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lr_finder.py
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lr_finder.py
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# Based on https://github.com/davidtvs/pytorch-lr-finder
# which is licensed as follows:
#
# MIT License
#
# Copyright (c) 2018 davidtvs
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import copy
import os
import sys
import tempfile
import torch
import numpy as np
from tqdm.autonotebook import tqdm
from torch.optim.lr_scheduler import _LRScheduler
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from packaging import version
PYTORCH_VERSION = version.parse(torch.__version__)
TQDM_OPTIONS = {
"file": sys.stdout,
"dynamic_ncols": True,
}
try:
from apex import amp
IS_AMP_AVAILABLE = True
except ImportError:
IS_AMP_AVAILABLE = False
class DataLoaderIter:
def __init__(self, data_loader):
self.data_loader = data_loader
self._iterator = iter(data_loader)
@property
def dataset(self):
return self.data_loader.dataset
@staticmethod
def inputs_labels_from_batch(batch_data):
if not isinstance(batch_data, list) and not isinstance(batch_data, tuple):
raise ValueError(
f"Your batch type is not supported: {type(batch_data)}. Please inherit from "
"`TrainDataLoaderIter` or `ValDataLoaderIter` and override the "
"`inputs_labels_from_batch` method."
)
inputs, labels, *_ = batch_data
return inputs, labels
def __iter__(self):
return self
def __next__(self):
batch = next(self._iterator)
return self.inputs_labels_from_batch(batch)
class TrainDataLoaderIter(DataLoaderIter):
def __init__(self, data_loader, auto_reset=True):
super().__init__(data_loader)
self.auto_reset = auto_reset
def __next__(self):
try:
batch = next(self._iterator)
inputs, labels = self.inputs_labels_from_batch(batch)
except StopIteration:
if not self.auto_reset:
raise
self._iterator = iter(self.data_loader)
batch = next(self._iterator)
inputs, labels = self.inputs_labels_from_batch(batch)
return inputs, labels
class ValDataLoaderIter(DataLoaderIter):
"""This iterator will reset itself **only** when it is acquired by
the syntax of normal `iterator`. That is, this iterator just works
like a `torch.data.DataLoader`. If you want to restart it, you
should use it like:
```
loader_iter = ValDataLoaderIter(data_loader)
for batch in loader_iter:
...
# `loader_iter` should run out of values now, you can restart it by:
# 1. the way we use a `torch.data.DataLoader`
for batch in loader_iter: # __iter__ is called implicitly
...
# 2. passing it into `iter()` manually
loader_iter = iter(loader_iter) # __iter__ is called by `iter()`
```
"""
def __init__(self, data_loader):
super().__init__(data_loader)
self.run_limit = len(self.data_loader)
self.run_counter = 0
def __iter__(self):
if self.run_counter >= self.run_limit:
self._iterator = iter(self.data_loader)
self.run_counter = 0
return self
def __next__(self):
self.run_counter += 1
return super().__next__()
class LRFinder:
"""Learning rate range test.
The learning rate range test increases the learning rate in a pre-training run
between two boundaries in a linear or exponential manner. It provides valuable
information on how well the network can be trained over a range of learning rates
and what is the optimal learning rate.
Arguments:
model (torch.nn.Module): wrapped model.
optimizer (torch.optim.Optimizer): wrapped optimizer where the defined learning
is assumed to be the lower boundary of the range test.
criterion (torch.nn.Module): wrapped loss function.
device (str or torch.device, optional): a string ("cpu" or "cuda") with an
optional ordinal for the device type (e.g. "cuda:X", where is the ordinal).
Alternatively, can be an object representing the device on which the
computation will take place. Default: None, uses the same device as `model`.
memory_cache (boolean, optional): if this flag is set to True, `state_dict` of
model and optimizer will be cached in memory. Otherwise, they will be saved
to files under the `cache_dir`.
cache_dir (string, optional): path for storing temporary files. If no path is
specified, system-wide temporary directory is used. Notice that this
parameter will be ignored if `memory_cache` is True.
