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TROUBLESHOOTING.md

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Troubleshooting

Note that the information in this section is subject to be removed in future releases of the PyTorch/XLA software, since many of them are peculiar to a given internal implementation which might change.

To diagnose issues, we can use the execution metrics and counters provided by PyTorch/XLA The first thing to check when model is slow is to generate a metrics report.

Metrics report is extremely helpful in diagonsing issues. Please try to include it in your bug report sent to us if you have it.

Get A Metrics Report

Put the following line in your program to generate a report:

import torch_xla.debug.metrics as met

print(met.metrics_report())

Understand The Metrics Report

The report includes things like:

  • how many time we issue XLA compilations and time spent on issuing.
  • how many times we execute and time spent on execution
  • how many device data handles we create/destroy etc.

This information is reported in terms of percentiles of the samples. An example is:

Metric: CompileTime
  TotalSamples: 202
  Counter: 06m09s401ms746.001us
  ValueRate: 778ms572.062us / second
  Rate: 0.425201 / second
  Percentiles: 1%=001ms32.778us; 5%=001ms61.283us; 10%=001ms79.236us; 20%=001ms110.973us; 50%=001ms228.773us; 80%=001ms339.183us; 90%=001ms434.305us; 95%=002ms921.063us; 99%=21s102ms853.173us

We also provide counters, which are named integer variables which track internal software status. For example:

Counter: CachedSyncTensors
  Value: 395

In this report, any counter that starts with aten:: indicates a context switch between the XLA device and CPU, which can be a potential performance optimization area in the model code.

Counters are useful to understand which operations are routed back to the CPU engine of PyTorch. They are fully qualified with their C++ namespace:

Counter: aten::nonzero
  Value: 33

If you see aten:: ops other than nonzero and _local_scalar_dense, that usually means a missing lowering in PyTorch/XLA. Feel free to open a feature request for it on GitHub issues.

Known Performance Caveats

PyTorch/XLA behaves semantically like regular PyTorch and XLA tensors share the full tensor interface with CPU & GPU tensors. However, constraints in XLA/hardware and the lazy evaluation model suggest certain patterns might result in bad performance.

If your model shows bad performance, keep in mind the following caveats:

  1. XLA/TPU yield degraded performance with too many recompilations.

    XLA compilation is expensive. PyTorch/XLA automatically recompiles the graph every time new shapes are encountered. Usually models should stabilize within a few steps and you can see huge speedup for the rest of training.

    In order to avoid recompilations, not only shapes must be constant, but also computations accross XLA devices in all hosts.

    Possible sources:

    • Direct or indirect uses of nonzero introduce dynamic shapes; for example, masked indexing base[index] where index is a mask tensor.
    • Loops with a different number of iterations between steps can result in different execution graphs, thus require recompilations.

    Solution:

    • Tensor shapes should be the same between iterations, or a low number of shape variations should be used.
    • Pad tensors to fixed sizes when possible.

  2. Certain operations don't have native translations to XLA.

    For these operations PyTorch/XLA automatically transfer to the CPU memory, evaluate on CPU, and transfer the result back to XLA device. Doing too many such operations during the training step can lead to significant slowdowns.

    Possible sources:

    • The item() operation explicitly asks to evaluate the result. Don't use it unless it's necessary.

    Solution:

    • For most ops we can lower them to XLA to fix it. Checkout metrics report section to find out the missing ops and open a feature request on GitHub.
    • Even when a PyTorch tensor is known as a scalar, avoid using tensor.item(). Keep it as a tensor and use tensor operations on it.
    • Use torch.where to substitute control flow when applicable. E.g. The control flow with item() used in clip_grad_norm_ is problematic and impacts performance, so we have patched clip_grad_norm_ by calling torch.where instead, which gives us a dramatic performance improvement. 
      ...
else:
  device = parameters[0].device
  total_norm = torch.zeros([], device=device if parameters else None)
  for p in parameters:
    param_norm = p.grad.data.norm(norm_type) ** norm_type
    total_norm.add_(param_norm)
  total_norm = (total_norm ** (1. / norm_type))
clip_coef = torch.tensor(max_norm, device=device) / (total_norm + 1e-6)
for p in parameters:
  p.grad.data.mul_(torch.where(clip_coef < 1, clip_coef, torch.tensor(1., device=device)))

  3. Iterators in torch_xla.distributed.data_parallel may drop the last few batches in the input iterator.

    This is to make sure we do the same amount of work on all XLA devices.

    Solution:

    • When dataset is small, and there are too few steps, this may result in a no-op epoch. Therefore, it is better to use small batch sizes in those cases.

XLA Tensor Quirks

  1. XLA tensor internals are opaque. XLA tensors always appear to be contiguous and without storage. Networks should not try to check the strides of XLA tensors.

  2. XLA tensors should be moved to the CPU before saving them. Saving XLA tensors directly causes them to be loaded back on the device(s) they were saved from. If a device is unavailable at load time then the load will fail. Moving XLA tensors to the CPU before saving them lets you decide which device(s) to put the loaded tensors on. This is necessary if you want to load the tensors on a machine without XLA devices. Care should be taken moving the XLA tensors to the CPU before saving them, however, as moving tensors across device types does not preserve view relationships. Instead, views should be reconstructed as necessary after the tensors are loaded.

  3. Copying an XLA Tensor with Python's copy.copy returns a deep copy, not a shallow copy. Use a view of an XLA tensor to get a shallow copy of it.

