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import argparse
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import time
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from time import perf_counter
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from optimum.utils import NormalizedTextConfig, NormalizedConfigManager
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from optimum.intel.openvino import OVModelForCausalLM
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from optimum.intel.openvino.utils import OV_XML_FILE_NAME
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from transformers import (PretrainedConfig, AutoTokenizer, AutoConfig,
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TextIteratorStreamer, StoppingCriteriaList, StoppingCriteria)
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from typing import Optional, Union, Dict, List, Tuple
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from pathlib import Path
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from threading import Thread
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import torch
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class StopOnTokens(StoppingCriteria):
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def __init__(self, token_ids):
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self.token_ids = token_ids
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def __call__(
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self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
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) -> bool:
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for stop_id in self.token_ids:
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if input_ids[0][-1] == stop_id:
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return True
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return False
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class OVCHATGLMModel(OVModelForCausalLM):
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"""
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Optimum intel compatible model wrapper for CHATGLM2
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"""
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def _reshape(
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self,
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model: "Model",
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*args, **kwargs
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):
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shapes = {}
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for inputs in model.inputs:
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shapes[inputs] = inputs.get_partial_shape()
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shapes[inputs][0] = -1
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input_name = inputs.get_any_name()
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if input_name.startswith('beam_idx'):
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continue
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if input_name.startswith('past_key_values'):
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shapes[inputs][1] = -1
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shapes[inputs][2] = 2
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elif shapes[inputs].rank.get_length() > 1:
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shapes[inputs][1] = -1
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model.reshape(shapes)
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return model
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument('-h',
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'--help',
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action='help',
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help='Show this help message and exit.')
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parser.add_argument('-m',
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'--model_path',
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required=True,
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type=str,
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help='Required. model path')
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parser.add_argument('-l',
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'--max_sequence_length',
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default=256,
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required=False,
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type=int,
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help='Required. maximun length of output')
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parser.add_argument('-d',
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'--device',
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default='CPU',
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required=False,
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type=str,
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help='Required. device for inference')
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args = parser.parse_args()
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model_dir = args.model_path
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ov_config = {"PERFORMANCE_HINT": "LATENCY",
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"NUM_STREAMS": "1", "CACHE_DIR": ""}
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#model start time
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model_start_time=time.perf_counter()
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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print("====Compiling model====")
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ov_model = OVCHATGLMModel.from_pretrained(
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model_dir,
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device=args.device,
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ov_config=ov_config,
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config=AutoConfig.from_pretrained(model_dir, trust_remote_code=True),
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trust_remote_code=True,
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)
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model_end_time=time.perf_counter()
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print("Model_loading_before Inference:::: ", model_end_time-model_start_time )
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