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Pytorch v1.0.1 Release Notes

Release Date: 2019-02-07 // about 5 years ago

  • Note: our conda install commands have slightly changed. Version specifiers such as cuda100 in conda install pytorch cuda100 -c pytorch have changed to conda install pytorch cudatoolkit=10.0 -c pytorch

    💥 Breaking Changes

    🚀 There are no breaking changes in this release.

    🐛 Bug Fixes

    Serious

    • 🛠 Higher order gradients for CPU Convolutions have been fixed (regressed in 1.0.0 under MKL-DNN setting) #15686
    • Correct gradients for non-contiguous weights in CPU Convolutions #16301
    • 🛠 Fix ReLU on CPU Integer Tensors by fixing vec256 inversions #15634
    • 🛠 Fix bincount for non-contiguous Tensors #15109
    • 🛠 Fix torch.norm on CPU for large Tensors #15602
    • 🛠 Fix eq_ to do equality on GPU (was doing greater-equal due to a typo) (#15475)
    • ↪ Workaround a CuDNN bug that gave wrong results in certain strided convolution gradient setups
      • blacklist fft algorithms for strided dgrad (#16626)

    Correctness

    • 🛠 Fix cuda native loss_ctc for varying input length (#15798)
      • this avoids NaNs in variable length settings
    • C++ Frontend: Fix serialization (#15033)
      • Fixes a bug where (de-)/serializing a hierarchy of submodules where one submodule doesn't have any parameters, but its submodules do
    • 🛠 Fix derivative for mvlgamma (#15049)
    • 🛠 Fix numerical stability in log_prob for Gumbel distribution (#15878)
    • multinomial: fix detection and drawing of zero probability events (#16075)

    Crashes

    • ⚡️ PyTorch binaries were crashing on AWS Lambda and a few other niche systems, stemming from CPUInfo handling certain warnings as errors. Updated CPUInfo with relevant fixes.
    • MKL-DNN is now statically built, to avoid conflicts with system versions
    • 👍 Allow ReadyQueue to handle empty tasks (#15791)
      • Fixes a segfault with a DataParallel + Checkpoint neural network setting
    • Avoid integer divide by zero error in index_put_ (#14984)
    • 🛠 Fix for model inference crash on Win10 (#15919) (#16092)
    • 👉 Use CUDAGuard when serializing Tensors:
      • Before this change, torch.save and torch.load would initialize the CUDA context on GPU 0 if it hadn't been initialized already, even if the serialized tensors are only on GPU 1.
    • Fix error with handling scalars and rpow , for example 1 ^^ x, where x is a PyTorch scalar (#16687)
    • Switch to CUDA implementation instead of CuDNN if batch size >= 65536 for affine_grid (#16403)
      • CuDNN crashes when batch size >= 65536
    • [Distributed] TCP init method race condition fix (#15684)
    • [Distributed] Fix a memory leak in Gloo's CPU backend
    • [C++ Frontend] Fix LBFGS issue around using inplace ops (#16167)
    • [Hub] Fix github branch prefix v (#15552)
    • 🛠 [Hub] url download bugfix for URLs served without Content-Length header

    🐎 Performance

    • 🛠 LibTorch binaries now ship with CuDNN enabled. Without this change, many folks saw significant perf differences while using LibTorch vs PyTorch, this should be fixed now. #14976
    • 👉 Make btriunpack work for high dimensional batches and faster than before (#15286)
    • 👌 improve performance of unique with inverse indices (#16145)
    • 🔨 Re-enable OpenMP in binaries (got disabled because of a CMake refactor)

    Other

    • create type hint stub files for module torch (#16089)
      • This will restore auto-complete functionality in PyCharm, VSCode etc.
    • 🛠 Fix sum_to behavior with zero dimensions (#15796)
    • Match NumPy by considering NaNs to be larger than any number when sorting (#15886)
    • 🛠 Fixes various error message / settings in dynamic weight GRU / LSTMs (#15766)
    • C++ Frontend: Make call operator on module holder call forward (#15831)
    • C++ Frontend: Add the normalize transform to the core library (#15891)
    • 🛠 Fix bug in torch::load and unpack torch::optim::detail namespace (#15926)
    • Implements Batched upper triangular, lower triangular (#15257)
    • ➕ Add torch.roll to documentation (#14880)
    • 👍 (better errors) Add backend checks for batch norm (#15955)

    JIT

    • ➕ Add better support for bools in the graph fuser (#15057)
    • 👍 Allow tracing with fork/wait (#15184)
    • 👌 improve script/no script save error (#15321)
    • ➕ Add self to Python printer reserved words (#15318)
    • 👍 Better error when torch.load-ing a JIT model (#15578)
    • 🛠 fix select after chunk op (#15672)
    • ➕ Add script standard library documentation + cleanup (#14912)