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Merged
merged 2 commits into from
Feb 22, 2024
Merged

Add support for google/pegasus-xsum #1325

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Support for google/pegasus-xsum
levkk Feb 22, 2024
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levkk Feb 22, 2024
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51 changes: 39 additions & 12 deletions pgml-extension/src/bindings/transformers/transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,9 @@
PegasusTokenizer,
TrainingArguments,
Trainer,
GPTQConfig
GPTQConfig,
PegasusForConditionalGeneration,
PegasusTokenizer,
)
import threading

Expand Down Expand Up @@ -254,6 +256,8 @@ def __init__(self, model_name, **kwargs):
if "use_auth_token" in kwargs:
kwargs["token"] = kwargs.pop("use_auth_token")

self.model_name = model_name

if (
"task" in kwargs
and model_name is not None
Expand All @@ -278,29 +282,55 @@ def __init__(self, model_name, **kwargs):
model_name, **kwargs
)
elif self.task == "summarization" or self.task == "translation":
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name, **kwargs)
if model_name == "google/pegasus-xsum":
# HF auto model doesn't detect GPUs
self.model = PegasusForConditionalGeneration.from_pretrained(
model_name
)
else:
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name, **kwargs
)
elif self.task == "text-generation" or self.task == "conversational":
# See: https://huggingface.co/docs/transformers/main/quantization
if "quantization_config" in kwargs:
quantization_config = kwargs.pop("quantization_config")
quantization_config = GPTQConfig(**quantization_config)
self.model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, **kwargs)
self.model = AutoModelForCausalLM.from_pretrained(
model_name, quantization_config=quantization_config, **kwargs
)
else:
self.model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs)
self.model = AutoModelForCausalLM.from_pretrained(
model_name, **kwargs
)
else:
raise PgMLException(f"Unhandled task: {self.task}")

if model_name == "google/pegasus-xsum":
kwargs.pop("token", None)

if "token" in kwargs:
self.tokenizer = AutoTokenizer.from_pretrained(
model_name, token=kwargs["token"]
)
else:
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
if model_name == "google/pegasus-xsum":
self.tokenizer = PegasusTokenizer.from_pretrained(model_name)
else:
self.tokenizer = AutoTokenizer.from_pretrained(model_name)

pipe_kwargs = {
"model": self.model,
"tokenizer": self.tokenizer,
}

# https://huggingface.co/docs/transformers/en/model_doc/pegasus
if model_name == "google/pegasus-xsum":
pipe_kwargs["device"] = kwargs.get("device", "cpu")

self.pipe = transformers.pipeline(
self.task,
model=self.model,
tokenizer=self.tokenizer,
**pipe_kwargs,
)
else:
self.pipe = transformers.pipeline(**kwargs)
Expand All @@ -320,7 +350,7 @@ def stream(self, input, timeout=None, **kwargs):
self.tokenizer,
timeout=timeout,
skip_prompt=True,
skip_special_tokens=True
skip_special_tokens=True,
)
if "chat_template" in kwargs:
input = self.tokenizer.apply_chat_template(
Expand All @@ -343,9 +373,7 @@ def stream(self, input, timeout=None, **kwargs):
)
else:
streamer = TextIteratorStreamer(
self.tokenizer,
timeout=timeout,
skip_special_tokens=True
self.tokenizer, timeout=timeout, skip_special_tokens=True
)
input = self.tokenizer(input, return_tensors="pt", padding=True).to(
self.model.device
Expand Down Expand Up @@ -496,7 +524,6 @@ def embed(transformer, inputs, kwargs):
return embed_using(model, transformer, inputs, kwargs)



def clear_gpu_cache(memory_usage: None):
if not torch.cuda.is_available():
raise PgMLException(f"No GPU available")
Expand Down