numel() == 1:
|
|
tot_sumsq += (grad**2).sum() # sum() to change shape [1] to []
|
|
else:
|
|
tot_sumsq += (grad**2).sum() * (state["param_rms"] ** 2)
|
|
tot_norm = tot_sumsq.sqrt()
|
|
if not "model_norms" in state:
|
|
state["model_norms"] = torch.zeros(clipping_update_period,
|
|
device=p.device)
|
|
state["model_norms"][step % clipping_update_period] = tot_norm
|
|
|
|
if step % clipping_update_period == 0:
|
|
# We don't reach here if step == 0 because we
|
|
# would have returned above.
|
|
sorted_norms = state["model_norms"].sort()[0].to('cpu')
|
|
quartiles = []
|
|
for n in range(0, 5):
|
|
index = min(clipping_update_period - 1,
|
|
(clipping_update_period // 4) * n)
|
|
quartiles.append(sorted_norms[index].item())
|
|
|
|
median = quartiles[2]
|
|
threshold = clipping_scale * median
|
|
state["model_norm_threshold"] = threshold
|
|
percent_clipped = (state["num_clipped"] * 100.0 / clipping_update_period
|
|
if "num_clipped" in state else 0.0)
|
|
state["num_clipped"] = 0
|
|
quartiles = ' '.join([ '%.3e' % x for x in quartiles ])
|
|
logging.info(f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
|
|
f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}")
|
|
|
|
if step < clipping_update_period:
|
|
return 1.0 # We have not yet estimated a norm to clip to.
|
|
else:
|
|
model_norm_threshold = state["model_norm_threshold"]
|
|
ans = min(1.0,
|
|
(model_norm_threshold / (tot_norm + 1.0e-20)).item())
|
|
if ans < 1.0:
|
|
state["num_clipped"] += 1
|
|
if ans < 0.1:
|
|
logging.warn("Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}")
|
|
return ans
|
|
|
|
|
|
def _step_one_batch(self,
|
|
group: dict,
|
|
p: Tensor,
|
|
state: dict,
|
|
clipping_scale: float):
|
|
"""
|
|
Do the step for parameter p.
|
|
Args:
|
|
group: dict to look up configuration values
|
|
p: parameter to update
|
|
state: state-dict for p, to look up the optimizer state
|
|
"""
|
|
lr = group["lr"]
|
|
size_update_period = group["size_update_period"]
|
|
beta1 = group["betas"][0]
|
|
|
|
grad = p.grad
|
|
if clipping_scale != 1.0:
|
|
grad = grad * clipping_scale
|
|
step = state["step"]
|
|
delta = state["delta"]
|
|
|
|
delta.mul_(beta1)
|
|
numel = p.numel()
|
|
if numel > 1:
|
|
# Update the size/scale of p, and set param_rms
|
|
scale_grads = state["scale_grads"]
|
|
scale_grads[step % size_update_period] = (p * grad).sum()
|
|
if step % size_update_period == size_update_period - 1:
|
|
param_rms = state["param_rms"]
|
|
param_rms.copy_((p**2).mean().sqrt())
|
|
if step > 0:
|
|
# self._size_update() learns the overall scale on the
|
|
# parameter, by shrinking or expanding it.
|
|
self._size_update(group, scale_grads, p, state)
|
|
|
|
|
|
if numel == 1:
|
|
# For parameters with 1 element we just use regular Adam.
|
|
# Updates delta.
|
|
self._step_scalar(group, p, state)
|
|
else:
|
|
self._step(group, p, state)
|
|
|
|
state["step"] = step + 1
|
|
|
|
|
|
def _size_update(self,
|
|
group: dict,
|
|
scale_grads: Tensor,
|
|
p: Tensor,
|
|
state: dict) -> None:
|
|
"""
|
|
Called only where p.numel() > 1, this updates the scale of the parameter.
|
|
If we imagine: p = underlying_param * scale.exp(), and we are doing
|
|
gradient descent on underlying param and on scale, this function does the update
|
|
on `scale`.
|
|
|
|
Args:
|
|
group: dict to look up configuration values
|
|
scale_grads: a tensor of shape (size_update_period) containing
|
|
grads w.r.t. the scales.
