Updated to custom SGMV kernel to fix issue with certain ranks

[tgaddair]

The existing cutlass based SGMV kernel produces erroneous output for certain LoRA ranks (ex: 32). More recent versions of Punica have introduced a custom shrink kernel that resolves the issue, but only supports ranks [16, 128]. As such, we introduce logic in this PR to use the custom kernel when the rank falls within the valid range, otherwise fallback to the legacy kernel (which does work for rank 8, but may not work for other ranks, need additional testing).

Example:

curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs": "<|system|> You are a helpful assistant <|user|> What is deep learning? </s> <|assistant|>", "parameters": {"max_new_tokens": 64, "adapter_id": "qblocks/mistral_7b_norobots"}}' \
    -H 'Content-Type: application/json'

Compared with transformers:

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

config = PeftConfig.from_pretrained("qblocks/mistral_7b_norobots")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
model = PeftModel.from_pretrained(model, "qblocks/mistral_7b_norobots")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

prompt = "<|system|> You are a helpful assistant <|user|> What is deep learning? </s> <|assistant|>"
inputs = tokenizer(prompt, return_tensors="pt")
generation_output = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(generation_output[0], skip_special_tokens=True))

Output:

Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. It is a powerful tool for solving complex problems in fields such as natural language processing, computer vision, and speech recognition. Deep learning algorithms can learn from large amounts of data and make predictions or decisions based on that data. They can

Updated vendored Kernels to punica-ai/punica@87cb9f5

Related: #68.