
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.
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)
</pt>
<tf>py
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 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.

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
</pt>
<tf>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
</pt>
<tf>py

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 and the results are
available here.
With the new benchmark tools, it is easier than ever to share your benchmark results with the community

PyTorch Benchmarking Results.
TensorFlow Benchmarking Results.
