A library for providing a simple interface to create new metrics and an easy-to-use toolkit for metric computations and checkpointing.
Project description
TorchEval
This library is currently in Alpha and currently does not have a stable release. The API may change and may not be backward compatible. If you have suggestions for improvements, please open a GitHub issue. We'd love to hear your feedback.
A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.
Installing TorchEval
Requires Python >= 3.7 and PyTorch >= 1.11
From pip:
pip install torcheval
For nighly build version
pip install --pre torcheval-nightly
From source:
git clone https://github.com/pytorch/torcheval
cd torcheval
pip install -r requirements.txt
python setup.py install
Quick Start
Take a look at the quickstart notebook, or fork it on Colab.
There are more examples in the examples directory:
cd torcheval
python examples/simple_example.py
Documentation
Documentation can be found at at pytorch.org/torcheval
Using TorchEval
TorchEval can be run on CPU, GPU, and in a multi-process or multi-GPU setting. Metrics are provided in two interfaces, functional and class based. The functional interfaces can be found in torcheval.metrics.functional and are useful when your program runs in a single process setting. To use multi-process or multi-gpu configurations, the class-based interfaces, found in torcheval.metrics provide a much simpler experience. The class based interfaces also allow you to defer some of the computation of the metric by calling update() multiple times before compute(). This can be advantageous even in a single process setting due to saved computation overhead.
Single Process
For use in a single process program, the simplest use case utilizes a functional metric. We simply import the metric function and feed in our outputs and targets. The example below shows a minimal PyTorch training loop that evaluates the multiclass accuracy of every fourth batch of data.
Functional Version (immediate computation of metric)
import torch
from torcheval.metrics.functional import multiclass_accuracy
NUM_BATCHES = 16
BATCH_SIZE = 8
INPUT_SIZE = 10
NUM_CLASSES = 6
eval_frequency = 4
model = torch.nn.Sequential(torch.nn.Linear(INPUT_SIZE, NUM_CLASSES), torch.nn.ReLU())
optim = torch.optim.Adagrad(model.parameters(), lr=0.001)
loss_fn = torch.nn.CrossEntropyLoss()
metric_history = []
for batch in range(NUM_BATCHES):
input = torch.rand(size=(BATCH_SIZE, INPUT_SIZE))
target = torch.randint(size=(BATCH_SIZE,), high=NUM_CLASSES)
outputs = model(input)
loss = loss_fn(outputs, target)
optim.zero_grad()
loss.backward()
optim.step()
# metric only computed every 4 batches,
# data from previous three batches is lost
if (batch + 1) % eval_frequency == 0:
metric_history.append(multiclass_accuracy(outputs, target))
Single Process with Deferred Computation
Class Version (enables deferred computation of metric)
import torch
from torcheval.metrics import MulticlassAccuracy
NUM_BATCHES = 16
BATCH_SIZE = 8
INPUT_SIZE = 10
NUM_CLASSES = 6
eval_frequency = 4
model = torch.nn.Sequential(torch.nn.Linear(INPUT_SIZE, NUM_CLASSES), torch.nn.ReLU())
optim = torch.optim.Adagrad(model.parameters(), lr=0.001)
loss_fn = torch.nn.CrossEntropyLoss()
metric = MulticlassAccuracy()
metric_history = []
for batch in range(NUM_BATCHES):
input = torch.rand(size=(BATCH_SIZE, INPUT_SIZE))
target = torch.randint(size=(BATCH_SIZE,), high=NUM_CLASSES)
outputs = model(input)
loss = loss_fn(outputs, target)
optim.zero_grad()
loss.backward()
optim.step()
# metric only computed every 4 batches,
# data from previous three batches is included
metric.update(input, target)
if (batch + 1) % eval_frequency == 0:
metric_history.append(metric.compute())
# remove old data so that the next call
# to compute is only based off next 4 batches
metric.reset()
Multi-Process or Multi-GPU
For usage on multiple devices a minimal example is given below. In the normal torch.distributed paradigm, each device is allocated its own process gets a unique numerical ID called a "global rank", counting up from 0.
Class Version (enables deferred computation and multi-processing)
import torch
from torcheval.metrics.toolkit import sync_and_compute
from torcheval.metrics import MulticlassAccuracy
# Using torch.distributed
local_rank = int(os.environ["LOCAL_RANK"]) #rank on local machine, i.e. unique ID within a machine
global_rank = int(os.environ["RANK"]) #rank in global pool, i.e. unique ID within the entire process group
world_size = int(os.environ["WORLD_SIZE"]) #total number of processes or "ranks" in the entire process group
device = torch.device(
f"cuda:{local_rank}"
if torch.cuda.is_available() and torch.cuda.device_count() >= world_size
else "cpu"
)
metric = MulticlassAccuracy(device=device)
num_epochs, num_batches = 4, 8
for epoch in range(num_epochs):
for i in range(num_batches):
input = torch.randint(high=5, size=(10,), device=device)
target = torch.randint(high=5, size=(10,), device=device)
# Add data to metric locally
metric.update(input, target)
# metric.compute() will returns metric value from
# all seen data on the local process since last reset()
local_compute_result = metric.compute()
# sync_and_compute(metric) sends metric data across all processes to the process with rank 0,
# the output on rank 0 is the computed metric for the entire process group, on other ranks None is returned.
global_compute_result = sync_and_compute(metric)
if global_rank == 0:
print(global_compute_result)
# if sync_and_compute(metric, recipient_rank="all") is called, the computation is done on rank 0, and the output is synced
# across processes so that each rank returns the computed metric.
# metric.reset() clears the data on each process so that subsequent
# calls to compute() only act on new data
metric.reset()
See the example directory for more examples.
Contributing
We welcome PRs! See the CONTRIBUTING file.
License
TorchEval is BSD licensed, as found in the LICENSE file.
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