# A mirror to gradio launch stream # By Xuan Phi Nguyen at DAMO Academy, Alibaba Group """ Load FasterLlama with original VLLM codebase, require changing config names to LlamaForCausalLM tensor_parallel must == 1 """ import os import numpy as np import argparse import torch import gradio as gr from typing import Any, Iterator from typing import Iterator, List, Optional, Tuple import filelock import glob import json from gradio_client.documentation import document, set_documentation_group from typing import List, Optional, Union, Dict, Tuple from tqdm.auto import tqdm from huggingface_hub import snapshot_download # @@ constants ================ DEBUG = bool(int(os.environ.get("DEBUG", "1"))) BLOCK_ZH = bool(int(os.environ.get("BLOCK_ZH", "0"))) TENSOR_PARALLEL = int(os.environ.get("TENSOR_PARALLEL", "1")) DTYPE = os.environ.get("DTYPE", "bfloat16") # DTYPE = 'float16' # MODEL_PATH = os.environ.get("MODEL_PATH", "notfound, please set `export MODEL_PATH=`") MODEL_PATH = os.environ.get("MODEL_PATH", "seal_13b_a") PORT = int(os.environ.get("PORT", "7860")) STREAM_YIELD_MULTIPLE = int(os.environ.get("STREAM_YIELD_MULTIPLE", "1")) MAX_TOKENS = 2048 # @@ constants ================ if not DEBUG: # vllm import from vllm import LLM, SamplingParams from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast from vllm.engine.arg_utils import EngineArgs from vllm.engine.llm_engine import LLMEngine from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams from vllm.utils import Counter from vllm.sequence import (Sequence, SequenceData, SequenceGroup, SequenceGroupMetadata, SequenceOutputs, SequenceStatus) # ! reconfigure vllm to faster llama from vllm.model_executor.model_loader import _MODEL_REGISTRY from vllm.model_executor.models import LlamaForCausalLM _MODEL_REGISTRY['FasterLlamaForCausalLM'] = LlamaForCausalLM def _detect_lang(text): from langdetect import detect as detect_lang from langdetect.detector import LangDetectException dlang = None try: dlang = detect_lang(text) except Exception as e: # No features in text. print(f'Error: {e}') if "No features in text." in str(e): return "en" else: return "zh" return dlang def hf_model_weights_iterator( model_name_or_path: str, cache_dir: Optional[str] = None, use_np_cache: bool = False, ) -> Iterator[Tuple[str, torch.Tensor]]: from vllm.model_executor.weight_utils import Disabledtqdm # Prepare file lock directory to prevent multiple processes from # downloading the same model weights at the same time. lock_dir = cache_dir if cache_dir is not None else "/tmp" lock_file_name = model_name_or_path.replace("/", "-") + ".lock" lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name)) # Download model weights from huggingface. is_local = os.path.isdir(model_name_or_path) if not is_local: with lock: hf_folder = snapshot_download(model_name_or_path, allow_patterns="*.bin", cache_dir=cache_dir, local_files_only=True, tqdm_class=Disabledtqdm) else: hf_folder = model_name_or_path hf_bin_files = [ # x for x in glob.glob(os.path.join(hf_folder, "*.bin")) x for x in glob.glob(os.path.join(hf_folder, "*model*.bin")) if not x.endswith("training_args.bin") ] hf_safetensors_files = [ x for x in glob.glob(os.path.join(hf_folder, "*model*.safetensors")) if not x.endswith("training_args.bin") ] # print(F'Load bin files: {hf_bin_files} // safetensors: {hf_safetensors_files}') if use_np_cache: # Convert the model weights from torch tensors to numpy arrays for # faster loading. np_folder = os.path.join(hf_folder, "np") os.makedirs(np_folder, exist_ok=True) weight_names_file = os.path.join(np_folder, "weight_names.json") with lock: if not os.path.exists(weight_names_file): weight_names = [] for bin_file in hf_bin_files: state = torch.load(bin_file, map_location="cpu") for name, param