2019年05月01日，Pytorch 1.1.0 版本正式發(fā)布啦~https://github.com/pytorch/pytorch/releases/tag/v1.1.0

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主要的幾個功能：
1. TensorBoard (currently experimental)
2. JIT 的升級
· [JIT] Attributes in ScriptModules
· [JIT] Dictionary and List Support in TorchScript
· [JIT] User-defined classes in TorchScript (experimental)
3. DistributedDataParallel new functionality and tutorials

TensorBoard (currently experimental)

• PyTorch now supports TensorBoard logging with a simplefrom torch.utils.tensorboard import SummaryWritercommand.
• Histograms, embeddings, scalars, images, text, graphs, and more can be visualized across training runs.
• TensorBoard support is currently experimental. You can browse the docs here.

JIT

• Attributes in ScriptModules

Attributes can be assigned on a ScriptModule by wrapping them with torch.jit.Attribute and specifying the type.

They will be serialized along with any paramters/buffers when you call torch.jit.save() , so they are a great way to store arbitrary state in your model.

See the docs for more info.

Example:

class Foo(torch.jit.ScriptModule):def __init__(self, a_dict):super(Foo, self).__init__(False)self.words = torch.jit.Attribute([], List[str])self.some_dict = torch.jit.Attribute(a_dict, Dict[str, int])@torch.jit.script_methoddef forward(self, input: str) -> int:self.words.append(input)return self.some_dict[input]

• Dictionary and List Support in TorchScript

TorchScript now has robust support for list and dictionary types. They behave much like Python lists and dictionaries, supporting most built-in methods, as well as simple comprehensions and for…in constructs.

• User-defined classes in TorchScript (experimental)

For more complex stateful operations, TorchScript now supports annotating a class with @torch.jit.script. Classes used this way can be JIT-compiled and loaded in C++ like other TorchScript modules.

See the docs for more info.

Example:

@torch.jit.script class Pair:def __init__(self, first, second)self.first = firstself.second = seconddef sum(self):return self.first + self.second

DistributedDataParallel new functionality and tutorials

• nn.parallel.DistributedDataParallel: can now wrap multi-GPU modules, which enables use cases such as model parallel (tutorial) on one server and data parallel (tutorial) across servers. (19271).

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Breaking Changes

• Tensor.set_: the device of a Tensor can no longer be changed via Tensor.set_. This would most commonly happen when setting up a Tensor with the default CUDA device and later swapping in a Storage on a different CUDA device. Instead, set up the Tensor on the correct device from the beginning. (18832).
• Pay attention to the order change of lr_scheduler.step(). (7889).
• torch.unique: changed the default value of sorted to True. (15379).
• [JIT] Rename isTensor api -> isCompleteTensor. #18437
• [JIT] Remove GraphExecutor's python bindings. #19141
• [C++]: many methods on Type no longer exist; use the functional or Tensor method equivalent. (17991).
• [C++]: the Backend constructor of TensorOptions no longer exists. (18137).
• [C++, Distributed]: Remove c10d ProcessGroup::getGroupRank has been removed. (19147).

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【New Features】
這次的版本更新添加了很多可調用的方法。

Operators

• torch.tril_indices, torch.triu_indices: added operator with same behavior as NumPy. (14904, 15203).
• torch.combinations, torch.cartesian_prod: added new itertools-like operators. (9393).
• torch.repeat_interleave: new operator similar to numpy.repeat. (18395).
• torch.from_file: new operator similar to Storage.from_file, but returning a tensor. (18688).
• torch.unique_consecutive: new operator with semantics similar to std::unique in C++. (19060).
• torch.tril, torch.triu, torch.trtrs: now support batching. (15257, 18025).
• torch.gather: add support for sparse_grad option. (17182).
• torch.std, torch.max_values, torch.min_values, torch.logsumexp can now operate over multiple dimensions at once. (14535, 15892, 16475).
• torch.cdist: added operator equivalent to scipy.spatial.distance.cdist. (16168, 17173).
• torch.__config__.show(): reports detailed version of all libraries. (18579).

NN

• nn.MultiheadedAttention: new module implementing MultiheadedAttention from Attention Is All You Need. (18334).
• nn.functional.interpolate: added support for bicubic. (9849).
• nn.SyncBatchNorm: support synchronous Batch Normalization. (14267).
• nn.Conv: added support for Circular Padding via mode='circular'. (17240).
• nn.EmbeddingBag: now supports trainable per_sample_weights. (18799).
• nn.EmbeddingBag: add support for from_pretrained method, as in nn.Embedding. (15273).
• RNNs: automatically handle unsorted variable-length sequences via enforce_sorted. (15225).
• nn.Identity: new module for easier model surgery. (19249).