Example:
>>> lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
>>> lr_finder.range_test(dataloader, end_lr=100, num_iter=100)
>>> lr_finder.plot() # to inspect the loss-learning rate graph
>>> lr_finder.reset() # to reset the model and optimizer to their initial state
Reference:
Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
fastai/lr_find: https://github.com/fastai/fastai
"""
def __init__(
self,
model,
optimizer,
criterion,
device=None,
memory_cache=True,
cache_dir=None,
):
# Check if the optimizer is already attached to a scheduler
self.optimizer = optimizer
self._check_for_scheduler()
self.model = model
self.criterion = criterion
self.history = {"lr": [], "loss": []}
self.best_loss = None
self.memory_cache = memory_cache
self.cache_dir = cache_dir
# Save the original state of the model and optimizer so they can be restored if
# needed
self.model_device = next(self.model.parameters()).device
self.state_cache = StateCache(memory_cache, cache_dir=cache_dir)
self.state_cache.store("model", self.model.state_dict())
self.state_cache.store("optimizer", self.optimizer.state_dict())
# If device is None, use the same as the model
if device:
self.device = device
else:
self.device = self.model_device
def reset(self):
"""Restores the model and optimizer to their initial states."""
self.model.load_state_dict(self.state_cache.retrieve("model"))
self.optimizer.load_state_dict(self.state_cache.retrieve("optimizer"))
self.model.to(self.model_device)
def range_test(
self,
train_loader,
val_loader=None,
start_lr=None,
end_lr=10,
num_iter=100,
step_mode="exp",
smooth_f=0.05,
diverge_th=5,
accumulation_steps=1,
non_blocking_transfer=True,
):
"""Performs the learning rate range test.
Arguments:
train_loader (`torch.utils.data.DataLoader`
or child of `TrainDataLoaderIter`, optional):
the training set data loader.
If your dataset (data loader) returns a tuple (inputs, labels,*) then
Pytorch data loader object can be provided. However, if a dataset
returns different outputs e.g. dicts, then you should inherit
from `TrainDataLoaderIter` class and redefine `inputs_labels_from_batch`
method so that it outputs (inputs, labels).
val_loader (`torch.utils.data.DataLoader`
or child of `ValDataLoaderIter`, optional): if `None` the range test
will only use the training loss. When given a data loader, the model is
evaluated after each iteration on that dataset and the evaluation loss
is used. Note that in this mode the test takes significantly longer but
generally produces more precise results.
Similarly to `train_loader`, if your dataset outputs are not standard
you should inherit from `ValDataLoaderIter` class and
redefine method `inputs_labels_from_batch` so that
it outputs (inputs, labels). Default: None.
start_lr (float, optional): the starting learning rate for the range test.
Default: None (uses the learning rate from the optimizer).
end_lr (float, optional): the maximum learning rate to test. Default: 10.
num_iter (int, optional): the number of iterations over which the test
occurs. Default: 100.
step_mode (str, optional): one of the available learning rate policies,
linear or exponential ("linear", "exp"). Default: "exp".
smooth_f (float, optional): the loss smoothing factor within the [0, 1[
interval. Disabled if set to 0, otherwise the loss is smoothed using
exponential smoothing. Default: 0.05.
diverge_th (int, optional): the test is stopped when the loss surpasses the
threshold: diverge_th * best_loss. Default: 5.
accumulation_steps (int, optional): steps for gradient accumulation. If it
is 1, gradients are not accumulated. Default: 1.
non_blocking_transfer (bool, optional): when non_blocking_transfer is set,
tries to convert/move data to the device asynchronously if possible,
e.g., moving CPU Tensors with pinned memory to CUDA devices. Default: True.