|
|
p: The parameter to update
|
|
state: The state-dict of p
|
|
"""
|
|
|
|
param_rms = state["param_rms"]
|
|
beta1, beta2 = group["betas"]
|
|
size_lr = group["lr"] * group["scalar_lr_scale"]
|
|
param_min_rms = group["param_min_rms"]
|
|
param_max_rms = group["param_max_rms"]
|
|
eps = group["eps"]
|
|
step = state["step"]
|
|
|
|
size_update_period = scale_grads.shape[0]
|
|
# correct beta2 for the size update period: we will have
|
|
# faster decay at this level.
|
|
beta2_corr = beta2 ** size_update_period
|
|
|
|
scale_exp_avg_sq = state["scale_exp_avg_sq"] # scalar
|
|
scale_exp_avg_sq.mul_(beta2_corr).add_(
|
|
(scale_grads ** 2).mean(), # mean over `size_update_period` elements
|
|
alpha=1-beta2_corr)
|
|
|
|
# The 1st time we reach here is when size_step == 1.
|
|
size_step = (step + 1) // size_update_period
|
|
bias_correction2 = 1 - beta2_corr ** size_step
|
|
# we don't bother with bias_correction1; this will help prevent divergence
|
|
# at the start of training.
|
|
|
|
denom = scale_exp_avg_sq.sqrt() + eps
|
|
|
|
scale_step = -size_lr * (bias_correction2 ** 0.5) * scale_grads.sum() / denom
|
|
|
|
is_too_small = (param_rms < param_min_rms)
|
|
is_too_large = (param_rms > param_max_rms)
|
|
|
|
# when the param gets too small, just don't shrink it any further.
|
|
scale_step.masked_fill_(is_too_small, 0.0)
|
|
# when it gets too large, stop it from getting any larger.
|
|
scale_step.masked_fill_(is_too_large, -size_lr * size_update_period)
|
|
delta = state["delta"]
|
|
# the factor of (1-beta1) relates to momentum.
|
|
delta.add_(p * scale_step, alpha=(1-beta1))
|
|
|
|
|
|
def _step(self,
|
|
group: dict,
|
|
p: Tensor,
|
|
state: dict):
|
|
"""
|
|
This function does the core update of self.step(), in the case where the tensor
|
|
has more than 1 elements.
|
|
|
|
Args:
|
|
group: A dict which will be used to look up configuration values
|
|
p: The parameter to be updated
|
|
grad: The grad of p
|
|
state: The state-dict corresponding to parameter p
|
|
|
|
This function modifies p.
|
|
"""
|
|
grad = p.grad
|
|
lr = group["lr"]
|
|
beta1, beta2 = group["betas"]
|
|
eps = group["eps"]
|
|
param_min_rms = group["param_min_rms"]
|
|
step = state["step"]
|
|
|
|
|
|
exp_avg_sq = state["exp_avg_sq"]
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad,
|
|
value=(1-beta2))
|
|
|
|
this_step = state["step"] - (state["zero_step"] if "zero_step" in state else 0)
|
|
bias_correction2 = 1 - beta2 ** (this_step + 1)
|
|
if bias_correction2 < 0.99:
|
|
# note: not in-place.
|
|
exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
|
|
|
|
denom = exp_avg_sq.sqrt()
|
|
denom += eps
|
|
grad = grad / denom
|
|
|
|
alpha = -lr * (1-beta1) * state["param_rms"].clamp(min=param_min_rms)
|
|
|
|
delta = state["delta"]
|
|
delta.add_(grad * alpha)
|
|
p.add_(delta)
|
|
|
|
|
|
def _step_scalar(self,
|
|
group: dict,
|
|
p: Tensor,
|
|
state: dict):
|
|
"""
|
|
A simplified form of the core update for scalar tensors, where we cannot get a good
|
|
estimate of the parameter rms.
