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pytorch/pytorch

版本发布时间: 2018-12-08 03:19:01

pytorch/pytorch最新发布版本:v2.5.1(2024-10-30 01:58:24)

Table of Contents

Highlights

JIT

The JIT is a set of compiler tools for bridging the gap between research in PyTorch and production. It allows for the creation of models that can run without a dependency on the Python interpreter and which can be optimized more aggressively. Using program annotations existing models can be transformed into Torch Script, a subset of Python that PyTorch can run directly. Model code is still valid Python code and can be debugged with the standard Python toolchain. PyTorch 1.0 provides two ways in which you can make your existing code compatible with the JIT, using torch.jit.trace or torch.jit.script. Once annotated, Torch Script code can be aggressively optimized and it can be serialized for later use in our new C++ API, which doesn't depend on Python at all.

# Write in Python, run anywhere!
@torch.jit.script
def RNN(x, h, W_h, U_h, b_h):
  y = []
  for t in range(x.size(0)):
    h = torch.tanh(x[t] @ W_h + h @ U_h + b_h)
    y += [h]
  return torch.stack(y), h

As an example, see a tutorial on deploying a seq2seq model, loading an exported model from C++, or browse the docs.

Brand New Distributed Package

The torch.distributed package and torch.nn.parallel.DistributedDataParallel module are backed by a brand new re-designed distributed library. The main highlights of the new library are:

C++ Frontend [API Unstable].

The C++ frontend is a pure C++ interface to the PyTorch backend that follows the API and architecture of the established Python frontend. It is intended to enable research in high performance, low latency and bare metal C++ applications. It provides equivalents to torch.nn, torch.optim, torch.data and other components of the Python frontend. Here is a minimal side-by-side comparison of the two language frontends:

PythonC++
import torch


model = torch.nn.Linear(5, 1)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
prediction = model.forward(torch.randn(3, 5))
loss = torch.nn.functional.mse_loss(prediction, torch.ones(3, 1))
loss.backward()
optimizer.step()
      
#include <torch/torch.h>


torch::nn::Linear model(5, 1);
torch::optim::SGD optimizer(model->parameters(), /*lr=*/0.1);
torch::Tensor prediction = model->forward(torch::randn({3, 5}));
auto loss = torch::mse_loss(prediction, torch::ones({3, 1}));
loss.backward();
optimizer.step();

We are releasing the C++ frontend marked as "API Unstable" as part of PyTorch 1.0. This means it is ready to be used for your research application, but still has some open construction sites that will stabilize over the next couple of releases. Some parts of the API may undergo breaking changes during this time.

See https://pytorch.org/cppdocs for detailed documentation on the greater PyTorch C++ API as well as the C++ frontend.

Torch Hub

Torch Hub is a pre-trained model repository designed to facilitate research reproducibility.

Torch Hub supports publishing pre-trained models (model definitions and pre-trained weights) to a github repository using a simple hubconf.py file; see hubconf for resnet models in pytorch/vision as an example. Once published, users can load the pre-trained models using the torch.hub.load API.

For more details, see the torch.hub documentation. Expect a more-detailed blog post introducing Torch Hub in the near future!

Breaking Changes

Additional New Features

N-dimensional empty tensors

New Operators

New Distributions

Sparse API Improvements

Additions to existing Operators and Distributions

Bug Fixes

Serious

Backwards Compatibility

Correctness

Error checking

Miscellaneous

Other Improvements

Deprecations

CPP Extensions

torch.distributed

Performance

Documentation Improvements

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