Jetstream-PyTorch

JetStream Engine implementation in PyTorch

Latest Release:

The latest release version is tagged with jetstream-v0.2.3. If you are running the release version Please follow the README of the that version here: https://github.com/google/jetstream-pytorch/blob/jetstream-v0.2.3/README.md

Commandline Flags might have changed between the release version to HEAD.

Outline

Ssh to Cloud TPU VM (using v5e-8 TPU VM) a. Create a Cloud TPU VM if you haven’t
Download jetstream-pytorch github repo
Clone repo and install dependencies
Download and convert weights
Run checkpoint converter (quantizer)
Local run
Run the server
Run benchmarks
Typical Errors

Ssh to Cloud TPU VM (using v5e-8 TPU VM)

gcloud compute config-ssh
gcloud compute tpus tpu-vm ssh "your-tpu-vm" --project "your-project" --zone "your-project-zone"

Create a Cloud TPU VM in a GCP project if you haven’t

Follow the steps in

https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm

Clone repo and install dependencies

Get the jetstream-pytorch code

git clone https://github.com/google/jetstream-pytorch.git
git checkout jetstream-v0.2.3

(optional) Create a virtual env using venv or conda and activate it.

2. Run installation script:

cd jetstream-pytorch
source install_everything.sh

Download and convert weights

LLaMA

Get official llama weights from meta-llama

Following instructions here:

Llama-2: https://github.com/meta-llama/llama#download
Llama-3: https://github.com/meta-llama/llama3/#download

After you have downloaded the weights, it will also download a tokenizer.model file that is the tokenizer that we will use.

Gemma

Get Gemma Checkpoint from HuggingFace

Please sign agreement on Huggingface website to access Gemma checkpoints. Download Gemma PyTorch checkpoint using huggingface-cli. Gemma Tokenizer is included in the checkpoint.

# Install huggingface-cli and login if it's not set up.
pip install -U "huggingface_hub[cli]"
huggingface-cli login
huggingface-cli download google/gemma-7b-pytorch --local-dir $input_ckpt_dir

Mixtral

Get Mixtral Checkpoint from HuggingFace

Please sign agreement on Huggingface website to access Mixtral checkpoints. Download Mixtral PyTorch checkpoint using huggingface-cli. Mixtral Tokenizer is included in the checkpoint.

huggingface-cli download mistralai/Mixtral-8x7B-v0.1 --local-dir $input_ckpt_dir

Run weight safetensor convert

export input_ckpt_dir=Original llama weights directory
export output_ckpt_dir=The output directory
export model_name="llama-3" # or "llama-2", "gemma", "mixtral"
export quantize_weights=True # Whether to quantize weights
export quantize_type="int8_per_channel" # "quantize_weights" needs to be turned on. Availabe quantize type: {"int8", "int4"} x {"per_channel", "blockwise"}, "int8_per_channel" is the default option if not specified.
python -m convert_checkpoints --model_name=$model_name --input_checkpoint_dir=$input_ckpt_dir --output_checkpoint_dir=$output_ckpt_dir --quantize_type=$quantize_type --quantize_weights=$quantize_weights

Local run

Set tokenizer path

export tokenizer_path=tokenizer model file path

Llama-2 7b

python run_interactive.py --size=7b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml

Llama-2 13b

python run_interactive.py --size=13b --model_name=$model_name --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml

Llama-3 8b

python run_interactive.py --size=8b --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml

Llama-3 70b

python run_interactive.py --size=70b --model_name=$model_name --batch_size=8 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/llama.yaml

Gemma 7b

python run_interactive.py --model_name=$model_name --size=7b --batch_size=64 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml

Mixtral 8x7b

python run_interactive.py --model_name=$model_name --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir --tokenizer_path=$tokenizer_path --sharding_config=default_shardings/$model_name.yaml

Run the server

Here is an example to run the server with llama2 7B config.

python run_server.py --model_name=$model_name --size=7b --batch_size=128 --max_cache_length=2048 --quantize_weights=$quantize_weights --quantize_type=$quantize_type --quantize_kv_cache=$quantize_weights --checkpoint_path=$output_ckpt_dir   --tokenizer_path=$tokenizer_path --sharding_config="default_shardings/llama.yaml"

Now you can fire gRPC to it.

Optional flags:

--shard_on_batch=1 This makes the model to shard on the batch dimension. I.e. this runs in data parallel mode instead of model parallel. This will ignore the sharding config. This is recommended for Gemma 2B model, because Gemma 2B is small enough to fit on a single TPU chip.
--sharding_config=<path> This makes use of alternative sharding config instead of the ones in default_shardings directory.

Run the server with ray

Below are steps run server with ray:

Ssh to Cloud Multiple Host TPU VM (v5e-16 TPU VM)
Step 2 to step 5 in Outline
Setup ray cluster
Run server with ray

Setup Ray Cluster

Login host 0 VM, start ray head with below command:

ray start --head

Login other host VMs, start ray head with below command:

ray start --address='$ip:$port'

Note: Get address ip and port information from ray head.

Run server with ray

Here is an example to run the server with ray for llama2 7B model:

python run_server_with_ray.py --tpu_chips=16 -model_name=$model_name --size=7b --batch_size=96 --max_cache_length=2048 --quantize_weights=$quantize --quantize_type=$quantize_type --quantize_kv_cache=$quantize --checkpoint_path=$output_ckpt_dir   --tokenizer_path=$tokenizer_path --sharding_config="default_shardings/llama.yaml"

Run benchmark

Start the server and then go to the deps/JetStream folder (downloaded during install_everything.sh)

cd deps/JetStream
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
export dataset_path=ShareGPT_V3_unfiltered_cleaned_split.json
python benchmarks/benchmark_serving.py --tokenizer $tokenizer_path --num-prompts 2000  --dataset-path  $dataset_path --dataset sharegpt --save-request-outputs --warmup-first=True

Please look at deps/JetStream/benchmarks/README.md for more information.

Typical Errors

Unexpected keyword argument 'device'

Fix:

Uninstall jax and jaxlib dependencies
Reinstall using `source install_everything.sh

Out of memory

Fix:

Use smaller batch size
Use quantization

Links

JetStream

https://github.com/google/JetStream

MaxText

https://github.com/google/maxtext

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