|
|
"""
|
|
beta1, beta2 = group["betas"]
|
|
scalar_max = group["scalar_max"]
|
|
eps = group["eps"]
|
|
lr = group["lr"] * group["scalar_lr_scale"]
|
|
grad = p.grad
|
|
|
|
exp_avg_sq = state["exp_avg_sq"] # scalar
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad,
|
|
value=1-beta2)
|
|
|
|
# bias_correction2 is like in Adam. Don't bother with bias_correction1;
|
|
# slower update at the start will help stability anyway.
|
|
bias_correction2 = 1 - beta2 ** (state["step"] + 1)
|
|
denom = (exp_avg_sq / bias_correction2).sqrt() + eps
|
|
|
|
delta = state["delta"]
|
|
delta.add_(grad / denom, alpha=-lr*(1-beta1))
|
|
p.clamp_(min=-scalar_max, max=scalar_max)
|
|
p.add_(delta)
|
|
|
|
|
|
|
|
class LRScheduler(object):
|
|
"""
|
|
Base-class for learning rate schedulers where the learning-rate depends on both the
|
|
batch and the epoch.
|
|
"""
|
|
|
|
def __init__(self, optimizer: Optimizer, verbose: bool = False):
|
|
# Attach optimizer
|
|
if not isinstance(optimizer, Optimizer):
|
|
raise TypeError(
|
|
"{} is not an Optimizer".format(type(optimizer).__name__)
|
|
)
|
|
self.optimizer = optimizer
|
|
self.verbose = verbose
|
|
|
|
for group in optimizer.param_groups:
|
|
group.setdefault("initial_lr", group["lr"])
|
|
|
|
self.base_lrs = [
|
|
group["initial_lr"] for group in optimizer.param_groups
|
|
]
|
|
|
|
self.epoch = 0
|
|
self.batch = 0
|
|
|
|
def state_dict(self):
|
|
"""Returns the state of the scheduler as a :class:`dict`.
|
|
|
|
It contains an entry for every variable in self.__dict__ which
|
|
is not the optimizer.
|
|
"""
|
|
return {
|
|
"base_lrs": self.base_lrs,
|
|
"epoch": self.epoch,
|
|
"batch": self.batch,
|
|
}
|
|
|
|
def load_state_dict(self, state_dict):
|
|
"""Loads the schedulers state.
|
|
|
|
Args:
|
|
state_dict (dict): scheduler state. Should be an object returned
|
|
from a call to :meth:`state_dict`.
|
|
"""
|
|
self.__dict__.update(state_dict)
|
|
|
|
def get_last_lr(self) -> List[float]:
|
|
"""Return last computed learning rate by current scheduler. Will be a list of float."""
|
|
return self._last_lr
|
|
|
|
def get_lr(self):
|
|
# Compute list of learning rates from self.epoch and self.batch and
|
|
# self.base_lrs; this must be overloaded by the user.
|
|
# e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
|
|
raise NotImplementedError
|
|
|
|
def step_batch(self, batch: Optional[int] = None) -> None:
|
|
# Step the batch index, or just set it. If `batch` is specified, it
|
|
# must be the batch index from the start of training, i.e. summed over
|
|
# all epochs.
|
|
# You can call this in any order; if you don't provide 'batch', it should
|
|
# of course be called once per batch.
|
|
if batch is not None:
|
|
self.batch = batch
|
|
else:
|
|
self.batch = self.batch + 1
|
|
self._set_lrs()
|
|
|
|
def step_epoch(self, epoch: Optional[int] = None):
|
|
# Step the epoch index, or just set it. If you provide the 'epoch' arg,
|
|
# you should call this at the start of the epoch; if you don't provide the 'epoch'
|
|
# arg, you should call it at the end of the epoch.
|
|
if epoch is not None:
|
|
self.epoch = epoch
|
|
else:
|
|
self.epoch = self.epoch + 1
|
|
self._set_lrs()
|
|
|
|
def _set_lrs(self):
|
|
values = self.get_lr()
|
|
assert len(values) == len(self.optimizer.param_groups)
|
|
|
|
for i, data in enumerate(zip(self.optimizer.param_groups, values)):
|
|
param_group, lr = data
|
|
param_group["lr"] = lr
|
|
self.print_lr(self.verbose, i, lr)
|
|
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
|
|
|
|
def print_lr(self, is_verbose, group, lr):
|
|
"""Display the current learning rate."""
|
|
if is_verbose:
|
|
logging.info(
|
|
f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
|
|
f" of group {group} to {lr:.4e}."
|
|
)
|
|
|
|
|
|
class Eden(LRScheduler):
|
|
"""
|
|
Eden scheduler.
|
|
lr = initial_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
|
|
(((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25))
|
|
|
|
E.g. suggest initial-lr = 0.003 (passed to optimizer).