Tensors / dtypes

• torch.bool: added support for torch.bool dtype and Tensors with that dtype (1-byte storage). NumPy conversion is supported, but operations are currently limited. (16810).

Optim

• optim.lr_scheduler.CyclicLR: Support for Cyclical Learning Rate and Momentum. (18001).
• optim.lr_scheduler.CosineAnnealingWarmRestarts: new scheduler: Stochastic Gradient Descent with Warm Restarts). (17226).
• Support multiple simultaneous LR schedulers. (14010)

Distributions

• torch.distributions: now support multiple inheritance. (16772).

Samplers

• quasirandom.SobolEngine: new sampler. (10505).

DistributedDataParallel

• nn.parallel.DistributedDataParallel: now supports modules with unused parameters (e.g. control flow, like adaptive softmax, etc). (18251, 18953).

TorchScript and Tracer

• Allow early returns from if-statements. (#154463)
• Add an @ignore annotation, which statically tells the TorchScript compiler to ignore the Python function. (#16055)
• Simple for...in loops on lists. (#16726)
• Ellipses (...) in Tensor indexing. (#17763)
• None in Tensor indexing. (#18615)
• Support for basic list comprehensions. (#17267)
• Add implicit unwrapping of optionals on if foo is not None. (#15587)
• Tensors, ints, and floats will once again be implicitly cast to bool if used in a conditional. (#18755).
• Implement to(), cpu(), and cuda() on ScriptModules. (#15340 , #15904)
• Add support for various methods on lists: (clear(), pop(), reverse(), copy() , extend(),index(), count(), insert(), remove() ).
• Add support for sort() on lists of specialized type (Tensors, int, float, bool). (#19572)
• Add support for various methods on strings: (index(), slice(), len())
• Support Tensor.to() in TorchScript. ( #15976 )
• Support for Torch.tensor() in TorchScript. (#14913, #19445)
• Support for torch.manual_seed() in TorchScript. (#19510)
• Support for nn.LSTM in TorchScript. (#15744)
• Support for nn.init in TorchScript. (#19640)
• Add hash() builtin. (#18258)
• Add min() and max() builtins for numerical types. (#15680)
• Add isinstance() builtin, which performs a static type check. (#15076)
• Add train() / eval() / is_training() to C++ ScriptModule API. (#16044)
• Allow List arguments to Python functions called from TorchScript. (#15721)
• Allow using std::vector and std::unordered_map as arguments to custom operators. (#17587)
• Tracer: now allows passing static dicts and lists as trace inputs. (#18092, #19580)
• Allow generic containers as ScriptModule inputs. (#16482)
• Allow nn.Sequential in ModuleList. (#16882)

Experimental Features

• [Quantization] (API unstable): added limited support for quantized datatypes via torch.qint8 dtype, torch.quantize_linear conversion function. (18230).
• [MKLDNN tensor] (API unstable): Added limited (opaque) support for MKLDNN tensors via Tensor.to_mkldnn(); operators are currently limited to ResNext101 operators. (17748).

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另外，日志里還有關于【Improvements】【Bug Fixes】【Deprecations】【Performance】【Documentation】【ONNX】的說明。

下邊是已經(jīng)修復了的比較嚴重的一些Bug。

• torch.prod: correct erroneous calculation on large tensors. (15653).
• torch.mean (and other reductions): fix incorrect calculation on CUDA on large inputs. (16023).
• nn.Conv: correctly handle non-contiguous inputs on MKLDNN convolution codepath. (16300).
• Tensor.eq_: Fix erroneous calculation. (15475).
• torch.mean: Fix fp16 output calculation. (14878).
• nn.PoissonNLLLoss: Properly handle reduction=None. (17358).
• [JIT] Fix bug where custom ops could get optimized out if their outputs weren't used. (#18711).
• [JIT] Fix bug where the model serializer would accidentally reorder statements. (#17557).

下邊是挑出的幾個比較顯著的【Performance】。

• nn.BatchNorm CPU inference speed increased up to ~19x.(19152).
• nn.AdaptiveAvgPool: speed up common-case of size=1 output by ~30x. (17011).
• nn.EmbeddingBag CPU performance increased by ~4x. (19329).
• Tensor.copy_: sped up larger tensor copy ~2-3x, small regression in small tensor copy. (18618).
• torch.nonzero: is now ~2x faster than numpy on CPU. (15190)
• Improve caching allocator for Pascal and newer GPUs; 10-20% better memory utilization on Mask-RCNN. (17120).
• reduction functions: Speed up some large Tensor cases by 50-80%. (17428).
• [JIT] Graph fuser: better fusion for backwards graphs in the presence of broadcasting. (#14957)
• [JIT] Graph fuser: batch_norm fusion for inference. (#15146)
• [JIT] Graph fuser: layer_norm fusion for inference. (#18266)

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