Pytorch Versions Save

Tensors and Dynamic neural networks in Python with strong GPU acceleration

v2.3.0

2 weeks ago

v2.2.2

1 month ago

This release is meant to fix the following issues (regressions / silent correctness):

Release tracker https://github.com/pytorch/pytorch/issues/120999 contains all relevant pull requests related to this release as well as links to related issues.

v2.2.1

2 months ago

This release is meant to fix the following issues (regressions / silent correctness):

Release tracker https://github.com/pytorch/pytorch/issues/119295 contains all relevant pull requests related to this release as well as links to related issues.

v2.2.0

3 months ago

v2.1.2

4 months ago

This release is meant to fix the following issues (regressions / silent correctness):

The Cherry pick tracker https://github.com/pytorch/pytorch/issues/113962 contains all relevant pull requests related to this release as well as links to related issues.

v2.1.1

5 months ago

This release is meant to fix the following issues (regressions / silent correctness):

  • Remove spurious warning in comparison ops (#112170)
  • Fix segfault in foreach_* operations when input list length does not match (#112349)
  • Fix cuda driver API to load the appropriate .so file (#112996)
  • Fix missing CUDA initialization when calling FFT operations (#110326)
  • Ignore beartype==0.16.0 within the onnx package as it is incompatible (#111861)
  • Fix the behavior of torch.new_zeros in onnx due to TorchScript behavior change (#111694)
  • Remove unnecessary slow code in torch.distributed.checkpoint.optimizer.load_sharded_optimizer_state_dict (#111687)
  • Add planner argument to torch.distributed.checkpoint.optimizer.load_sharded_optimizer_state_dict (#111393)
  • Continue if param not exist in sharded load in torch.distributed.FSDP (#109116)
  • Fix handling of non-contiguous bias_mask in torch.nn.functional.scaled_dot_product_attention (#112673)
  • Fix the meta device implementation for nn.functional.scaled_dot_product_attention (#110893)
  • Fix copy from mps to cpu device when storage_offset is non-zero (#109557)
  • Fix segfault in torch.sparse.mm for non-contiguous inputs (#111742)
  • Fix circular import between Dynamo and einops (#110575)
  • Verify flatbuffer module fields are initialized for mobile deserialization (#109794)

The https://github.com/pytorch/pytorch/issues/110961 contains all relevant pull requests related to this release as well as links to related issues.

v2.1.0

7 months ago

v2.0.1

11 months ago

This release is meant to fix the following issues (regressions / silent correctness):

  • Fix _canonical_mask throws warning when bool masks passed as input to TransformerEncoder/TransformerDecoder (#96009, #96286)
  • Fix Embedding bag max_norm=-1 causes leaf Variable that requires grad is being used in an in-place operation #95980
  • Fix type hint for torch.Tensor.grad_fn, which can be a torch.autograd.graph.Node or None. #96804
  • Can’t convert float to int when the input is a scalar np.ndarray. #97696
  • Revisit torch._six.string_classes removal #97863
  • Fix module backward pre-hooks to actually update gradient #97983
  • Fix load_sharded_optimizer_state_dict error on multi node #98063
  • Warn once for TypedStorage deprecation #98777
  • cuDNN V8 API, Fix incorrect use of emplace in the benchmark cache #97838

Torch.compile:

  • Add support for Modules with custom getitem method to torch.compile #97932
  • Fix improper guards with on list variables. #97862
  • Fix Sequential nn module with duplicated submodule #98880

Distributed:

  • Fix distributed_c10d's handling of custom backends #95072
  • Fix MPI backend not properly initialized #98545

NN_frontend:

  • Update Multi-Head Attention's doc string #97046
  • Fix incorrect behavior of is_causal paremeter for torch.nn.TransformerEncoderLayer.forward #97214
  • Fix error for SDPA on sm86 and sm89 hardware #99105
  • Fix nn.MultiheadAttention mask handling #98375

DataLoader:

  • Fix regression for pin_memory recursion when operating on bytes #97737
  • Fix collation logic #97789
  • Fix Ppotentially backwards incompatible change with DataLoader and is_shardable Datapipes #97287

MPS:

  • Fix LayerNorm crash when input is in float16 #96208
  • Add support for cumsum on int64 input #96733
  • Fix issue with setting BatchNorm to non-trainable #98794

Functorch:

  • Fix Segmentation Fault for vmaped function accessing BatchedTensor.data #97237
  • Fix index_select support when dim is negative #97916
  • Improve docs for autograd.Function support #98020
  • Fix Exception thrown when running Migration guide example for jacrev #97746

Releng:

Torch.optim:

  • Fix fused AdamW causes NaN loss #95847
  • Fix Fused AdamW has worse loss than Apex and unfused AdamW for fp16/AMP #98620

The release tracker should contain all relevant pull requests related to this release as well as links to related issues

v2.0.0

1 year ago

v1.13.1

1 year ago

This release is meant to fix the following issues (regressions / silent correctness):

  • RuntimeError by torch.nn.modules.activation.MultiheadAttention with bias=False and batch_first=True #88669
  • Installation via pip on Amazon Linux 2, regression #88869
  • Installation using poetry on Mac M1, failure #88049
  • Missing masked tensor documentation #89734
  • torch.jit.annotations.parse_type_line is not safe (command injection) #88868
  • Use the Python frame safely in _pythonCallstack #88993
  • Double-backward with full_backward_hook causes RuntimeError #88312
  • Fix logical error in get_default_qat_qconfig #88876
  • Fix cuda/cpu check on NoneType and unit test #88854 and #88970
  • Onnx ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504
  • Onnx operator_export_type on the new registry #87735
  • torchrun AttributeError caused by file_based_local_timer on Windows #85427

The release tracker should contain all relevant pull requests related to this release as well as links to related issues