Example (fastai approach):
>>> lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
>>> lr_finder.range_test(dataloader, end_lr=100, num_iter=100)
Example (Leslie Smith's approach):
>>> lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
>>> lr_finder.range_test(trainloader, val_loader=val_loader, end_lr=1, num_iter=100, step_mode="linear")
Gradient accumulation is supported; example:
>>> train_data = ... # prepared dataset
>>> desired_bs, real_bs = 32, 4 # batch size
>>> accumulation_steps = desired_bs // real_bs # required steps for accumulation
>>> dataloader = torch.utils.data.DataLoader(train_data, batch_size=real_bs, shuffle=True)
>>> acc_lr_finder = LRFinder(net, optimizer, criterion, device="cuda")
>>> acc_lr_finder.range_test(dataloader, end_lr=10, num_iter=100, accumulation_steps=accumulation_steps)
If your DataLoader returns e.g. dict, or other non standard output, intehit from TrainDataLoaderIter,
redefine method `inputs_labels_from_batch` so that it outputs (inputs, lables) data:
>>> class TrainIter(TrainDataLoaderIter):
>>> def inputs_labels_from_batch(self, batch_data):
>>> return (batch_data['user_features'], batch_data['user_history']), batch_data['y_labels']
>>> train_data_iter = TrainIter(train_dl)
>>> finder = LRFinder(model, optimizer, criterion)
>>> finder.range_test(train_data_iter, end_lr=10, num_iter=300, diverge_th=10)
Reference:
[Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups](https://medium.com/huggingface/ec88c3e51255)
[thomwolf/gradient_accumulation](https://gist.github.com/thomwolf/ac7a7da6b1888c2eeac8ac8b9b05d3d3)
"""
# Reset test results
self.history = {"lr": [], "loss": [], "apt": []}
self.best_loss = None
# Move the model to the proper device
self.model.to(self.device)
# Check if the optimizer is already attached to a scheduler
self._check_for_scheduler()
# Set the starting learning rate
if start_lr:
self._set_learning_rate(start_lr)
# Initialize the proper learning rate policy
if step_mode.lower() == "exp":
lr_schedule = ExponentialLR(self.optimizer, end_lr, num_iter)
elif step_mode.lower() == "linear":
lr_schedule = LinearLR(self.optimizer, end_lr, num_iter)
else:
raise ValueError(f"expected one of (exp, linear), got {step_mode}")
if smooth_f < 0 or smooth_f >= 1:
raise ValueError("smooth_f is outside the range [0, 1[")
# Create an iterator to get data batch by batch
if isinstance(train_loader, DataLoader):
train_iter = TrainDataLoaderIter(train_loader)
elif isinstance(train_loader, TrainDataLoaderIter):
train_iter = train_loader
else:
raise ValueError(
f"`train_loader` has unsupported type: {type(train_loader)}."
"Expected types are `torch.utils.data.DataLoader`"
"or child of `TrainDataLoaderIter`."
)
if val_loader:
if isinstance(val_loader, DataLoader):
val_iter = ValDataLoaderIter(val_loader)
elif isinstance(val_loader, ValDataLoaderIter):
val_iter = val_loader
else:
raise ValueError(
f"`val_loader` has unsupported type: {type(val_loader)}."
"Expected types are `torch.utils.data.DataLoader`"
"or child of `ValDataLoaderIter`."
)
for iteration in tqdm(range(num_iter), **TQDM_OPTIONS):
# Train on batch and retrieve loss
loss, apt = self._train_batch(
train_iter,
accumulation_steps,
non_blocking_transfer=non_blocking_transfer,
)
if val_loader:
loss = self._validate(
val_iter, non_blocking_transfer=non_blocking_transfer
)
# append APT
self.history["apt"].append(apt)
# Update the learning rate
self.history["lr"].append(lr_schedule.get_lr()[0])
lr_schedule.step()
# Track the best loss and smooth it if smooth_f is specified
if iteration == 0:
self.best_loss = loss
else:
if smooth_f > 0:
loss = smooth_f * loss + (1 - smooth_f) * self.history["loss"][-1]
if loss < self.best_loss:
self.best_loss = loss
# Check if the loss has diverged; if it has, stop the test
self.history["loss"].append(loss)
if loss > diverge_th * self.best_loss:
print("Stopping early, the loss has diverged")
break
print("Learning rate search finished. See the graph with LRFinder.plot()")
def _set_learning_rate(self, new_lrs):
if not isinstance(new_lrs, list):
new_lrs = [new_lrs] * len(self.optimizer.param_groups)
if len(new_lrs) != len(self.optimizer.param_groups):
raise ValueError(
"Length of `new_lrs` is not equal to the number of parameter groups "
+ "in the given optimizer"
)
for param_group, new_lr in zip(self.optimizer.param_groups, new_lrs):
param_group["lr"] = new_lr
def _check_for_scheduler(self):
for param_group in self.optimizer.param_groups:
if "initial_lr" in param_group:
raise RuntimeError("Optimizer already has a scheduler attached to it")
def _train_batch(self, train_iter, accumulation_steps, non_blocking_transfer=True):
