# Copyright: DAMO Academy, Alibaba Group # By Xuan Phi Nguyen at DAMO Academy, Alibaba Group # Description: """ VLLM-based demo script to launch Language chat model for Southeast Asian Languages """ 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 # @@ environments ================ DEBUG = bool(int(os.environ.get("DEBUG", "1"))) BLOCK_ZH = bool(int(os.environ.get("BLOCK_ZH", "1"))) # for lang block, wether to block in history too LANG_BLOCK_HISTORY = bool(int(os.environ.get("LANG_BLOCK_HISTORY", "0"))) TENSOR_PARALLEL = int(os.environ.get("TENSOR_PARALLEL", "1")) DTYPE = os.environ.get("DTYPE", "bfloat16") # ! (no debug) whether to download HF_MODEL_NAME and save to MODEL_PATH DOWNLOAD_SNAPSHOT = bool(int(os.environ.get("DOWNLOAD_SNAPSHOT", "0"))) LOG_RESPONSE = bool(int(os.environ.get("LOG_RESPONSE", "0"))) # ! show model path in the demo page, only for internal DISPLAY_MODEL_PATH = bool(int(os.environ.get("DISPLAY_MODEL_PATH", "1"))) # ! uploaded model path, will be downloaded to MODEL_PATH HF_MODEL_NAME = os.environ.get("HF_MODEL_NAME", "DAMO-NLP-SG/seal-13b-chat-a") # ! if model is private, need HF_TOKEN to access the model HF_TOKEN = os.environ.get("HF_TOKEN", None) # ! path where the model is downloaded, either on ./ or persistent disc MODEL_PATH = os.environ.get("MODEL_PATH", "./seal-13b-chat-a") # ! list of keywords to disabled as security measures to comply with local regulation KEYWORDS = os.environ.get("KEYWORDS", "").strip() KEYWORDS = KEYWORDS.split(";") if len(KEYWORDS) > 0 else [] KEYWORDS = [x.lower() for x in KEYWORDS] # gradio config PORT = int(os.environ.get("PORT", "7860")) # how many iterations to yield response STREAM_YIELD_MULTIPLE = int(os.environ.get("STREAM_YIELD_MULTIPLE", "1")) # how many iterations to perform safety check on response STREAM_CHECK_MULTIPLE = int(os.environ.get("STREAM_CHECK_MULTIPLE", "0")) # self explanatory MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "2048")) TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.1")) FREQUENCE_PENALTY = float(os.environ.get("FREQUENCE_PENALTY", "0.4")) gpu_memory_utilization = float(os.environ.get("gpu_memory_utilization", "0.9")) # whether to enable quantization, currently not in use QUANTIZATION = str(os.environ.get("QUANTIZATION", "")) """ Internal instructions of how to configure the DEMO 1. Upload SFT model as a model to huggingface: hugginface/models/seal_13b_a 2. If the model weights is private, set HF_TOKEN= in https://huggingface.co/spaces/????/?????/settings 3. space config env: `HF_MODEL_NAME=DAMO-NLP-SG/seal-13b-chat-a` or the underlining model 4. If enable persistent storage: set HF_HOME=/data/.huggingface MODEL_PATH=/data/.huggingface/seal-13b-chat-a if not: MODEL_PATH=./seal-13b-chat-a """ # ============================== print(f'DEBUG mode: {DEBUG}') print(f'Torch version: {torch.__version__}') try: print(f'Torch CUDA version: {torch.version.cuda}') except Exception as e: print(f'Failed to print cuda version: {e}') try: compute_capability = torch.cuda.get_device_capability() print(f'Torch CUDA compute_capability: {compute_capability}') except Exception as e: print(f'Failed to print compute_capability version: {e}') # @@ constants ================ DTYPES = { 'float16': torch.float16, 'bfloat16': torch.bfloat16 } llm = None demo = None BOS_TOKEN = '' EOS_TOKEN = '' B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<>\n", "\n<>\n\n" SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. Your name is SeaLLM and you are built by DAMO Academy, Alibaba Group. \ Please 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. """ # ============ CONSTANT ============ # https://github.com/gradio-app/gradio/issues/884 MODEL_NAME = "SeaLLM-13B" MODEL_TITLE = "SeaLLM-13B - An Assistant for Southeast Asian Languages" MODEL_TITLE = """

SeaLLM-13B - An Assistant for Southeast Asian Languages

""" MODEL_DESC = """ This is SeaLLM-13B - a chatbot assistant optimized for Southeast Asian Languages. It can produce helpful responses in English 🇬🇧, Vietnamese 🇻🇳, Indonesian 🇮🇩 and Thai 🇹🇭.
NOTICE: The chatbot may produce inaccurate and harmful information about people, places, or facts. By using our service, you are required to agree to the following terms:
 """.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 = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Sharifah Mahani Aljunied*, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, } ``` """ path_markdown = """ #### Model path: {model_path} """ def _detect_lang(text): # Disable language that may have safety risk from langdetect import detect as detect_lang dlang = None try: dlang = detect_lang(text) except Exception as e: print(f'Error: {e}') if "No features in text." in str(e): return "en" else: return "zh" return dlang def custom_hf_model_weights_iterator( model_name_or_path: str, cache_dir: Optional[str] = None, use_np_cache: bool = False, ) -> Iterator[Tuple[str, torch.Tensor]]: # ! if use vllm==0.1.4, use this to augment hf_model_weights_iterator loader 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, "*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") ] 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", revision: Optional[str] = None ): # if use vllm==0.1.4 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 iterator = custom_hf_model_weights_iterator(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=}') 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 ) 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) ) _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}') def new_llama_load_weights( self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None ): # If use newest vllm, not been thoroughly tested yet. from vllm.model_executor.weight_utils import ( load_tensor_parallel_weights, hf_model_weights_iterator ) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) if self.quant_config is None: weight_suffixes = ["weight"] else: weight_suffixes = self.quant_config.get_tp_tensor_names() column_parallel_weights: List[str] = [] for layer in self._column_parallel_layers: for suffix in weight_suffixes: column_parallel_weights.append(f"{layer}.{suffix}") row_parallel_weights: List[str] = [] for layer in self._row_parallel_layers: for suffix in weight_suffixes: row_parallel_weights.append(f"{layer}.{suffix}") tp_size = get_tensor_model_parallel_world_size() tp_rank = get_tensor_model_parallel_rank() assert tp_size == 1, f'tensorparallel >=2 not allowed. {tp_size}' q_proj_shard_size = (self.config.hidden_size // tp_size) num_kv_heads_replicas = max(1, tp_size // self.config.num_key_value_heads) num_kv_heads_per_gpu = max(1, self.config.num_key_value_heads // tp_size) kv_proj_shard_size = (self.config.hidden_size // self.config.num_attention_heads * num_kv_heads_per_gpu) 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 for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): if "rotary_emb.inv_freq" in name: continue is_packed = False is_transposed = False if self.quant_config is not None: is_packed = self.quant_config.is_packed(name) is_transposed = self.quant_config.is_transposed(name) if is_transposed: loaded_weight = convert_pyslice_to_tensor(loaded_weight) loaded_weight = loaded_weight.T 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")] if is_transposed: param = param.T if is_packed: shard_size //= self.quant_config.pack_factor offset //= self.quant_config.pack_factor if weight_name in ["k_proj", "v_proj"]: shard_id = tp_rank // num_kv_heads_replicas else: shard_id = tp_rank loaded_weight = loaded_weight[shard_size * shard_id:shard_size * (shard_id + 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 # TODO: need to figure out to do sharding with qkv_proj fused 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")] if is_transposed: param = param.T shard_size = param.shape[0] // 2 loaded_weight = loaded_weight[shard_size * tp_rank:shard_size * (tp_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 # TODO: need to figure out to do sharding with gate_up_proj fused param = state_dict[name] if is_transposed: param = param.T if "embed_tokens" in name or "lm_head" in name: load_padded_tensor_parallel_vocab(param, loaded_weight, tp_rank) loaded += 1 continue load_tensor_parallel_weights(param, loaded_weight, name, column_parallel_weights, row_parallel_weights, tp_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: try: import vllm from vllm.model_executor.model_loader import _MODEL_REGISTRY from vllm.model_executor.models import LlamaForCausalLM _MODEL_REGISTRY['FasterLlamaForCausalLM'] = LlamaForCausalLM if vllm.__version__ == "0.1.4": LlamaForCausalLM.load_weights = llama_load_weights else: LlamaForCausalLM.load_weights = new_llama_load_weights if DTYPE == "bfloat16": try: compute_capability = torch.cuda.get_device_capability() if compute_capability[0] < 8: gpu_name = torch.cuda.get_device_name() print( "Bfloat16 is only supported on GPUs with compute capability " f"of at least 8.0. Your {gpu_name} GPU has compute capability " f"{compute_capability[0]}.{compute_capability[1]}. --> Move to FLOAT16") DTYPE = "float16" except Exception as e: print(f'Unable to obtain compute_capability: {e}') except Exception as e: print(f'Failing import and reconfigure VLLM: {str(e)}') # ! ================================================================== set_documentation_group("component") 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 from gradio.components import Button from gradio.events import Dependency, EventListenerMethod # replace events so that submit button is disabled during generation, if stop_btn not found # this prevent weird behavior 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, ) # upon clear, cancel the submit event as well if self.clear_btn: self.clear_btn.click( lambda: ([], [], None, Button.update(interactive=True)), None, [self.chatbot, self.chatbot_state, self.saved_input, self.submit_btn], queue=False, api_name=False, cancels=event_to_cancel, ) # TODO: reconfigure clear button as stop and clear button def _setup_events(self) -> None: has_on = False try: from gradio.events import Dependency, EventListenerMethod, on has_on = True except ImportError as ie: has_on = False submit_fn = self._stream_fn if self.is_generator else self._submit_fn if has_on: # new version submit_triggers = ( [self.textbox.submit, self.submit_btn.click] if self.submit_btn else [self.textbox.submit] ) submit_event = ( on( submit_triggers, self._clear_and_save_textbox, [self.textbox], [self.textbox, self.saved_input], api_name=False, queue=False, ) .then( self._display_input, [self.saved_input, self.chatbot_state], [self.chatbot, self.chatbot_state], api_name=False, queue=False, ) .then( submit_fn, [self.saved_input, self.chatbot_state] + self.additional_inputs, [self.chatbot, self.chatbot_state], api_name=False, ) ) self._setup_stop_events(submit_triggers, submit_event) else: raise ValueError(f'Better install new gradio version than 3.44.0') if self.retry_btn: retry_event = ( self.retry_btn.click( self._delete_prev_fn, [self.chatbot_state], [self.chatbot, self.saved_input, self.chatbot_state], api_name=False, queue=False, ) .then( self._display_input, [self.saved_input, self.chatbot_state], [self.chatbot, self.chatbot_state], api_name=False, queue=False, ) .then( submit_fn, [self.saved_input, self.chatbot_state] + self.additional_inputs, [self.chatbot, self.chatbot_state], api_name=False, ) ) self._setup_stop_events([self.retry_btn.click], retry_event) if self.undo_btn: self.undo_btn.click( self._delete_prev_fn, [self.chatbot_state], [self.chatbot, self.saved_input, self.chatbot_state], api_name=False, queue=False, ).then( lambda x: x, [self.saved_input], [self.textbox], api_name=False, queue=False, ) # Reconfigure clear_btn to stop and clear text box # if self.clear_btn: # self.clear_btn.click( # lambda: ([], [], None), # None, # [self.chatbot, self.chatbot_state, self.saved_input], # queue=False, # api_name=False, # cancels=submit_event, # ) # replace gr.ChatInterface._setup_stop_events = _setup_stop_events gr.ChatInterface._setup_events = _setup_events def vllm_abort(self: Any): from vllm.sequence import SequenceStatus 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: Any, use_tqdm: bool = False) -> Dict[str, Any]: from vllm.outputs import RequestOutput # 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 if len(outputs) > 0: yield 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. """ from vllm import LLM, SamplingParams 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) # ! avoid saying LANG_BLOCK_MESSAGE = """Sorry, the language you have asked is currently not supported. If you have questions in other supported languages, I'll be glad to help. \ Please also consider clearing the chat box for a better experience.""" KEYWORD_BLOCK_MESSAGE = "Sorry, I cannot fulfill your request. If you have any unrelated question, I'll be glad to help." def block_zh( message: str, history: List[Tuple[str, str]] = None, ) -> str: # relieve history base block if LANG_BLOCK_HISTORY and history is not None and any((LANG_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 else: return False def log_responses(history, message, response): pass def safety_check(text, history=None, ) -> Optional[str]: """ Despite our effort in safety tuning and red teaming, our models may still generate harmful or illegal content. This provides an additional security measure to enhance safety and compliance with local regulations. """ if BLOCK_ZH: if history is not