import asyncio
import json
from threading import Lock
from typing import List, Union
from enum import Enum
import base64
from fastapi import APIRouter, Request, status, HTTPException
from sse_starlette.sse import EventSourceResponse
from pydantic import BaseModel, Field
import numpy as np
import tiktoken
from utils.rwkv import *
from utils.log import quick_log
import global_var
router = APIRouter()
class Role(Enum):
User = "user"
Assistant = "assistant"
System = "system"
class Message(BaseModel):
role: Role
content: str = Field(min_length=0)
raw: bool = Field(False, description="Whether to treat content as raw text")
default_stop = [
"\n\nUser",
"\n\nQuestion",
"\n\nQ",
"\n\nHuman",
"\n\nBob",
]
class ChatCompletionBody(ModelConfigBody):
messages: Union[List[Message], None]
model: Union[str, None] = "rwkv"
stream: bool = False
stop: Union[str, List[str], None] = default_stop
user_name: Union[str, None] = Field(
None, description="Internal user name", min_length=1
)
assistant_name: Union[str, None] = Field(
None, description="Internal assistant name", min_length=1
)
presystem: bool = Field(
True, description="Whether to insert default system prompt at the beginning"
)
model_config = {
"json_schema_extra": {
"example": {
"messages": [
{"role": Role.User.value, "content": "hello", "raw": False}
],
"model": "rwkv",
"stream": False,
"stop": None,
"user_name": None,
"assistant_name": None,
"presystem": True,
"max_tokens": 1000,
"temperature": 1.2,
"top_p": 0.5,
"presence_penalty": 0.4,
"frequency_penalty": 0.4,
}
}
}
class CompletionBody(ModelConfigBody):
prompt: Union[str, List[str], None]
model: Union[str, None] = "rwkv"
stream: bool = False
stop: Union[str, List[str], None] = None
model_config = {
"json_schema_extra": {
"example": {
"prompt": "The following is an epic science fiction masterpiece that is immortalized, "
+ "with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n",
"model": "rwkv",
"stream": False,
"stop": None,
"max_tokens": 100,
"temperature": 1.2,
"top_p": 0.5,
"presence_penalty": 0.4,
"frequency_penalty": 0.4,
}
}
}
completion_lock = Lock()
requests_num = 0
async def eval_rwkv(
model: AbstractRWKV,
request: Request,
body: ModelConfigBody,
prompt: str,
stream: bool,
stop: Union[str, List[str], None],
chat_mode: bool,
):
global requests_num
requests_num = requests_num + 1
quick_log(request, None, "Start Waiting. RequestsNum: " + str(requests_num))
while completion_lock.locked():
if await request.is_disconnected():
requests_num = requests_num - 1
print(f"{request.client} Stop Waiting (Lock)")
quick_log(
request,
None,
"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
)
return
await asyncio.sleep(0.1)
else:
with completion_lock:
if await request.is_disconnected():
requests_num = requests_num - 1
print(f"{request.client} Stop Waiting (Lock)")
quick_log(
request,
None,
"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
)
return
set_rwkv_config(model, global_var.get(global_var.Model_Config))
set_rwkv_config(model, body)
response, prompt_tokens, completion_tokens = "", 0, 0
for response, delta, prompt_tokens, completion_tokens in model.generate(
prompt,
stop=stop,
):
if await request.is_disconnected():
break
if stream:
yield json.dumps(
{
"object": "chat.completion.chunk"
if chat_mode
else "text_completion",
# "response": response,
"model": model.name,
"choices": [
{
"delta": {"content": delta},
"index": 0,
"finish_reason": None,
}
if chat_mode
else {
"text": delta,
"index": 0,
"finish_reason": None,
}
],
}
)
# torch_gc()
requests_num = requests_num - 1
if await request.is_disconnected():
print(f"{request.client} Stop Waiting")
quick_log(
request,
body,
response + "\nStop Waiting. RequestsNum: " + str(requests_num),
)
return
quick_log(
request,
body,
response + "\nFinished. RequestsNum: " + str(requests_num),
)
if stream:
yield json.dumps(
{
"object": "chat.completion.chunk"
if chat_mode
else "text_completion",
# "response": response,
"model": model.name,
