#
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.

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.

Pytorch
Hide Pytorch content

    >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

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

TensorFlow
Hide TensorFlow content

    >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

    >>> args = TensorFlowBenchmarkArguments(
    ...     models=["google-bert/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.

Pytorch
Hide Pytorch content

    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
    --------------------------------------------------------------------------------
    google-bert/bert-base-uncased          8               8             0.006
    google-bert/bert-base-uncased          8               32            0.006
    google-bert/bert-base-uncased          8              128            0.018
    google-bert/bert-base-uncased          8              512            0.088
    --------------------------------------------------------------------------------

    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length    Memory in MB
    --------------------------------------------------------------------------------
    google-bert/bert-base-uncased          8               8             1227
    google-bert/bert-base-uncased          8               32            1281
    google-bert/bert-base-uncased          8              128            1307
    google-bert/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

TensorFlow
Hide TensorFlow content

    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
    --------------------------------------------------------------------------------
    google-bert/bert-base-uncased          8               8             0.005
    google-bert/bert-base-uncased          8               32            0.008
    google-bert/bert-base-uncased          8              128            0.022
    google-bert/bert-base-uncased          8              512            0.105
    --------------------------------------------------------------------------------

    ====================      INFERENCE - MEMORY - RESULT       ====================
    --------------------------------------------------------------------------------
    Model Name             Batch Size     Seq Length    Memory in MB
    --------------------------------------------------------------------------------
    google-bert/bert-base-uncased          8               8             1330
    google-bert/bert-base-uncased          8               32            1330
    google-bert/bert-base-uncased          8              128            1330
    google-bert/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. `google-bert/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.

Pytorch
Hide Pytorch content

    >>> 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

TensorFlow
Hide TensorFlow content

    >>> 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.

< > Update on GitHub