mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:12:07 +00:00
62b2a8b67b
This PR adds paligemma modeling code Blog post: https://huggingface.co/blog/paligemma Transformers PR: https://github.com/huggingface/transformers/pull/30814 install the latest changes and run with ```bash # get the weights # text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf # run TGI text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf ``` basic example sending various requests ```python from huggingface_hub import InferenceClient client = InferenceClient("http://127.0.0.1:3000") images = [ "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png", ] prompts = [ "What animal is in this image?", "Name three colors in this image.", "What are 10 colors in this image?", "Where is the cow standing?", "answer en Where is the cow standing?", "Is there a bird in the image?", "Is ther a cow in the image?", "Is there a rabbit in the image?", "how many birds are in the image?", "how many rabbits are in the image?", ] for img in images: print(f"\nImage: {img.split('/')[-1]}") for prompt in prompts: inputs = f"{prompt}\n" json_data = { "inputs": inputs, "parameters": { "max_new_tokens": 30, "do_sample": False, }, } generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False) print([f"{prompt}\n{generated_output}"]) ``` --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import torch
|
|
import torch.distributed
|
|
|
|
from opentelemetry import trace
|
|
from typing import Optional
|
|
from transformers import AutoConfig, AutoTokenizer
|
|
|
|
from text_generation_server.models import FlashCausalLM
|
|
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
|
|
FlashGemmaForCausalLM,
|
|
)
|
|
from text_generation_server.utils import (
|
|
initialize_torch_distributed,
|
|
weight_files,
|
|
Weights,
|
|
)
|
|
|
|
tracer = trace.get_tracer(__name__)
|
|
|
|
|
|
class FlashGemma(FlashCausalLM):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
speculator: Optional[str] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
self.process_group, rank, world_size = initialize_torch_distributed()
|
|
if torch.cuda.is_available():
|
|
device = torch.device(f"cuda:{rank}")
|
|
dtype = torch.bfloat16 if dtype is None else dtype
|
|
else:
|
|
raise NotImplementedError("FlashGemma is only available on GPU")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
|
|
config = AutoConfig.from_pretrained(
|
|
model_id, revision=revision, trust_remote_code=trust_remote_code
|
|
)
|
|
config.quantize = quantize
|
|
config.speculator = speculator
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
|
|
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
|
weights = Weights(filenames, device, dtype, process_group=self.process_group)
|
|
if config.quantize in ["gptq", "awq"]:
|
|
weights._set_gptq_params(model_id, revision)
|
|
|
|
# TODO hardcoded
|
|
prefix = "language_model"
|
|
model = FlashGemmaForCausalLM(prefix, config, weights, causal=True)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
super(FlashGemma, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
num_layers=len(model.model.layers),
|
|
num_kv_heads=model.model.num_key_value_heads,
|
|
head_size=model.model.head_size,
|
|
dtype=dtype,
|
|
device=device,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|