"choices": [
{
"delta": {},
"index": 0,
"finish_reason": "stop",
}
if chat_mode
else {
"text": "",
"index": 0,
"finish_reason": "stop",
}
],
}
)
yield "[DONE]"
else:
yield {
"object": "chat.completion" if chat_mode else "text_completion",
# "response": response,
"model": model.name,
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
},
"choices": [
{
"message": {
"role": Role.Assistant.value,
"content": response,
},
"index": 0,
"finish_reason": "stop",
}
if chat_mode
else {
"text": response,
"index": 0,
"finish_reason": "stop",
}
],
}
@router.post("/v1/chat/completions", tags=["Completions"])
@router.post("/chat/completions", tags=["Completions"])
async def chat_completions(body: ChatCompletionBody, request: Request):
model: TextRWKV = global_var.get(global_var.Model)
if model is None:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
if body.messages is None or body.messages == []:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "messages not found")
interface = model.interface
user = model.user if body.user_name is None else body.user_name
bot = model.bot if body.assistant_name is None else body.assistant_name
is_raven = model.rwkv_type == RWKVType.Raven
completion_text: str = ""
basic_system: Union[str, None] = None
if body.presystem:
if body.messages[0].role == Role.System:
basic_system = body.messages[0].content
if basic_system is None:
completion_text = (
f"""
The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
{bot} is very intelligent, creative and friendly. \
{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
{bot} likes to tell {user} a lot about herself and her opinions. \
{bot} usually gives {user} kind, helpful and informative advices.\n
"""
if is_raven
else (
f"{user}{interface} hi\n\n{bot}{interface} Hi. "
+ "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"
)
)
else:
if not body.messages[0].raw:
basic_system = (
basic_system.replace("\r\n", "\n")
.replace("\r", "\n")
.replace("\n\n", "\n")
.replace("\n", " ")
.strip()
)
completion_text = (
(
f"The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. "
if is_raven
else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
)
+ basic_system.replace("You are", f"{bot} is" if is_raven else "I am")
.replace("you are", f"{bot} is" if is_raven else "I am")
.replace("You're", f"{bot} is" if is_raven else "I'm")
.replace("you're", f"{bot} is" if is_raven else "I'm")
.replace("You", f"{bot}" if is_raven else "I")
.replace("you", f"{bot}" if is_raven else "I")
.replace("Your", f"{bot}'s" if is_raven else "My")
.replace("your", f"{bot}'s" if is_raven else "my")
.replace("你", f"{bot}" if is_raven else "我")
+ "\n\n"
)
for message in body.messages[(0 if basic_system is None else 1) :]:
append_message: str = ""
if message.role == Role.User:
append_message = f"{user}{interface} " + message.content
elif message.role == Role.Assistant:
append_message = f"{bot}{interface} " + message.content
elif message.role == Role.System:
append_message = message.content
if not message.raw:
append_message = (
append_message.replace("\r\n", "\n")
.replace("\r", "\n")
.replace("\n\n", "\n")
.strip()
)
completion_text += append_message + "\n\n"
completion_text += f"{bot}{interface}"
user_code = model.pipeline.decode([model.pipeline.encode(user)[0]])
bot_code = model.pipeline.decode([model.pipeline.encode(bot)[0]])
if type(body.stop) == str:
body.stop = [body.stop, f"\n\n{user_code}", f"\n\n{bot_code}"]
elif type(body.stop) == list:
body.stop.append(f"\n\n{user_code}")
body.stop.append(f"\n\n{bot_code}")
elif body.stop is None:
body.stop = default_stop
if body.stream:
return EventSourceResponse(
eval_rwkv(
model, request, body, completion_text, body.stream, body.stop, True
)
)
else:
try:
return await eval_rwkv(
model, request, body, completion_text, body.stream, body.stop, True
).__anext__()
except StopAsyncIteration:
return None
@router.post("/v1/completions", tags=["Completions"])
@router.post("/completions", tags=["Completions"])
