add usage
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4b2509e643
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@ -109,8 +109,8 @@ async def eval_rwkv(
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set_rwkv_config(model, global_var.get(global_var.Model_Config))
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set_rwkv_config(model, body)
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response = ""
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for response, delta in model.generate(
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response, prompt_tokens, completion_tokens = "", 0, 0
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for response, delta, prompt_tokens, completion_tokens in model.generate(
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prompt,
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stop=stop,
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):
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@ -184,6 +184,11 @@ async def eval_rwkv(
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"object": "chat.completion" if chat_mode else "text_completion",
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"response": response,
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"model": model.name,
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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},
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"choices": [
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{
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"message": {
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@ -384,6 +389,7 @@ async def embeddings(body: EmbeddingsBody, request: Request):
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base64_format = True
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embeddings = []
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prompt_tokens = 0
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if type(body.input) == list:
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if type(body.input[0]) == list:
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encoding = tiktoken.model.encoding_for_model("text-embedding-ada-002")
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@ -391,7 +397,8 @@ async def embeddings(body: EmbeddingsBody, request: Request):
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if await request.is_disconnected():
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break
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input = encoding.decode(body.input[i])
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embedding = model.get_embedding(input, body.fast_mode)
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embedding, token_len = model.get_embedding(input, body.fast_mode)
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prompt_tokens = prompt_tokens + token_len
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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@ -399,12 +406,15 @@ async def embeddings(body: EmbeddingsBody, request: Request):
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for i in range(len(body.input)):
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if await request.is_disconnected():
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break
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embedding = model.get_embedding(body.input[i], body.fast_mode)
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embedding, token_len = model.get_embedding(
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body.input[i], body.fast_mode
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)
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prompt_tokens = prompt_tokens + token_len
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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else:
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embedding = model.get_embedding(body.input, body.fast_mode)
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embedding, prompt_tokens = model.get_embedding(body.input, body.fast_mode)
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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@ -438,4 +448,5 @@ async def embeddings(body: EmbeddingsBody, request: Request):
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"object": "list",
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"data": ret_data,
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"model": model.name,
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"usage": {"prompt_tokens": prompt_tokens, "total_tokens": prompt_tokens},
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}
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@ -1,7 +1,7 @@
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import os
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import pathlib
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import copy
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from typing import Dict, List
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from typing import Dict, List, Tuple
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from utils.log import quick_log
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from fastapi import HTTPException
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from pydantic import BaseModel, Field
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@ -71,7 +71,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
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+ "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
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)
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logits = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
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logits, _ = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
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try:
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state_cache.add_state(
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state_cache.AddStateBody(
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@ -92,6 +92,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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def run_rnn(self, _tokens: List[str], newline_adj: int = 0):
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tokens = [int(x) for x in _tokens]
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token_len = len(tokens)
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self.model_tokens += tokens
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while len(tokens) > 0:
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@ -104,23 +105,24 @@ The following is a coherent verbose detailed conversation between a girl named {
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if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
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out[self.model_tokens[-1]] = -999999999
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return out
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return out, token_len
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def get_embedding(self, input: str, fast_mode: bool) -> List[float]:
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def get_embedding(self, input: str, fast_mode: bool) -> Tuple[List[float], int]:
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if fast_mode:
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embedding = self.fast_embedding(
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embedding, token_len = self.fast_embedding(
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self.fix_tokens(self.pipeline.encode(input)), None
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)
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else:
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self.model_state = None
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self.model_tokens = []
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self.run_rnn(self.fix_tokens(self.pipeline.encode(input)))
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_, token_len = self.run_rnn(self.fix_tokens(self.pipeline.encode(input)))
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embedding = self.model_state[-5].tolist()
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embedding = (embedding / np.linalg.norm(embedding)).tolist()
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return embedding
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return embedding, token_len
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def fast_embedding(self, tokens: List[str], state):
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tokens = [int(x) for x in tokens]
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token_len = len(tokens)
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self = self.model
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with torch.no_grad():
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@ -253,7 +255,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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ory,
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)
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return state[0].tolist()
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return state[0].tolist(), token_len
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def generate(self, prompt: str, stop: str = None):
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quick_log(None, None, "Generation Prompt:\n" + prompt)
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@ -274,8 +276,11 @@ The following is a coherent verbose detailed conversation between a girl named {
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self.model_tokens = copy.deepcopy(cache["tokens"])
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logits = copy.deepcopy(cache["logits"])
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prompt_token_len = 0
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if delta_prompt != "":
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logits = self.run_rnn(self.fix_tokens(self.pipeline.encode(delta_prompt)))
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logits, prompt_token_len = self.run_rnn(
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self.fix_tokens(self.pipeline.encode(delta_prompt))
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)
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try:
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state_cache.add_state(
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state_cache.AddStateBody(
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@ -293,6 +298,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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occurrence: Dict = {}
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completion_token_len = 0
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response = ""
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for i in range(self.max_tokens_per_generation):
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for n in occurrence:
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@ -305,14 +311,15 @@ The following is a coherent verbose detailed conversation between a girl named {
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)
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if token == END_OF_TEXT:
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yield response, ""
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yield response, "", prompt_token_len, completion_token_len
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break
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if token not in occurrence:
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occurrence[token] = 1
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else:
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occurrence[token] += 1
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logits = self.run_rnn([token])
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logits, _ = self.run_rnn([token])
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completion_token_len = completion_token_len + 1
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delta: str = self.pipeline.decode(self.model_tokens[out_last:])
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if "\ufffd" not in delta: # avoid utf-8 display issues
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response += delta
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@ -330,7 +337,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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)
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except HTTPException:
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pass
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yield response, ""
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yield response, "", prompt_token_len, completion_token_len
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break
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out_last = begin + i + 1
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if i == self.max_tokens_per_generation - 1:
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@ -345,7 +352,7 @@ The following is a coherent verbose detailed conversation between a girl named {
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)
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except HTTPException:
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pass
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yield response, delta
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yield response, delta, prompt_token_len, completion_token_len
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class ModelConfigBody(BaseModel):
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