How to use BOINC AI Accelerate with Intel® Extension for PyTorch for cpu
Intel® Extension for PyTorch
IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs with only AVX2 (e.g., AMD CPUs or older Intel CPUs) might result in a better performance under IPEX, but not guaranteed. IPEX provides performance optimizations for CPU training with both Float32 and BFloat16. The usage of BFloat16 is the main focus of the following sections.
Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision.
IPEX installation:
IPEX release is following PyTorch, to install via pip:
🌍 Accelerate has integrated IPEX, all you need to do is enabling it through the config.
Scenario 1: Acceleration of No distributed CPU training
Run accelerate config on your machine:
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$ accelerate config
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In which compute environment are you running?
This machine
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Which type of machine are you using?
No distributed training
Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:yes
Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes
Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
Do you want to use DeepSpeed? [yes/NO]: NO
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Do you wish to use FP16 or BF16 (mixed precision)?
bf16
This will generate a config file that will be used automatically to properly set the default options when doing
For instance, here is how you would run the NLP example examples/nlp_example.py (from the root of the repo) with IPEX enabled. default_config.yaml that is generated after accelerate config
Scenario 2: Acceleration of distributed CPU training we use Intel oneCCL for communication, combined with Intel® MPI library to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. you could refer the here for the installation guide
Run accelerate config on your machine(node0):
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$ accelerate config
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In which compute environment are you running?
This machine
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Which type of machine are you using?
multi-CPU
How many different machines will you use (use more than 1 for multi-node training)? [1]: 4
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What is the rank of this machine?
0
What is the IP address of the machine that will host the main process? 36.112.23.24
What is the port you will use to communicate with the main process? 29500
Are all the machines on the same local network? Answer `no` if nodes are on the cloud and/or on different network hosts [YES/no]: yes
Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes
Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
How many CPU(s) should be used for distributed training? [1]:16
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Do you wish to use FP16 or BF16 (mixed precision)?
bf16
For instance, here is how you would run the NLP example examples/nlp_example.py (from the root of the repo) with IPEX enabled for distributed CPU training.
default_config.yaml that is generated after accelerate config
Set following env and using intel MPI to launch the training
In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument.
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$ cat hostfile
xxx.xxx.xxx.xxx #node0 ip
xxx.xxx.xxx.xxx #node1 ip
xxx.xxx.xxx.xxx #node2 ip
xxx.xxx.xxx.xxx #node3 ip
Now, run the following command in node0 and 16DDP will be enabled in node0,node1,node2,node3 with BF16 mixed precision:
