diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index bd3fc784b..aea5b947b 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -6,20 +6,26 @@ jobs: runs-on: ubuntu-latest strategy: max-parallel: 5 + matrix: + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 - - name: Set up Python 3.11 - uses: actions/setup-python@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 with: - python-version: 3.11.2 + python-version: ${{ matrix.python-version }} + - name: Display Python version + run: python -c "import sys; print(sys.version)" - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - poetry install --all-extras + poetry lock + poetry install --with test -E chat - name: Type-checking package with mypy run: | # Run this mypy instance against our main package. poetry run pip install types-protobuf==4.24.0.4 poetry run mypy --install-types --non-interactive sotopia + poetry run mypy --install-types --non-interactive sotopia-chat poetry run mypy --strict . diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml new file mode 100644 index 000000000..ce091d243 --- /dev/null +++ b/.github/workflows/publish-to-pypi.yml @@ -0,0 +1,38 @@ +# This workflow will upload a Python Package using Twine when a release is created +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + release: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.11 + uses: actions/setup-python@v4 + with: + python-version: 3.11.2 + - name: Install dependencies + run: | + curl -sSL https://install.python-poetry.org | python3 + + - name: Get release version + run: echo "RELEASE_VERSION=$(poetry version | awk '{print $2}')" >> $GITHUB_ENV + + - name: Build and publish Python package + run: poetry publish --build + env: + POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index de119fc0f..c0a430b89 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,7 +16,8 @@ jobs: - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - poetry install --all-extras + poetry lock + poetry install --with test -E chat - name: Test with pytest env: # Or as an environment variable OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} diff --git a/docs/pages/examples.md b/docs/pages/examples.mdx similarity index 72% rename from docs/pages/examples.md rename to docs/pages/examples.mdx index e2cc0cfc0..aac169c80 100644 --- a/docs/pages/examples.md +++ b/docs/pages/examples.mdx @@ -1,6 +1,10 @@ +import { Callout } from 'nextra/components' + # Example Scripts For Using The Library -## Example 1: Evaluating existing episodes + + Before running examples, please install extra dependencies with `pip install sotopia[examples]` or `poetry install -E examples` if you are using poetry. + ```python python examples/evaluate_existing_episodes.py --tag= --model= --batch_size= --push-to-db diff --git a/docs/pages/index.mdx b/docs/pages/index.mdx index cd36a89d8..7da796513 100644 --- a/docs/pages/index.mdx +++ b/docs/pages/index.mdx @@ -3,7 +3,7 @@ import { Lumiflex } from 'uvcanvas' import { Button } from "../components/ui/button" import { MoveRight } from "lucide-react" import { Accordion, AccordionItem, AccordionContent, AccordionTrigger } from "../components/ui/accordion" -import { Steps } from 'nextra/components' +import { Steps, Callout } from 'nextra/components' # Sotopia - Getting Started @@ -30,15 +30,20 @@ https://github.com/sotopia-lab/sotopia ## Installation -
+ + +
+ Check out Open in Colab for a quick tutorial
+ + ### Basic Usage -This package supports Python 3.11 and above. +This package supports Python 3.10 and above. Tested on Ubuntu 22.04 with python 3.11. @@ -80,6 +85,19 @@ pip install sotopia ``` + + What if I want to run examples and sotopia-chat? + +

Install the package with the following command:

+ ```bash + pip install sotopia[examples, chat] + ``` + if you are using poetry, you can install the package with: + ```bash + poetry install -E examples -E chat + ``` + + #### Set up Redis stack diff --git a/examples/generate_scenarios.py b/examples/generate_scenarios.py index 25a246c36..88a83ce7e 100644 --- a/examples/generate_scenarios.py +++ b/examples/generate_scenarios.py @@ -12,6 +12,8 @@ from sotopia.database.persistent_profile import RelationshipType from sotopia.generation_utils import ( LLM_Name, +) +from .generate_specific_envs import ( # type:ignore[import-untyped] generate_craigslist_bargains_envs, generate_mutual_friend_envs, ) diff --git a/sotopia/generation_utils/generate_specific_envs.py b/examples/generate_specific_envs.py similarity index 97% rename from sotopia/generation_utils/generate_specific_envs.py rename to examples/generate_specific_envs.py index 4b3149139..bfcd27355 100644 --- a/sotopia/generation_utils/generate_specific_envs.py +++ b/examples/generate_specific_envs.py @@ -4,13 +4,15 @@ Here are the datasets we have so far: 1. Mutual-Friend (https://huggingface.co/datasets/mutual_friends) 2. Craigslist-Bargains (https://huggingface.co/datasets/craigslist_bargains) + +You will have to install the datasets library to use this file. """ import names import numpy as np from datasets import DatasetDict, load_dataset -from .generate import StrOutputParser, generate +from sotopia.generation_utils.generate import StrOutputParser, generate async def generate_mutual_friend_envs() -> tuple[str, list[str]]: diff --git a/lmlib/__init__.py b/lmlib/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lmlib/api_service/__init__.py b/lmlib/api_service/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lmlib/api_service/chat_model.py b/lmlib/api_service/chat_model.py deleted file mode 100644 index f00975399..000000000 --- a/lmlib/api_service/chat_model.py +++ /dev/null @@ -1,197 +0,0 @@ -from typing import Any, List - -import torch -from pydantic import BaseModel - -from lmlib.utils.conversation import ( - Conversation, - SeparatorStyle, - compute_skip_echo_len, -) - - -class ChatMessage(BaseModel): - system: str - messages: List[List[str]] # list of [role, message] - input: str - - -def get_conv_from_json(message: ChatMessage) -> Conversation: - system = message.system - messages = message.messages - return Conversation( - system=system, - roles=["INTERVIEWER", "ME"], - messages=messages, - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", - ) - - -class ChatModel: - def __init__( - self, - model: Any, - tokenizer: Any, - device: str, - context_len: int = 2048, - stream_interval: int = 2, - ): - self.model = model - self.tokenizer = tokenizer - self.device = device - self.context_len = context_len - self.stream_interval = stream_interval - - @torch.inference_mode() - def generate_stream( - self, - params: Any, - device: str, - context_len: int = 2048, - stream_interval: int = 2, - ) -> Any: - model = self.model - tokenizer = self.tokenizer - prompt = params["prompt"] - l_prompt = len(prompt) - temperature = float(params.get("temperature", 1.0)) - max_new_tokens = int(params.get("max_new_tokens", 256)) - stop_str = params.get("stop", None) - stop_token_ids = params.get("stop_ids", [tokenizer.eos_token_id]) - - input_ids = tokenizer(prompt).input_ids - output_ids = list(input_ids) - - max_src_len = context_len - max_new_tokens - 8 - input_ids = input_ids[-max_src_len:] - token = 0 - - for i in range(max_new_tokens): - if i == 0: - if model.config.is_encoder_decoder: - encoder_outputs = model.encoder( - input_ids=torch.as_tensor([input_ids], device=device) - ) - out = model( - torch.as_tensor([input_ids], device=device), - decoder_input_ids=torch.as_tensor( - [[model.generation_config.decoder_start_token_id]], - device=device, - ), - encoder_outputs=encoder_outputs, - use_cache=True, - ) - logits = out.logits - past_key_values = out.past_key_values - else: - out = model( - torch.as_tensor([input_ids], device=device), - use_cache=True, - ) - logits = out.logits - past_key_values = out.past_key_values - else: - if model.config.is_encoder_decoder: - out = model( - input_ids=torch.as_tensor([input_ids], device=device), - use_cache=True, - encoder_outputs=encoder_outputs, - decoder_input_ids=torch.as_tensor([[token]], device=device), - past_key_values=past_key_values, - ) - logits = out.logits - past_key_values = out.past_key_values - else: - out = model( - input_ids=torch.as_tensor([[token]], device=device), - use_cache=True, - past_key_values=past_key_values, - ) - logits = out.logits - past_key_values = out.past_key_values - - last_token_logits = logits[0][-1] - - if device == "mps": - # Switch to CPU by avoiding some bugs in mps backend. - last_token_logits = last_token_logits.float().to("cpu") - - if temperature < 1e-4: - token = int(torch.argmax(last_token_logits)) - else: - probs = torch.softmax(last_token_logits / temperature, dim=-1) - token = int(torch.multinomial(probs, num_samples=1)) - - output_ids.append(token) - - if token in stop_token_ids: - stopped = True - else: - stopped = False - - if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped: - output = tokenizer.decode(output_ids, skip_special_tokens=True) - if stop_str: - pos = output.rfind(stop_str, l_prompt) - if pos != -1: - output = output[:pos] - stopped = True - yield output - - if stopped: - break - - del past_key_values - - def stream_output(self, output_stream: List[Any], skip_echo_len: int) -> Any: - pre = 0 - for outputs in output_stream: - outputs = outputs[skip_echo_len:].strip() - outputs = outputs.split(" ") - now = len(outputs) - 1 - if now > pre: - yield " ".join(outputs[pre:now]) + " " - pre = now - yield " ".join(outputs[pre:]) - - def prompt_for_input(self, role: str) -> str: - return input(f"{role}: ") - - def prompt_for_output(self, role: str) -> None: - print(f"{role}: ", end="", flush=True) - - def chat_oneshot( - self, - msg: ChatMessage, - temperature: float = 0.7, - max_new_tokens: int = 2048, - ) -> Any: - conv = get_conv_from_json(msg) - inp = msg.input - conv.append_message(conv.roles[0], inp) - conv.append_message(conv.roles[1], "") - prompt = conv.get_prompt() - - skip_echo_len = compute_skip_echo_len(conv, prompt) - stop_str = ( - conv.sep - if conv.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.BAIZE] - else None - ) - - params = { - "prompt": prompt, - "temperature": temperature, - "max_new_tokens": max_new_tokens, - "stop": stop_str, - } - - # self.prompt_for_output(conv.roles[1]) - outstream = self.generate_stream(params, self.device) - return self.stream_output(outstream, skip_echo_len) - - # output_stream = self.generate_stream(params, self.device) # self.device is "cuda" - # return chatio.stream_output(output_stream, skip_echo_len) diff --git a/lmlib/api_service/client_api.py b/lmlib/api_service/client_api.py deleted file mode 100644 index 0902c7ca9..000000000 --- a/lmlib/api_service/client_api.py +++ /dev/null @@ -1,69 +0,0 @@ -import json -import os.path as osp -from typing import Any, Dict - -import requests - -from lmlib.api_service.chat_model import ChatMessage - - -# write an api service to get the response from the server -class ChatCharacterGPT: - def __init__(self, public_url: str) -> None: - self.public_url = public_url - - def check_msg(self, message: Dict[str, Any]) -> bool: - try: - ChatMessage.parse_raw(json.dumps(message)) - except Exception as e: - print(e) - print("Invalid message type") - return False - return True - - def generate_stream(self, message: Dict[str, Any]) -> Any: - if not self.check_msg(message): - return - post_url = osp.join(self.public_url, "message") - with requests.post(post_url, json=message, stream=True) as response: - # Set the chunk size to 1024 bytes - for chunk in response.iter_content(chunk_size=1024): - yield chunk.decode("utf-8") - - def generate(self, message: Dict[str, Any]) -> str | None: - if not self.check_msg(message): - return None - post_url = osp.join(self.public_url, "message") - with requests.post(post_url, json=message, stream=True) as response: - # Set the chunk size to 1024 bytes - res = "" - for chunk in response.iter_content(chunk_size=1024): - res += chunk.decode("utf-8") - return res - - -# # post method -# public_url='https://a986-128-2-205-154.ngrok-free.app/message/' -# #url = 'http://127.0.0.1:8000/message/' -# with requests.post(public_url, json=js, stream=True) as response: -# # Set the chunk size to 1024 bytes -# for chunk in response.iter_content(chunk_size=100): -# # Print each chunk to the console -# print(chunk) - -if __name__ == "__main__": - js = { - "system": "My name is Eli Dawson, and I'm a 52-year-old forensic psychiatrist. I would like to answer any questions about myself.", - "messages": [ - ["INTERVIEWER", "Hello"], - ["ME", "Thank you for having me. It's a pleasure to be here."], - ], - "input": "what is your job?", - } - public_url = "https://a986-128-2-205-154.ngrok-free.app" - chat = ChatCharacterGPT(public_url) - for chunk in chat.generate_stream(js): - print(chunk) - print(chat.generate(js)) - # for chunk in chat.generate(js): - # print(chunk, end='') diff --git a/lmlib/api_service/main.py b/lmlib/api_service/main.py deleted file mode 100644 index 9db7c7258..000000000 --- a/lmlib/api_service/main.py +++ /dev/null @@ -1,43 +0,0 @@ -import transformers -from fastapi import FastAPI -from fastapi.responses import StreamingResponse - -from lmlib.api_service.chat_model import ChatMessage, ChatModel -from lmlib.api_service.setup_args import setup_inf_args -from lmlib.arguments import InferenceArguments, LoraArguments -from lmlib.serve.lm_inference import load_gen_model - -parser = transformers.HfArgumentParser((InferenceArguments, LoraArguments)) # type: ignore -inf_args, lora_args = setup_inf_args() - - -# hard code model param -model, tokenizer = load_gen_model( - inf_args.model_path, - inf_args.cache_dir, - large_model=inf_args.load_8bit, - device="cuda:0", - lora_path=lora_args.lora_weight_path, -) -chat = ChatModel(model, tokenizer, "cuda", 2048, 2) - -app = FastAPI() - - -@app.post("/message/") -async def gen_from_msg(msg: ChatMessage) -> StreamingResponse: - return StreamingResponse(chat.chat_oneshot(msg)) - - -# for later use, current post method is enough -# @app.put("/message/{msg_id}") -# async def gen_from_id_msg(msg_id: int, msg: ChatMessage): -# # save id -# print(f"id: {msg_id}") -# return StreamingResponse(chat.chat_oneshot(msg)) - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, port=8000, log_level="info") diff --git a/lmlib/api_service/setup_args.py b/lmlib/api_service/setup_args.py deleted file mode 100644 index 697c62e32..000000000 --- a/lmlib/api_service/setup_args.py +++ /dev/null @@ -1,114 +0,0 @@ -import os.path as osp -from argparse import Namespace -from typing import Any, List, Tuple - -from transformers import HfArgumentParser # type: ignore - -from lmlib.arguments import ( - DataArguments, - InferenceArguments, - LoraArguments, - ModelArguments, -) -from lmlib.arguments import PreTrainingArguments as TrainingArguments - -base_dir = "/usr1/data/ruohongz/ds" -save_dir = f"{base_dir}/save" -cache_dir = f"{base_dir}/cache" -data_dir = f"{base_dir}/data" - -exp_dict_default = Namespace( - data_name="info_eli_dawson.json", - model_name="decapoda-research/llama-13b-hf", - lora_model_name=osp.join("eli-lora-llama-13b-v1"), -) - - -def set_path(exp_dict: Any = exp_dict_default) -> Namespace: - data_name = exp_dict.data_name - - paths = Namespace( - base_dir=base_dir, - save_dir=save_dir, - cache_dir=cache_dir, - data_dir=data_dir, - data_name=data_name, - model_name=exp_dict.model_name, - lora_model_name=exp_dict.lora_model_name, - lora_path=osp.join(save_dir, exp_dict.lora_model_name), - ) - return paths - - -def get_args(paths: Namespace) -> List[str]: - args = [ - "--exp_name", - "seed100", - "--seed", - "100", - "--data_dir", - paths.data_dir, - "--output_dir", - paths.save_dir, - "--cache_dir", - paths.cache_dir, - "--max_steps", - "500", - "--model_name_or_path", - paths.model_name, - "--per_device_eval_batch_size", - "32", - "--per_device_train_batch_size", - "32", - "--num_train_epochs", - "1", - "--learning_rate", - "1e-5", - "--max_text_length", - "128", - "--dataloader_num_workers", - "1", - "--model_max_length", - "2048", - ] - return args - - -def setup_args( - exp_dict: Namespace = exp_dict_default, additional_args: List[str] = [] -) -> Tuple[Namespace, Namespace, Namespace, Namespace]: - paths = set_path(exp_dict) - args = get_args(paths) - parser = HfArgumentParser( - (ModelArguments, DataArguments, TrainingArguments, LoraArguments) - ) - ( - model_args, - data_args, - training_args, - lora_args, - ) = parser.parse_args_into_dataclasses(args + additional_args) - return model_args, data_args, training_args, lora_args - - -def get_inf_args(paths: Namespace) -> List[str]: - args = [ - "--model_path", - paths.model_name, - "--cache_dir", - paths.cache_dir, - "--lora_weight_path", - paths.lora_path, - "--load_8bit", - ] - return args - - -def setup_inf_args( - exp_dict: Namespace = exp_dict_default, additional_args: List[str] = [] -) -> Tuple[Namespace, Namespace]: - paths = set_path(exp_dict) - args = get_inf_args(paths) - parser = HfArgumentParser((InferenceArguments, LoraArguments)) - inf_args, lora_args = parser.parse_args_into_dataclasses(args + additional_args) - return inf_args, lora_args diff --git a/lmlib/arguments.py b/lmlib/arguments.py deleted file mode 100644 index dbb21dc50..000000000 --- a/lmlib/arguments.py +++ /dev/null @@ -1,149 +0,0 @@ -from dataclasses import dataclass, field -from typing import Optional, Union - -from transformers.training_args import TrainingArguments - - -@dataclass -class ModelArguments: - model_name_or_path: str = field( - metadata={ - "help": "Path to pretrained model or model identifier from huggingface.co/models" - } - ) - model_name_short: Union[str, None] = field(default=None) - gen_model_name: Union[str, None] = field(default=None) - gen_model_name_short: Union[str, None] = field(default=None) - target_model_path: Union[str, None] = field( - default=None, - metadata={"help": "Path to pretrained reranker target model"}, - ) - config_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained config name or path if not the same as model_name" - }, - ) - tokenizer_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained tokenizer name or path if not the same as model_name" - }, - ) - cache_dir: Optional[str] = field( - default=None, - metadata={ - "help": "Where do you want to store the pretrained models downloaded from s3" - }, - ) - - # out projection - add_pooler: bool = field(default=False) - projection_in_dim: int = field(default=768) - projection_out_dim: int = field(default=768) - - decay_rate: Optional[float] = field( - default=0.6, metadata={"help": "Decay learning rate"} - ) - pooling: Optional[str] = field( - default="pooler", metadata={"help": "Pooling method"} - ) - device: Optional[str] = field(default="cuda:1", metadata={"help": "Device to use"}) - gen_device: Optional[str] = field( - default="cuda:0", metadata={"help": "Device to use"} - ) - max_text_length: int = field(default=128) - - -@dataclass -class DataArguments: - # data dir, path, name related - train_dir: Union[str, None] = field( - default=None, metadata={"help": "Path to train directory"} - ) - train_path: Union[str, None] = field( - default=None, metadata={"help": "Path to single train file"} - ) - eval_path: Union[str, None] = field( - default=None, metadata={"help": "Path to eval file"} - ) - corpus_path: Union[str, None] = field( - default=None, metadata={"help": "Path to corpus file"} - ) - data_dir: Union[str, None] = field( - default=None, metadata={"help": "Path to data directory"} - ) - data_path: Union[str, None] = field( - default=None, metadata={"help": "Path to the single data file"} - ) - processed_data_path: Union[str, None] = field( - default=None, metadata={"help": "Path to processed data directory"} - ) - context_path: Union[str, None] = field(default=None) - dataset_name: Union[str, None] = field( - default=None, metadata={"help": "huggingface dataset name"} - ) - prompt_path: Union[str, None] = field(default=None) - label_name_path: Union[str, None] = field(default=None) - label_path: Union[str, None] = field(default=None) - save_path: Union[str, None] = field(default=None) - - # data format related - data_cache_dir: Union[str, None] = field( - default=None, - metadata={ - "help": "Where do you want to store the data downloaded from huggingface" - }, - ) - exp_name: str = field(default="exp") - conv_template: Union[str, None] = field(default=None) - - -@dataclass -class PreTrainingArguments(TrainingArguments): # type: ignore[misc] - # generate - generation_mode: Union[str, None] = field(default=None) - load_large_model: bool = field(default=False) - num_samples: int = field(default=5) - - top_k: int = field(default=10) - top_p: float = field(default=0.9) - temperature: float = field(default=1.0) - - gen_batch_size: int = field(default=45) - max_length: Union[str, None] = field(default=None) - min_length: Union[str, None] = field(default=None) - do_sample: bool = field(default=True) - num_beams: int = field(default=1) - - max_iter: int = field(default=1000) - model_max_length: int = field(default=512) - - -@dataclass -class LoraArguments: - lora_r: int = 8 - lora_alpha: int = 16 - lora_dropout: float = 0.05 - # lora_target_modules: typing.List[str] = field( - # default_factory=lambda: ["q_proj", "v_proj"] - # ) - lora_target_modules: str = "q_proj,v_proj" - lora_weight_path: str = "" - bias: str = "none" - - -@dataclass -class InferenceArguments: - model_path: str = field(default="decapoda-research/llama-13b-hf") - cache_dir: Union[str, None] = field(default=None) - device: str = field(default="cuda") - gpus: Union[str, None] = field(default=None) - num_gpus: str = field(default="1") - conv_template: Union[str, None] = field(default=None) - temperature: float = field(default=0.7) - max_new_tokens: int = field(default=512) - style: str = field(default="simple") - load_8bit: bool = field(default=False) - max_gpu_memory: bool = field(default=False) - debug: bool = field(default=False) diff --git a/lmlib/model_tools.py b/lmlib/model_tools.py deleted file mode 100644 index b173b3450..000000000 --- a/lmlib/model_tools.py +++ /dev/null @@ -1,332 +0,0 @@ -import os -import os.path as osp -from argparse import Namespace -from typing import Any, Dict, Tuple - -import torch - -# from transformers.trainer_pt_utils import IterableDatasetShard -from logzero import logger -from transformers import ( # type: ignore[attr-defined] - AutoModel, - AutoModelForCausalLM, - AutoTokenizer, -) - -default_args = Namespace(max_len=256) -default_gen_params: Dict[str, str] = {} - - -def load_and_cache_model( - model_name: str, cache_dir: str, margs: Dict[str, Any] = {} -) -> Any: - save_name = osp.basename(model_name) - model_cache_dir = osp.join(cache_dir, save_name) - model_path = osp.join(model_cache_dir, "model.pt") - if osp.exists(model_path): - logger.info(f"load model from {model_path}") - model = torch.load(model_path) - return model - MODEL_CLASS = AutoModelForCausalLM - if margs == {}: - margs = {"torch_dtype": torch.float16} - - margs["cache_dir"] = model_cache_dir - logger.info("load and cache model") - model = MODEL_CLASS.from_pretrained(model_name, **margs) - torch.save(model, model_path) - return model - - -def load_ret_model( - model_args: Any, device: str = "cuda", dtype: Any = torch.float32 -) -> Any: - logger.info(f"ret model dtype: {dtype}") - cache_dir = model_args.cache_dir - ret_model_name = model_args.model_name_or_path - ret_model_name_short = os.path.basename(ret_model_name) - ret_model_cache_dir = osp.join(cache_dir, ret_model_name_short) - ret_model = AutoModel.from_pretrained(ret_model_name, cache_dir=ret_model_cache_dir) - ret_model = ret_model.to(device=device, dtype=dtype) - ret_tokenizer = AutoTokenizer.from_pretrained( - ret_model_name, cache_dir=ret_model_cache_dir - ) - return ret_model, ret_tokenizer - - -def load_and_cache_large_model(model_name: str, cache_dir: str, device: str) -> Any: - MODEL_CLASS = AutoModelForCausalLM - save_name = osp.basename(model_name) - model_cache_dir = osp.join(cache_dir, save_name) - # config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir) - # with init_empty_weights(config): - # model = MODEL_CLASS.from_config(config) - - # max_memory = {1: "49GIB"} - # device_map = infer_auto_device_map(model, max_memory=max_memory) - # model = load_checkpoint_and_dispatch( - # model, - # model_cache_dir, - # device_map=device_map, - # ) - # save_name = osp.basename(model_name) - device_map = {"": int(device[-1])} - print(device_map) - margs = {"load_in_8bit": True, "device_map": device_map} - model_cache_dir = osp.join(cache_dir, save_name) - margs["cache_dir"] = model_cache_dir - model = MODEL_CLASS.from_pretrained(model_name, **margs) - return model - - -def load_gen_model( - model_args: Any, - large_model: bool = False, - device: str = "cuda", - margs: Dict[str, Any] = {}, -) -> Any: - cache_dir = model_args.cache_dir - gen_model_name = model_args.gen_model_name - if large_model: - gen_model = load_and_cache_large_model( - gen_model_name, cache_dir=cache_dir, device=device - ) - else: - # margs = {"revision": "float16", "torch_dtype": torch.float16, "low_cpu_mem_usage": True} - gen_model = load_and_cache_model( - gen_model_name, cache_dir=cache_dir, margs=margs - ) - gen_model = gen_model.to(device) - if ( - "vicuna" in gen_model_name - or "llama" in gen_model_name - or "alpaca" in gen_model_name - ): - from transformers.models.llama.tokenization_llama import ( - LlamaTokenizer, - ) - - gen_tokenizer = LlamaTokenizer.from_pretrained( - gen_model_name, cache_dir=cache_dir - ) - else: - gen_tokenizer = AutoTokenizer.from_pretrained( - gen_model_name, cache_dir=cache_dir - ) - return gen_model, gen_tokenizer - - -def load_model( - model_args: Any, - large_model: bool = False, - device: str = "cuda", - gen_device: str = "cuda", - dtype: Any = torch.float32, -) -> Dict[str, Tuple[Any, Any]]: - ret_model, ret_tokenizer = load_ret_model(model_args, device=device, dtype=dtype) - gen_model, gen_tokenizer = load_gen_model( - model_args, large_model=large_model, device=gen_device - ) - return { - "ret_model": (ret_model, ret_tokenizer), - "gen_model": (gen_model, gen_tokenizer), - } - - -""" -for later use - -def get_context(torch_dtype: str=None, training_args=None): - if torch_dtype is None: # infer dtype from training_args - assert training_args is not None - if training_args.fp16: - torch_dtype = torch.float16 - elif training_args.bf16: - torch_dtype = torch.bfloat16 - else: - torch_dtype = torch.float32 - context = nullcontext() - if torch_dtype == torch.float16: - context = amp.autocast() - elif torch_dtype == torch.bfloat16: - context = amp.autocast(dtype=torch.bfloat16) - return context - -class RetreiverTool: - def __init__(self, model, tokenizer, args=default_args): - #self.model = model.to(args.device) - self.model = model - self.tokenizer = tokenizer - self.args = args - self.max_len = args.max_len - self.pooling = args.pooling # default simcse is pooler - - def embed( - self, - input_ids=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - output_attentions=None, - output_hidden_states=None, - normalize=False, - ): - - model_output = self.model.forward( - input_ids=input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - ) - - if self.pooling == "average": - last_hidden = model_output["last_hidden_state"] - last_hidden = last_hidden.masked_fill(~attention_mask[..., None].bool(), 0.0) - emb = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] - elif self.pooling == "cls": - emb = model_output["last_hidden_state"][:, 0] - elif self.pooling == "pooler": - emb = model_output["pooler_output"] - - if normalize: - emb = torch.nn.functional.normalize(emb, dim=-1) - return emb - - @torch.no_grad() - def encode(self, texts): - self.model.eval() - feature = self.tokenizer(texts, - padding='max_length', truncation=True, max_length=self.max_len, - return_tensors="pt" - ).to(self.model.device) - embeddings = self.embed(**feature) - return embeddings.cpu().detach().numpy() - - def encode_batch(self, texts, batch_size=512): - self.model.eval() - embeddings = [] - for i in range(0, len(texts), batch_size): - batch_texts = texts[i:i+batch_size] - embeddings.append(self.encode(batch_texts)) - return np.concatenate(embeddings, axis=0) - - def predict(self, emb_text, emb_label): - pred = emb_text @ emb_label.T - return pred - - @classmethod - def load_model(cls, model_name, args, device="cuda:0"): - tokenizer = AutoTokenizer.from_pretrained(model_name) - model = AutoModel.from_pretrained(model_name).to(device) - return cls(model, tokenizer, args) - - -class GeneratorTool: - def __init__(self, model, tokenizer, args=default_args, lora_train=False): - #self.model = model.to(args.device) - self.model = model - self.model.eval() - self.tokenizer = tokenizer - - if lora_train: - model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk - model.config.bos_token_id = 1 - model.config.eos_token_id = 2 - else: - # set pad token - if self.tokenizer.pad_token is None or self.tokenizer.pad_token == "": - if self.tokenizer.eos_token is None or self.tokenizer.eos_token == "": - self.tokenizer.eos_token = '' - #self.tokenizer.add_special_tokens({'pad_token': '[PAD]'}) - #self.model.resize_token_embeddings(len(self.tokenizer)) - - self.tokenizer.pad_token = self.tokenizer.eos_token - - self.args = args - self.max_len = args.max_len - #self.model.config.pad_token_id = tokenizer.eos_token_ - - self.loss_fn = torch.nn.CrossEntropyLoss(reduction='none', ignore_index=tokenizer.pad_token_id) - - def enc(self, texts): - texts = list(texts) # can't take np array - return self.tokenizer(texts, - padding='max_length', truncation=True, max_length=self.max_len, - return_tensors="pt" - ).to(self.model.device) - - def enc_for_generation(self, texts): - texts = list(texts) # can't take np array - inputs = self.tokenizer(texts, - padding='max_length', truncation=True, max_length=self.max_len, - return_tensors="pt" - ).to(self.model.device) - batch_size, seq_len = inputs['input_ids'].shape - - inputs['attention_mask'] = torch.flip(inputs['attention_mask'], dims=[1]) - shifts = seq_len - inputs['attention_mask'].sum(dim=-1) - for batch_idx in range(batch_size): - inputs['input_ids'][batch_idx] = inputs['input_ids'][batch_idx].roll(shifts[batch_idx].item()) - return inputs, seq_len - - @torch.no_grad() - def calc_loss(self, model, texts): - self.model.eval() - batch = self.enc(texts) - outputs = model(**batch) - shift_logits = outputs.logits[..., :-1, :].contiguous() - shift_labels = batch["input_ids"][..., 1:].contiguous() - loss = self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)).view( - shift_labels.size()) - # pad position loss is 0 - avg_loss = loss.sum(-1) / (loss > 0).sum(-1) - return avg_loss.tolist() - - @torch.no_grad() - def generate(self, prompt_texts, - num_samples=1, min_length=16, max_length=64, - gen_params=default_gen_params): - torch_dtype = self.model.config.torch_dtype - context = get_context(torch_dtype) - with context: - batch, seq_len = self.enc_for_generation(prompt_texts) - - min_length = min_length + seq_len - max_length = min(self.model.config.max_position_embeddings, max_length + seq_len) - - output_ids = self.model.generate( - **batch, - num_return_sequences=num_samples, min_length=min_length, max_length=max_length, - generation_config=gen_params - ) - # get generated text - output_ids = output_ids[:, seq_len:] - gen_text = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) - # for i, output_text in enumerate(gen_text): - # output_text = post_process(output_text, self.min_length) - return gen_text - - def generate_batch(self, prompt_texts, - num_samples=1, min_length=16, max_length=64, - gen_params=default_gen_params, batch_size=32, verbose=False): - # actual batch is 32 on gpu - bsz = batch_size // num_samples - gen_texts = [] - if verbose: - loop = tqdm(range(0, len(prompt_texts), bsz)) - else: - loop = range(0, len(prompt_texts), bsz) - for i in loop: - gen_texts.extend(self.generate(prompt_texts[i:i+bsz], num_samples, min_length, max_length, gen_params)) - return gen_texts -""" diff --git a/lmlib/serve/__init__.py b/lmlib/serve/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lmlib/serve/cli.py b/lmlib/serve/cli.py deleted file mode 100644 index 82353a479..000000000 --- a/lmlib/serve/cli.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Chat with a model with command line interface. -""" - -import os -import re -from argparse import Namespace -from typing import Any, List - -import transformers -from prompt_toolkit import PromptSession -from prompt_toolkit.auto_suggest import AutoSuggestFromHistory -from prompt_toolkit.completion import WordCompleter -from prompt_toolkit.history import InMemoryHistory -from rich.console import Console -from rich.live import Live -from rich.markdown import Markdown - -from lmlib.arguments import InferenceArguments, LoraArguments -from lmlib.serve.lm_inference import ChatIO, chat_loop - - -class SimpleChatIO(ChatIO): - def prompt_for_input(self, role: str) -> str: - return input(f"{role}: ") - - def prompt_for_output(self, role: str) -> None: - print(f"{role}: ", end="", flush=True) - - def stream_output(self, output_stream: List[Any], skip_echo_len: int) -> str: - pre = 0 - for outputs in output_stream: - outputs = outputs[skip_echo_len:].strip() - outputs = outputs.split(" ") - now = len(outputs) - 1 - if now > pre: - print(" ".join(outputs[pre:now]), end=" ", flush=True) - pre = now - print(" ".join(outputs[pre:]), flush=True) - return " ".join(outputs) - - -class RichChatIO(ChatIO): - _prompt_session: Any - - def __init__(self) -> None: - self._prompt_session = PromptSession(history=InMemoryHistory()) - self._completer = WordCompleter( - words=["!exit", "!reset"], pattern=re.compile("$") - ) - self._console = Console() - - def prompt_for_input(self, role: str) -> Any: - self._console.print(f"[bold]{role}:") - # TODO(suquark): multiline input has some issues. fix it later. - prompt_input = self._prompt_session.prompt( - completer=self._completer, - multiline=False, - auto_suggest=AutoSuggestFromHistory(), - key_bindings=None, - ) - self._console.print() - return prompt_input - - def prompt_for_output(self, role: str) -> None: - self._console.print(f"[bold]{role}:") - - def stream_output(self, output_stream: List[Any], skip_echo_len: int) -> Any: - """Stream output from a role.""" - # TODO(suquark): the console flickers when there is a code block - # above it. We need to cut off "live" when a code block is done. - - # Create a Live context for updating the console output - with Live(console=self._console, refresh_per_second=4) as live: - # Read lines from the stream - for outputs in output_stream: - accumulated_text = outputs[skip_echo_len:] - if not accumulated_text: - continue - # Render the accumulated text as Markdown - # NOTE: this is a workaround for the rendering "unstandard markdown" - # in rich. The chatbots output treat "\n" as a new line for - # better compatibility with real-world text. However, rendering - # in markdown would break the format. It is because standard markdown - # treat a single "\n" in normal text as a space. - # Our workaround is adding two spaces at the end of each line. - # This is not a perfect solution, as it would - # introduce trailing spaces (only) in code block, but it works well - # especially for console output, because in general the console does not - # care about trailing spaces. - lines = [] - for line in accumulated_text.splitlines(): - lines.append(line) - if line.startswith("```"): - # Code block marker - do not add trailing spaces, as it would - # break the syntax highlighting - lines.append("\n") - else: - lines.append(" \n") - markdown = Markdown("".join(lines)) - # Update the Live console output - live.update(markdown) - self._console.print() - return outputs[skip_echo_len:] - - -def main(args: Namespace) -> None: - if args.gpus: - if args.num_gpus and len(args.gpus.split(",")) < int(args.num_gpus): - raise ValueError( - f"Larger --num-gpus ({args.num_gpus}) than --gpus {args.gpus}!" - ) - os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus - if args.style == "simple": - chatio: Any = SimpleChatIO() - elif args.style == "rich": - chatio = RichChatIO() - else: - raise ValueError(f"Invalid style for console: {args.style}") - try: - chat_loop( - args, - args.model_path, - args.cache_dir, - args.device, - args.num_gpus, - args.max_gpu_memory, - args.load_8bit, - args.conv_template, - args.temperature, - args.max_new_tokens, - chatio, - args.debug, - ) - except KeyboardInterrupt: - print("exit...") - - -if __name__ == "__main__": - parser = transformers.HfArgumentParser((InferenceArguments, LoraArguments)) # type: ignore - ( - inf_args, - lora_args, - ) = parser.parse_args_into_dataclasses() - - args = Namespace(**vars(inf_args), **vars(lora_args)) - main(args) diff --git a/lmlib/serve/lm_inference.py b/lmlib/serve/lm_inference.py deleted file mode 100644 index bc3bf17d4..000000000 --- a/lmlib/serve/lm_inference.py +++ /dev/null @@ -1,325 +0,0 @@ -"""Inference for FastChat models.""" - -import abc -import os -import os.path as osp -from typing import Any, Dict, Optional, Union - -import torch -from logzero import logger -from peft import PeftModel, set_peft_model_state_dict -from transformers import LlamaTokenizer # type: ignore[attr-defined] - -from lmlib.utils.conversation import ( - SeparatorStyle, - compute_skip_echo_len, - conv_templates, - get_default_conv_template, -) -from lmlib.utils.data_utils import load_json - -# try: -# from transformers import ( -# AutoModel, -# AutoModelForCausalLM, -# AutoModelForSeq2SeqLM, -# LlamaForCausalLM, -# LlamaTokenizer, -# ) -# except ImportError: -# from transformers import ( -# AutoModelForCausalLM, -# LLaMATokenizer, -# LLamaForCausalLM, -# AutoModel, -# AutoModelForSeq2SeqLM, -# ) - - -# from lmlib.serve.compression import compress_module -# from lmlib.serve.monkey_patch_non_inplace import ( -# replace_llama_attn_with_non_inplace_operations, -# ) -# from lmlib.serve.serve_chatglm import chatglm_generate_stream - - -def get_gpu_memory(max_gpus: Union[int, None] = None) -> list[float]: - gpu_memory = [] - num_gpus = ( - torch.cuda.device_count() - if max_gpus is None - else min(max_gpus, torch.cuda.device_count()) - ) - - for gpu_id in range(num_gpus): - with torch.cuda.device(gpu_id): - device = torch.cuda.current_device() - gpu_properties = torch.cuda.get_device_properties(device) - total_memory = gpu_properties.total_memory / (1024**3) - allocated_memory = torch.cuda.memory_allocated() / (1024**3) - available_memory = total_memory - allocated_memory - gpu_memory.append(available_memory) - return gpu_memory - - -def load_gen_model( - gen_model_name: str, - cache_dir: str, - large_model: bool = False, - device: str = "cuda", - lora_path: Union[str, None] = None, - margs: Dict[str, Any] = {}, -) -> Any: - from lmlib.model_tools import ( - load_and_cache_large_model, - load_and_cache_model, - ) - - if large_model: - gen_model = load_and_cache_large_model( - gen_model_name, cache_dir=cache_dir, device=device - ) - else: - # margs = {"revision": "float16", "torch_dtype": torch.float16, "low_cpu_mem_usage": True} - gen_model = load_and_cache_model( - gen_model_name, cache_dir=cache_dir, margs=margs - ) - gen_model = gen_model.to(device) - if lora_path is not None: - logger.info(f"load lora from {lora_path}") - from argparse import Namespace - - from peft import get_peft_model - from transformers.trainer_utils import get_last_checkpoint - - lora_config_json = load_json(osp.join(lora_path, "adapter_config.json")) - lora_config = Namespace(**lora_config_json) - gen_model = get_peft_model(gen_model, lora_config) - - ckpt_path = get_last_checkpoint(lora_path) - if ckpt_path is None: - gen_model = PeftModel.from_pretrained( - gen_model, - lora_path, - torch_dtype=torch.float16, - ) - else: - checkpoint_name = os.path.join(ckpt_path, "pytorch_model.bin") - adapter_weigths = torch.load(checkpoint_name) - set_peft_model_state_dict(gen_model, adapter_weigths) - - gen_tokenizer = LlamaTokenizer.from_pretrained(gen_model_name, cache_dir=cache_dir) - # use the vicuna version of special tokens - gen_tokenizer.add_special_tokens( - { - "bos_token": "", - "eos_token": "", - "unk_token": "", - "pad_token": "", - } - ) - return gen_model, gen_tokenizer - - -@torch.inference_mode() -def generate_stream( - model: Any, - tokenizer: Any, - params: Any, - device: str, - context_len: int = 2048, - stream_interval: int = 2, -) -> Any: - prompt = params["prompt"] - l_prompt = len(prompt) - temperature = float(params.get("temperature", 1.0)) - max_new_tokens = int(params.get("max_new_tokens", 256)) - stop_str = params.get("stop", None) - stop_token_ids = params.get("stop_ids", [tokenizer.eos_token_id]) - - input_ids = tokenizer(prompt).input_ids - output_ids = list(input_ids) - - max_src_len = context_len - max_new_tokens - 8 - input_ids = input_ids[-max_src_len:] - token = 0 - - for i in range(max_new_tokens): - if i == 0: - if model.config.is_encoder_decoder: - encoder_outputs = model.encoder( - input_ids=torch.as_tensor([input_ids], device=device) - ) - out = model( - torch.as_tensor([input_ids], device=device), - decoder_input_ids=torch.as_tensor( - [[model.generation_config.decoder_start_token_id]], - device=device, - ), - encoder_outputs=encoder_outputs, - use_cache=True, - ) - logits = out.logits - past_key_values = out.past_key_values - else: - out = model(torch.as_tensor([input_ids], device=device), use_cache=True) - logits = out.logits - past_key_values = out.past_key_values - else: - if model.config.is_encoder_decoder: - out = model( - input_ids=torch.as_tensor([input_ids], device=device), - use_cache=True, - encoder_outputs=encoder_outputs, - decoder_input_ids=torch.as_tensor([[token]], device=device), - past_key_values=past_key_values, - ) - logits = out.logits - past_key_values = out.past_key_values - else: - out = model( - input_ids=torch.as_tensor([[token]], device=device), - use_cache=True, - past_key_values=past_key_values, - ) - logits = out.logits - past_key_values = out.past_key_values - - last_token_logits = logits[0][-1] - - if device == "mps": - # Switch to CPU by avoiding some bugs in mps backend. - last_token_logits = last_token_logits.float().to("cpu") - - if temperature < 1e-4: - token = int(torch.argmax(last_token_logits)) - else: - probs = torch.softmax(last_token_logits / temperature, dim=-1) - token = int(torch.multinomial(probs, num_samples=1)) - - output_ids.append(token) - - if token in stop_token_ids: - stopped = True - else: - stopped = False - - if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped: - output = tokenizer.decode(output_ids, skip_special_tokens=True) - if