  4. Handling shared weights. Modules can share weights by setting the Parameters of one module to another. This "tying" of module weights should be done AFTER the modules are moved to an XLA device. Otherwise two independent copies of the shared tensor will be made on the XLA device.

More Debugging Tools

We don't expect users to use tools in this section to debug their models. But we might ask for them when you submit a bug report since they provide additional information that metrics report doesn't have.

Environment Variables

There are also a number of environment variables which control the behavior of the PyTorch/XLA software stack.

Setting such variables will cause different degrees of performance degradation, so they should only be enabled for debugging.

  • XLA_IR_DEBUG: Enables the Python stack trace to be catpured where creating IR nodes, hence allowing to understand which PyTorch operation was responsible of generating such IR.

  • XLA_HLO_DEBUG: Enables the Python stack frame captured when XLA_IR_DEBUG is active, to be propagated to the XLA HLO metadata.

  • XLA_SAVE_TENSORS_FILE: The path to a file which will be used to dump the IR graphs during execution. Note that the file can become really big if the option is left enabled and the PyTorch program let run for long time. The graphs are appended to the file, so to have a clean sheet from run to run, the file should be explicitly removed.

  • XLA_SAVE_TENSORS_FMT: The format of the graphs stored within the XLA_SAVE_TENSORS_FILE file. Can be text (the default), dot (the Graphviz format) or hlo.

  • XLA_METRICS_FILE: If set, the path to a local file where the internal metrics will be saved at every step. Metrics will be appended to the file, if already existing.

  • XLA_GET_TENSORS_OPBYOP: Enables pure OpByOp dispatch. The PyTorch/XLA software tries to fuse together many PyTorch operations into a single computation graph, but sometimes, either for debugging, or in case the PyTorch code have a very dynamic nature (in shapes or graph terms), it is better to force the execution in OpByOp mode (every IR node is lowered into a separate XLA computation, and chain-executed). This environment variable, if set to 1, enables OpByOp during the "get tensors" operation (the operation used by PyTorch/XLA to fetch intermediate values back from the TPU device into PyTorch CPU tensors).

  • XLA_SYNC_TENSORS_OPBYOP: The same as XLA_GET_TENSORS_OPBYOP but for "sync tensors" operation (the operation used at the end of a step, to flush pending IR computations and materialize them into TPU device data).

  • XLA_SYNC_WAIT: Forces the XLA tensor sync operation to wait for its completion, before moving to the next step.

  • XLA_USE_BF16: If set to 1, tranforms all the PyTorch Float values into BiFloat16 when sending to the TPU device.

  • XLA_USE_F16: If set to 1, tranforms all the PyTorch Float values into Float16 (PyTorch Half type) when sending to devices which supports them.

  • XLA_USE_32BIT_LONG: If set to 1, maps PyTorch Long types to XLA 32bit type. On the versions of the TPU HW at the time of writing, 64bit integer computations are expensive, so setting this flag might help. It should be verified by the user that truncating to 32bit values is a valid operation according to the use of PyTorch Long values in it.

  • TF_CPP_LOG_THREAD_ID: If set to 1, the TF logs will show the thread ID helping with debugging multithreaded processes.

  • TF_CPP_VMODULE: Environment variable used for TF VLOGs and takes the form of TF_CPP_VMODULE=name=value,.... For PyTorch/XLA using a configuration like TF_CPP_VMODULE=tensor=5 would enable logging such as:

    2019-10-03 17:23:56.419040: I   27891 torch_xla/csrc/tensor.cpp:1104]
Executing IR graph hash 4211381954965020633 on device TPU:3 done!
2019-10-03 17:23:56.419448: I   27890 torch_xla/csrc/tensor.cpp:1104]
Executing IR graph hash 15483856951158150605 on device TPU:5 done!
2019-10-03 17:23:56.419539: I   27896 torch_xla/csrc/tensor.cpp:1104]
Executing IR graph hash 4211381954965020633 on device TPU:4 done!
...

Retrieving Stack Traces

In the event that the PyTorch process is hanging, it might be useful to include the stack traces together with the GitHub issue.

First thing is to find out which PID the PyTorch process is associated with. Using the ps command it is possible to find that information. It will be a python process running your main python file.

In order to allow GDB to attach a user process the following command should be run as root:

echo 0 > /proc/sys/kernel/yama/ptrace_scope

The above command remains active until the machine is rebooted.

The, given the PID, it is possible to grab the stack traces with the following command:

./scripts/dump_stacks.py PID > /tmp/stack-traces.log

Using debug_run.py To Collect Debug Information

A utility is provided in scripts/debug_run.py which can be used to create a tar.gz archive with the information required to debug PyTorch/XLA executions.

Example:

./scripts/debug_run.py --outfile /tmp/debug_run.tar.gz -- python -u SCRIPT [ARGS...]

The python -u flag is suggested to disable buffering so that captured logs are correctly interleaved (otherwise STDOUT will be rendered after all STDERR).

The above command line example will leave the temporary folder containing the archived information on the filesystem. Use the --tidy flag to have that removed on exit:

./scripts/debug_run.py --tidy --outfile /tmp/debug_run.tar.gz -- python -u SCRIPT [ARGS...]

The debug_run.tar.gz file should then be attached to bug reports when necessary.

Since the script will collect a lot of data, it should usually be let run for no more than hundred steps or so. If the SCRIPT has arguments to control the number of steps, those should be used, otherwise hitting CTRL^C will interrupt the run.

It is also sugested to run in single-core mode, to minimize the amount of data. Running in single-core mode is also strongly suggested when debugging execution issues.