in state.items(): param_path = os.path.join(np_folder, name) with open(param_path, "wb") as f: np.save(f, param.cpu().detach().numpy()) weight_names.append(name) with open(weight_names_file, "w") as f: json.dump(weight_names, f) with open(weight_names_file, "r") as f: weight_names = json.load(f) for name in weight_names: param_path = os.path.join(np_folder, name) with open(param_path, "rb") as f: param = np.load(f) yield name, torch.from_numpy(param) else: if len(hf_bin_files) > 0: print(F'Load bin files: {hf_bin_files}') for bin_file in hf_bin_files: state = torch.load(bin_file, map_location="cpu") for name, param in state.items(): yield name, param del state torch.cuda.empty_cache() elif len(hf_safetensors_files) > 0: print(F'Load safetensor files: {hf_safetensors_files}') from safetensors.torch import load_file for safe_file in hf_safetensors_files: # state = torch.load(bin_file, map_location="cpu") state = load_file(safe_file) for name, param in state.items(): yield name, param del state torch.cuda.empty_cache() else: raise ValueError(f'no files available either bin or safe') def convert_pyslice_to_tensor(x: Any) -> torch.Tensor: """convert PySafeSlice object from safetensors to torch.Tensor PySafeSlice object supports indexing, which is done before loading the actual tensor and can reduce the amount of memory being read into the memory. However, it does not support more advanced functionalities like `.view()` or `.t()`. Therefore, if we need to modify the loaded tensor with these more complicated operators, we need to convert to tensor first. """ if not isinstance(x, torch.Tensor): x = x[:] return x def load_padded_tensor_parallel_vocab( param: torch.Tensor, loaded_weight: Any, # `torch.Tensor` or `PySafeSlice` tensor_model_parallel_rank: int, ) -> None: shard_size = param.shape[0] start_idx = tensor_model_parallel_rank * shard_size end_idx = (tensor_model_parallel_rank + 1) * shard_size loaded_weight = loaded_weight[start_idx:end_idx] loaded_weight = convert_pyslice_to_tensor(loaded_weight) param[:loaded_weight.shape[0]].copy_(loaded_weight) def llama_load_weights( self, model_name_or_path: str, cache_dir: Optional[str] = None, use_np_cache: bool = False, load_format: str = "auto", # load_format: str = "pt", revision: Optional[str] = None ): from vllm.model_executor.weight_utils import ( load_tensor_parallel_weights ) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) tp_size = get_tensor_model_parallel_world_size() tensor_model_parallel_rank = get_tensor_model_parallel_rank() q_proj_shard_size = (self.config.hidden_size // tp_size) kv_proj_shard_size = (self.config.hidden_size // self.config.num_attention_heads * getattr(self.config, "num_key_value_heads", self.config.num_attention_heads) // tp_size) attention_weight_specs = [ # (weight_name, shard_size, offset) ("q_proj", q_proj_shard_size, 0), ("k_proj", kv_proj_shard_size, q_proj_shard_size), ("v_proj", kv_proj_shard_size, q_proj_shard_size + kv_proj_shard_size), ] state_dict = self.state_dict() need_to_load = len(state_dict) loaded = 0 # try: # iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, use_np_cache) # except Exception as e: # iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, load_format, revision) iterator = hf_model_weights_iterator(model_name_or_path, cache_dir, use_np_cache) # for name, loaded_weight in hf_model_weights_iterator( # model_name_or_path, cache_dir, load_format, revision): # model_name_or_path, cache_dir, use_np_cache): for name, loaded_weight in iterator: if "rotary_emb.inv_freq" in name: continue if "embed_tokens" in name or "lm_head" in name: param = state_dict[name] # Consider padding in the vocab size. padded_vocab_size = (param.shape[0] * tp_size) # num_extra_rows = padded_vocab_size - self.config.vocab_size num_extra_rows = padded_vocab_size - loaded_weight.size(0) load_size = loaded_weight.size() extra_rows = torch.empty(num_extra_rows, loaded_weight.shape[1]) extra_rows = extra_rows.to(loaded_weight) loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0) if num_extra_rows > 0: print(f'Add empty to {num_extra_rows} extra row for {name}') print(f'Load: {name} | {padded_vocab_size=} | {self.config.vocab_size=} | {num_extra_rows=} | {param.size()=} | {loaded_weight.size()=} | {load_size=}') # if "embed_tokens" in name or "lm_head" in name: # param = state_dict[name] # load_padded_tensor_parallel_vocab(param, loaded_weight, tensor_model_parallel_rank) # loaded += 1 # continue is_attention_weight = False for weight_name, shard_size, offset in attention_weight_specs: if weight_name not in name or "qkv_proj" in name: continue param = state_dict[name.replace(weight_name, "qkv_proj")] loaded_weight = loaded_weight[ shard_size * tensor_model_parallel_rank:shard_size * (tensor_model_parallel_rank + 1)] param_slice = param.data[offset:offset + shard_size] assert param_slice.shape == loaded_weight.shape param_slice.copy_(loaded_weight) loaded += 1.0 / 3 is_attention_weight = True break if is_attention_weight: continue # ! qkv_proj is sharded differently if concatenated into qkv # qkv: qqqq kkkk vvvv # lweight: qq0qq1 kk0kk1 vv0vv1 # q_shard_size: hidden_size // tp_size = qq # qkv_s0: qq0_kk0_vv0 # qkv_s1: qq1_kk1_vv1 if "qkv_proj" in name: param = state_dict[name] # loaded_weight qsize = self.config.hidden_size kvsize = self.config.hidden_size // self.config.num_attention_heads * getattr(self.config, "num_key_value_heads", self.config.num_attention_heads) q_offsets = ( q_proj_shard_size * tensor_model_parallel_rank, q_proj_shard_size * (tensor_model_parallel_rank + 1) ) k_offsets = ( qsize + kv_proj_shard_size * tensor_model_parallel_rank, qsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1) ) v_offsets = ( qsize + kvsize + kv_proj_shard_size * tensor_model_parallel_rank, qsize + kvsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1) ) _loaded_weight = torch.cat( [ loaded_weight[q_offsets[0]:q_offsets[1]], loaded_weight[k_offsets[0]:k_offsets[1]], loaded_weight[v_offsets[0]:v_offsets[1]], ], 0 ) # print(f'{name} | {q_offsets} | {k_offsets} | {v_offsets}') assert param.shape == _loaded_weight.shape, f'{param.shape=} != {_loaded_weight.shape=}' param.data.copy_(_loaded_weight) loaded += 1.0 is_attention_weight = True if is_attention_weight: continue is_gate_up_weight = False for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]): if weight_name not in name or "gate_up_proj" in name: continue param = state_dict[name.replace(weight_name, "gate_up_proj")] shard_size = param.shape[0] // 2 loaded_weight = loaded_weight[ shard_size * tensor_model_parallel_rank:shard_size * (tensor_model_parallel_rank + 1)] param_slice = param.data[shard_size * stride_id:shard_size * (stride_id + 1)] assert param_slice.shape == loaded_weight.shape param_slice.copy_(loaded_weight) loaded += 1.0 / 2 is_gate_up_weight = True break if is_gate_up_weight: continue if "gate_up_proj" in name: param = state_dict[name] shard_size = param.shape[0] // 2 intermediate_size = self.config.intermediate_size g_offsets = ( shard_size * tensor_model_parallel_rank, shard_size * (tensor_model_parallel_rank + 1) ) u_offsets = ( intermediate_size + shard_size * tensor_model_parallel_rank, intermediate_size + shard_size * (tensor_model_parallel_rank + 1) ) # print(f'{name} {param.size()} | {g_offsets} | {u_offsets}') _loaded_weight = torch.cat( [ loaded_weight[g_offsets[0]:g_offsets[1]], loaded_weight[u_offsets[0]:u_offsets[1]], ], 0 ) assert param.shape == _loaded_weight.shape param.data.copy_(_loaded_weight) loaded += 1.0 is_gate_up_weight = True if is_gate_up_weight: continue param = state_dict[name] load_tensor_parallel_weights(param, loaded_weight, name, self._column_parallel_weights, self._row_parallel_weights, tensor_model_parallel_rank) loaded += 1 if np.abs(loaded - need_to_load) < 0.01: print(f'WARNING: only {loaded} params loaded out of {need_to_load}') else: print(f'Loaded all {loaded} params loaded out of {need_to_load}') # Reassign LlamaForCausalLM.load_weights with llama_load_weights if not DEBUG: LlamaForCausalLM.load_weights = llama_load_weights # ! ================================================================== set_documentation_group("component") DATA_ROOT = os.environ.get("dataroot", "/mnt/workspace/workgroup/phi") MODEL_CACHE_DIR = os.path.join(DATA_ROOT, "pret_models") DTYPES = { 'float16': torch.float16, 'bfloat16': torch.bfloat16 } llm = None demo = None RELOAD_SIGNAL = '<<>\n\n" SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. Your name is SeaL and you are built by DAMO Academy, Alibaba Group. 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. As a multilingual assistant, you must respond and follow instructions in the native language of the user by default, unless told otherwise. \ Your response should adapt to the norms and customs of the respective language and culture. """ RES_PRINTED = False def llama_chat_sys_input_seq_constructor(text, sys_prompt=SYSTEM_PROMPT_1, bos_token=BOS_TOKEN, eos_token=EOS_TOKEN): return f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {text} {E_INST}" def llama_chat_multiturn_sys_input_seq_constructor( message: str, history: List[Tuple[str, str]], sys_prompt=SYSTEM_PROMPT_1, bos_token=BOS_TOKEN, eos_token=EOS_TOKEN, ): """ ``` [INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer [INST] Prompt [/INST] Answer [INST] Prompt [/INST] ``` """ text = '' for i, (prompt, res) in enumerate(history): if i == 0: text += f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {prompt} {E_INST}" else: text += f"{bos_token}{B_INST} {prompt} {E_INST}" if res is not None: text += f" {res} {eos_token} " if len(history) == 0 or text.strip() == '': text = f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {message} {E_INST}" else: text += f"{bos_token}{B_INST} {message} {E_INST}" return text @document() class ChatBot(gr.Chatbot): def _postprocess_chat_messages( self, chat_message ): x = super()._postprocess_chat_messages(chat_message) if isinstance(x, str): x = x.strip().replace("\n", "
") return x # gr.ChatInterface from gradio.components import Button from gradio.events import Dependency, EventListenerMethod def _setup_stop_events( self, event_triggers: list[EventListenerMethod], event_to_cancel: Dependency ) -> None: event_triggers = event_triggers if isinstance(event_triggers, (list, tuple)) else [event_triggers] if self.stop_btn and self.is_generator: if self.submit_btn: for event_trigger in event_triggers: event_trigger( lambda: ( Button.update(visible=False), Button.update(visible=True), ), None, [self.submit_btn, self.stop_btn], api_name=False, queue=False, ) event_to_cancel.then( lambda: (Button.update(visible=True), Button.update(visible=False)), None, [self.submit_btn, self.stop_btn], api_name=False, queue=False, ) else: for event_trigger in event_triggers: event_trigger( lambda: Button.update(visible=True), None, [self.stop_btn], api_name=False, queue=False, ) event_to_cancel.then( lambda: Button.update(visible=False), None, [self.stop_btn], api_name=False, queue=False, ) self.stop_btn.click( None, None, None, cancels=event_to_cancel, api_name=False, ) else: if self.submit_btn: for event_trigger in event_triggers: event_trigger( lambda: Button.update(interactive=False), None, [self.submit_btn], api_name=False, queue=False, ) event_to_cancel.then( lambda: Button.update(interactive=True), None, [self.submit_btn], api_name=False, queue=False, ) gr.ChatInterface._setup_stop_events = _setup_stop_events def chat_response(message, history, temperature: float, max_tokens: int, system_prompt: str = '') -> str: global llm assert llm is not None temperature = float(temperature) max_tokens = int(max_tokens) if system_prompt.strip() != '': # chat version, add system prompt message = llama_chat_sys_input_seq_constructor( message.strip(), sys_prompt=system_prompt ) sampling_params = SamplingParams(temperature=temperature, max_tokens=max_tokens) gen = llm.generate(message, sampling_params) out = gen[0].outputs[0].text return f'{out}' def vllm_abort(self: Any): scheduler = self.llm_engine.scheduler for state_queue in [scheduler.waiting, scheduler.running, scheduler.swapped]: for seq_group in state_queue: # if seq_group.request_id == request_id: # Remove the sequence group from the state queue. state_queue.remove(seq_group) for seq in seq_group.seqs: if seq.is_finished(): continue scheduler.free_seq(seq, SequenceStatus.FINISHED_ABORTED) # def _vllm_run_engine(self: LLM, use_tqdm: bool = False) -> Dict[str, RequestOutput]: def _vllm_run_engine(self: Any, use_tqdm: bool = False) -> Dict[str, Any]: # Initialize tqdm. if use_tqdm: num_requests = self.llm_engine.get_num_unfinished_requests() pbar = tqdm(total=num_requests, desc="Processed prompts") # Run the engine. outputs: Dict[str, RequestOutput] = {} while self.llm_engine.has_unfinished_requests(): step_outputs = self.llm_engine.step() for output in step_outputs: outputs[output.request_id] = output # outputs = sorted(outputs, key=lambda x: int(x.request_id)) if len(outputs) > 0: yield outputs # if use_tqdm: # pbar.close() # Sort the outputs by request ID. # This is necessary because some requests may be finished earlier than # its previous requests. # outputs = sorted(outputs, key=lambda x: int(x.request_id)) # return outputs def vllm_generate_stream( self: Any, prompts: Optional[Union[str, List[str]]] = None, sampling_params: Optional[Any] = None, prompt_token_ids: Optional[List[List[int]]] = None, use_tqdm: bool = False, ) -> Dict[str, Any]: """Generates the completions for the input prompts. NOTE: This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: A list of prompts to generate completions for. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. prompt_token_ids: A list of token IDs for the prompts. If None, we use the tokenizer to convert the prompts to token IDs. use_tqdm: Whether to use tqdm to display the progress bar. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. """ if prompts is None and prompt_token_ids is None: raise ValueError("Either prompts or prompt_token_ids must be " "provided.") if isinstance(prompts, str): # Convert a single prompt to a list. prompts = [prompts] if prompts is not None and prompt_token_ids is not None: if len(prompts) != len(prompt_token_ids): raise ValueError("The lengths of prompts and prompt_token_ids " "must be the same.") if sampling_params is None: # Use default sampling params. sampling_params = SamplingParams() # Add requests to the engine. if prompts is not None: num_requests = len(prompts) else: num_requests = len(prompt_token_ids) for i in range(num_requests): prompt = prompts[i] if prompts is not None else None if prompt_token_ids is None: token_ids = None else: token_ids = prompt_token_ids[i] self._add_request(prompt, sampling_params, token_ids) # return self._run_engine(use_tqdm) yield from _vllm_run_engine(self, use_tqdm) # def chat_response_stream( # message: str, # history: List[Tuple[str, str]], # temperature: float, # max_tokens: int, # frequency_penalty: float, # system_prompt: str # ) -> str: # global llm, RES_PRINTED # assert llm is not None # # force removing all # vllm_abort(llm) # temperature = float(temperature) # frequency_penalty = float(frequency_penalty) # max_tokens = int(max_tokens) # if system_prompt.strip() != '': # # chat version, add system prompt # message = llama_chat_sys_input_seq_constructor( # message.strip(), # sys_prompt=system_prompt # ) # sampling_params = SamplingParams( # temperature=temperature, max_tokens=max_tokens, # frequency_penalty=frequency_penalty, # ) # cur_out = None # for j, gen in enumerate(vllm_generate_stream(llm, message, sampling_params)): # if cur_out is not None and (STREAM_YIELD_MULTIPLE < 1 or j % STREAM_YIELD_MULTIPLE == 0) and j > 0: # yield cur_out # assert len(gen) == 1, f'{gen}' # item = next(iter(gen.values())) # cur_out = item.outputs[0].text # if not RES_PRINTED: # print(f'{message}<<<{cur_out}>>>') # RES_PRINTED = True # if cur_out is not None: # yield cur_out BLOCK_MESSAGE = """Sorry, Chinese is not currently supported. Please clear the chat box for a new conversation. 抱歉，目前不支持中文。 请清除聊天框以进行新对话。""" def block_zh( message: str, history: List[Tuple[str, str]] ) -> str: # if any((BLOCK_MESSAGE in x[0].strip() or BLOCK_MESSAGE in x[1].strip()) for x in history): if any((BLOCK_MESSAGE in x[1].strip()) for x in history): return True elif 'zh' in _detect_lang(message): print(f'Detect zh: {message}') return True # ! optionally detect every responses message else: return False # 抱歉，目前不支持中文。 def chat_response_stream_multiturn( message: str, history: List[Tuple[str, str]], temperature: float, max_tokens: int, frequency_penalty: float, system_prompt: Optional[str] = SYSTEM_PROMPT_1 ) -> str: """Build multi turn [INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer [INST] Prompt [/INST] Answer [INST] Prompt [/INST] message is incoming prompt history don't have the current messauge """ global llm, RES_PRINTED assert llm is not None assert system_prompt.strip() != '', f'system prompt is empty' # force removing all vllm_abort(llm) temperature = float(temperature) frequency_penalty = float(frequency_penalty) max_tokens = int(max_tokens) message = message.strip() # detect_ = _detect_lang(message) # print(f'Message language: {detect_}') # ! lang detect if BLOCK_ZH: if block_zh(message, history): yield BLOCK_MESSAGE return # history.append([message, None]) # history will be appended with message later on full_prompt = llama_chat_multiturn_sys_input_seq_constructor( message, history, sys_prompt=system_prompt ) # print(full_prompt) sampling_params = SamplingParams( temperature=temperature, max_tokens=max_tokens, frequency_penalty=frequency_penalty, ) cur_out = None # for gen in vllm_generate_stream(llm, full_prompt, sampling_params): for j, gen in enumerate(vllm_generate_stream(llm, full_prompt, sampling_params)): if cur_out is not None and (STREAM_YIELD_MULTIPLE < 1 or j % STREAM_YIELD_MULTIPLE == 0) and j > 0: yield cur_out assert len(gen) == 1, f'{gen}' item = next(iter(gen.values())) cur_out = item.outputs[0].text # if not RES_PRINTED: print(f'{full_prompt}<<<{cur_out}>>>\n') # RES_PRINTED = True if cur_out is not None: yield cur_out # print(f'Output: {_detect_lang(cur_out)}') if BLOCK_ZH: if "zh" in _detect_lang(cur_out): yield BLOCK_MESSAGE def debug_chat_response_echo( message: str, history: List[Tuple[str, str]], temperature: float = 0.0, max_tokens: int = 4096, frequency_penalty: float = 0.4, system_prompt: str = SYSTEM_PROMPT_1, ) -> str: import time time.sleep(0.5) yield f"repeat: {message}" # ============ CONSTANT ============ # https://github.com/gradio-app/gradio/issues/884 MODEL_NAME = "SeaL-13B" MODEL_TITLE = "SeaL-13B - An Assistant for South East Asian Languages" # ! add icon: "image One" MODEL_DESC = """ This is a DAMO SeaL-13B chatbot assistant built by DAMO Academy, Alibaba Group. It can produce helpful responses in English 🇬🇧, Vietnamese 🇻🇳, Indonesian 🇮🇩 and Thai 🇹🇭. """.strip() #