self.model.train()
total_loss = None # for late initialization
net_APT_temp = None
self.optimizer.zero_grad()
for i in range(accumulation_steps):
inputs, labels = next(train_iter)
inputs, labels = self._move_to_device(
inputs, labels, non_blocking=non_blocking_transfer
)
# enable APT
if hasattr(self.model, "temperature") and i == accumulation_steps - 1:
self.model.reset_APT()
# Forward pass
outputs = self.model(inputs)
loss = self.criterion(outputs, labels)
# Loss should be averaged in each step
loss /= accumulation_steps
# Backward pass
if IS_AMP_AVAILABLE and hasattr(self.optimizer, "_amp_stash"):
# For minor performance optimization, see also:
# https://nvidia.github.io/apex/advanced.html#gradient-accumulation-across-iterations
delay_unscale = ((i + 1) % accumulation_steps) != 0
with amp.scale_loss(
loss, self.optimizer, delay_unscale=delay_unscale
) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if total_loss is None:
total_loss = loss
else:
total_loss += loss
self.optimizer.step()
# get APT
if hasattr(self.model, "temperature") and accumulation_steps == 1:
self.model(inputs)
net_APT_temp = self.model.temperature.item()
self.model.disable_APT()
return total_loss.item(), net_APT_temp
def _move_to_device(self, inputs, labels, non_blocking=True):
def move(obj, device, non_blocking=True):
if hasattr(obj, "to"):
return obj.to(device, non_blocking=non_blocking)
if isinstance(obj, tuple):
return tuple(move(o, device, non_blocking) for o in obj)
if isinstance(obj, list):
return [move(o, device, non_blocking) for o in obj]
if isinstance(obj, dict):
return {k: move(o, device, non_blocking) for k, o in obj.items()}
return obj
inputs = move(inputs, self.device, non_blocking=non_blocking)
labels = move(labels, self.device, non_blocking=non_blocking)
return inputs, labels
def _validate(self, val_iter, non_blocking_transfer=True):
# Set model to evaluation mode and disable gradient computation
running_loss = 0
self.model.eval()
with torch.no_grad():
for inputs, labels in val_iter:
# Move data to the correct device
inputs, labels = self._move_to_device(
inputs, labels, non_blocking=non_blocking_transfer
)
# Forward pass and loss computation
outputs = self.model(inputs)
loss = self.criterion(outputs, labels)
running_loss += loss.item() * len(labels)
return running_loss / len(val_iter.dataset)
def plot(
self,
skip_start=10,
skip_end=5,
log_lr=True,
show_lr=None,
ax=None,
suggest_lr=True,
):
"""Plots the learning rate range test.
Arguments:
skip_start (int, optional): number of batches to trim from the start.
Default: 10.
skip_end (int, optional): number of batches to trim from the start.
Default: 5.
log_lr (bool, optional): True to plot the learning rate in a logarithmic
scale; otherwise, plotted in a linear scale. Default: True.
show_lr (float, optional): if set, adds a vertical line to visualize the
specified learning rate. Default: None.
ax (matplotlib.axes.Axes, optional): the plot is created in the specified
matplotlib axes object and the figure is not be shown. If `None`, then
the figure and axes object are created in this method and the figure is
shown . Default: None.
suggest_lr (bool, optional): suggest a learning rate by
- 'steepest': the point with steepest gradient (minimal gradient)
you can use that point as a first guess for an LR. Default: True.
Returns:
The matplotlib.axes.Axes object that contains the plot,
and the suggested learning rate (if set suggest_lr=True).
"""
if skip_start < 0:
raise ValueError("skip_start cannot be negative")
if skip_end < 0:
raise ValueError("skip_end cannot be negative")
if show_lr is not None and not isinstance(show_lr, float):
raise ValueError("show_lr must be float")
# Get the data to plot from the history dictionary. Also, handle skip_end=0
# properly so the behaviour is the expected
lrs = self.history["lr"]
losses = self.history["loss"]
apt = self.history["apt"]
if skip_end == 0:
lrs = lrs[skip_start:]
losses = losses[skip_start:]
apt = apt[skip_start:]
else:
lrs = lrs[skip_start:-skip_end]
losses = losses[skip_start:-skip_end]
apt = apt[skip_start:-skip_end]
# Create the figure and axes object if axes was not already given
fig = None
if ax is None:
fig, ax = plt.subplots()
ax2 = ax.twinx()
# Plot loss as a function of the learning rate
ax.plot(lrs, losses)
ax2.plot(lrs, apt, color="green")
# Plot the suggested LR
if suggest_lr:
# 'steepest': the point with steepest gradient (minimal gradient)
print("LR suggestion: steepest gradient")
min_grad_idx = None
try:
min_grad_idx = (np.gradient(np.array(losses))).argmin()
except ValueError:
print(
"Failed to compute the gradients, there might not be enough points."
