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from typing import List, Union, Optional, Literal
import dataclasses
from tenacity import (
retry,
stop_after_attempt, # type: ignore
wait_random_exponential, # type: ignore
)
import openai
import requests
import json
import os
from groq import Groq
MessageRole = Literal["system", "user", "assistant"]
@dataclasses.dataclass()
class Message():
role: MessageRole
content: str
def message_to_str(message: Message) -> str:
return f"{message.role}: {message.content}"
def messages_to_str(messages: List[Message]) -> str:
return "\n".join([message_to_str(message) for message in messages])
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def gpt_completion(
model: str,
prompt: str,
max_tokens: int = 1024,
stop_strs: Optional[List[str]] = None,
temperature: float = 0.0,
num_comps=1,
) -> Union[List[str], str]:
response = openai.Completion.create(
model=model,
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
top_p=1,
frequency_penalty=0.0,
presence_penalty=0.0,
stop=stop_strs,
n=num_comps,
)
if num_comps == 1:
return response.choices[0].text # type: ignore
return [choice.text for choice in response.choices] # type: ignore
@retry(wait=wait_random_exponential(min=1, max=180), stop=stop_after_attempt(6))
def gpt_chat(
model: str,
messages: List[Message],
max_tokens: int = 1024,
temperature: float = 0.0,
num_comps=1,
) -> Union[List[str], str]:
response = openai.ChatCompletion.create(
model=model,
messages=[dataclasses.asdict(message) for message in messages],
max_tokens=max_tokens,
temperature=temperature,
top_p=1,
frequency_penalty=0.0,
presence_penalty=0.0,
n=num_comps,
)
if num_comps == 1:
return response.choices[0].message.content # type: ignore
print("temp", temperature)
return [choice.message.content for choice in response.choices] # type: ignore
class ModelBase():
def __init__(self, name: str):
self.name = name
self.is_chat = False
def __repr__(self) -> str:
return f'{self.name}'
def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
raise NotImplementedError
def generate(self, prompt: str, max_tokens: int = 1024, stop_strs: Optional[List[str]] = None, temperature: float = 0.0, num_comps=1) -> Union[List[str], str]:
raise NotImplementedError
class GroqBase():
def __init__(self):
self.is_chat = True
self.client = Groq(
api_key=os.environ.get("GROQ_API_KEY"),
)
def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
resps = []
for i in range(num_comps):
chat_completion = self.client.chat.completions.create(
messages=[dataclasses.asdict(message) for message in messages],
model="llama3-8b-8192",
)
response_text = chat_completion.choices[0].message.content
resps.append(response_text)
if num_comps == 1:
return resps[0]
else:
return resps
class Samba():
def __init__(self):
self.is_chat = True
def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
resps = []
for i in range(num_comps):
payload = {
"inputs": [dataclasses.asdict(message) for message in messages],
"params": {
"do_sample": {"type": "bool", "value": True},
"max_tokens_allowed_in_completion": {"type": "int", "value": 500},
"min_token_capacity_for_completion": {"type": "int", "value": 2},
"temperature": {"type": "float", "value": 0.7},
"top_p": {"type": "float", "value": 0.1},
"top_k": {"type": "int", "value": 40},
"skip_special_token": {"type": "bool", "value": True},
"repetition_penalty": {"type": "float", "value": 1.15},
"stop_sequences": {"type": "list", "value": ["[INST]", "[INST]", "[/INST]", "[/INST]"]}
},
"expert": "llama3-8b"
}
url = 'https://kjddazcq2e2wzvzv.snova.ai/api/v1/chat/completion'
headers = {
"Authorization": "Basic bGlnaHRuaW5nOlUyM3pMcFlHY3dmVzRzUGFy",
"Content-Type": "application/json"
}
post_response = requests.post(url, json=payload, headers=headers, stream=True)
response_text = ""
for line in post_response.iter_lines():
if line.startswith(b"data: "):
data_str = line.decode('utf-8')[6:]
try:
line_json = json.loads(data_str)
content = line_json.get("stream_token", "")
if content:
response_text += content
except json.JSONDecodeError as e:
pass
resps.append(response_text)
if num_comps == 1:
return resps[0]
else:
return resps
class GPTChat(ModelBase):
def __init__(self, model_name: str):
self.name = model_name
self.is_chat = True
def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
return gpt_chat(self.name, messages, max_tokens, temperature, num_comps)
class GPT4(GPTChat):
def __init__(self):
super().__init__("gpt-4")
class GPT4o(GPTChat):
def __init__(self):
super().__init__("gpt-4o")
class GPT35(GPTChat):
def __init__(self):
super().__init__("gpt-3.5-turbo")
class GPTDavinci(ModelBase):
def __init__(self, model_name: str):
self.name = model_name
def generate(self, prompt: str, max_tokens: int = 1024, stop_strs: Optional[List[str]] = None, temperature: float = 0, num_comps=1) -> Union[List[str], str]:
return gpt_completion(self.name, prompt, max_tokens, stop_strs, temperature, num_comps)
class HFModelBase(ModelBase):
"""
Base for huggingface chat models
"""
def __init__(self, model_name: str, model, tokenizer, eos_token_id=None):
self.name = model_name
self.model = model
self.tokenizer = tokenizer
self.eos_token_id = eos_token_id if eos_token_id is not None else self.tokenizer.eos_token_id
