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Performance and Bottleneck Profiler

Profiling your training run can help you understand if there are any bottlenecks in your code.

Built-in checks

PyTorch Lightning supports profiling standard actions in the training loop out of the box, including:

  • on_epoch_start

  • on_epoch_end

  • on_batch_start

  • tbptt_split_batch

  • model_forward

  • model_backward

  • on_after_backward

  • optimizer_step

  • on_batch_end

  • training_step_end

  • on_training_end

Enable simple profiling

If you only wish to profile the standard actions, you can set profiler=”simple” when constructing your Trainer object.

trainer = Trainer(..., profiler="simple")

The profiler’s results will be printed at the completion of a training fit().

Profiler Report

Action                  |  Mean duration (s)    |  Total time (s)
-----------------------------------------------------------------
on_epoch_start          |  5.993e-06            |  5.993e-06
get_train_batch         |  0.0087412            |  16.398
on_batch_start          |  5.0865e-06           |  0.0095372
model_forward           |  0.0017818            |  3.3408
model_backward          |  0.0018283            |  3.4282
on_after_backward       |  4.2862e-06           |  0.0080366
optimizer_step          |  0.0011072            |  2.0759
on_batch_end            |  4.5202e-06           |  0.0084753
on_epoch_end            |  3.919e-06            |  3.919e-06
on_train_end            |  5.449e-06            |  5.449e-06

Advanced Profiling

If you want more information on the functions called during each event, you can use the AdvancedProfiler. This option uses Python’s cProfiler to provide a report of time spent on each function called within your code.

trainer = Trainer(..., profiler="advanced")

# or

profiler = AdvancedProfiler()
trainer = Trainer(..., profiler=profiler)

The profiler’s results will be printed at the completion of a training fit(). This profiler report can be quite long, so you can also specify an output_filename to save the report instead of logging it to the output in your terminal. The output below shows the profiling for the action get_train_batch.

Profiler Report

Profile stats for: get_train_batch
        4869394 function calls (4863767 primitive calls) in 18.893 seconds
Ordered by: cumulative time
List reduced from 76 to 10 due to restriction <10>
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
3752/1876    0.011    0.000   18.887    0.010 {built-in method builtins.next}
    1876     0.008    0.000   18.877    0.010 dataloader.py:344(__next__)
    1876     0.074    0.000   18.869    0.010 dataloader.py:383(_next_data)
    1875     0.012    0.000   18.721    0.010 fetch.py:42(fetch)
    1875     0.084    0.000   18.290    0.010 fetch.py:44(<listcomp>)
    60000    1.759    0.000   18.206    0.000 mnist.py:80(__getitem__)
    60000    0.267    0.000   13.022    0.000 transforms.py:68(__call__)
    60000    0.182    0.000    7.020    0.000 transforms.py:93(__call__)
    60000    1.651    0.000    6.839    0.000 functional.py:42(to_tensor)
    60000    0.260    0.000    5.734    0.000 transforms.py:167(__call__)

You can also reference this profiler in your LightningModule to profile specific actions of interest. If you don’t want to always have the profiler turned on, you can optionally pass a PassThroughProfiler which will allow you to skip profiling without having to make any code changes. Each profiler has a method profile() which returns a context handler. Simply pass in the name of your action that you want to track and the profiler will record performance for code executed within this context.

from pytorch_lightning.profiler import Profiler, PassThroughProfiler


class MyModel(LightningModule):
    def __init__(self, profiler=None):
        self.profiler = profiler or PassThroughProfiler()

    def custom_processing_step(self, data):
        with profiler.profile("my_custom_action"):
            ...
        return data


profiler = Profiler()
model = MyModel(profiler)
trainer = Trainer(profiler=profiler, max_epochs=1)

PyTorch Profiling

Autograd includes a profiler that lets you inspect the cost of different operators inside your model - both on the CPU and GPU.

To read more about the PyTorch Profiler and all its options, have a look at its docs

trainer = Trainer(..., profiler="pytorch")

# or

profiler = PyTorchProfiler(...)
trainer = Trainer(..., profiler=profiler)

This profiler works with PyTorch DistributedDataParallel. If filename is provided, each rank will save their profiled operation to their own file. The profiler report can be quite long, so you setting a filename will save the report instead of logging it to the output in your terminal. If no filename is given, it will be logged only on rank 0.

The profiler’s results will be printed on the completion of {fit,validate,test,predict}.