None: if block_zh(text, history): return LANG_BLOCK_MESSAGE else: if "zh" in _detect_lang(text): return LANG_BLOCK_MESSAGE if len(KEYWORDS) > 0 and any(x in text.lower() for x in KEYWORDS): return KEYWORD_BLOCK_MESSAGE return None 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: from vllm import LLM, SamplingParams """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() message_safety = safety_check(message, history=history) if message_safety is not None: yield message_safety return # history will be appended with message later on full_prompt = llama_chat_multiturn_sys_input_seq_constructor( message, history, 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, full_prompt, sampling_params)): if cur_out is not None and (STREAM_YIELD_MULTIPLE < 1 or j % STREAM_YIELD_MULTIPLE == 0) and j > 0: # optionally check safety, and respond if STREAM_CHECK_MULTIPLE > 0 and j % STREAM_CHECK_MULTIPLE == 0: message_safety = safety_check(cur_out, history=None) if message_safety is not None: yield message_safety return yield cur_out assert len(gen) == 1, f'{gen}' item = next(iter(gen.values())) cur_out = item.outputs[0].text print(f'{full_prompt}<<<{cur_out}>>>\n\n') if cur_out is not None: yield cur_out message_safety = safety_check(cur_out, history=None) if message_safety is not None: yield message_safety return if LOG_RESPONSE: log_responses(history, message, cur_out) 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}" def check_model_path(model_path) -> str: assert os.path.exists(model_path), f'{model_path} not found' ckpt_info = "None" if os.path.isdir(model_path): if os.path.exists(f'{model_path}/info.txt'): with open(f'{model_path}/info.txt', 'r') as f: ckpt_info = f.read() print(f'Checkpoint info:\n{ckpt_info}\n-----') else: print(f'info.txt not found in {model_path}') print(f'model path dir: {list(os.listdir(model_path))}') return ckpt_info def launch(): global demo, llm, DEBUG model_desc = MODEL_DESC model_path = MODEL_PATH model_title = MODEL_TITLE hf_model_name = HF_MODEL_NAME tensor_parallel = TENSOR_PARALLEL assert tensor_parallel > 0 , f'{tensor_parallel} invalid' dtype = DTYPE sys_prompt = SYSTEM_PROMPT_1 max_tokens = MAX_TOKENS temperature = TEMPERATURE frequence_penalty = FREQUENCE_PENALTY ckpt_info = "None" print( f'Launch config: {tensor_parallel=} / {dtype=} / {max_tokens} | {BLOCK_ZH=} ' f'\n| model_title=`{model_title}` ' f'\n| STREAM_YIELD_MULTIPLE={STREAM_YIELD_MULTIPLE} ' f'\n| STREAM_CHECK_MULTIPLE={STREAM_CHECK_MULTIPLE} ' f'\n| DISPLAY_MODEL_PATH={DISPLAY_MODEL_PATH} ' f'\n| LANG_BLOCK_HISTORY={LANG_BLOCK_HISTORY} ' f'\n| frequence_penalty={frequence_penalty} ' f'\n| temperature={temperature} ' f'\n| hf_model_name={hf_model_name} ' f'\n| model_path={model_path} ' f'\n| DOWNLOAD_SNAPSHOT={DOWNLOAD_SNAPSHOT} ' f'\n| gpu_memory_utilization={gpu_memory_utilization} ' f'\n| KEYWORDS={KEYWORDS} ' f'\n| Sys={SYSTEM_PROMPT_1}' f'\n| Desc={model_desc}' ) if DEBUG: model_desc += "\n
!!!!! This is in debug mode, responses will copy original" response_fn = debug_chat_response_echo print(f'Creating in DEBUG MODE') else: # ! load the model if DOWNLOAD_SNAPSHOT: print(f'Downloading from HF_MODEL_NAME={hf_model_name} -> {model_path}') if HF_TOKEN is not None: print(f'Load with HF_TOKEN: {HF_TOKEN}') snapshot_download(hf_model_name, local_dir=model_path, use_auth_token=True, token=HF_TOKEN) else: snapshot_download(hf_model_name, local_dir=model_path) import vllm from vllm import LLM print(F'VLLM: {vllm.__version__}') ckpt_info = check_model_path(model_path) print(f'Load path: {model_path} | {ckpt_info}') if QUANTIZATION == 'awq': print(F'Load model in int4 quantization') llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel, gpu_memory_utilization=gpu_memory_utilization, quantization="awq") else: llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel, gpu_memory_utilization=gpu_memory_utilization) try: print(llm.llm_engine.workers[0].model) except Exception as e: print(f'Cannot print model worker: {e}') 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}", additional_inputs=[ gr.Number(value=temperature, label='Temperature (higher -> more random)'), gr.Number(value=max_tokens, label='Max generated tokens (increase if want more generation)'), gr.Number(value=frequence_penalty, label='Frequency penalty (> 0 encourage new tokens)'), # ! Remove the system prompt textbox to avoid jailbreaking # gr.Textbox(value=sys_prompt, label='System prompt', lines=8) ], ) demo.title = MODEL_NAME with demo: # gr.Markdown(warning_markdown) gr.Markdown(cite_markdown) if DISPLAY_MODEL_PATH: gr.Markdown(path_markdown.format(model_path=model_path)) demo.queue() demo.launch(server_port=PORT) def main(): launch() if __name__ == "__main__": main()