)
if min_grad_idx is not None:
print(f"Suggested LR: {lrs[min_grad_idx]:.2E}")
ax.scatter(
lrs[min_grad_idx],
losses[min_grad_idx],
s=75,
marker="o",
color="red",
zorder=3,
label="steepest gradient",
)
ax.legend()
if log_lr:
ax.set_xscale("log")
ax.set_xlabel("Learning rate")
ax.set_ylabel("Loss")
ax2.set_ylabel("APT")
if show_lr is not None:
ax.axvline(x=show_lr, color="red")
# Show only if the figure was created internally
if fig is not None:
plt.show()
if suggest_lr and min_grad_idx:
return ax, lrs[min_grad_idx]
return ax
class LinearLR(_LRScheduler):
"""Linearly increases the learning rate between two boundaries over a number of
iterations.
Arguments:
optimizer (torch.optim.Optimizer): wrapped optimizer.
end_lr (float): the final learning rate.
num_iter (int): the number of iterations over which the test occurs.
last_epoch (int, optional): the index of last epoch. Default: -1.
"""
def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
self.end_lr = end_lr
if num_iter <= 1:
raise ValueError("`num_iter` must be larger than 1")
self.num_iter = num_iter
super().__init__(optimizer, last_epoch)
def get_lr(self):
# In earlier Pytorch versions last_epoch starts at -1, while in recent versions
# it starts at 0. We need to adjust the math a bit to handle this. See
# discussion at: https://github.com/davidtvs/pytorch-lr-finder/pull/42
if PYTORCH_VERSION < version.parse("1.1.0"):
curr_iter = self.last_epoch + 1
r = curr_iter / (self.num_iter - 1)
else:
r = self.last_epoch / (self.num_iter - 1)
return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
class ExponentialLR(_LRScheduler):
"""Exponentially increases the learning rate between two boundaries over a number of
iterations.
Arguments:
optimizer (torch.optim.Optimizer): wrapped optimizer.
end_lr (float): the final learning rate.
num_iter (int): the number of iterations over which the test occurs.
last_epoch (int, optional): the index of last epoch. Default: -1.
"""
def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
self.end_lr = end_lr
if num_iter <= 1:
raise ValueError("`num_iter` must be larger than 1")
self.num_iter = num_iter
super().__init__(optimizer, last_epoch)
def get_lr(self):
# In earlier Pytorch versions last_epoch starts at -1, while in recent versions
# it starts at 0. We need to adjust the math a bit to handle this. See
# discussion at: https://github.com/davidtvs/pytorch-lr-finder/pull/42
if PYTORCH_VERSION < version.parse("1.1.0"):
curr_iter = self.last_epoch + 1
r = curr_iter / (self.num_iter - 1)
else:
r = self.last_epoch / (self.num_iter - 1)
return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
class StateCache:
def __init__(self, in_memory, cache_dir=None):
self.in_memory = in_memory
self.cache_dir = cache_dir
if self.cache_dir is None:
self.cache_dir = tempfile.gettempdir()
else:
if not os.path.isdir(self.cache_dir):
raise ValueError("Given `cache_dir` is not a valid directory.")
self.cached = {}
def store(self, key, state_dict):
if self.in_memory:
self.cached.update({key: copy.deepcopy(state_dict)})
return
fn = os.path.join(self.cache_dir, f"state_{key}_{id(self)}.pt")
self.cached.update({key: fn})
torch.save(state_dict, fn)
def retrieve(self, key):
if key not in self.cached:
raise KeyError(f"Target {key} was not cached.")
if self.in_memory:
return self.cached.get(key)
fn = self.cached.get(key)
if not os.path.exists(fn):
raise RuntimeError(
f"Failed to load state in {fn}. File doesn't exist anymore."
)
state_dict = torch.load(fn, map_location=lambda storage, location: storage)
return state_dict
def __del__(self):
"""Check whether there are unused cached files existing in `cache_dir` before
this instance being destroyed."""
if self.in_memory:
return
for i in self.cached.values():
if os.path.exists(i):
os.remove(i)