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Removed GPTQ pipeline class #1165

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Removed GPTQ pipeline class #1165

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45 changes: 0 additions & 45 deletions pgml-extension/src/bindings/transformers/transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,46 +119,6 @@ def __next__(self):
return value


class GPTQPipeline(object):
def __init__(self, model_name, **task):
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from huggingface_hub import snapshot_download

model_path = snapshot_download(model_name)

quantized_config = BaseQuantizeConfig.from_pretrained(model_path)
self.model = AutoGPTQForCausalLM.from_quantized(
model_path, quantized_config=quantized_config, **task
)
if "use_fast_tokenizer" in task:
self.tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=task.pop("use_fast_tokenizer")
)
else:
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.task = "text-generation"

def stream(self, inputs, **kwargs):
streamer = TextIteratorStreamer(self.tokenizer)
inputs = self.tokenizer(inputs, return_tensors="pt").to(self.model.device)
generation_kwargs = dict(inputs, streamer=streamer, **kwargs)
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
return streamer

def __call__(self, inputs, **kwargs):
outputs = []
for input in inputs:
tokens = (
self.tokenizer(input, return_tensors="pt")
.to(self.model.device)
.input_ids
)
token_ids = self.model.generate(input_ids=tokens, **kwargs)[0]
outputs.append(self.tokenizer.decode(token_ids))
return outputs


class ThreadedGeneratorIterator:
def __init__(self, output, starting_input):
self.output = output
Expand Down Expand Up @@ -294,17 +254,12 @@ def create_pipeline(task):
ensure_device(task)
convert_dtype(task)
model_name = task.get("model", None)
model_type = None
if "model_type" in task:
model_type = task["model_type"]
if model_name:
lower = model_name.lower()
else:
lower = None
if lower and ("-ggml" in lower or "-gguf" in lower):
pipe = GGMLPipeline(model_name, **task)
elif lower and "-gptq" in lower and not (model_type == "mistral" or model_type == "llama"):
pipe = GPTQPipeline(model_name, **task)
else:
try:
pipe = StandardPipeline(model_name, **task)
Expand Down