# Benchmarks

Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed 

and memory complexity of Transformer models.

[[open-in-colab]]

Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.

A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/main/examples/benchmark.ipynb).

## How to benchmark 🤗 Transformers models

The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.

Hereby, _inference_ is defined by a single forward pass, and _training_ is defined by a single forward pass and

backward pass.

The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an object of type [`PyTorchBenchmarkArguments`] and

[`TensorFlowBenchmarkArguments`], respectively, for instantiation. [`PyTorchBenchmarkArguments`] and [`TensorFlowBenchmarkArguments`] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type _bert-base-cased_ can be benchmarked.

>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])

>>> benchmark = PyTorchBenchmark(args)

>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

>>> args = TensorFlowBenchmarkArguments(

     models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]

 )

>>> benchmark = TensorFlowBenchmark(args)

Here, three arguments are given to the benchmark argument data classes, namely `models`, `batch_sizes`, and

`sequence_lengths`. The argument `models` is required and expects a `list` of model identifiers from the

[model hub](https://huggingface.co/models) The `list` arguments `batch_sizes` and `sequence_lengths` define

the size of the `input_ids` on which the model is benchmarked. There are many more parameters that can be configured

via the benchmark argument data classes. For more detail on these one can either directly consult the files

`src/transformers/benchmark/benchmark_args_utils.py`, `src/transformers/benchmark/benchmark_args.py` (for PyTorch)

and `src/transformers/benchmark/benchmark_args_tf.py` (for Tensorflow). Alternatively, running the following shell

commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow

respectively.

```bash

python examples/pytorch/benchmarking/run_benchmark.py --help

An instantiated benchmark object can then simply be run by calling `benchmark.run()`.

>>> results = benchmark.run()

>>> print(results)

====================       INFERENCE - SPEED - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length     Time in s                  

--------------------------------------------------------------------------------

bert-base-uncased          8               8             0.006     

bert-base-uncased          8               32            0.006     

bert-base-uncased          8              128            0.018     

bert-base-uncased          8              512            0.088     

--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length    Memory in MB 

--------------------------------------------------------------------------------

bert-base-uncased          8               8             1227

bert-base-uncased          8               32            1281

bert-base-uncased          8              128            1307

bert-base-uncased          8              512            1539

--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================

- transformers_version: 2.11.0

- framework: PyTorch

- use_torchscript: False

- framework_version: 1.4.0

- python_version: 3.6.10

- system: Linux

- cpu: x86_64

- architecture: 64bit

- date: 2020-06-29

- time: 08:58:43.371351

- fp16: False

- use_multiprocessing: True

- only_pretrain_model: False

- cpu_ram_mb: 32088

- use_gpu: True

- num_gpus: 1

- gpu: TITAN RTX

- gpu_ram_mb: 24217

- gpu_power_watts: 280.0

- gpu_performance_state: 2

- use_tpu: False

```bash

python examples/tensorflow/benchmarking/run_benchmark_tf.py --help

An instantiated benchmark object can then simply be run by calling `benchmark.run()`.

>>> results = benchmark.run()

>>> print(results)

>>> results = benchmark.run()

>>> print(results)

====================       INFERENCE - SPEED - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length     Time in s                  

--------------------------------------------------------------------------------

bert-base-uncased          8               8             0.005

bert-base-uncased          8               32            0.008

bert-base-uncased          8              128            0.022

bert-base-uncased          8              512            0.105

--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length    Memory in MB 

--------------------------------------------------------------------------------

bert-base-uncased          8               8             1330

bert-base-uncased          8               32            1330

bert-base-uncased          8              128            1330

bert-base-uncased          8              512            1770

--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================

- transformers_version: 2.11.0

- framework: Tensorflow

- use_xla: False

- framework_version: 2.2.0

- python_version: 3.6.10

- system: Linux

- cpu: x86_64

- architecture: 64bit

- date: 2020-06-29

- time: 09:26:35.617317

- fp16: False

- use_multiprocessing: True

- only_pretrain_model: False

- cpu_ram_mb: 32088

- use_gpu: True

- num_gpus: 1

- gpu: TITAN RTX

- gpu_ram_mb: 24217

- gpu_power_watts: 280.0

- gpu_performance_state: 2

- use_tpu: False

By default, the _time_ and the _required memory_ for _inference_ are benchmarked. In the example output above the first

two sections show the result corresponding to _inference time_ and _inference memory_. In addition, all relevant

information about the computing environment, _e.g._ the GPU type, the system, the library versions, etc are printed

out in the third section under _ENVIRONMENT INFORMATION_. This information can optionally be saved in a _.csv_ file

when adding the argument `save_to_csv=True` to [`PyTorchBenchmarkArguments`] and

[`TensorFlowBenchmarkArguments`] respectively. In this case, every section is saved in a separate

_.csv_ file. The path to each _.csv_ file can optionally be defined via the argument data classes.

Instead of benchmarking pre-trained models via their model identifier, _e.g._ `bert-base-uncased`, the user can

alternatively benchmark an arbitrary configuration of any available model class. In this case, a `list` of

configurations must be inserted with the benchmark args as follows.

