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https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Add types to the encode/decode/token-count endpoints
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commit
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@ -27,7 +27,12 @@ from .typing import (
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ChatCompletionResponse,
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CompletionRequest,
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CompletionResponse,
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DecodeRequest,
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DecodeResponse,
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EncodeRequest,
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EncodeResponse,
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ModelInfoResponse,
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TokenCountResponse,
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to_dict
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)
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@ -206,26 +211,21 @@ async def handle_moderations(request: Request):
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return JSONResponse(response)
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@app.post("/v1/internal/encode")
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async def handle_token_encode(request: Request):
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body = await request.json()
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encoding_format = body.get("encoding_format", "")
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response = token_encode(body["input"], encoding_format)
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@app.post("/v1/internal/encode", response_model=EncodeResponse)
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async def handle_token_encode(request_data: EncodeRequest):
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response = token_encode(request_data.text)
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return JSONResponse(response)
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@app.post("/v1/internal/decode")
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async def handle_token_decode(request: Request):
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body = await request.json()
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encoding_format = body.get("encoding_format", "")
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response = token_decode(body["input"], encoding_format)
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return JSONResponse(response, no_debug=True)
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@app.post("/v1/internal/decode", response_model=DecodeResponse)
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async def handle_token_decode(request_data: DecodeRequest):
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response = token_decode(request_data.tokens)
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return JSONResponse(response)
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@app.post("/v1/internal/token-count")
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async def handle_token_count(request: Request):
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body = await request.json()
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response = token_count(body['prompt'])
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@app.post("/v1/internal/token-count", response_model=TokenCountResponse)
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async def handle_token_count(request_data: EncodeRequest):
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response = token_count(request_data.text)
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return JSONResponse(response)
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@ -3,34 +3,24 @@ from modules.text_generation import decode, encode
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def token_count(prompt):
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tokens = encode(prompt)[0]
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return {
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'results': [{
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'tokens': len(tokens)
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}]
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'length': len(tokens)
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}
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def token_encode(input, encoding_format):
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# if isinstance(input, list):
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def token_encode(input):
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tokens = encode(input)[0]
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if tokens.__class__.__name__ in ['Tensor', 'ndarray']:
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tokens = tokens.tolist()
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return {
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'results': [{
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'tokens': tokens,
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'length': len(tokens),
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}]
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'tokens': tokens,
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'length': len(tokens),
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}
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def token_decode(tokens, encoding_format):
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# if isinstance(input, list):
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# if encoding_format == "base64":
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# tokens = base64_to_float_list(tokens)
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output = decode(tokens)[0]
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def token_decode(tokens):
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output = decode(tokens)
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return {
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'results': [{
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'text': output
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}]
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'text': output
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}
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@ -121,6 +121,27 @@ class ChatCompletionResponse(BaseModel):
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usage: dict
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class EncodeRequest(BaseModel):
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text: str
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class DecodeRequest(BaseModel):
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tokens: List[int]
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class EncodeResponse(BaseModel):
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tokens: List[int]
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length: int
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class DecodeResponse(BaseModel):
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text: str
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class TokenCountResponse(BaseModel):
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length: int
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class ModelInfoResponse(BaseModel):
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model_name: str
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lora_names: List[str]
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@ -101,7 +101,7 @@ class LlamaCppModel:
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return self.model.tokenize(string)
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def decode(self, ids):
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def decode(self, ids, **kwargs):
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return self.model.detokenize(ids).decode('utf-8')
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def get_logits(self, tokens):
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@ -145,7 +145,7 @@ def decode(output_ids, skip_special_tokens=True):
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if shared.tokenizer is None:
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raise ValueError('No tokenizer is loaded')
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return shared.tokenizer.decode(output_ids, skip_special_tokens)
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return shared.tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens)
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def get_encoded_length(prompt):
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