parallelformers
An Efficient Model Parallelization Toolkit for Deployment.
- Parallelformers, which is based on Megatron LM, is designed to make model parallelization easier.
- You can parallelize various models in HuggingFace Transformers on multiple GPUs with a single line of code.
- Currently, Parallelformers only supports inference. Training features are NOT included.
Why Parallelformers?
You can load a model that is too large for a single GPU. For example, using Parallelformers, you can load a model of 12GB on two 8 GB GPUs. In addition, you can save your precious money because usually multiple smaller size GPUs are less costly than a single larger size GPU.
Installation
Parallelformers can be easily installed using the pip package manager. All the dependencies such as torch, transformers, and dacite should be installed automatically with the following command. Be careful that the name is plural.
pip install parallelformers
Getting Started
1. Create a HuggingFace transformers model.
You don't need to call .half() or .cuda() as those functions will be invoked automatically. It is more memory efficient to start parallelization on the CPU.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
2. Put the model in the parallelize() function.
from parallelformers import parallelize
parallelize(model, num_gpus=2, fp16=True, verbose='detail')
Since nvidia-smi shows the reserved cache area, it is difficult to check the exact allocated memory. To check the allocated memory state well, you can set the verbose option as 'detail' or 'simple'. (default is None)
|===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 2721 MB | 2967 MB | 2967 MB | 251905 KB |
| from large pool | 2720 MB | 2966 MB | 2966 MB | 251904 KB |
| from small pool | 1 MB | 1 MB | 1 MB | 1 KB |
|---------------------------------------------------------------------------|
GPU:0 => 2.72GB
|===========================================================================|
| PyTorch CUDA memory summary, device ID 1 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 2721 MB | 2967 MB | 2967 MB | 251905 KB |
| from large pool | 2720 MB | 2966 MB | 2966 MB | 251904 KB |
| from small pool | 1 MB | 1 MB | 1 MB | 1 KB |
|---------------------------------------------------------------------------|
GPU:1 => 2.72GB
3. Do Inference as usual.
You don't have to call .cuda() when creating input tokens. Note that you should input both input tokens and attention masks to the model. (**inputs is the recommended way for this)
inputs = tokenizer("Parallelformers is", return_tensors="pt")
outputs = model.generate(
**inputs,
num_beams=5,
no_repeat_ngram_size=4,
max_length=15,
)
print(f"Output: {tokenizer.batch_decode(outputs)[0]}")
Output: Parallelformers is an open-source library for parallel programming ...
4. Deploy the model to the server as usual.
The parallelization process does not affect the web server because they are automatically synchronized.
from flask import Flask
app = Flask(__name__)
@app.route("/generate_text/<text>")
def generate_text(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(
**inputs,
num_beams=5,
no_repeat_ngram_size=4,
max_length=15,
)
outputs = tokenizer.batch_decode(
outputs,
skip_special_tokens=True,
)
return {
"inputs": text,
"outputs": outputs[0],
}
app.run(host="0.0.0.0", port=5000)
You can send a request to the web server as follows:
$ curl -X get "YOUR_IP:5000/generate_text/Messi"
And the following result should be returned.
{"inputs": "Messi", "outputs": "Messi is the best player in the world right now. He is the"}
5. Check the current GPU states.
You can check GPU states using .memory_allocated(), .memory_reserved() and .memory_chached() to make sure the parallelization is successful.
model.memory_allocated()
model.memory_reserved()
model.memory_chached()
{'cuda:0':XXXXXX, 'cuda:1':XXXXXX}
6. Manage the model parallelization states.
You can manage model parallelization states using .cuda(), .cpu() and .to(). The model parallelization process ends if you call those functions.
model.cuda()
print(torch.cuda.memory_summary(0))
print(torch.cuda.memory_summary(1))
Check the allocated memory status using torch.cuda.memory_summary().
|===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 5121 MB | 5121 MB | 5121 MB | 1024 B |
| from large pool | 5120 MB | 5120 MB | 5120 MB | 0 B |
| from small pool | 1 MB | 1 MB | 1 MB | 1024 B |
|---------------------------------------------------------------------------|
GPU0 => 5.12GB
|===========================================================================|
| PyTorch CUDA memory summary, device ID 1 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 0 B | 1024 B | 1024 B | 1024 B |
| from large pool | 0 B | 0 B | 0 B | 0 B |
| from small pool | 0 B | 1024 B | 1024 B | 1024 B |
|---------------------------------------------------------------------------|
GPU1 => 0.00GB
If you switch to the CPU mode, it works like this.
model.cpu()
print(torch.cuda.memory_summary(0))
print(torch.cuda.memory_summary(1))
|===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 0 B | 5121 MB | 5121 MB | 5121 MB |
| from large pool | 0 B | 5120 MB | 5120 MB | 5120 MB |
| from small pool | 0 B | 1 MB | 1 MB | 1 MB |
|---------------------------------------------------------------------------|
GPU0 => 0.00GB
|===========================================================================|
| PyTorch CUDA memory summary, device ID 1 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 0 B | 1024 B | 1024 B | 1024 B |
| from large pool | 0 B | 0 B | 0 B | 0 B |
| from small pool | 0 B | 1024 B | 1024 B | 1024 B |
|---------------------------------------------------------------------------|
GPU1 => 0.00GB
