torch.profiler¶
Overview¶
PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference. Profiler’s context manager API can be used to better understand what model operators are the most expensive, examine their input shapes and stack traces, study device kernel activity and visualize the execution trace.
Note
An earlier version of the API in torch.autograd
module is considered legacy and will be deprecated.
API Reference¶
- class torch.profiler._KinetoProfile(*, activities=None, record_shapes=False, profile_memory=False, with_stack=False, with_flops=False, with_modules=False, experimental_config=None, execution_trace_observer=None, acc_events=False)[source]¶
Low-level profiler wrap the autograd profile
- Parameters
activities (iterable) – list of activity groups (CPU, CUDA) to use in profiling, supported values:
torch.profiler.ProfilerActivity.CPU
,torch.profiler.ProfilerActivity.CUDA
,torch.profiler.ProfilerActivity.XPU
. Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA or (when available) ProfilerActivity.XPU.record_shapes (bool) – save information about operator’s input shapes.
profile_memory (bool) – track tensor memory allocation/deallocation (see
export_memory_timeline
for more details).with_stack (bool) – record source information (file and line number) for the ops.
with_flops (bool) – use formula to estimate the FLOPS of specific operators (matrix multiplication and 2D convolution).
with_modules (bool) – record module hierarchy (including function names) corresponding to the callstack of the op. e.g. If module A’s forward call’s module B’s forward which contains an aten::add op, then aten::add’s module hierarchy is A.B Note that this support exist, at the moment, only for TorchScript models and not eager mode models.
experimental_config (_ExperimentalConfig) – A set of experimental options used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
execution_trace_observer (ExecutionTraceObserver) – A PyTorch Execution Trace Observer object. PyTorch Execution Traces offer a graph based representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators. When this argument is included the observer start() and stop() will be called for the same time window as PyTorch profiler.
acc_events (bool) – Enable the accumulation of FunctionEvents across multiple profiling cycles
Note
This API is experimental and subject to change in the future.
Enabling shape and stack tracing results in additional overhead. When record_shapes=True is specified, profiler will temporarily hold references to the tensors; that may further prevent certain optimizations that depend on the reference count and introduce extra tensor copies.
- add_metadata(key, value)[source]¶
Adds a user defined metadata with a string key and a string value into the trace file
- add_metadata_json(key, value)[source]¶
Adds a user defined metadata with a string key and a valid json value into the trace file
- events()[source]¶
Returns the list of unaggregated profiler events, to be used in the trace callback or after the profiling is finished
- export_chrome_trace(path)[source]¶
Exports the collected trace in Chrome JSON format. If kineto is enabled, only last cycle in schedule is exported.
- export_memory_timeline(path, device=None)[source]¶
Export memory event information from the profiler collected tree for a given device, and export a timeline plot. There are 3 exportable files using
export_memory_timeline
, each controlled by thepath
’s suffix.For an HTML compatible plot, use the suffix
.html
, and a memory timeline plot will be embedded as a PNG file in the HTML file.For plot points consisting of
[times, [sizes by category]]
, wheretimes
are timestamps andsizes
are memory usage for each category. The memory timeline plot will be saved a JSON (.json
) or gzipped JSON (.json.gz
) depending on the suffix.For raw memory points, use the suffix
.raw.json.gz
. Each raw memory event will consist of(timestamp, action, numbytes, category)
, whereaction
is one of[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]
, andcategory
is one of the enums fromtorch.profiler._memory_profiler.Category
.
Output: Memory timeline written as gzipped JSON, JSON, or HTML.
- key_averages(group_by_input_shape=False, group_by_stack_n=0)[source]¶
Averages events, grouping them by operator name and (optionally) input shapes and stack.
Note
To use shape/stack functionality make sure to set record_shapes/with_stack when creating profiler context manager.
- preset_metadata_json(key, value)[source]¶
Preset a user defined metadata when the profiler is not started and added into the trace file later. Metadata is in the format of a string key and a valid json value
- toggle_collection_dynamic(enable, activities)[source]¶
Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops (CPU) and CUDA activity supported in Kineto
- Parameters
activities (iterable) – list of activity groups to use in profiling, supported values:
torch.profiler.ProfilerActivity.CPU
,torch.profiler.ProfilerActivity.CUDA
Examples:
with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ] ) as p: code_to_profile_0() // turn off collection of all CUDA activity p.toggle_collection_dynamic(False, [torch.profiler.ProfilerActivity.CUDA]) code_to_profile_1() // turn on collection of all CUDA activity p.toggle_collection_dynamic(True, [torch.profiler.ProfilerActivity.CUDA]) code_to_profile_2() print(p.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1))
- class torch.profiler.profile(*, activities=None, schedule=None, on_trace_ready=None, record_shapes=False, profile_memory=False, with_stack=False, with_flops=False, with_modules=False, experimental_config=None, execution_trace_observer=None, acc_events=False, use_cuda=None)[source]¶
Profiler context manager.
