# TorchServe with Intel® Extension for PyTorch\* TorchServe can be used with Intel® Extension for PyTorch\* to give performance boost on Intel hardware.1 Here we show how to use TorchServe with Intel® Extension for PyTorch\*. 1. While Intel® Extension for PyTorch\* benefits all platforms, platforms with AVX512 benefit the most. ## Contents of this Document * [Install Intel® Extension for PyTorch\*](#install-intel-extension-for-pytorch) * [Serving model with Intel® Extension for PyTorch\*](#serving-model-with-intel-extension-for-pytorch) * [TorchServe with Launcher](#torchserve-with-launcher) * [Creating and Exporting INT8 model for Intel® Extension for PyTorch\*](#creating-and-exporting-int8-model-for-intel-extension-for-pytorch) * [Benchmarking with Launcher](#benchmarking-with-launcher) * [Performance Boost with Intel® Extension for PyTorch\* and Launcher](#performance-boost-with-intel-extension-for-pytorch-and-launcher) ## Install Intel® Extension for PyTorch\* Refer to the documentation [here](../installation.md). ## Serving model with Intel® Extension for PyTorch\* After installation, all it needs to use TorchServe with Intel® Extension for PyTorch\* is to enable it in `config.properties`. ``` ipex_enable=true ``` Once Intel® Extension for PyTorch\* is enabled, deploying PyTorch model follows the same procedure shown [here](https://pytorch.org/serve/use_cases.html). TorchServe with Intel® Extension for PyTorch\* can deploy any model and do inference. ## TorchServe with Launcher Launcher is a script to automate the process of tunining configuration setting on Intel hardware to boost performance. Tuning configurations such as OMP_NUM_THREADS, thread affinity, memory allocator can have a dramatic effect on performance. Refer to [Performance Tuning Guide](./tuning_guide.md) and [Launch Script Usage Guide](./launch_script.md) for details on performance tuning with launcher. All it needs to use TorchServe with launcher is to set its configuration in `config.properties`. Add the following lines in `config.properties` to use launcher with its default configuration. ``` ipex_enable=true cpu_launcher_enable=true ``` Launcher by default uses `numactl` if it's installed to ensure socket is pinned and thus memory is allocated from local numa node. To use launcher without numactl, add the following lines in `config.properties`. ``` ipex_enable=true cpu_launcher_enable=true cpu_launcher_args=--disable_numactl ``` Launcher by default uses only non-hyperthreaded cores if hyperthreading is present to avoid core compute resource sharing. To use launcher with all cores, both physical and logical, add the following lines in `config.properties`. ``` ipex_enable=true cpu_launcher_enable=true cpu_launcher_args=--use_logical_core ``` Below is an example of passing multiple args to `cpu_launcher_args`. ``` ipex_enable=true cpu_launcher_enable=true cpu_launcher_args=--use_logical_core --disable_numactl ``` Below are some useful `cpu_launcher_args` to note. Italic values are default if applicable. 1. Memory Allocator: [ PTMalloc `--use_default_allocator` | *TCMalloc `--enable_tcmalloc`* | JeMalloc `--enable_jemalloc`] * PyTorch by default uses PTMalloc. TCMalloc/JeMalloc generally gives better performance. 2. OpenMP library: [GNU OpenMP `--disable_iomp` | *Intel OpenMP*] * PyTorch by default uses GNU OpenMP. Launcher by default uses Intel OpenMP. Intel OpenMP library generally gives better performance. 3. Node id: [`--node_id`] * Launcher by default uses all NUMA nodes. Limit memory access to local memories on the Nth Numa node to avoid Non-Uniform Memory Access (NUMA). Refer to [Launch Script Usage Guide](./launch_script.md) for a full list of tunable configuration of launcher. And refer to [Performance Tuning Guide](./tuning_guide.md) for more details. ### Launcher Core Pinning to Boost Performance of TorchServe Multi Worker Inference When running [multi-worker inference](https://pytorch.org/serve/management_api.html#scale-workers) with Torchserve (Required torchserve>=0.6.1), launcher pin cores to workers to boost performance. Internally, launcher equally divides the number of cores by the number of workers such that each worker is pinned to assigned cores. Doing so avoids core overlap among workers which can signficantly boost performance for TorchServe multi-worker inference. For example, assume running 4 workers on a machine with Intel(R) Xeon(R) Platinum 8180 CPU, 2 sockets, 28 cores per socket, 2 threads per core. Launcher will bind worker 0 to cores 0-13, worker 1 to cores 14-27, worker 2 to cores 28-41, and worker 3 to cores 42-55. CPU usage is shown below. 