|
|
|
|
Args:
|
|
optimizer: the optimizer to change the learning rates on
|
|
lr_batches: the number of batches after which we start significantly
|
|
decreasing the learning rate, suggest 5000.
|
|
lr_epochs: the number of epochs after which we start significantly
|
|
decreasing the learning rate, suggest 6 if you plan to do e.g.
|
|
20 to 40 epochs, but may need smaller number if dataset is huge
|
|
and you will do few epochs.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
optimizer: Optimizer,
|
|
lr_batches: Union[int, float],
|
|
lr_epochs: Union[int, float],
|
|
verbose: bool = False,
|
|
):
|
|
super(Eden, self).__init__(optimizer, verbose)
|
|
self.lr_batches = lr_batches
|
|
self.lr_epochs = lr_epochs
|
|
|
|
def get_lr(self):
|
|
factor = (
|
|
(self.batch ** 2 + self.lr_batches ** 2) / self.lr_batches ** 2
|
|
) ** -0.25 * (
|
|
((self.epoch ** 2 + self.lr_epochs ** 2) / self.lr_epochs ** 2)
|
|
** -0.25
|
|
)
|
|
return [x * factor for x in self.base_lrs]
|
|
|
|
|
|
def _test_eden():
|
|
m = torch.nn.Linear(100, 100)
|
|
optim = ScaledAdam(m.parameters(), lr=0.03)
|
|
|
|
scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
|
|
|
|
for epoch in range(10):
|
|
scheduler.step_epoch(epoch) # sets epoch to `epoch`
|
|
|
|
for step in range(20):
|
|
x = torch.randn(200, 100).detach()
|
|
x.requires_grad = True
|
|
y = m(x)
|
|
dy = torch.randn(200, 100).detach()
|
|
f = (y * dy).sum()
|
|
f.backward()
|
|
|
|
optim.step()
|
|
scheduler.step_batch()
|
|
optim.zero_grad()
|
|
|
|
logging.info(f"last lr = {scheduler.get_last_lr()}")
|
|
logging.info(f"state dict = {scheduler.state_dict()}")
|
|
|
|
|
|
# This is included mostly as a baseline for ScaledAdam.
|
|
class Eve(Optimizer):
|
|
"""
|
|
Implements Eve algorithm. This is a modified version of AdamW with a special
|
|
way of setting the weight-decay / shrinkage-factor, which is designed to make the
|
|
rms of the parameters approach a particular target_rms (default: 0.1). This is
|
|
for use with networks with 'scaled' versions of modules (see scaling.py), which
|
|
will be close to invariant to the absolute scale on the parameter matrix.
|
|
|
|
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
|
|
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
|
|
Eve is unpublished so far.
|
|
|
|
Arguments:
|
|
params (iterable): iterable of parameters to optimize or dicts defining
|
|
parameter groups
|
|
lr (float, optional): learning rate (default: 1e-3)
|
|
betas (Tuple[float, float], optional): coefficients used for computing
|
|
running averages of gradient and its square (default: (0.9, 0.999))
|
|
eps (float, optional): term added to the denominator to improve
|
|
numerical stability (default: 1e-8)
|
|
weight_decay (float, optional): weight decay coefficient (default: 3e-4;
|
|
this value means that the weight would decay significantly after
|
|
about 3k minibatches. Is not multiplied by learning rate, but
|
|
is conditional on RMS-value of parameter being > target_rms.
|
|
target_rms (float, optional): target root-mean-square value of
|
|
parameters, if they fall below this we will stop applying weight decay.