This profiler will record training_step_and_backward, training_step, backward, validation_step, test_step, and predict_step by default. The output below shows the profiling for the action training_step_and_backward. The user can provide PyTorchProfiler(record_functions={...}) to extend the scope of profiled functions.

Note

When using the PyTorch Profiler, wall clock time will not not be representative of the true wall clock time. This is due to forcing profiled operations to be measured synchronously, when many CUDA ops happen asynchronously. It is recommended to use this Profiler to find bottlenecks/breakdowns, however for end to end wall clock time use the SimpleProfiler. # noqa E501

Profiler Report

Profile stats for: training_step_and_backward
---------------------  ---------------  ---------------  ---------------  ---------------  ---------------
Name                   Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg
---------------------  ---------------  ---------------  ---------------  ---------------  ---------------
t                      62.10%           1.044ms          62.77%           1.055ms          1.055ms
addmm                  32.32%           543.135us        32.69%           549.362us        549.362us
mse_loss               1.35%            22.657us         3.58%            60.105us         60.105us
mean                   0.22%            3.694us          2.05%            34.523us         34.523us
div_                   0.64%            10.756us         1.90%            32.001us         16.000us
ones_like              0.21%            3.461us          0.81%            13.669us         13.669us
sum_out                0.45%            7.638us          0.74%            12.432us         12.432us
transpose              0.23%            3.786us          0.68%            11.393us         11.393us
as_strided             0.60%            10.060us         0.60%            10.060us         3.353us
to                     0.18%            3.059us          0.44%            7.464us          7.464us
empty_like             0.14%            2.387us          0.41%            6.859us          6.859us
empty_strided          0.38%            6.351us          0.38%            6.351us          3.175us
fill_                  0.28%            4.782us          0.33%            5.566us          2.783us
expand                 0.20%            3.336us          0.28%            4.743us          4.743us
empty                  0.27%            4.456us          0.27%            4.456us          2.228us
copy_                  0.15%            2.526us          0.15%            2.526us          2.526us
broadcast_tensors      0.15%            2.492us          0.15%            2.492us          2.492us
size                   0.06%            0.967us          0.06%            0.967us          0.484us
is_complex             0.06%            0.961us          0.06%            0.961us          0.481us
stride                 0.03%            0.517us          0.03%            0.517us          0.517us
---------------------  ---------------  ---------------  ---------------  ---------------  ---------------
Self CPU time total: 1.681ms

When running with PyTorchProfiler(emit_nvtx=True). You should run as following:

nvprof --profile-from-start off -o trace_name.prof -- <regular command here>

To visualize the profiled operation, you can either:

Use:

nvvp trace_name.prof

Or:

python -c 'import torch; print(torch.autograd.profiler.load_nvprof("trace_name.prof"))'
class pytorch_lightning.profiler.AbstractProfiler[source]

Bases: abc.ABC

Specification of a profiler.

abstract setup(**kwargs)[source]

Execute arbitrary pre-profiling set-up steps as defined by subclass.

Return type

None

abstract start(action_name)[source]

Defines how to start recording an action.

Return type

None

abstract stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

abstract summary()[source]

Create profiler summary in text format.

Return type

str

abstract teardown(**kwargs)[source]

Execute arbitrary post-profiling tear-down steps as defined by subclass.

Return type

None

class pytorch_lightning.profiler.AdvancedProfiler(dirpath=None, filename=None, line_count_restriction=1.0, output_filename=None)[source]

Bases: pytorch_lightning.profiler.base.BaseProfiler

This profiler uses Python’s cProfiler to record more detailed information about time spent in each function call recorded during a given action. The output is quite verbose and you should only use this if you want very detailed reports.

Parameters
  • dirpath (Union[str, Path, None]) – Directory path for the filename. If dirpath is None but filename is present, the trainer.log_dir (from TensorBoardLogger) will be used.

  • filename (Optional[str]) – If present, filename where the profiler results will be saved instead of printing to stdout. The .txt extension will be used automatically.

  • line_count_restriction (float) – this can be used to limit the number of functions reported for each action. either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines)

Raises

ValueError – If you attempt to stop recording an action which was never started.

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

teardown(stage=None)[source]

Execute arbitrary post-profiling tear-down steps.

Closes the currently open file and stream.

Return type

None

class pytorch_lightning.profiler.BaseProfiler(dirpath=None, filename=None, output_filename=None)[source]

Bases: pytorch_lightning.profiler.base.AbstractProfiler

If you wish to write a custom profiler, you should inherit from this class.

describe()[source]

Logs a profile report after the conclusion of run.