async def completions(body: CompletionBody, request: Request):
model: AbstractRWKV = global_var.get(global_var.Model)
if model is None:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
if body.prompt is None or body.prompt == "" or body.prompt == []:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "prompt not found")
if type(body.prompt) == list:
body.prompt = body.prompt[0] # TODO: support multiple prompts
if body.stream:
return EventSourceResponse(
eval_rwkv(model, request, body, body.prompt, body.stream, body.stop, False)
)
else:
try:
return await eval_rwkv(
model, request, body, body.prompt, body.stream, body.stop, False
).__anext__()
except StopAsyncIteration:
return None
class EmbeddingsBody(BaseModel):
input: Union[str, List[str], List[List[int]], None]
model: Union[str, None] = "rwkv"
encoding_format: str = None
fast_mode: bool = False
model_config = {
"json_schema_extra": {
"example": {
"input": "a big apple",
"model": "rwkv",
"encoding_format": None,
"fast_mode": False,
}
}
}
def embedding_base64(embedding: List[float]) -> str:
return base64.b64encode(np.array(embedding).astype(np.float32)).decode("utf-8")
@router.post("/v1/embeddings", tags=["Embeddings"])
@router.post("/embeddings", tags=["Embeddings"])
@router.post("/v1/engines/text-embedding-ada-002/embeddings", tags=["Embeddings"])
@router.post("/engines/text-embedding-ada-002/embeddings", tags=["Embeddings"])
async def embeddings(body: EmbeddingsBody, request: Request):
model: AbstractRWKV = global_var.get(global_var.Model)
if model is None:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
if body.input is None or body.input == "" or body.input == [] or body.input == [[]]:
raise HTTPException(status.HTTP_400_BAD_REQUEST, "input not found")
global requests_num
requests_num = requests_num + 1
quick_log(request, None, "Start Waiting. RequestsNum: " + str(requests_num))
while completion_lock.locked():
if await request.is_disconnected():
requests_num = requests_num - 1
print(f"{request.client} Stop Waiting (Lock)")
quick_log(
request,
None,
"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
)
return
await asyncio.sleep(0.1)
else:
with completion_lock:
if await request.is_disconnected():
requests_num = requests_num - 1
print(f"{request.client} Stop Waiting (Lock)")
quick_log(
request,
None,
"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
)
return
base64_format = False
if body.encoding_format == "base64":
base64_format = True
embeddings = []
prompt_tokens = 0
if type(body.input) == list:
if type(body.input[0]) == list:
encoding = tiktoken.model.encoding_for_model(
"text-embedding-ada-002"
)
for i in range(len(body.input)):
if await request.is_disconnected():
break
input = encoding.decode(body.input[i])
embedding, token_len = model.get_embedding(
input, body.fast_mode
)
prompt_tokens = prompt_tokens + token_len
if base64_format:
embedding = embedding_base64(embedding)
embeddings.append(embedding)
else:
for i in range(len(body.input)):
if await request.is_disconnected():
break
embedding, token_len = model.get_embedding(
body.input[i], body.fast_mode
)
prompt_tokens = prompt_tokens + token_len
if base64_format:
embedding = embedding_base64(embedding)
embeddings.append(embedding)
else:
embedding, prompt_tokens = model.get_embedding(
body.input, body.fast_mode
)
if base64_format:
embedding = embedding_base64(embedding)
embeddings.append(embedding)
requests_num = requests_num - 1
if await request.is_disconnected():
print(f"{request.client} Stop Waiting")
quick_log(
request,
None,
"Stop Waiting. RequestsNum: " + str(requests_num),
)
return
quick_log(
request,
None,
"Finished. RequestsNum: " + str(requests_num),
)
ret_data = [
{
"object": "embedding",
"index": i,
"embedding": embedding,
}
for i, embedding in enumerate(embeddings)
]
return {
"object": "list",
"data": ret_data,
"model": model.name,
"usage": {
"prompt_tokens": prompt_tokens,
"total_tokens": prompt_tokens,
},
}