- Parameters
activities (iterable) – list of activity groups (CPU, CUDA) to use in profiling, supported values:
torch.profiler.ProfilerActivity.CPU
,torch.profiler.ProfilerActivity.CUDA
,torch.profiler.ProfilerActivity.XPU
. Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA or (when available) ProfilerActivity.XPU.schedule (Callable) – callable that takes step (int) as a single parameter and returns
ProfilerAction
value that specifies the profiler action to perform at each step.on_trace_ready (Callable) – callable that is called at each step when
schedule
returnsProfilerAction.RECORD_AND_SAVE
during the profiling.record_shapes (bool) – save information about operator’s input shapes.
profile_memory (bool) – track tensor memory allocation/deallocation.
with_stack (bool) – record source information (file and line number) for the ops.
with_flops (bool) – use formula to estimate the FLOPs (floating point operations) of specific operators (matrix multiplication and 2D convolution).
with_modules (bool) – record module hierarchy (including function names) corresponding to the callstack of the op. e.g. If module A’s forward call’s module B’s forward which contains an aten::add op, then aten::add’s module hierarchy is A.B Note that this support exist, at the moment, only for TorchScript models and not eager mode models.
experimental_config (_ExperimentalConfig) – A set of experimental options used for Kineto library features. Note, backward compatibility is not guaranteed.
execution_trace_observer (ExecutionTraceObserver) – A PyTorch Execution Trace Observer object. PyTorch Execution Traces offer a graph based representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators. When this argument is included the observer start() and stop() will be called for the same time window as PyTorch profiler. See the examples section below for a code sample.
acc_events (bool) – Enable the accumulation of FunctionEvents across multiple profiling cycles
use_cuda (bool) –
Deprecated since version 1.8.1: use
activities
instead.
Note
Use
schedule()
to generate the callable schedule. Non-default schedules are useful when profiling long training jobs and allow the user to obtain multiple traces at the different iterations of the training process. The default schedule simply records all the events continuously for the duration of the context manager.Note
Use
tensorboard_trace_handler()
to generate result files for TensorBoard:on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)
After profiling, result files can be found in the specified directory. Use the command:
tensorboard --logdir dir_name
to see the results in TensorBoard. For more information, see PyTorch Profiler TensorBoard Plugin
Note
Enabling shape and stack tracing results in additional overhead. When record_shapes=True is specified, profiler will temporarily hold references to the tensors; that may further prevent certain optimizations that depend on the reference count and introduce extra tensor copies.
Examples:
with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ] ) as p: code_to_profile() print(p.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1))
Using the profiler’s
schedule
,on_trace_ready
andstep
functions:# Non-default profiler schedule allows user to turn profiler on and off # on different iterations of the training loop; # trace_handler is called every time a new trace becomes available def trace_handler(prof): print(prof.key_averages().table( sort_by="self_cuda_time_total", row_limit=-1)) # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json") with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ], # In this example with wait=1, warmup=1, active=2, repeat=1, # profiler will skip the first step/iteration, # start warming up on the second, record # the third and the forth iterations, # after which the trace will become available # and on_trace_ready (when set) is called; # the cycle repeats starting with the next step schedule=torch.profiler.schedule( wait=1, warmup=1, active=2, repeat=1), on_trace_ready=trace_handler # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log') # used when outputting for tensorboard ) as p: for iter in range(N): code_iteration_to_profile(iter) # send a signal to the profiler that the next iteration has started p.step()
The following sample shows how to setup up an Execution Trace Observer (execution_trace_observer)
with torch.profiler.profile( ... execution_trace_observer=( ExecutionTraceObserver().register_callback("./execution_trace.json") ), ) as p: for iter in range(N): code_iteration_to_profile(iter) p.step()
You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py. Note: One can also pass any object satisfying the _ITraceObserver interface.
- class torch.profiler.ProfilerAction(value)[source]¶
Profiler actions that can be taken at the specified intervals
- torch.profiler.schedule(*, wait, warmup, active, repeat=0, skip_first=0)[source]¶
Returns a callable that can be used as profiler
schedule
argument. The profiler will skip the firstskip_first
steps, then wait forwait
steps, then do the warmup for the nextwarmup
steps, then do the active recording for the nextactive
steps and then repeat the cycle starting withwait
steps. The optional number of cycles is specified with therepeat
parameter, the zero value means that the cycles will continue until the profiling is finished.- Return type
- torch.profiler.tensorboard_trace_handler(dir_name, worker_name=None, use_gzip=False)[source]¶
Outputs tracing files to directory of
dir_name
, then that directory can be directly delivered to tensorboard as logdir.worker_name
should be unique for each worker in distributed scenario, it will be set to ‘[hostname]_[pid]’ by default.
Intel Instrumentation and Tracing Technology APIs¶
- torch.profiler.itt.mark(msg)[source]¶
Describe an instantaneous event that occurred at some point.
- Parameters
msg (str) – ASCII message to associate with the event.