Return type

None

profile(action_name)[source]

Yields a context manager to encapsulate the scope of a profiled action.

Example:

with self.profile('load training data'):
    # load training data code

The profiler will start once you’ve entered the context and will automatically stop once you exit the code block.

Return type

None

setup(stage=None, local_rank=None, log_dir=None)[source]

Execute arbitrary pre-profiling set-up steps.

Return type

None

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

teardown(stage=None)[source]

Execute arbitrary post-profiling tear-down steps.

Closes the currently open file and stream.

Return type

None

class pytorch_lightning.profiler.PassThroughProfiler(dirpath=None, filename=None, output_filename=None)[source]

Bases: pytorch_lightning.profiler.base.BaseProfiler

This class should be used when you don’t want the (small) overhead of profiling. The Trainer uses this class by default.

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

class pytorch_lightning.profiler.PyTorchProfiler(dirpath=None, filename=None, group_by_input_shapes=False, emit_nvtx=False, export_to_chrome=True, row_limit=20, sort_by_key=None, record_functions=None, record_module_names=True, profiled_functions=None, output_filename=None, **profiler_kwargs)[source]

Bases: pytorch_lightning.profiler.base.BaseProfiler

This profiler uses PyTorch’s Autograd Profiler and lets you inspect the cost of different operators inside your model - both on the CPU and GPU

Parameters
  • dirpath (Union[str, Path, None]) – Directory path for the filename. If dirpath is None but filename is present, the trainer.log_dir (from TensorBoardLogger) will be used.

  • filename (Optional[str]) – If present, filename where the profiler results will be saved instead of printing to stdout. The .txt extension will be used automatically.

  • group_by_input_shapes (bool) – Include operator input shapes and group calls by shape.

  • emit_nvtx (bool) –

    Context manager that makes every autograd operation emit an NVTX range Run:

    nvprof --profile-from-start off -o trace_name.prof -- <regular command here>
    

    To visualize, you can either use:

    nvvp trace_name.prof
    torch.autograd.profiler.load_nvprof(path)
    

  • export_to_chrome (bool) – Whether to export the sequence of profiled operators for Chrome. It will generate a .json file which can be read by Chrome.

  • row_limit (int) – Limit the number of rows in a table, -1 is a special value that removes the limit completely.

  • sort_by_key (Optional[str]) – Attribute used to sort entries. By default they are printed in the same order as they were registered. Valid keys include: cpu_time, cuda_time, cpu_time_total, cuda_time_total, cpu_memory_usage, cuda_memory_usage, self_cpu_memory_usage, self_cuda_memory_usage, count.

  • record_functions (Optional[Set[str]]) – Set of profiled functions which will create a context manager on. Any other will be pass through.

  • record_module_names (bool) – Whether to add module names while recording autograd operation.

  • profiler_kwargs (Any) – Keyword arguments for the PyTorch profiler. This depends on your PyTorch version

Raises

MisconfigurationException – If arg sort_by_key is not present in AVAILABLE_SORT_KEYS. If arg schedule is not a Callable. If arg schedule does not return a torch.profiler.ProfilerAction.

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

teardown(stage=None)[source]

Execute arbitrary post-profiling tear-down steps.

Closes the currently open file and stream.

Return type

None

class pytorch_lightning.profiler.SimpleProfiler(dirpath=None, filename=None, extended=True, output_filename=None)[source]

Bases: pytorch_lightning.profiler.base.BaseProfiler

This profiler simply records the duration of actions (in seconds) and reports the mean duration of each action and the total time spent over the entire training run.

Parameters
  • dirpath (Union[str, Path, None]) – Directory path for the filename. If dirpath is None but filename is present, the trainer.log_dir (from TensorBoardLogger) will be used.

  • filename (Optional[str]) – If present, filename where the profiler results will be saved instead of printing to stdout. The .txt extension will be used automatically.

Raises

ValueError – If you attempt to start an action which has already started, or if you attempt to stop recording an action which was never started.

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

class pytorch_lightning.profiler.XLAProfiler(port=9012)[source]

Bases: pytorch_lightning.profiler.base.BaseProfiler

This Profiler will help you debug and optimize training workload performance for your models using Cloud TPU performance tools.

start(action_name)[source]

Defines how to start recording an action.

Return type

None

stop(action_name)[source]

Defines how to record the duration once an action is complete.

Return type

None

summary()[source]

Create profiler summary in text format.

Return type

str

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