>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig

>>> args = PyTorchBenchmarkArguments(

     models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]

 )

>>> config_base = BertConfig()

>>> config_384_hid = BertConfig(hidden_size=384)

>>> config_6_lay = BertConfig(num_hidden_layers=6)

>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])

>>> benchmark.run()

====================       INFERENCE - SPEED - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length       Time in s                  

--------------------------------------------------------------------------------

bert-base                  8              128            0.006

bert-base                  8              512            0.006

bert-base                  8              128            0.018     

bert-base                  8              512            0.088     

bert-384-hid              8               8             0.006     

bert-384-hid              8               32            0.006     

bert-384-hid              8              128            0.011     

bert-384-hid              8              512            0.054     

bert-6-lay                 8               8             0.003     

bert-6-lay                 8               32            0.004     

bert-6-lay                 8              128            0.009     

bert-6-lay                 8              512            0.044

--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length      Memory in MB 

--------------------------------------------------------------------------------

bert-base                  8               8             1277

bert-base                  8               32            1281

bert-base                  8              128            1307     

bert-base                  8              512            1539     

bert-384-hid              8               8             1005     

bert-384-hid              8               32            1027     

bert-384-hid              8              128            1035     

bert-384-hid              8              512            1255     

bert-6-lay                 8               8             1097     

bert-6-lay                 8               32            1101     

bert-6-lay                 8              128            1127     

bert-6-lay                 8              512            1359

--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================

- transformers_version: 2.11.0

- framework: PyTorch

- use_torchscript: False

- framework_version: 1.4.0

- python_version: 3.6.10

- system: Linux

- cpu: x86_64

- architecture: 64bit

- date: 2020-06-29

- time: 09:35:25.143267

- fp16: False

- use_multiprocessing: True

- only_pretrain_model: False

- cpu_ram_mb: 32088

- use_gpu: True

- num_gpus: 1

- gpu: TITAN RTX

- gpu_ram_mb: 24217

- gpu_power_watts: 280.0

- gpu_performance_state: 2

- use_tpu: False

>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig

>>> args = TensorFlowBenchmarkArguments(

     models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]

 )

>>> config_base = BertConfig()

>>> config_384_hid = BertConfig(hidden_size=384)

>>> config_6_lay = BertConfig(num_hidden_layers=6)

>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])

>>> benchmark.run()

====================       INFERENCE - SPEED - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length       Time in s                  

--------------------------------------------------------------------------------

bert-base                  8               8             0.005

bert-base                  8               32            0.008

bert-base                  8              128            0.022

bert-base                  8              512            0.106

bert-384-hid              8               8             0.005

bert-384-hid              8               32            0.007

bert-384-hid              8              128            0.018

bert-384-hid              8              512            0.064

bert-6-lay                 8               8             0.002

bert-6-lay                 8               32            0.003

bert-6-lay                 8              128            0.0011

bert-6-lay                 8              512            0.074

--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================

--------------------------------------------------------------------------------

Model Name             Batch Size     Seq Length      Memory in MB 

--------------------------------------------------------------------------------

bert-base                  8               8             1330

bert-base                  8               32            1330

bert-base                  8              128            1330

bert-base                  8              512            1770

bert-384-hid              8               8             1330

bert-384-hid              8               32            1330

bert-384-hid              8              128            1330

bert-384-hid              8              512            1540

bert-6-lay                 8               8             1330

bert-6-lay                 8               32            1330

bert-6-lay                 8              128            1330

bert-6-lay                 8              512            1540

--------------------------------------------------------------------------------

====================        ENVIRONMENT INFORMATION         ====================

- transformers_version: 2.11.0

- framework: Tensorflow

- use_xla: False

- framework_version: 2.2.0

- python_version: 3.6.10

- system: Linux

- cpu: x86_64

- architecture: 64bit

- date: 2020-06-29

- time: 09:38:15.487125

- fp16: False

- use_multiprocessing: True

- only_pretrain_model: False

- cpu_ram_mb: 32088

- use_gpu: True

- num_gpus: 1

- gpu: TITAN RTX

- gpu_ram_mb: 24217

- gpu_power_watts: 280.0

- gpu_performance_state: 2

- use_tpu: False

Again, _inference time_ and _required memory_ for _inference_ are measured, but this time for customized configurations

of the `BertModel` class. This feature can especially be helpful when deciding for which configuration the model

should be trained.

## Benchmark best practices

This section lists a couple of best practices one should be aware of when benchmarking a model.

- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user

  specifies on which device the code should be run by setting the `CUDA_VISIBLE_DEVICES` environment variable in the

  shell, _e.g._ `export CUDA_VISIBLE_DEVICES=0` before running the code.

- The option `no_multi_processing` should only be set to `True` for testing and debugging. To ensure accurate

  memory measurement it is recommended to run each memory benchmark in a separate process by making sure

  `no_multi_processing` is set to `True`.

- One should always state the environment information when sharing the results of a model benchmark. Results can vary

  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very

  useful for the community.

## Sharing your benchmark

Previously all available core models (10 at the time) have been benchmarked for _inference time_, across many different

settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were

done across CPUs (except for TensorFlow XLA) and GPUs.

The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2) and the results are

available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).

With the new _benchmark_ tools, it is easier than ever to share your benchmark results with the community

- [PyTorch Benchmarking Results](https://github.com/huggingface/transformers/tree/main/examples/pytorch/benchmarking/README.md).

- [TensorFlow Benchmarking Results](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/benchmarking/README.md).