4 main worker threads were launched, each launching 14 threads affinitized to the assigned physical cores. ![26](https://user-images.githubusercontent.com/93151422/170373651-fd8a0363-febf-4528-bbae-e1ddef119358.gif) #### Scaling workers Additionally when dynamically [scaling the number of workers](https://pytorch.org/serve/management_api.html#scale-workers), cores that were pinned to killed workers by the launcher could be left unutilized. To address this problem, launcher internally restarts the workers to re-distribute cores that were pinned to killed workers to the remaining, alive workers. This is taken care internally, so users do not have to worry about this. Continuing with the above example with 4 workers, assume killing workers 2 and 3. If cores were not re-distributed after the scale down, cores 28-55 would be left unutilized. Instead, launcher re-distributes cores 28-55 to workers 0 and 1 such that now worker 0 binds to cores 0-27 and worker 1 binds to cores 28-55.2 CPU usage is shown below. 4 main worker threads were initially launched. Then after scaling down the number of workers from 4 to 2, 2 main worker threads were launched, each launching 28 threads affinitized to the assigned physical cores. ![worker_scaling](https://user-images.githubusercontent.com/93151422/170374697-7497c2d5-4c17-421b-9993-1434d1f722f6.gif) 2. Serving is interrupted for few seconds while re-distributing cores to scaled workers. Again, all it needs to use TorchServe with launcher core pinning for multiple workers as well as scaling workers is to set its configuration in `config.properties`. Add the following lines in `config.properties` to use launcher with its default configuration. ``` cpu_launcher_enable=true ``` ## Creating and Exporting INT8 model for Intel® Extension for PyTorch\* Intel® Extension for PyTorch\* supports both eager and torchscript mode. In this section, we show how to deploy INT8 model for Intel® Extension for PyTorch\*. Refer to [here](../features/int8_overview.md) for more details on Intel® Extension for PyTorch\* optimizations for quantization. ### 1. Creating a serialized file First create `.pt` serialized file using Intel® Extension for PyTorch\* INT8 inference. Here we show two examples with BERT and ResNet50. #### BERT ``` import torch import intel_extension_for_pytorch as ipex from transformers import BertModel # load the model model = BertModel.from_pretrained('bert-base-uncased') model = model.eval() # define dummy input tensor to use for the model's forward call to record operations in the model for tracing vocab_size = model.config.vocab_size batch_size = 1 seq_length = 384 dummy_tensor = torch.randint(vocab_size, size=[batch_size, seq_length]) from intel_extension_for_pytorch.quantization import prepare, convert # ipex supports two quantization schemes: static and dynamic # default dynamic qconfig qconfig = ipex.quantization.default_dynamic_qconfig # prepare and calibrate model = prepare(model, qconfig, example_inputs=dummy_tensor) # convert and deploy model = convert(model) with torch.no_grad(): model = torch.jit.trace(model, dummy_tensor, check_trace=False, strict=False) model = torch.jit.freeze(model) torch.jit.save(model, 'bert_int8_jit.pt') ``` #### ResNet50 ``` import torch import intel_extension_for_pytorch as ipex import torchvision.models as models # load the model model = models.resnet50(pretrained=True) model = model.eval() # define dummy input tensor to use for the model's forward call to record operations in the model for tracing N, C, H, W = 1, 3, 224, 224 dummy_tensor = torch.randn(N, C, H, W) from intel_extension_for_pytorch.quantization import prepare, convert # ipex supports two quantization schemes: static and dynamic # default static qconfig qconfig = ipex.quantization.default_static_qconfig # prepare and calibrate model = prepare(model, qconfig, example_inputs=dummy_tensor, inplace=False) n_iter = 100 for i in range(n_iter): model(dummy_tensor) # convert and deploy model = convert(model) with torch.no_grad(): model = torch.jit.trace(model, dummy_tensor) model = torch.jit.freeze(model) torch.jit.save(model, 'rn50_int8_jit.pt') ``` ### 2. Creating a Model Archive Once the serialized file ( `.pt`) is created, it can be used with `torch-model-archiver` as ususal. Use the following command to package `rn50_int8_jit.pt` into `rn50_ipex_int8.mar`. ``` torch-model-archiver --model-name rn50_ipex_int8 --version 1.0 --serialized-file rn50_int8_jit.pt --handler image_classifier ``` Similarly, use the following command in the [Huggingface_Transformers