因此,TORCH_LIBRARY_init_是 PyTorch 用来管理动态算子库的关键步骤,它自动初始化了与扩展相关的算子。 关键点总结 作用: TORCH_LIBRARY_init_是 PyTorch 的内部实现,用于初始化算子库。 它与TORCH_LIBRARY和TORCH_LIBRARY_IMPL配合使用,管理自定义算子的注册和初始化。 2. 什么时候被调用: 在动态库(如 C++ 扩展)...
C10_UID)#define _TORCH_LIBRARY_IMPL(ns, k, m, uid) \\static void C10_CONCATENATE( \\TORCH_LIBRARY_IMPL_init_##ns##_##k##_, uid)(torch::Library&); \\static const torch::detail::TorchLibraryInit C10_CONCATENATE( \\TORCH_LIBRARY_IMPL_static_init_##ns##_##k##_, uid)( \\torc...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Replace torch.library.impl_abstract with torch.library.register_fake …· pytorch/pytorch@853081a
torch.cuda.device_count()) def get_tensor_sharded_model(model, use_ddp): if use_ddp: ...
TorchRecis a PyTorch domain library built to provide common sparsity and parallelism primitives needed for large-scale recommender systems (RecSys). TorchRec allows training and inference of models with large embedding tables sharded across many GPUs andpowers many production RecSys models at Meta. ...
torch.fx是Pytorch 1.8出来的一套工具或者说一个库,是做python-to-python code transformation,大意就是可以把pytorch中的python前向代码转换为你想要的样子,官方介绍如下: We apply this principle in torch.fx, a program capture and transformation library for PyTorch written entirely in Python and optimized fo...
transformation library for PyTorch written entirely in Python and optimized for high developer productivity by ML practitioners 上述来源于FX的论文,感兴趣的可以看TORCH.FX: PRACTICAL PROGRAM CAPTURE AND TRANSFORMATION FOR DEEP LEARNING IN PYTHON这篇,知乎上也有一篇不错的解读,这里就不复述了。不过本文也会介...
importtorch torch.__version__# 如正常则返回版本号 "2.4.1+cu121"torch.cuda.is_available()# 如正常则返回 "True"a = torch.Tensor([1.]) a.cuda()# 如正常则返回"tensor([ 1.], device='cuda:0')"torch.backends.cudnn.is_acceptable(a.cuda())# 如正常则返回 "True" ...
XLA (Accelerated Linear Algebra)是一个开源的机器学习编译器,对PyTorch、Tensorflow、JAX等多个深度学习框架都有支持。最初XLA实际上是跟Tensorflow深度结合的,很好地服务了Tensorflow和TPU,而与XLA的结合主要依赖于社区的支持,即torch-xla。 torch-xla在支持XLA编译的基础上,较大限度地保持了PyTorch的易用性,贴一个...
SUPPORTED_TASKS = { "audio-classification": { "impl": AudioClassificationPipeline, # 当前NLP任务的pipeline类接口 "tf": (),# 指定TensorFloew框架下的自动类模型 "pt": (AutoModelForAudioClassification,) if is_torch_available() else (), # 指定Pytorch框架下的自动类模型 "default": {"model": ...