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Posted to discuss-archive@tvm.apache.org by popojames via Apache TVM Discuss <no...@discuss.tvm.ai> on 2021/10/23 22:18:13 UTC
[Apache TVM Discuss] [Questions] Setting the CPU affinity and number
of cores locally without RPC Remote
Hello,
This is a continuing discussion from [Use all cores in a big.LITTLE architecture](https://discuss.tvm.apache.org/t/use-all-cores-in-a-big-little-architecture/8474/6):
I am wondering how can we adjust CPU affinity and the numbers of the thread **locally** without using "remote setting".
Now I was able to set the numbers of the thread through RPC.remtoe as the previous discussion shows (command as shown below).
[quote="FrozenGene, post:5, topic:8474"]
```
config_threadpool = remote.get_function('runtime.config_threadpool')
# affinity_mode: kBig = 1, kLittle = -1. kDefault = 0. pass 1 or -1 to control the cores
config_threadpool(affinity_mode, num_threads)
```
[/quote]
However, I am still wondering can we set it locally without RPC Remote like the following figure shows? According to https://discuss.tvm.apache.org/t/can-tvm-split-work-into-different-layers-and-assign-layers-into-different-cores/11161/10?u=popojames, it seems it is possible to do so.
Does anyone have any idea about this question?
Thanks :)
cc @FrozenGene @hjiang
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[Apache TVM Discuss] [Questions] Setting the CPU affinity and number
of cores locally without RPC Remote
Posted by popojames via Apache TVM Discuss <no...@discuss.tvm.ai>.
This problem is fixed by myslef.
I was able to set it locally without RPC Remote.
What I did is very similar with the aforementioned setting: hooks c++ into python
[quote="popojames, post:3, topic:11306"]
I tried to create a function in “module” (python/runtime/module.py) that hooks c++ into python so that I can directly use
> module.module.get_global_function(‘runtime.config_threadpool’)
[/quote]
In my **python/runtime/module.py**, I add "conf_set" function inside,
to set the configuration, I can direct use this command just like we did in remote.get_function
> config_threadpool = module.module.config_set(remote.get_function('runtime.config_threadpool')
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[Apache TVM Discuss] [Questions] Setting the CPU affinity and number
of cores locally without RPC Remote
Posted by popojames via Apache TVM Discuss <no...@discuss.tvm.ai>.
According to this post:
[quote="FrozenGene, post:5, topic:8474"]
I assume you have got ‘remote’ handle correctly. Then we could get the func:
```
config_threadpool = remote.get_function('runtime.config_threadpool')
# affinity_mode: kBig = 1, kLittle = -1. kDefault = 0. pass 1 or -1 to control the cores
config_threadpool(affinity_mode, num_threads)
```
[/quote]
I tried to create a function in "module" (python/runtime/module.py) that hooks c++ into python so that I can directly use
> module.module.get_global_function('runtime.config_threadpool')
, which is very similar to what we did in remote.get_function('runtime.config_threadpool')
remote is "RPsession(object)" with "get_function" which use "get_global_function" that can get global function. Therefore, I put **get_global_function** into **python/runtime/module.py**
And **python/runtime/module.py** will use ""get_global_func"" from **tvm/_ffi/_ctypes/packed_func.py**
May I ask are the above steps the correct way to follow to enable such setting?
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[Apache TVM Discuss] [Questions] Setting the CPU affinity and number
of cores locally without RPC Remote
Posted by popojames via Apache TVM Discuss <no...@discuss.tvm.ai>.
To be more precise on this question, I am working on Hikey 970 with 4 little cores (core id: 0123) and 4 big cores (core id:4567). I am working on splitting the entire relay graph into graphs and running it in pipeline format.
I know that with the following command, I can control the CPU number of threads **locally.**
> export TVM_BIND_THREADS=1 (enable CPU affinity)
> export TVM_NUM_THREADS= N (N=1~4 control Number of CPU you wanna use)
However, I have two questions on this problem:
1. This command only allows me to use 1/2/3/4 big cores only and it seems that the default setting doesn't allow users to use the small cores. Even if I set TVM_NUM_THREADS larger than 4, it still uses 4 big cores only. Is it possible to use small cores, or big and small cores (e.g. 3 big 4 small)?
2. Moreover, this command would set all of the benchmarks using assign cores. Since I split the original graph into subgraphs, what I would like to have is actually **assigning specific subgraphs to use specific cores** (something like subgraph 0 -> 2 big cores, subgraph 1 -> 2 big cores, subgraph 2 -> 4 small cores) instead of changing the entire setting globally.
@hjiang may I ask is it possible to share some of your implementations even though it's still under development?
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