directory](https://github.com/pytorch/serve/tree/master/examples/Huggingface_Transformers) to package `bert_int8_jit.pt` into `bert_ipex_int8.mar`. ``` torch-model-archiver --model-name bert_ipex_int8 --version 1.0 --serialized-file bert_int8_jit.pt --handler ./Transformer_handler_generalized.py --extra-files "./setup_config.json,./Seq_classification_artifacts/index_to_name.json" ``` ### 3. Start TorchServe to serve the model Make sure to set `ipex_enable=true` in `config.properties`. Use the following command to start TorchServe with Intel® Extension for PyTorch\*. ``` torchserve --start --ncs --model-store model_store --ts-config config.properties ``` ### 4. Registering and Deploying model Registering and deploying the model follows the same steps shown [here](https://pytorch.org/serve/use_cases.html). ## Benchmarking with Launcher Launcher can be used with TorchServe official [benchmark](https://github.com/pytorch/serve/tree/master/benchmarks) to launch server and benchmark requests with optimal configuration on Intel hardware. In this section we provide examples of benchmarking with launcher with its default configuration. Add the following lines to `config.properties` in the benchmark directory to use launcher with its default setting. ``` ipex_enable=true cpu_launcher_enable=true ``` The rest of the steps for benchmarking follows the same steps shown [here](https://github.com/pytorch/serve/tree/master/benchmarks). `model_log.log` contains information and command that were used for this execution launch. CPU usage on a machine with Intel(R) Xeon(R) Platinum 8180 CPU, 2 sockets, 28 cores per socket, 2 threads per core is shown as below: ![launcher_default_2sockets](https://user-images.githubusercontent.com/93151422/144373537-07787510-039d-44c4-8cfd-6afeeb64ac78.gif) ``` $ cat logs/model_log.log 2021-12-01 21:22:40,096 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home//.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance 2021-12-01 21:22:40,096 - __main__ - INFO - OMP_NUM_THREADS=56 2021-12-01 21:22:40,096 - __main__ - INFO - Using Intel OpenMP 2021-12-01 21:22:40,096 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0 2021-12-01 21:22:40,096 - __main__ - INFO - KMP_BLOCKTIME=1 2021-12-01 21:22:40,096 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so 2021-12-01 21:22:40,096 - __main__ - WARNING - Numa Aware: cores:[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55] in different NUMA node ``` CPU usage on a machine with Intel(R) Xeon(R) Platinum 8375C CPU, 1 socket, 2 cores per socket, 2 threads per socket is shown as below: ![launcher_default_1socket](https://user-images.githubusercontent.com/93151422/144372993-92b2ca96-f309-41e2-a5c8-bf2143815c93.gif) ``` $ cat logs/model_log.log 2021-12-02 06:15:03,981 - __main__ - WARNING - Both TCMalloc and JeMalloc are not found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or /home//.local/lib/ so the LD_PRELOAD environment variable will not be set. This may drop the performance 2021-12-02 06:15:03,981 - __main__ - INFO - OMP_NUM_THREADS=2 2021-12-02 06:15:03,982 - __main__ - INFO - Using Intel OpenMP 2021-12-02 06:15:03,982 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0 2021-12-02 06:15:03,982 - __main__ - INFO - KMP_BLOCKTIME=1 2021-12-02 06:15:03,982 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so ``` ### Benchmarking with Launcher Core Pinning As described previously in [TorchServe with Launcher](#torchserve-with-launcher), launcher core pinning boosts performance of multi-worker inference. We'll demonstrate launcher core pinning with TorchServe benchmark, but keep in mind that launcher core pinning is a generic feature applicable to any TorchServe multi-worker inference use casese. For example, assume running 4 workers ``` python benchmark-ab.py --workers 4 ``` on a machine with Intel(R) Xeon(R) Platinum 8180 CPU, 2 sockets, 28 cores per socket, 2 threads per core. Launcher will bind worker 0 to cores 0-13, worker 1 to cores 14-27, worker 2 to cores 28-41, and worker 3 to cores 42-55. All it needs to use TorchServe with launcher's core pinning is to enable launcher in `config.properties`. Add the following lines to `config.properties` in the benchmark directory to use launcher's core pinning: ``` cpu_launcher_enable=true ``` CPU usage is shown as below: ![launcher_core_pinning](https://user-images.githubusercontent.com/93151422/159063975-e7e8d4b0-e083-4733-bdb6-4d92bdc10556.gif) 4 main worker threads were launched, then each launched a num_physical_cores/num_workers number (14) of threads affinitized to the assigned physical cores. 