|
|
|
|
|
|
.. _Adam\: A Method for Stochastic Optimization:
|
|
https://arxiv.org/abs/1412.6980
|
|
.. _Decoupled Weight Decay Regularization:
|
|
https://arxiv.org/abs/1711.05101
|
|
.. _On the Convergence of Adam and Beyond:
|
|
https://openreview.net/forum?id=ryQu7f-RZ
|
|
"""
|
|
def __init__(
|
|
self,
|
|
params,
|
|
lr=1e-3,
|
|
betas=(0.9, 0.98),
|
|
eps=1e-8,
|
|
weight_decay=1e-3,
|
|
target_rms=0.1,
|
|
):
|
|
if not 0.0 <= lr:
|
|
raise ValueError("Invalid learning rate: {}".format(lr))
|
|
if not 0.0 <= eps:
|
|
raise ValueError("Invalid epsilon value: {}".format(eps))
|
|
if not 0.0 <= betas[0] < 1.0:
|
|
raise ValueError(
|
|
"Invalid beta parameter at index 0: {}".format(betas[0])
|
|
)
|
|
if not 0.0 <= betas[1] < 1.0:
|
|
raise ValueError(
|
|
"Invalid beta parameter at index 1: {}".format(betas[1])
|
|
)
|
|
if not 0 <= weight_decay <= 0.1:
|
|
raise ValueError(
|
|
"Invalid weight_decay value: {}".format(weight_decay)
|
|
)
|
|
if not 0 < target_rms <= 10.0:
|
|
raise ValueError("Invalid target_rms value: {}".format(target_rms))
|
|
defaults = dict(
|
|
lr=lr,
|
|
betas=betas,
|
|
eps=eps,
|
|
weight_decay=weight_decay,
|
|
target_rms=target_rms,
|
|
)
|
|
super(Eve, self).__init__(params, defaults)
|
|
|
|
def __setstate__(self, state):
|
|
super(Eve, self).__setstate__(state)
|
|
|
|
@torch.no_grad()
|
|
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:
|
|
with torch.enable_grad():
|
|
loss = closure()
|
|
|
|
for group in self.param_groups:
|
|
for p in group["params"]:
|
|
if p.grad is None:
|
|
continue
|
|
|
|
# Perform optimization step
|
|
grad = p.grad
|
|
if grad.is_sparse:
|
|
raise RuntimeError(
|
|
"AdamW does not support sparse gradients"
|
|
)
|
|
|
|
state = self.state[p]
|
|
|
|
# State initialization
|
|
if len(state) == 0:
|
|
state["step"] = 0
|
|
# Exponential moving average of gradient values
|
|
state["exp_avg"] = torch.zeros_like(
|
|
p, memory_format=torch.preserve_format
|
|
)
|
|
# Exponential moving average of squared gradient values
|
|
state["exp_avg_sq"] = torch.zeros_like(
|
|
p, memory_format=torch.preserve_format
|
|
)
|
|
|
|
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
|
|
|
|
beta1, beta2 = group["betas"]
|
|
|
|
state["step"] += 1
|
|
bias_correction1 = 1 - beta1 ** state["step"]
|
|
bias_correction2 = 1 - beta2 ** state["step"]
|
|
|
|
# Decay the first and second moment running average coefficient
|
|
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
|
|
denom = (exp_avg_sq.sqrt() * (bias_correction2 ** -0.5)).add_(
|
|
group["eps"]
|
|
)
|
|
|
|
step_size = group["lr"] / bias_correction1
|
|
target_rms = group["target_rms"]
|
|
weight_decay = group["weight_decay"]
|
|
|
|
if p.numel() > 1:
|
|
# avoid applying this weight-decay on "scaling factors"
|
|
# (which are scalar).
|
|
is_above_target_rms = p.norm() > (
|
|
target_rms * (p.numel() ** 0.5)
|
|
)
|
|
p.mul_(1 - (weight_decay * is_above_target_rms))
|
|
|
|
p.addcdiv_(exp_avg, denom, value=-step_size)
|
|
|
|
if random.random() < 0.0005:
|
|
step = (exp_avg/denom) * step_size
|
|
logging.info(f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}")
|
|
|
|
|
|
return loss
|
|
|
|
|
|
def _test_scaled_adam(hidden_dim: int):
|
|
import timeit
|
|
from scaling import ScaledLinear
|
|
E = 100
|
|
B = 4
|
|
T = 2
|
|
logging.info("in test_eve_cain")
|
|
#device = torch.device('cuda')
|
|
device = torch.device('cpu')
|
|
dtype = torch.float32
|
|
|
|
fix_random_seed(42)
|
|
# these input_magnitudes and output_magnitudes are to test that
|
|
# Abel is working as we expect and is able to adjust scales of
|
|
# different dims differently.
|
|
input_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
|
|
output_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
|
|
|
|
for iter in [1, 0]:
|
|
fix_random_seed(42)
|
|
Linear = torch.nn.Linear if iter == 0 else ScaledLinear
|
|
|
|
m = torch.nn.Sequential(Linear(E, hidden_dim),
|
|
torch.nn.PReLU(),
|
|
Linear(hidden_dim, hidden_dim),
|
|
torch.nn.PReLU(),
|
|
Linear(hidden_dim, E),
|
|
).to(device)
|
|
|
|
train_pairs = [ (100.0 * torch.randn(B, T, E, device=device, dtype=dtype) * input_magnitudes,
|
|
torch.randn(B, T, E, device=device, dtype=dtype) * output_magnitudes) for _ in range(20) ]
|
|
|
|
if iter == 0: optim = Eve(m.parameters(), lr=0.003)
|
|
elif iter == 1: optim = ScaledAdam(m.parameters(), lr=0.03, clipping_scale=2.0)
|
|
scheduler = Eden(optim, lr_batches=200, lr_epochs=5, verbose=False)
|
|
|
|
|
|
start = timeit.default_timer()
|
|
avg_loss = 0.0
|
|
for epoch in range(180):
|
|
scheduler.step_epoch()
|
|
#if epoch == 100 and iter in [2,3]:
|
|
# optim.reset_speedup() # check it doesn't crash.
|
|
|
|
#if epoch == 130:
|
|
# opts = diagnostics.TensorDiagnosticOptions(
|
|
# 2 ** 22
|
|
# ) # allow 4 megabytes per sub-module
|
|
# diagnostic = diagnostics.attach_diagnostics(m, opts)
|
|
|
|
|
|
for n, (x,y) in enumerate(train_pairs):
|
|
y_out = m(x)
|
|
loss = ((y_out - y)**2).mean() * 100.0
|
|
if epoch == 0 and n == 0:
|
|
avg_loss = loss.item()
|
|
else:
|
|
avg_loss = 0.98 * avg_loss + 0.02 * loss.item()
|
|
if n == 0 and epoch % 5 == 0:
|
|
#norm1 = '%.2e' % (m[0].weight**2).mean().sqrt().item()
|
|
#norm1b = '%.2e' % (m[0].bias**2).mean().sqrt().item()
|
|
#norm2 = '%.2e' % (m[2].weight**2).mean().sqrt().item()
|
|
#norm2b = '%.2e' % (m[2].bias**2).mean().sqrt().item()
|
|
#scale1 = '%.2e' % (m[0].weight_scale.exp().item())
|
|
#scale1b = '%.2e' % (m[0].bias_scale.exp().item())
|
|
#scale2 = '%.2e' % (m[2].weight_scale.exp().item())
|
|
#scale2b = '%.2e' % (m[2].bias_scale.exp().item())
|
|
lr = scheduler.get_last_lr()[0]
|
|
logging.info(f"Iter {iter}, epoch {epoch}, batch {n}, avg_loss {avg_loss:.4g}, lr={lr:.4e}") #, norms={norm1,norm1b,norm2,norm2b}") # scales={scale1,scale1b,scale2,scale2b}
|
|
loss.log().backward()
|
|
optim.step()
|
|
optim.zero_grad()
|
|
scheduler.step_batch()
|
|
|
|
#diagnostic.print_diagnostics()
|
|
|
|
stop = timeit.default_timer()
|
|
logging.info(f"Iter={iter}, Time taken: {stop - start}")
|
|
|
|
logging.info(f"last lr = {scheduler.get_last_lr()}")
|
|
#logging.info("state dict = ", scheduler.state_dict())
|
|
#logging.info("optim state_dict = ", optim.state_dict())
|
|
logging.info(f"input_magnitudes = {input_magnitudes}")
|
|
logging.info(f"output_magnitudes = {output_magnitudes}")
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
torch.set_num_threads(1)
|
|
torch.set_num_interop_threads(1)
|
|
logging.getLogger().setLevel(logging.INFO)
|
|
import subprocess
|
|
s = subprocess.check_output("git status -uno .; git log -1; git diff HEAD .", shell=True)
|
|
logging.info(s)
|
|
import sys
|
|
if len(sys.argv) > 1:
|
|
hidden_dim = int(sys.argv[1])
|
|
else:
|
|
hidden_dim = 200
|
|
|
|
_test_scaled_adam(hidden_dim)
|
|
_test_eden()
|