mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import os
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from pathlib import Path
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from queue import Queue
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from threading import Thread
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import numpy as np
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from tokenizers import Tokenizer
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import modules.shared as shared
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from modules.callbacks import Iteratorize
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np.set_printoptions(precision=4, suppress=True, linewidth=200)
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os.environ['RWKV_JIT_ON'] = '1'
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os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
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from rwkv.model import RWKV
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from rwkv.utils import PIPELINE, PIPELINE_ARGS
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class RWKVModel:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path, dtype="fp16", device="cuda"):
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tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
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if shared.args.rwkv_strategy is None:
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model = RWKV(model=os.path.abspath(path), strategy=f'{device} {dtype}')
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else:
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model = RWKV(model=os.path.abspath(path), strategy=shared.args.rwkv_strategy)
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pipeline = PIPELINE(model, os.path.abspath(tokenizer_path))
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result = self()
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result.pipeline = pipeline
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return result
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def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
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args = PIPELINE_ARGS(
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temperature = temperature,
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top_p = top_p,
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top_k = top_k,
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alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3)
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alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3)
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token_ban = token_ban, # ban the generation of some tokens
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token_stop = token_stop
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)
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return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
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def generate_with_streaming(self, **kwargs):
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with Iteratorize(self.generate, kwargs, callback=None) as generator:
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reply = kwargs['context']
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for token in generator:
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reply += token
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yield reply
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class RWKVTokenizer:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path):
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tokenizer_path = path / "20B_tokenizer.json"
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tokenizer = Tokenizer.from_file(os.path.abspath(tokenizer_path))
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result = self()
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result.tokenizer = tokenizer
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return result
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def encode(self, prompt):
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return self.tokenizer.encode(prompt).ids
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def decode(self, ids):
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return self.tokenizer.decode(ids)
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