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:orphan:
.. _profiler_intermediate:
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Find bottlenecks in your code (intermediate)
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**Audience** : Users who want to see more granular profiling information
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Profile pytorch operations
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To understand the cost of each PyTorch operation, use the :class: `~lightning.pytorch.profilers.pytorch.PyTorchProfiler` built on top of the `PyTorch profiler <https://pytorch.org/docs/master/profiler.html> `__ .
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.. code-block :: python
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from lightning.pytorch.profilers import PyTorchProfiler
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profiler = PyTorchProfiler()
trainer = Trainer(profiler=profiler)
The profiler will generate an output like this:
.. code-block ::
Profiler Report
Profile stats for: training_step
--------------------- --------------- --------------- --------------- --------------- ---------------
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
.. 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 `` .
----
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Profile a distributed model
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To profile a distributed model, use the :class: `~lightning.pytorch.profilers.pytorch.PyTorchProfiler` with the *filename* argument which will save a report per rank.
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.. code-block :: python
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from lightning.pytorch.profilers import PyTorchProfiler
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profiler = PyTorchProfiler(filename="perf-logs")
trainer = Trainer(profiler=profiler)
With two ranks, it will generate a report like so:
.. code-block ::
Profiler Report: rank 0
Profile stats for: training_step
--------------------- --------------- --------------- --------------- --------------- ---------------
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
.. code-block ::
Profiler Report: rank 1
Profile stats for: training_step
--------------------- --------------- --------------- --------------- --------------- ---------------
Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg
--------------------- --------------- --------------- --------------- --------------- ---------------
t 42.10% 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
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This profiler will record `` training_step `` , `` validation_step `` , `` test_step `` , and `` predict_step `` .
The output above shows the profiling for the action `` training_step `` .
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.. 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 `` .
----
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Visualize profiled operations
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To visualize the profiled operations, enable **emit_nvtx** in the :class: `~lightning.pytorch.profilers.pytorch.PyTorchProfiler` .
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.. code-block :: python
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from lightning.pytorch.profilers import PyTorchProfiler
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profiler = PyTorchProfiler(emit_nvtx=True)
trainer = Trainer(profiler=profiler)
Then run as following:
.. code-block ::
nvprof --profile-from-start off -o trace_name.prof -- <regular command here>
To visualize the profiled operation, you can either use **nvvp** :
.. code-block ::
nvvp trace_name.prof
or python:
.. code-block ::
python -c 'import torch; print(torch.autograd.profiler.load_nvprof("trace_name.prof"))'