$ cat logs/model_log.log
2022-03-24 10:41:32,223 - __main__ - INFO - Use TCMalloc memory allocator
2022-03-24 10:41:32,223 - __main__ - INFO - OMP_NUM_THREADS=14
2022-03-24 10:41:32,223 - __main__ - INFO - Using Intel OpenMP
2022-03-24 10:41:32,223 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-03-24 10:41:32,223 - __main__ - INFO - KMP_BLOCKTIME=1
2022-03-24 10:41:32,223 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so:/lib/libtcmalloc.so
2022-03-24 10:41:32,223 - __main__ - INFO - numactl -C 0-13 -m 0 /bin/python -u /lib/python/site-packages/ts/model_service_worker.py --sock-type unix --sock-name /tmp/.ts.sock.9000

2022-03-24 10:49:03,760 - __main__ - INFO - Use TCMalloc memory allocator
2022-03-24 10:49:03,761 - __main__ - INFO - OMP_NUM_THREADS=14
2022-03-24 10:49:03,762 - __main__ - INFO - Using Intel OpenMP
2022-03-24 10:49:03,762 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-03-24 10:49:03,762 - __main__ - INFO - KMP_BLOCKTIME=1
2022-03-24 10:49:03,762 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so:/lib/libtcmalloc.so
2022-03-24 10:49:03,763 - __main__ - INFO - numactl -C 14-27 -m 0 /bin/python -u /lib/python/site-packages/ts/model_service_worker.py --sock-type unix --sock-name /tmp/.ts.sock.9001

2022-03-24 10:49:26,274 - __main__ - INFO - Use TCMalloc memory allocator
2022-03-24 10:49:26,274 - __main__ - INFO - OMP_NUM_THREADS=14
2022-03-24 10:49:26,274 - __main__ - INFO - Using Intel OpenMP
2022-03-24 10:49:26,274 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-03-24 10:49:26,274 - __main__ - INFO - KMP_BLOCKTIME=1
2022-03-24 10:49:26,274 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so:/lib/libtcmalloc.so
2022-03-24 10:49:26,274 - __main__ - INFO - numactl -C 28-41 -m 1 /bin/python -u /lib/python/site-packages/ts/model_service_worker.py --sock-type unix --sock-name /tmp/.ts.sock.9002

2022-03-24 10:49:42,975 - __main__ - INFO - Use TCMalloc memory allocator
2022-03-24 10:49:42,975 - __main__ - INFO - OMP_NUM_THREADS=14
2022-03-24 10:49:42,975 - __main__ - INFO - Using Intel OpenMP
2022-03-24 10:49:42,975 - __main__ - INFO - KMP_AFFINITY=granularity=fine,compact,1,0
2022-03-24 10:49:42,975 - __main__ - INFO - KMP_BLOCKTIME=1
2022-03-24 10:49:42,975 - __main__ - INFO - LD_PRELOAD=/lib/libiomp5.so:/lib/libtcmalloc.so
2022-03-24 10:49:42,975 - __main__ - INFO - numactl -C 42-55 -m 1 /bin/python -u /lib/python/site-packages/ts/model_service_worker.py --sock-type unix --sock-name /tmp/.ts.sock.9003
## Performance Boost with Intel® Extension for PyTorch\* and Launcher ![pdt_perf](https://user-images.githubusercontent.com/93151422/159067306-dfd604e3-8c66-4365-91ae-c99f68d972d5.png) Above shows performance improvement of Torchserve with Intel® Extension for PyTorch\* and launcher on ResNet50 and BERT-base-uncased. Torchserve official [apache-bench benchmark](https://github.com/pytorch/serve/tree/master/benchmarks#benchmarking-with-apache-bench) on Amazon EC2 m6i.24xlarge was used to collect the results2. Add the following lines in ```config.properties``` to reproduce the results. Notice that launcher is configured such that a single instance uses all physical cores on a single socket to avoid cross socket communication and core overlap. ``` ipex_enable=true cpu_launcher_enable=true cpu_launcher_args=--node_id 0 --enable_jemalloc ``` Use the following command to reproduce the results. ``` python benchmark-ab.py --url {modelUrl} --input {inputPath} --concurrency 1 ``` For example, run the following command to reproduce latency performance of ResNet50 with data type of Intel® Extension for PyTorch\* int8 and batch size of 1. Refer to [Creating and Exporting INT8 model for Intel® Extension for PyTorch\*](#creating-and-exporting-int8-model-for-intel-extension-for-pytorch) for steps to creating ```rn50_ipex_int8.mar``` file for ResNet50 with Intel® Extension for PyTorch\* int8 data type. ``` python benchmark-ab.py --url 'file:///model_store/rn50_ipex_int8.mar' --concurrency 1 ``` For example, run the following command to reproduce latency performance of BERT with data type of Intel® Extension for PyTorch\* int8 and batch size of 1. Refer to [Creating and Exporting INT8 model for Intel® Extension for PyTorch\*](#creating-and-exporting-int8-model-for-intel-extension-for-pytorch) for steps to creating ```bert_ipex_int8.mar``` file for BERT with Intel® Extension for PyTorch\* int8 data type. ``` python benchmark-ab.py --url 'file:///model_store/bert_ipex_int8.mar' --input '../examples/Huggingface_Transformers/Seq_classification_artifacts/sample_text_captum_input.txt' --concurrency 1 ``` 3. Amazon EC2 m6i.24xlarge was used for benchmarking purpose only. For multi-core instances, Intel® Extension for PyTorch\* optimizations automatically scale and leverage full instance resources.