virginie-d
commited on
Commit
•
380cd6b
1
Parent(s):
aba07ec
First batch of updates
Browse files- handler.py +43 -1
- requirements.txt +10 -0
handler.py
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from typing import Dict, List, Any
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from transformers import pipeline
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import torch, PIL, transformers, triton, sentencepiece, protobuf
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import torchvision, einops
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import xformers, accelerate
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from transformers import AutoModelForCausalLM, LlamaTokenizer
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class EndpointHandler():
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def __init__(self, path=""):
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self.model = AutoModelForCausalLM.from_pretrained(
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'THUDM/cogvlm-chat-hf',
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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# cache_dir='/tmp'
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)
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self.tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
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# create inference pipeline
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# self.pipeline = pipeline(model=model, tokenizer=tokenizer)
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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Args:
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data (:obj:):
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includes the input data and the parameters for the inference.
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Return:
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A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
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- "label": A string representing what the label/class is. There can be multiple labels.
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- "score": A score between 0 and 1 describing how confident the model is for this label/class.
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"""
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inputs = data.pop("inputs", data)
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gen_kwargs = {"max_length": 2048, "do_sample": False}
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# pass inputs with all kwargs in data
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# prediction = self.pipeline(inputs)
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outputs = self.model.generate(**inputs, **gen_kwargs)
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outputs = outputs[:, inputs['input_ids'].shape[1]:]
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prediction = self.tokenizer.decode(outputs[0])
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# post process the prediction
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return prediction
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requirements.txt
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torch
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Pillow
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transformers
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triton
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sentencepiece
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protobuf
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torchvision
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einops
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xformers
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accelerate
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