The Neuron Model Cache is a remote cache for compiled Neuron models in the neff
format.
It is integrated into the NeuronTrainer
and NeuronModelForCausalLM
classes to enable loading pretrained models from the cache instead of compiling them locally.
Note: it is not available for models exported using any other NeuronModelXX classes, that use a different export mechanism.
The Neuron Model Cache is hosted on the Hugging Face Hub and includes compiled files for all popular and supported optimum-neuron
pre-trained models.
Before training a Transformers or Diffusion model or loading a NeuronModelForCausalLM on Neuron platforms, it needs to be exported to neuron format
with torch-neuronx
.
When exporting a model, torch-neuronx
will:
The first step is relatively fast, but the compilation takes a lot of time.
To avoid recompiling all NEFF files every time a model is loaded on a NeuronX host, torch-neuronx
stores NEFF files in a local directory, usually /var/tmp/neuron-compile-cache
.
However, this local cache is not shared between platforms, which means that every time you train or export a model on a new host, you need to recompile it.
We created the Neuron Model Cache to solve this limitation by providing a public repository of precompiled model graphs.
Note: we also support the creation of private, secured, remote model cache.
The public model cache will be used when you use the NeuronTrainer
or NeuronModelForCausalLM
classes. There are no additional changes needed.
When exporting a model to neuron format, optimum-neuron
will simply look for cached NEFF files in the hub repository during the compilation of the
model subgraphs.
If the NEFF files are cached, they will be fetched from the hub and directly loaded instead of being recompiled.
The Optimum Neuron Cache is built on top of the NeuronX compiler cache.
It is important to understand that the cache operates on NEFF binaries, and not on the model itself.
As explained previously, each model exported to Neuron using the NeuronTrainer
or NeuronModelForCausalLM
is composed of XLA subgraphs.
Each subgraph is unique, and results from the combination of:
transformers
or transformers_neuronx
python modeling code,transformers
model config,input_shapes
selected during the export,When compiling a subgraph to a NEFF file, other parameters influence the result:
All these parameters are combined together to create a unique hash that identifies a NEFF file.
This has two very important consequences:
It is therefore very difficult to know in advance if the NEFFs associated to a specific model configuration are cached.
The neuron cache lookup is a feature allowing users to look for compatible cached model configurations before exporting a model for inference.
It is based on a dedicated registry composed of stored cached configurations.
Cached model configurations are stored as entries under a specific subfolder in the Neuron Model Cache:
neuronxcc-2.12.54.0+f631c2365
├── 0_REGISTRY
│ └── 0.0.18
│ └── llama
│ └── meta-llama
│ └── Llama-2-7b-chat-hf
│ └── 54c1f6689cd88f246fce.json
Each entry corresponds to the combination of a model configuration and its export parameters: this is as close as we can get to uniquely identify the exported model.
You can use the optimum-cli
to lookup for compatible cached entries by passing it a hub model_id or the path to a file
containing a model config.json
.
$ optimum-cli neuron cache lookup meta-llama/Llama-2-7b-chat-hf
*** 1 entrie(s) found in cache for meta-llama/Llama-2-7b-chat-hf ***
task: text-generation
batch_size: 1
num_cores: 24
auto_cast_type: fp16
sequence_length: 2048
compiler_type: neuronx-cc
compiler_version: 2.12.54.0+f631c2365
checkpoint_id: meta-llama/Llama-2-7b-chat-hf
checkpoint_revision: c1b0db933684edbfe29a06fa47eb19cc48025e93
Note that even if compatible cached entries exist, this does not always guarantee that the model will not be recompiled during export if you modified the compilation parameters or updated the neuronx packages.
The repository for the public cache is aws-neuron/optimum-neuron-cache
. This repository includes all precompiled files for commonly used models so that it is publicly available and free to use for everyone. But there are two limitations:
To alleviate that you can create your own private cache repository using the optimum-cli
or set the environment variable CUSTOM_CACHE_REPO
.
The Optimum CLI offers 2 subcommands for cache creation and setting:
create
: To create a new cache repository that you can use as a private Neuron Model cache.set
: To set the name of the Neuron cache repository locally, the repository needs to exists
and will be used by default by optimum-neuron
.Create a new Neuron cache repository:
optimum-cli neuron cache create --help
usage: optimum-cli neuron cache create [-h] [-n NAME] [--public]
optional arguments:
-h, --help show this help message and exit
-n NAME, --name NAME The name of the repo that will be used as a remote cache for the compilation files.
--public If set, the created repo will be public. By default the cache repo is private.
