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Troubleshoot

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.

Sanity Check

Before performing any in depth debugging, we want to do a sanity check on the installed PyTorch/XLA.

Check PyTorch/XLA Version

PyTorch and PyTorch/XLA version should match. Check out our README for more detials on versions available.

vm:~$ python
>>> import torch
>>> import torch_xla
>>> print(torch.__version__)
2.1.0+cu121
>>> print(torch_xla.__version__)
2.1.0

Perform A Simple Calculation

vm:~$ export PJRT_DEVICE=TPU
vm:~$ python3
>>> import torch
>>> import torch_xla.core.xla_model as xm
>>> t1 = torch.tensor(100, device=xm.xla_device())
>>> t2 = torch.tensor(200, device=xm.xla_device())
>>> print(t1 + t2)
tensor(300, device='xla:0')

Run Resnet With Fake Data

For nightly

vm:~$ git clone https://github.com/pytorch/xla.git
vm:~$ python xla/test/test_train_mp_imagenet.py --fake_data

For release version x.y, you want to use the branch rx.y. For example if you installed 2.1 release, you should do

vm:~$ git clone --branch r2.1 https://github.com/pytorch/xla.git
vm:~$ python xla/test/test_train_mp_imagenet.py --fake_data

If you can get the resnet to run we can conclude that torch_xla is installed correctly.

Performance Debugging

To diagnose performance 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 diagnosing issues. Please try to include it in your bug report sent to us if you have it.

PyTorch/XLA Debugging Tool

You can enable the PyTorch/XLA debugging tool by setting PT_XLA_DEBUG_LEVEL=2, which provides a couple useful debugging features. You can also lower the debug level to 1 to slip the execution analysis.

Perform A Auto-Metrics Analysis

The debugging tool will analyze the metrics report and provide a summary. Some example output would be

pt-xla-profiler: CompileTime too frequent: 21 counts during 11 steps
pt-xla-profiler: TransferFromDeviceTime too frequent: 11 counts during 11 steps
pt-xla-profiler: Op(s) not lowered: aten::_ctc_loss, aten::_ctc_loss_backward,  Please open a GitHub issue with the above op lowering requests.
pt-xla-profiler: CompileTime too frequent: 23 counts during 12 steps
pt-xla-profiler: TransferFromDeviceTime too frequent: 12 counts during 12 steps

Compilation & Execution Analysis

The debugging tool will analyze every compilation and execution for your model. Some example output would be:

Compilation Analysis: ================================================================================
Compilation Analysis: Compilation Cause
Compilation Analysis:   mark_step in parallel loader at step end
Compilation Analysis: Graph Info: 
Compilation Analysis:   Graph Hash: c74c3b91b855b2b123f833b0d5f86943
Compilation Analysis:   Number of Graph Inputs: 35
Compilation Analysis:   Number of Graph Outputs: 107
Compilation Analysis: Python Frame Triggered Execution: 
Compilation Analysis:   mark_step (/workspaces/dk3/pytorch/xla/torch_xla/core/xla_model.py:1055)
Compilation Analysis:   next (/workspaces/dk3/pytorch/xla/torch_xla/distributed/parallel_loader.py:44)
Compilation Analysis:   __next__ (/workspaces/dk3/pytorch/xla/torch_xla/distributed/parallel_loader.py:32)
Compilation Analysis:   train_loop_fn (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:48)
Compilation Analysis:   start_training (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:65)
Compilation Analysis:   <module> (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:73)
Compilation Analysis: --------------------------------------------------------------------------------
Compilation Analysis: ================================================================================

Post Compilation Analysis: ================================================================================
Post Compilation Analysis: Graph input size: 1.548000 GB
Post Compilation Analysis: Graph output size: 7.922460 GB
Post Compilation Analysis: Aliased Input size: 1.547871 GB
Post Compilation Analysis: Intermediate tensor size: 12.124478 GB
Post Compilation Analysis: Compiled program size: 0.028210 GB
Post Compilation Analysis: --------------------------------------------------------------------------------
Post Compilation Analysis: ================================================================================

Execution Analysis: ================================================================================
Execution Analysis: Execution Cause
Execution Analysis:   mark_step in parallel loader at step end
Execution Analysis: Graph Info: 
Execution Analysis:   Graph Hash: c74c3b91b855b2b123f833b0d5f86943
Execution Analysis:   Number of Graph Inputs: 35
Execution Analysis:   Number of Graph Outputs: 107
Execution Analysis: Python Frame Triggered Execution: 
Execution Analysis:   mark_step (/workspaces/dk3/pytorch/xla/torch_xla/core/xla_model.py:1055)
Execution Analysis:   next (/workspaces/dk3/pytorch/xla/torch_xla/distributed/parallel_loader.py:44)
Execution Analysis:   __next__ (/workspaces/dk3/pytorch/xla/torch_xla/distributed/parallel_loader.py:32)
Execution Analysis:   train_loop_fn (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:48)
Execution Analysis:   start_training (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:65)
Execution Analysis:   <module> (/workspaces/dk3/pytorch/xla/examples/train_decoder_only_base.py:73)
Execution Analysis: --------------------------------------------------------------------------------
Execution Analysis: ================================================================================

Some common causes of Compilation/Executation are 1. User manually call mark_step. 2. Parallel loader call mark_step for every x (configurable) batch. 3. Exiting a profiler StepTrace region. 4. Dynamo decide to compile/execute the graph. 5. User trying to access(often due to logging) the value of a tensor before the mark_step.

