Comments (5)
As I mentioned, the PyTorch/TPU project is under heavy development, and we have switched the architecture from using the PyTorch/JIT, to using a new XTen (lazy tensor) approach.
In order for you to fetch a version working with TF 1.13, you will have to fetch the PyTorch and PyTorch/XLA commit IDs before Dec 19th 2018, which is a LONG time ago.
And you will be fetching a version using the old JIT approach, which has limited support (if you are planning to train MNIST or resnets, that is OK - anything more, you will likely find "unsupported operator" errors).
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Hi!
Thanks for the interest in the PyTorch/TPU project. The project is under heavy development, and unfortunately, as we move forward, we discover new features that we need on the TPU VM side (the TF service).
We expect the project to be stabilizing in the new couple of months, and we are also speeding up the TF release process, so that the gap between cutting edge TF, and the version inside the TPU VM, will be smaller.
from xla.
@dlibenzi Thanks for the reply! So do you have any recommendation of versions to use current xla? (eg. xla commit id, torch commit id, tpu pod tf version, ...)
from xla.
@dlibenzi Thanks for the hard work you put in but current repository is somewhat misguiding many people. It is not installable with the current version of readme. Please provide commit ids(pytorch, tensorflow, xla) and tpu pod version that we can test.
from xla.
Thanks I will look forward to your improved works!
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Related Issues (20)
- Need help on building torch_xla 2.3 from source HOT 8
- [torchbench] Inductor failing on training HOT 5
- Loading large sharded model weights HOT 4
- TPU crash during importing Trainer from transformers HOT 6
- GPT2 CasualLM Inference crashes when using transformers v4.39.0 HOT 6
- Export debug information to StableHLO HOT 11
- Migrate PyTorch/XLA's gradient checkpointing to upstream one HOT 3
- constant folding for AvgPool2d HOT 10
- [torchbench] The official benchmark for performance and accuracy check HOT 7
- model.to(xla_device) increases the number of named_parameters HOT 4
- Spmd whether expert parallelism is supported? HOT 3
- The behavior of `torch.einsum` significantly differs between TPU and other devices. HOT 1
- multi_tensor_sgd triggers extra xla execution HOT 4
- torchdynamo + XLA crash HOT 11
- Export nn.Module.forward with kwargs to StableHLO HOT 2
- Experiencing slow recompilation when manually building XLA HOT 10
- Cannot Import _XLAC HOT 4
- `is_master_ordinal` breaks dataloader HOT 8
- Failed to build with bazel: ERROR: Source forest creation failed HOT 1
- Mismatch between XLA Tensor and PyTorch Native Tensor Results for `torch.matmul` in FP16 Precision on NVIDIA GPU HOT 9
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