The -n
/ --name
option allows you to specify a name for the Neuron cache repo, if not set the default name will be used. The --public
flag allows you to make your Neuron cache public as it will be created as a private repository by default.
Example:
optimum-cli neuron cache create
Neuron cache created on the Hugging Face Hub: michaelbenayoun/optimum-neuron-cache [private].
Neuron cache name set locally to michaelbenayoun/optimum-neuron-cache in /home/michael/.cache/huggingface/optimum_neuron_custom_cache.
Set a different Trainiun cache repository:
usage: optimum-cli neuron cache set [-h] name
positional arguments:
name The name of the repo to use as remote cache.
optional arguments:
-h, --help show this help message and exit
Example:
optimum-cli neuron cache set michaelbenayoun/optimum-neuron-cache
Neuron cache name set locally to michaelbenayoun/optimum-neuron-cache in /home/michael/.cache/huggingface/optimum_neuron_custom_cache
The optimum-cli neuron cache set
command is useful when working on a new instance to use your own cache.
Using the CLI is not always feasible, and not very practical for small testing. In this case, you can simply set the environment variable CUSTOM_CACHE_REPO
.
For example, if your cache repo is called michaelbenayoun/my_custom_cache_repo
, you just need to do:
CUSTOM_CACHE_REPO="michaelbenayoun/my_custom_cache_repo" torchrun ...
or:
export CUSTOM_CACHE_REPO="michaelbenayoun/my_custom_cache_repo"
torchrun ...
You have to be logged into the Hugging Face Hub to be able to push and pull files from your private cache repository.
Cache system flow
At each the beginning of each training step, the NeuronTrainer computes a NeuronHash
and checks the cache repo(s) (official and custom) on the Hugging Face Hub to see if there are compiled files associated to this hash.
If that is the case, the files are downloaded directly to the local cache directory and no compilation is needed. Otherwise compilation is performed.
Just as for downloading compiled files, the NeuronTrainer will keep track of the newly created compilation files at each training step, and upload them to the Hugging Face Hub at save time or when training ends. This assumes that you have writing access to the cache repo, otherwise nothing will be pushed.
The Optimum CLI can be used to perform various cache-related tasks, as described by the optimum-cli neuron cache
command usage message:
usage: optimum-cli neuron cache [-h] {create,set,add,list} ...
positional arguments:
{create,set,add,list,synchronize,lookup}
create Create a model repo on the Hugging Face Hub to store Neuron X compilation files.
set Set the name of the Neuron cache repo to use locally (trainium only).
add Add a model to the cache of your choice (trainium only).
list List models in a cache repo (trainium only).
synchronize Synchronize local compiler cache with the hub cache (inferentia only).
lookup Lookup the neuronx compiler hub cache for the specified model id (inferentia only).
optional arguments:
-h, --help show this help message and exit
It is possible to add a model compilation files to a cache repo via the optimum-cli neuron cache add
command:
usage: optimum-cli neuron cache add [-h] -m MODEL --task TASK --train_batch_size TRAIN_BATCH_SIZE [--eval_batch_size EVAL_BATCH_SIZE] [--sequence_length SEQUENCE_LENGTH]
[--encoder_sequence_length ENCODER_SEQUENCE_LENGTH] [--decoder_sequence_length DECODER_SEQUENCE_LENGTH]
[--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] --precision {fp,bf16} --num_cores
{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} [--max_steps MAX_STEPS]
When running this command a small training session will be run and the resulting compilation files will be pushed.
If at least one of those requirements is not met, the command will fail.
Example:
optimum-cli neuron cache add \
--model prajjwal1/bert-tiny \
--task text-classification \
--train_batch_size 16 \
--eval_batch_size 16 \
--sequence_length 128 \
--gradient_accumulation_steps 32 \
--num_cores 32 \
--precision bf16
This will push compilation files for the prajjwal1/bert-tiny
model on the Neuron cache repo that was set up for the specified parameters.
It can also be convenient to request the cache repo to know which compilation files are available. This can be done via the optimum-cli neuron cache list
command:
usage: optimum-cli neuron cache list [-h] [-m MODEL] [-v VERSION] [name]
positional arguments:
name The name of the repo to list. Will use the locally saved cache repo if left unspecified.
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
The model name or path of the model to consider. If left unspecified, will list all available models.
-v VERSION, --version VERSION
The version of the Neuron X Compiler to consider. Will list all available versions if left unspecified.
As you can see, it is possible to:
-v / --version
argument.-m / --model
argument.Example:
optimum-cli neuron cache list aws-neuron/optimum-neuron-cache