cite_markdown = """ ### Citation If you find our project useful, hope you can star our repo and cite our paper as follows: ``` @article{damonlpsg2023seallm, author = {???}, title = {SeaL: A language model for South East Asian Languages}, year = 2023, } ``` """ warning_markdown = """ ### Warning: The chatbot may produce inaccurate and harmful information about people, places, or facts. We strongly advise against misuse of the chatbot to knowingly generate harmful or unethical content, \ or content that violates locally applicable and international laws or regulations, including hate speech, violence, pornography, deception, etc! """ path_markdown = """ #### Model path: {model_path} """ def launch(): global demo, llm, DEBUG model_desc = MODEL_DESC model_path = MODEL_PATH model_title = MODEL_TITLE tensor_parallel = TENSOR_PARALLEL assert tensor_parallel > 0 , f'{tensor_parallel} invalid' dtype = DTYPE sys_prompt = SYSTEM_PROMPT_1 max_tokens = MAX_TOKENS print(f'Launch config: {model_path=} / {model_title=} / {tensor_parallel=} / {dtype=} / {max_tokens}\n{SYSTEM_PROMPT_1} | {BLOCK_ZH=}') if DEBUG: model_desc += "\n
!!!!! This is in debug mode, responses will be copy original" response_fn = debug_chat_response_echo else: # ! load the model import vllm assert os.path.exists(model_path), f'{model_path} not found' print(F'VLLM: {vllm.__version__}') print(f'Load path: {model_path}') llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel) print(f'Use system prompt:\n{sys_prompt}') response_fn = chat_response_stream_multiturn print(F'respond: {response_fn}') demo = gr.ChatInterface( response_fn, chatbot=ChatBot( label=MODEL_NAME, bubble_full_width=False, latex_delimiters=[ { "left": "$", "right": "$", "display": False}, { "left": "$$", "right": "$$", "display": True}, ] ), textbox=gr.Textbox(placeholder='Type message', lines=8, max_lines=128, min_width=200), submit_btn=gr.Button(value='Submit', variant="primary", scale=0), # ! consider preventing the stop button stop_btn=None, title=f"{model_title}", description=f"{model_desc}", # ! decide if can change the system prompt. additional_inputs=[ gr.Number(value=0, label='Temperature (higher -> more random)'), gr.Number(value=max_tokens, label='Max generated tokens (increase if want more generation)'), gr.Number(value=0.4, label='Frequency penalty (> 0 encourage new tokens)'), # gr.Textbox(value=sys_prompt, label='System prompt', lines=8) ], ) with demo: gr.Markdown(warning_markdown) gr.Markdown(cite_markdown) gr.Markdown(path_markdown.format(model_path=model_path)) demo.queue() demo.launch(server_port=PORT) def main(): # launch(parser.parse_args()) launch() if __name__ == "__main__": main() """ export CUDA_VISIBLE_DEVICES=0 export MODEL_PATH=${dataroot}/hf_train/pretrain_lm/swpn/merlion13s108Hi8kPretFlCW8k.LMFromHf.a.gc.t5k0.vizhthid.mean_std.TrainTask.NLNL.Multi.Vi.FSePlCq13M.FSePlCq13M.m4k.b8.lr1e5.linear.wa0k.ms858k.grac1.se1.8g.v4c.zfsdp/step_4000 export MODEL_PATH=${dataroot}/llama-2-7b-lxxp-faster export MODEL_PATH=${dataroot}/llama-2-7b-chat-xp export DEBUG=0 export CUDA_VISIBLE_DEVICES=0 export MODEL_PATH=seal_13b_a export MODEL_PATH=${dataroot}/hf_train/pretrain_lm/swpn/merlion13s108Hi8kPretFlCW12k.LMFromHf.a.gc.t5k0.vizhthid.mean_std.TrainTask.NLNL.Multi.Vi.SeaV2Cq13M.SeaV2Cq13M.m4k.b8.lr1e5.linear.wa0k.ms858k.grac1.se1.8g.v4c.zfsdp/step_6000 export MODEL_PATH=${dataroot}/hf_train/pretrain_lm/swpn/mer13s108Hi16kPretFlCWNLP12k_SFT2.LMFromHf.a.gc.t5k0.vizhthid.mean_std.TrainTask.NLNL.Multi.Vi.Sft2Censor.Sft2Censor.m4k.b8.lr1e5.linear.wa0k.ms1144k.grac1.se1.6g.v4c.zfsdp/step_2000 export PORT=8799 export BLOCK_ZH=1 python app.py DEBUG=1 python app.py """