The executation caused by 1-4 are expected, and we want to avoid 5 by either reduce the frequency of accessing tensor values or manually add a mark_step before accessing.

Users should expect to see this Compilation Cause + Executation Cause pairs for first couple steps. After the model stabilize users should expect to only see Execution Cause(you can disable execution analysis by PT_XLA_DEBUG_LEVEL=1). To use PyTorch/XLA efficiently, we expect the same models code to be run for every step and compilation only happen once for every graph. If you keep seeing Compilation Cause, you should try to dump the IR/HLO following this section and compare the graphs for each step and understand the source of the differences.

Following section will explain how to get and understand a more detail metrics report.

Get A Metrics Report

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

import torch_xla.debug.metrics as met

# For short report that only contains a few key metrics.
print(met.short_metrics_report())
# For full report that includes all metrics.
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.

Clear The Metrics Report

If you want to clear the metrics between steps/epochs, you can use

import torch_xla.debug.metrics as met

met.clear_all()

PyTorch/XLA + Dynamo Debugging Tool

You can enable the PyTorch/XLA + Dynamo debugging tool by setting XLA_DYNAMO_DEBUG=1.

Performance Profiling

To profile your workload in depth to understand bottlenecks please check the following resources:

Simple Benchmarking

Take a look at:

examples/train_resnet_benchmark.py for how to benchmark a PyTorch/XLA model.

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 must shapes be constant, but computations across XLA devices in all hosts should also be constant.

    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 transfers to the CPU memory, evaluates on CPU, and transfers the result back to the 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.

  • print(torch_xla._XLAC._get_xla_tensors_text([res])) where res is the result tensor prints out the IR.
  • print(torch_xla._XLAC._get_xla_tensors_hlo([res])) where res is the result tensor prints out the generated XLA HLO.

Note these functions must be called prior to mark_step(), otherwise the tensor will already be materialized.

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 captured where creating IR nodes, hence allowing to understand which PyTorch operation was responsible for generating the 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_FLAGS=--xla_dump_to: If set to =/tmp/dir_name, XLA compiler will dump the unoptimized and optimzed HLO per compilation.
  • 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_SAVE_HLO_FILE: If set, the path to a local file where, in case of compilation/execution error, the offending HLO graph will be saved.
  • XLA_SYNC_WAIT: Forces the XLA tensor sync operation to wait for its completion, before moving to the next step.
  • XLA_USE_EAGER_DEBUG_MODE: Forces the XLA tensor to execute eagerly, meaning compile and execute the torch operations one by one. This is useful to bypass the long compilation time but overall step time will be a lot slower and memory usage will be higher since all compiler optimizaiton will be skipped.
  • 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,.... Note that for VLOGs you must set TF_CPP_MIN_LOG_LEVEL=0.
  • TF_CPP_MIN_LOG_LEVEL: Level to print messages for. TF_CPP_MIN_LOG_LEVEL=0 will turn on INFO logging, TF_CPP_MIN_LOG_LEVEL=1 WARNING and so on. Our PyTorch/XLA TF_VLOG uses tensorflow::INFO level by default so to see VLOGs set TF_CPP_MIN_LOG_LEVEL=0.
  • XLA_DUMP_HLO_GRAPH: If set to =1 in case of a compilation or execution error the offending HLO graph will be dumped as part of the runtime error raised by xla_util.cc.

Common Debugging Environment Variables Combinations

  • Record the graph execution in the IR format

    XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 XLA_SAVE_TENSORS_FMT="text" XLA_SAVE_TENSORS_FILE="/tmp/save1.ir"

  • Record the graph execution in the HLO format

    XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 XLA_SAVE_TENSORS_FMT="hlo" XLA_SAVE_TENSORS_FILE="/tmp/save1.hlo"

  • Show debugging VLOG for runtime and graph compilation/execution

    TF_CPP_MIN_LOG_LEVEL=0 TF_CPP_VMODULE="xla_graph_executor=5,pjrt_computation_client=3"

Reproducing PyTorch/XLA CI/CD unit test failures.

You may see some test failures for a PR such as:

To execute this test, run the following from the base repo dir:

PYTORCH_TEST_WITH_SLOW=1 python ../test/test_torch.py -k test_put_xla_uint8

Running this directly in the command line does not work. You need to set the environment variable TORCH_TEST_DEVICES to your local pytorch/xla/test/pytorch_test_base.py. For example:

TORCH_TEST_DEVICES=/path/to/pytorch/xla/test/pytorch_test_base.py PYTORCH_TEST_WITH_SLOW=1 python ../test/test_torch.py -k test_put_xla_uint8

should work.