#COUNT RUN TIME R HOW TO#
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Important because CUDA syncronization time is non-trivial Invocation, and results in a less biased measurement. Measure many replicates while keeping timer overhead to a minimum.Īt a high level, blocked_autorange executes the following pseudo-code:Ī small block size results in more replicates and generallyĪ large block size better amortizes the cost of timer Threadpool size which tries to utilize all cores.īlocked_autorange ( callback = None, min_run_time = 0.2 ) ¶ This is in contrast to the default PyTorch

Threaded performace is important as both a key inference workloadĪnd a good indicator of intrinsic algorithmic efficiency, so theĭefault is set to one. Num_threads – The size of the PyTorch threadpool when executing stmt. Treat Measurements with different env specification as distinct Instance when A/B testing a change to a kernel.

It is also included when printing a Measurement.Įnv – This tag indicates that otherwise identical tasks were run inĭifferent environments, and are therefore not equivilent, for For instance one might set itīased on the input size to create a table of the form: The principal use of description is to signal toĬompare the columns of data. String to distinguish measurements with identical label and When printing Measurements or summarizing using Compare. For instance, in our exampleĪbove sub_label might be “float” or “int”, so that it is easy Provide supplemental information to disambiguate measurements One might set label to “ReLU(x + 1)” to improve readability. “torch.nn.functional.relu(torch.add(x, 1, out=out))” This is the other method for providing variables which Globals – A dict which defines the global variables when stmt is beingĮxecuted. fault_timer otherwise it will synchronize CUDA before Without CUDA or there is no GPU present, this defaults to Timer – Callable which returns the current time. Used to define variables used in stmtĬode which is placed at the top level of the file for things like

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Stmt – Code snippet to be run in a loop and timed.

Label, sub_label, description, env, num_threads Parameters In addition to wall times, Timer can run a statement under Callgrindĭirectly analogous to timeit.Timer constructor arguments: The representation of result object and by the Compare class to group (Defined later) These fields are included in When defining a Timer, one can optionally specify label, sub_label,ĭescription, and env. Method is replicated for cases where an adaptive strategy is not (Exact algorithms are discussed in method docstrings.) The timeit

To that effect, this class deviates from the timeit API byĬonceptually merging and. Noise and allow median computation, which is more robust than mean. It isĮxpected that all measurements should include replicates to quantify Run-to-run variation is a significant confounding factor. When measuring code, and particularly complex kernels / models, Lazily initialized), set threadpool size so that comparisons areĪpples-to-apples, and synchronize asynchronous CUDA functions when Timer will perform warmups (important as some elements of PyTorch are Timeit.Timer internally), but with several key differences: The PyTorch Timer is based on timeit.Timer (and in fact uses

#COUNT RUN TIME R HOW TO#

Helper class for measuring execution time of PyTorch statements.įor a full tutorial on how to use this class, see: Timer ( stmt='pass', setup='pass', global_setup='', timer=, globals=None, label=None, sub_label=None, description=None, env=None, num_threads=1, language= ) ¶

PyTorch Governance | Persons of Interestīenchmark Utils - ¶ class.

CPU threading and TorchScript inference.

Count run time r