Update handler.py

5d3586d almost 4 years ago

1.71 kB

from typing import Dict, List, Any
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification,pipeline
from transformers import pipeline
import deepspeed


class EndpointHandler():
    def __init__(self, path=""):
        # load model and tokenizer
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoModelForSequenceClassification.from_pretrained(path)

        # init deepspeed inference engine
        ds_model = deepspeed.init_inference(
            model=model,      # Transformers models
            mp_size=1,        # Number of GPU
            dtype=torch.half, # dtype of the weights (fp16)
            # injection_policy={"BertLayer" : HFBertLayerPolicy}, # replace BertLayer with DS HFBertLayerPolicy
            replace_method="auto", # Lets DS autmatically identify the layer to replace
            replace_with_kernel_inject=True, # replace the model with the kernel injector
        )

        # create acclerated pipeline
        self.pipeline = pipeline("text-classification", model=ds_model, tokenizer=tokenizer, device=0)


    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str`)
            date (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        # pass inputs with all kwargs in data
        if parameters is not None:
            prediction = self.pipeline(inputs, **parameters)
        else:
            prediction = self.pipeline(inputs)
        # postprocess the prediction
        return prediction

	from typing import Dict, List, Any
	import torch
	from transformers import AutoTokenizer, AutoModelForSequenceClassification,pipeline
	from transformers import pipeline
	import deepspeed


	class EndpointHandler():
	def __init__(self, path=""):
	# load model and tokenizer
	tokenizer = AutoTokenizer.from_pretrained(path)
	model = AutoModelForSequenceClassification.from_pretrained(path)

	# init deepspeed inference engine
	ds_model = deepspeed.init_inference(
	model=model, # Transformers models
	mp_size=1, # Number of GPU
	dtype=torch.half, # dtype of the weights (fp16)
	# injection_policy={"BertLayer" : HFBertLayerPolicy}, # replace BertLayer with DS HFBertLayerPolicy
	replace_method="auto", # Lets DS autmatically identify the layer to replace
	replace_with_kernel_inject=True, # replace the model with the kernel injector
	)

	# create acclerated pipeline
	self.pipeline = pipeline("text-classification", model=ds_model, tokenizer=tokenizer, device=0)


	def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
	"""
	data args:
	inputs (:obj: `str`)
	date (:obj: `str`)
	Return:
	A :obj:`list` \| `dict`: will be serialized and returned
	"""
	inputs = data.pop("inputs", data)
	parameters = data.pop("parameters", None)

	# pass inputs with all kwargs in data
	if parameters is not None:
	prediction = self.pipeline(inputs, **parameters)
	else:
	prediction = self.pipeline(inputs)
	# postprocess the prediction
	return prediction