self.is_chat = True
def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
# NOTE: HF does not like temp of 0.0.
if temperature < 0.0001:
temperature = 0.0001
prompt = self.prepare_prompt(messages)
outputs = self.model.generate(
prompt,
max_new_tokens=min(
max_tokens, self.model.config.max_position_embeddings),
use_cache=True,
do_sample=True,
temperature=temperature,
top_p=0.95,
eos_token_id=self.eos_token_id,
num_return_sequences=num_comps,
)
outs = self.tokenizer.batch_decode(outputs, skip_special_tokens=False)
assert isinstance(outs, list)
for i, out in enumerate(outs):
assert isinstance(out, str)
outs[i] = self.extract_output(out)
if len(outs) == 1:
return outs[0] # type: ignore
else:
return outs # type: ignore
def prepare_prompt(self, messages: List[Message]):
raise NotImplementedError
def extract_output(self, output: str) -> str:
raise NotImplementedError
class StarChat(HFModelBase):
def __init__(self):
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"HuggingFaceH4/starchat-beta",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
"HuggingFaceH4/starchat-beta",
)
super().__init__("starchat", model, tokenizer, eos_token_id=49155)
def prepare_prompt(self, messages: List[Message]):
prompt = ""
for i, message in enumerate(messages):
prompt += f"<|{message.role}|>\n{message.content}\n<|end|>\n"
if i == len(messages) - 1:
prompt += "<|assistant|>\n"
return self.tokenizer.encode(prompt, return_tensors="pt").to(self.model.device)
def extract_output(self, output: str) -> str:
out = output.split("<|assistant|>")[1]
if out.endswith("<|end|>"):
out = out[:-len("<|end|>")]
return out
class CodeLlama(HFModelBase):
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
def __init__(self, version: Literal["34b", "13b", "7b"] = "34b"):
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
f"codellama/CodeLlama-{version}-Instruct-hf",
add_eos_token=True,
add_bos_token=True,
padding_side='left'
)
model = AutoModelForCausalLM.from_pretrained(
f"codellama/CodeLlama-{version}-Instruct-hf",
torch_dtype=torch.bfloat16,
device_map="auto",
)
super().__init__("codellama", model, tokenizer)
def prepare_prompt(self, messages: List[Message]):
if messages[0].role != "system":
messages = [
Message(role="system", content=self.DEFAULT_SYSTEM_PROMPT)
] + messages
messages = [
Message(role=messages[1].role, content=self.B_SYS +
messages[0].content + self.E_SYS + messages[1].content)
] + messages[2:]
assert all([msg.role == "user" for msg in messages[::2]]) and all(
[msg.role == "assistant" for msg in messages[1::2]]
), (
"model only supports 'system', 'user' and 'assistant' roles, "
"starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
)
messages_tokens: List[int] = sum(
[
self.tokenizer.encode(
f"{self.B_INST} {(prompt.content).strip()} {self.E_INST} {(answer.content).strip()} ",
)
for prompt, answer in zip(
messages[::2],
messages[1::2],
)
],
[],
)
assert messages[-1].role == "user", f"Last message must be from user, got {messages[-1].role}"
messages_tokens += self.tokenizer.encode(
f"{self.B_INST} {(messages[-1].content).strip()} {self.E_INST}",
)
# remove eos token from last message
messages_tokens = messages_tokens[:-1]
import torch
return torch.tensor([messages_tokens]).to(self.model.device)
def extract_output(self, output: str) -> str:
out = output.split("[/INST]")[-1].split("</s>")[0].strip()
return out