stop_str: - pos = output.rfind(stop_str, l_prompt) - if pos != -1: - output = output[:pos] - stopped = True - yield output - - if stopped: - break - - del past_key_values - - -class ChatIO(abc.ABC): - @abc.abstractmethod - def prompt_for_input(self, role: str) -> str: - """Prompt for input from a role.""" - - @abc.abstractmethod - def prompt_for_output(self, role: str) -> None: - """Prompt for output from a role.""" - - @abc.abstractmethod - def stream_output(self, output_stream: Any, skip_echo_len: int) -> str: - """Stream output.""" - - -def chat_loop( - args: Any, - model_path: str, - cache_dir: str, - device: str, - num_gpus: str, - max_gpu_memory: str, - load_8bit: bool, - conv_template: Optional[str], - temperature: float, - max_new_tokens: int, - chatio: ChatIO, - debug: bool, -) -> None: - # Model - int8 = args.load_8bit - lora_path = args.lora_weight_path - model, tokenizer = load_gen_model( - model_path, - cache_dir, - large_model=int8, - device="cuda:0", - lora_path=lora_path, - ) - - # Chat - if conv_template: - conv = conv_templates[conv_template].copy() - else: - conv = get_default_conv_template("vicuna_character").copy() - - # import pdb; pdb.set_trace() - def chat() -> None: - while True: - try: - inp = chatio.prompt_for_input(conv.roles[0]) - except EOFError: - inp = "" - if not inp: - print("exit...") - break - # import pdb; pdb.set_trace() - conv.append_message(conv.roles[0], inp) - conv.append_message(conv.roles[1], "") - - # if is_chatglm: - # prompt = conv.messages[conv.offset :] - # generate_stream_func = chatglm_generate_stream - # else: - generate_stream_func = generate_stream - prompt = conv.get_prompt() - - skip_echo_len = compute_skip_echo_len(conv, prompt) - stop_str = ( - conv.sep - if conv.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.BAIZE] - else None - ) - - params = { - "model": model_path, - "prompt": prompt, - "temperature": temperature, - "max_new_tokens": max_new_tokens, - "stop": stop_str, - } - - chatio.prompt_for_output(conv.roles[1]) - output_stream = generate_stream_func(model, tokenizer, params, device) - outputs = chatio.stream_output(output_stream, skip_echo_len) - # NOTE: strip is important to align with the training data. - conv.messages[-1][-1] = outputs.strip() - - if debug: - print("\n", {"prompt": prompt, "outputs": outputs}, "\n") - - while True: - try: - conv = get_default_conv_template("vicuna_character").copy() - chat() - except KeyboardInterrupt: - print("restart") - import pdb - - pdb.set_trace() - # x = input("input state") - # if x == "exit": - # break - # else: - # continue diff --git a/lmlib/utils/__init__.py b/lmlib/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lmlib/utils/conversation.py b/lmlib/utils/conversation.py deleted file mode 100644 index c78a85f76..000000000 --- a/lmlib/utils/conversation.py +++ /dev/null @@ -1,197 +0,0 @@ -""" -Conversation prompt template. -""" - -import dataclasses -from enum import Enum, auto -from typing import Any, List - - -class SeparatorStyle(Enum): - """Different separator style.""" - - SINGLE = auto() - TWO = auto() - DOLLY = auto() - OASST_PYTHIA = auto() - BAIZE = auto() - - -@dataclasses.dataclass -class Conversation: - """A class that keeps all conversation history.""" - - system: str - roles: list[str] - messages: List[List[str]] - offset: int - sep_style: SeparatorStyle = SeparatorStyle.SINGLE - sep: str = "###" - sep2: str | None = None - - # Used for the state in the gradio servers. - # TODO(lmzheng): refactor this - conv_id: Any = None - skip_next: bool = False - model_name: str | None = None - - def get_prompt(self) -> str: - if self.sep_style == SeparatorStyle.SINGLE: - ret = self.system - for role, message in self.messages: - if message: - ret += self.sep + " " + role + ": " + message - else: - ret += self.sep + " " + role + ":" - return ret - elif self.sep_style == SeparatorStyle.TWO: - assert ( - self.sep2 is not None - ), "sep2 should not be None when using TWO style." - seps = (self.sep, self.sep2) - ret = self.system + seps[0] - for i, (role, message) in enumerate(self.messages): - if message: - ret += role + ": " + message.strip() + seps[i % 2] - else: - ret += role + ":" - return ret - elif self.sep_style == SeparatorStyle.DOLLY: - assert ( - self.sep2 is not None - ), "sep2 should not be None when using DOLLY style." - seps = (self.sep, self.sep2) - ret = self.system - for i, (role, message) in enumerate(self.messages): - if message: - ret += role + ":\n" + message + seps[i % 2] - if i % 2 == 1: - ret += "\n\n" - else: - ret += role + ":\n" - return ret - elif self.sep_style == SeparatorStyle.OASST_PYTHIA: - ret = self.system - for role, message in self.messages: - if message: - ret += role + message + self.sep - else: - ret += role - return ret - elif self.sep_style == SeparatorStyle.BAIZE: - ret = self.system - for role, message in self.messages: - if message: - ret += "\n" + role + message - else: - ret += "\n" + role - return ret - else: - raise ValueError(f"Invalid style: {self.sep_style}") - - def append_message(self, role: str, message: str) -> None: - self.messages.append([role, message]) - - def to_gradio_chatbot(self) -> list[list[str | None]]: - ret = [] - for i, (role, msg) in enumerate(self.messages[self.offset :]): - if i % 2 == 0: - ret.append([msg, None]) - else: - ret[-1][-1] = msg - return ret - - def copy(self) -> "Conversation": - return Conversation( - system=self.system, - roles=self.roles, - messages=[[x, y] for x, y in self.messages], - offset=self.offset, - sep_style=self.sep_style, - sep=self.sep, - sep2=self.sep2, - conv_id=self.conv_id, - model_name=self.model_name, - ) - - def dict(self) -> dict[str, Any]: - return { - "system": self.system, - "roles": self.roles, - "messages": self.messages, - "offset": self.offset, - "sep": self.sep, - "sep2": self.sep2, - "conv_id": self.conv_id, - "model_name": self.model_name, - } - - -conv_vicuna_v1_1 = Conversation( - system="A chat between a curious user and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the user's questions.", - roles=["USER", "ASSISTANT"], - messages=[], - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_emily = Conversation( - system="My name is Emily Harrison and I am a 32-year-old woman. I am very compassionate and values caring for other people. I would like to answer any questions about myself.", - roles=["INTERVIEWER", "ME"], - messages=[], - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_sasha = Conversation( - system="My name is Sasha Ramirez and I am a 42-year-old female police officer. My decision-making style is logical, and my primary values are authority and loyalty. I would like to answer any questions about myself.", - roles=["INTERVIEWER", "ME"], - messages=[], - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_eli = Conversation( - system="My name is Eli Dawson, and I'm a 52-year-old forensic psychiatrist. I would like to answer any questions about myself.", - roles=["INTERVIEWER", "ME"], - messages=[], - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_templates = { - "vicuna_v1_1": conv_vicuna_v1_1, - "vicuna_emily": conv_emily, - "vicuna_sasha": conv_sasha, - "vicuna_eli": conv_eli, -} - - -def get_default_conv_template(model_name: str) -> Conversation: - return conv_vicuna_v1_1 - - -def compute_skip_echo_len( - conv: Conversation, prompt: str, is_chatglm: bool = False -) -> int: - if is_chatglm: - assert len(conv.messages) >= 2 - assert conv.messages[-2][1] is not None - skip_echo_len = len(conv.messages[-2][1]) + 1 - else: - skip_echo_len = len(prompt) + 1 - prompt.count("") * 3 - return skip_echo_len - - -if __name__ == "__main__": - default_conversation = conv_templates["vicuna_v1.1"] - print(default_conversation.get_prompt()) diff --git a/lmlib/utils/data_utils.py b/lmlib/utils/data_utils.py deleted file mode 100644 index f5d741364..000000000 --- a/lmlib/utils/data_utils.py +++ /dev/null @@ -1,61 +0,0 @@ -import json -import os -import random -from typing import Any, Dict, List - -import numpy as np -import torch - - -# IO -def load_data(text_path: str | os.PathLike[str], encoding: str = "utf-8") -> list[str]: - """ - load textual data from file - """ - with open(text_path, encoding=encoding) as fp: - texts = fp.readlines() - return [t.strip() for t in texts] - - -def save_data(filename: str, data: str) -> None: - """ - write textual data to file - """ - with open(filename, "w") as fout: - for d in data: - fout.write(str(d) + "\n") - fout.close() - - -def save_jsonl(path: str, entries: List[Dict[str, Any]]) -> None: - with open(path, "w", encoding="utf8") as fh: - for entry in entries: - fh.write(f"{json.dumps(entry)}\n") - - -def load_jsonl(path: str) -> List[Dict[str, Any]]: - pairs = [] - with open(path, "r", encoding="utf8") as fh: - for line in fh: - pairs.append(json.loads(line)) - return pairs - - -def load_json(path: str) -> dict[str, Any]: - with open(path, "r", encoding="utf8") as fh: - content: dict[str, Any] = json.load(fh) - return content - - -def save_json(path: str, content: Any) -> None: - with open(path, "w", encoding="utf8") as fh: - json.dump(content, fh, indent=4) - - -def set_seed(seed: int) -> None: - """Set RNG seeds for python's `random` module, numpy and torch""" - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) diff --git a/poetry.lock b/poetry.lock index 33e0f0272..3fb7d9cc2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -98,6 +98,7 @@ files = [ [package.dependencies] aiosignal = ">=1.1.2" +async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""} attrs = ">=17.3.0" frozenlist = ">=1.1.1" multidict = ">=4.5,<7.0" @@ -120,25 +121,52 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "anthropic" +version = "0.26.1" +description = "The official 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a/pyproject.toml b/pyproject.toml index 2c95fd80b..0a0ea312b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,43 +8,55 @@ readme = "README.md" packages = [{include = "sotopia"}] [tool.poetry.dependencies] -python = ">=3.11, <3.12" -datasets = "^2.16.1" +python = ">=3.10, <3.13" pandas = "^2.1.1" lxml = "^4.9.3" -scipy = "^1.11.3" openai = "^1.11.0" langchain = "0.1.5" -tiktoken = "^0.5.1" rich = "^13.6.0" -jinja2 = "^3.1.2" PettingZoo = "1.24.0" redis-om = "^0.2.1" pandas-stubs = "*" types-tqdm = "*" -fastapi = "^0.103.2" -uvicorn = "^0.23.2" gin-config = "^0.5.0" absl-py = "^2.0.0" -names = "^0.3.0" together = "^0.2.4" pydantic = "1.10.12" -mypy = "^1.8.0" beartype = "^0.14.0" -torch = ">=2.1 <2.3" -transformers = "^4.34.0" langchain-openai = "^0.0.5" litellm = "^1.23.12" +# dependency versions for extras +fastapi = { version = "^0.109.2", optional = true } +tabulate = { version = "^0.9.0", optional = true } +anthropic = { version = "^0.26.0", optional = true } +xmltodict = { version = "^0.13.0", optional = true } +groq = { version = "^0.4.2", optional = true } +cohere = { version = "^5.1.8", optional = true } +google-generativeai = { version = "^0.5.4", optional = true } +transformers = { version = "^4.41.0", optional = true } +datasets = { version = "^2.19.0", optional = true } +scipy = { version = "^1.13.1", optional = true } +torch = { version = "^2.3.0", optional = true } + [tool.poetry.group.dev.dependencies] pre-commit = "*" nbmake = "*" types-setuptools = "*" +ruff = "*" [tool.poetry.group.test.dependencies] pytest = "*" pytest-asyncio = "*" +mypy = "^1.8.0" +[tool.poetry.extras] +anthropic = ["anthropic"] +groq = ["groq"] +cohere = ["cohere"] +google-generativeai = ["google-generativeai"] +examples = ["transformers", "datasets", "scipy", "torch"] +chat = ["fastapi"] [build-system] requires = ["poetry-core"] diff --git a/sotopia-chat/fastapi_server.py b/sotopia-chat/fastapi_server.py index ae8c293b5..a0fdf6edd 100644 --- a/sotopia-chat/fastapi_server.py +++ b/sotopia-chat/fastapi_server.py @@ -338,11 +338,16 @@ async def _join_after_seconds( await asyncio.sleep(0.1) return str(response.text) - async with asyncio.timeout(200): - _ = await asyncio.gather( - _join_after_seconds(random.random() * 199), - _join_after_seconds(random.random() * 199), - _join_after_seconds(random.random() * 199), - _join_after_seconds(random.random() * 199), - _join_after_seconds(random.random() * 199), + try: + await asyncio.wait_for( + asyncio.gather( + _join_after_seconds(random.random() * 199), + _join_after_seconds(random.random() * 199), + _join_after_seconds(random.random() * 199), + _join_after_seconds(random.random() * 199), + _join_after_seconds(random.random() * 199), + ), + timeout=200, ) + except (TimeoutError, asyncio.TimeoutError) as _: + pass diff --git a/sotopia/generation_utils/__init__.py b/sotopia/generation_utils/__init__.py index c82cffb58..c5fb65099 100644 --- a/sotopia/generation_utils/__init__.py +++ b/sotopia/generation_utils/__init__.py @@ -5,10 +5,6 @@ fill_in_background, generate_goal, ) -from .generate_specific_envs import ( - generate_craigslist_bargains_envs, - generate_mutual_friend_envs, -) __all__ = [ "EnvResponse", @@ -16,6 +12,4 @@ "LLM_Name", "fill_in_background", "generate_goal", - "generate_mutual_friend_envs", - "generate_craigslist_bargains_envs", ] diff --git a/sotopia/renderers/xml_renderer.py b/sotopia/renderers/xml_renderer.py index 2ce119e59..c685a7a81 100644 --- a/sotopia/renderers/xml_renderer.py +++ b/sotopia/renderers/xml_renderer.py @@ -65,7 +65,8 @@ def __call__( self.parser, ).getroot() except etree.XMLSyntaxError as e: - e.add_note(f"The xml_string is {xml_string}") - raise e + raise etree.XMLSyntaxError( + f"Failed to parse xml_string: {xml_string}" + ) from e return _render_xml(root, context) diff --git a/stubs/peft/__init__.pyi b/stubs/peft/__init__.pyi deleted file mode 100644 index f01c1acdc..000000000 --- a/stubs/peft/__init__.pyi +++ /dev/null @@ -1,16 +0,0 @@ -from typing import Any, TypeVar - -import torch - -Model = TypeVar("Model", bound=PushToHubMixin) - -class PushToHubMixin: - @classmethod - def from_pretrained(cls: type[Model], *args: Any, **kwargs: Any) -> Model: ... - -class PeftModel(PushToHubMixin, torch.nn.Module): ... - -def set_peft_model_state_dict( - model: PeftModel, peft_model_state_dict: dict[str, Any] -) -> PeftModel: ... -def get_peft_model(model: PeftModel, peft_config: Any) -> PeftModel: ...