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 >= 3.8 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) syncs metric data across all ranks and computes the metric value
        global_compute_result = sync_and_compute(metric)
        if global_rank == 0:
            print(global_compute_result)

    # metric.reset() clears the data on each process so that subsequent
    # calls to compute() only act on new data
    metric.reset()