import ast import copy import functools import inspect import queue import sys import os import json import time import traceback import typing import uuid import warnings from datetime import datetime from random import randint import filelock import requests if os.path.dirname(os.path.abspath(__file__)) not in sys.path: sys.path.append(os.path.dirname(os.path.abspath(__file__))) try: from importlib.metadata import distribution, PackageNotFoundError assert distribution('hf_transfer') is not None have_hf_transfer = True except (PackageNotFoundError, AssertionError): have_hf_transfer = False if have_hf_transfer and os.getenv('HF_HUB_ENABLE_HF_TRANSFER', 'None') != '0': os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1' os.environ['SCARF_NO_ANALYTICS'] = 'true' os.environ['DO_NOT_TRACK'] = 'true' os.environ['OTEL_SDK_DISABLED'] = 'true' os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['BITSANDBYTES_NOWELCOME'] = '1' warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') os.environ['FIFTYONE_SHOW_PROGRESS_BARS'] = 'false' # more is not useful typically, don't let these go beyond limits and eat up resources max_cores = max(1, os.cpu_count() // 2) if os.getenv('NUMEXPR_MAX_THREADS') is None: os.environ['NUMEXPR_MAX_THREADS'] = str(min(8, max_cores)) if os.getenv('NUMEXPR_NUM_THREADS') is None: os.environ['NUMEXPR_NUM_THREADS'] = str(min(8, max_cores)) if os.getenv('OMP_NUM_THREADS') is None: os.environ['OMP_NUM_THREADS'] = str(min(8, max_cores)) if os.getenv('OPENBLAS_NUM_THREADS') is None: os.environ['OPENBLAS_NUM_THREADS'] = str(min(8, max_cores)) if os.getenv('DUCKDB_NUM_THREADS') is None: os.environ['DUCKDB_NUM_THREADS'] = str(min(4, max_cores)) if os.getenv('RAYON_RS_NUM_CPUS') is None: os.environ['RAYON_RS_NUM_CPUS'] = str(min(8, max_cores)) if os.getenv('RAYON_NUM_THREADS') is None: os.environ['RAYON_NUM_THREADS'] = str(min(8, max_cores)) from gradio_funcs import merge_chat_conversation_history from db_utils import fetch_user from model_utils import switch_a_roo_llama, get_score_model, get_model_retry, get_model, \ get_client_from_inference_server, model_lock_to_state from evaluate_params import eval_func_param_names, no_default_param_names, input_args_list, image_size_default, \ image_quality_choices from enums import DocumentSubset, LangChainMode, no_lora_str, no_model_str, \ LangChainAction, LangChainAgent, DocumentChoice, LangChainTypes, super_source_prefix, \ super_source_postfix, t5_type, get_langchain_prompts, gr_to_lg, invalid_key_msg, docs_joiner_default, \ docs_ordering_types_default, docs_token_handling_default, max_input_tokens_public, max_total_input_tokens_public, \ max_top_k_docs_public, max_top_k_docs_default, max_total_input_tokens_public_api, max_top_k_docs_public_api, \ max_input_tokens_public_api, anthropic_mapping, \ base_langchain_actions, generic_prefix, \ generic_postfix, langchain_modes_intrinsic, valid_imagechange_models, \ valid_imagegen_models, valid_imagestyle_models, \ langchain_modes0, langchain_mode_types0, langchain_mode_paths0, \ llava_num_max, response_formats, noop_prompt_type, unknown_prompt_type, \ json_object_prompt0, json_object_prompt_simpler0, json_code_prompt0, user_prompt_for_fake_system_prompt0, \ json_schema_instruction0, json_code_prompt_if_no_schema0, my_db_state0, empty_prompt_type, is_gradio_vision_model, \ is_json_model, is_vision_model, \ model_state_none0, other_model_state_defaults0, image_batch_image_prompt0, image_batch_final_prompt0, \ tokens_per_image, openai_supports_functiontools, openai_supports_parallel_functiontools, does_support_functiontools, \ json_object_post_prompt_reminder0, json_code_post_prompt_reminder0, json_code2_post_prompt_reminder0, \ max_stream_string_for_json from utils import set_seed, clear_torch_cache, NullContext, wrapped_partial, EThread, get_githash, \ import_matplotlib, get_device, makedirs, get_kwargs, start_faulthandler, get_hf_server, \ have_langchain, set_openai, cuda_vis_check, H2O_Fire, lg_to_gr, str_to_list, str_to_dict, get_token_count, \ have_wavio, have_soundfile, have_deepspeed, have_doctr, have_librosa, have_TTS, have_flash_attention_2, \ have_diffusers, sanitize_filename, get_gradio_tmp, get_is_gradio_h2oai, get_json, \ get_docs_tokens, deduplicate_names, have_autogen, get_model_name, is_empty, get_supports_schema start_faulthandler() import_matplotlib() SEED = 1236 set_seed(SEED) from typing import Union import torch from transformers import GenerationConfig, TextIteratorStreamer from prompter import Prompter, non_hf_types, PromptType, get_prompt, generate_prompt, \ openai_gpts, get_vllm_extra_dict, gradio_to_llm, history_for_llm, apply_chat_template, model_name_to_prompt_type from stopping import get_stopping from prompter_utils import get_use_chat_template, base64_decode_jinja_template langchain_actions = [x.value for x in list(LangChainAction)] langchain_agents_list = [x.value for x in list(LangChainAgent)] def main( load_8bit: bool = False, load_4bit: bool = False, low_bit_mode: int = 1, load_half: bool = None, use_flash_attention_2=False, load_gptq: str = '', use_autogptq: bool = False, load_awq: str = '', load_exllama: bool = False, use_safetensors: bool = True, revision: str = None, use_gpu_id: bool = True, base_model: str = '', display_name: str = None, tokenizer_base_model: str = '', lora_weights: str = "", gpu_id: int = 0, compile_model: bool = None, use_cache: bool = None, inference_server: str = "", regenerate_clients: bool = True, regenerate_gradio_clients: bool = False, validate_clients: bool = True, fail_if_invalid_client: bool = False, prompt_type: Union[int, str] = None, prompt_dict: typing.Dict = None, chat_template: str = '', system_prompt: str = 'auto', allow_chat_system_prompt: bool = True, # llama and gpt4all settings llamacpp_path: str = 'llamacpp_path', llamacpp_dict: typing.Dict = dict(n_gpu_layers=100, use_mlock=True, n_batch=1024, n_gqa=0), model_path_llama: str = '', model_name_gptj: str = '', model_name_gpt4all_llama: str = '', model_name_exllama_if_no_config: str = '', exllama_dict: typing.Dict = dict(), gptq_dict: typing.Dict = dict(), attention_sinks: bool = False, sink_dict: typing.Dict = dict(), truncation_generation: bool = False, hf_model_dict: typing.Dict = dict(), force_seq2seq_type: bool = False, force_t5_type: bool = False, model_lock: typing.List[typing.Dict[str, str]] = None, model_lock_columns: int = None, model_lock_layout_based_upon_initial_visible: bool = False, fail_if_cannot_connect: bool = False, # input to generation temperature: float = None, top_p: float = None, top_k: int = None, penalty_alpha: float = None, num_beams: int = None, repetition_penalty: float = None, num_return_sequences: int = None, do_sample: bool = None, seed: int = None, max_new_tokens: int = None, min_new_tokens: int = None, early_stopping: Union[bool, str] = None, max_time: float = None, memory_restriction_level: int = None, debug: bool = False, save_dir: str = None, local_files_only: bool = False, resume_download: bool = True, use_auth_token: Union[str, bool] = False, admin_pass: str = None, trust_remote_code: Union[str, bool] = True, rope_scaling: dict = None, max_seq_len: int = None, max_output_seq_len: int = None, offload_folder: str = "offline_folder", src_lang: str = "English", tgt_lang: str = "Russian", prepare_offline_level: int = 0, cli: bool = False, cli_loop: bool = True, eval: bool = False, gradio: bool = True, function: bool = False, force_streaming_on_to_handle_timeouts: bool = True, openai_server: bool = True, openai_port: int = 5001 if sys.platform == "darwin" else 5000, openai_workers: int = 1, function_server: bool = False, function_server_port: int = 5003 if sys.platform == "darwin" else 5002, function_server_workers: int = 1, function_api_key: str = None, agent_server: bool = False, # WIP agent_port: int = 5004 if sys.platform == "darwin" else 5004, agent_workers: int = 1, multiple_workers_gunicorn: bool = False, gradio_offline_level: int = 0, server_name: str = "0.0.0.0", share: bool = False, open_browser: bool = False, close_button: bool = True, shutdown_via_api: bool = False, root_path: str = "", ssl_verify: bool = True, ssl_keyfile: str | None = None, ssl_certfile: str | None = None, ssl_keyfile_password: str | None = None, chat: bool = True, chat_conversation: typing.List[typing.Tuple[str, str]] = None, text_context_list: typing.List[str] = None, stream_output: bool = True, enable_caching: bool = False, async_output: bool = True, num_async: int = 3, stream_map: bool = False, show_examples: bool = None, verbose: bool = False, h2ocolors: bool = True, dark: bool = False, # light tends to be best height: int = 600, render_markdown: bool = True, show_lora: bool = True, show_llama: bool = True, show_gpt4all: bool = False, login_mode_if_model0: bool = False, block_gradio_exit: bool = True, concurrency_count: int = None, api_open: bool = False, allow_api: bool = True, system_api_open: bool = False, input_lines: int = 1, gradio_size: str = None, show_copy_button: bool = True, large_file_count_mode: bool = False, gradio_ui_stream_chunk_size: int = None, gradio_ui_stream_chunk_min_seconds: float = 0.2, gradio_ui_stream_chunk_seconds: float = 2.0, gradio_api_use_same_stream_limits: bool = True, gradio_upload_to_chatbot: bool = False, gradio_upload_to_chatbot_num_max: bool = 2, gradio_errors_to_chatbot: bool = True, pre_load_embedding_model: bool = True, embedding_gpu_id: Union[int, str] = 'auto', auth: Union[typing.List[typing.Tuple[str, str]], str] = None, auth_filename: str = None, auth_access: str = 'open', auth_freeze: bool = False, auth_message: str = None, google_auth: bool = False, guest_name: str = None, enforce_h2ogpt_api_key: bool = None, enforce_h2ogpt_ui_key: bool = None, h2ogpt_api_keys: Union[list, str] = [], h2ogpt_key: str = None, extra_allowed_paths: list = [], blocked_paths: list = [], max_max_time=None, max_max_new_tokens=None, visible_models: list = None, max_visible_models: int = None, visible_ask_anything_high: bool = True, visible_visible_models: bool = True, visible_submit_buttons: bool = True, visible_side_bar: bool = True, visible_document_subset: bool = True, visible_max_quality: bool = True, visible_add_doc_to_chat: bool = True, visible_chat_history: bool = True, visible_doc_track: bool = True, visible_chat_tab: bool = True, visible_doc_selection_tab: bool = True, visible_doc_view_tab: bool = True, visible_chat_history_tab: bool = True, visible_expert_tab: bool = True, visible_models_tab: bool = True, visible_system_tab: bool = True, visible_tos_tab: bool = False, visible_login_tab: bool = True, visible_hosts_tab: bool = False, visible_langchain_action_radio: bool = True, visible_langchain_purge: bool = True, chat_tabless: bool = False, visible_h2ogpt_links: bool = False, visible_h2ogpt_qrcode: bool = False, visible_h2ogpt_logo: bool = True, visible_chatbot_label: bool = True, visible_all_prompter_models: bool = False, visible_curated_models: bool = True, actions_in_sidebar: bool = False, document_choice_in_sidebar: bool = True, enable_add_models_to_list_ui: bool = False, max_raw_chunks: int = None, pdf_height: int = 800, avatars: bool = True, add_disk_models_to_ui: bool = True, page_title: str = "Doc IQ", model_label_prefix: str = "Doc IQ", favicon_path: str = "./aibenfavicon.png", visible_ratings: bool = False, reviews_file: str = None, sanitize_user_prompt: bool = False, sanitize_bot_response: bool = False, extra_model_options: typing.List[str] = [], extra_lora_options: typing.List[str] = [], extra_server_options: typing.List[str] = [], score_model: str = 'auto', verifier_model: str = None, verifier_tokenizer_base_model: str = None, verifier_inference_server: str = None, eval_filename: str = None, eval_prompts_only_num: int = 0, eval_prompts_only_seed: int = 1234, eval_as_output: bool = False, langchain_mode: str = None, user_path: str = None, langchain_modes: list = langchain_modes0, langchain_mode_paths: dict = langchain_mode_paths0, langchain_mode_types: dict = langchain_mode_types0, detect_user_path_changes_every_query: bool = False, update_selection_state_from_cli: bool = True, langchain_action: str = LangChainAction.QUERY.value, langchain_agents: list = [], force_langchain_evaluate: bool = False, visible_langchain_actions: list = base_langchain_actions.copy(), visible_langchain_agents: list = langchain_agents_list.copy(), document_subset: str = DocumentSubset.Relevant.name, document_choice: list = [DocumentChoice.ALL.value], document_source_substrings: list = [], document_source_substrings_op: str = 'and', document_content_substrings: list = [], document_content_substrings_op: str = 'and', use_llm_if_no_docs: bool = True, load_db_if_exists: bool = True, keep_sources_in_context: bool = False, db_type: str = 'chroma', use_openai_embedding: bool = False, use_openai_model: bool = False, hf_embedding_model: str = None, migrate_embedding_model: str = False, cut_distance: float = 1.64, answer_with_sources: bool = True, append_sources_to_answer: bool = False, append_sources_to_chat: bool = True, sources_show_text_in_accordion: bool = True, top_k_docs_max_show: int = 10, show_link_in_sources: bool = True, langchain_instruct_mode: bool = True, pre_prompt_query: str = None, prompt_query: str = None, pre_prompt_summary: str = None, prompt_summary: str = None, hyde_llm_prompt: str = None, all_docs_start_prompt: str = 'auto', all_docs_finish_prompt: str = 'auto', user_prompt_for_fake_system_prompt: str = None, json_object_prompt: str = None, json_object_prompt_simpler: str = None, json_code_prompt: str = None, json_code_prompt_if_no_schema: str = None, json_schema_instruction: str = None, json_preserve_system_prompt: bool = False, json_object_post_prompt_reminder: str = None, json_code_post_prompt_reminder: str = None, json_code2_post_prompt_reminder: str = None, add_chat_history_to_context: bool = True, add_search_to_context: bool = False, context: str = '', iinput: str = '', allow_upload_to_user_data: bool = True, reload_langchain_state: bool = True, allow_upload_to_my_data: bool = True, enable_url_upload: bool = True, enable_text_upload: bool = True, enable_sources_list: bool = True, chunk: bool = True, chunk_size: int = 512, top_k_docs: int = None, docs_ordering_type: str = docs_ordering_types_default, min_max_new_tokens=512, max_input_tokens=None, max_total_input_tokens=None, docs_token_handling: str = docs_token_handling_default, docs_joiner: str = docs_joiner_default, hyde_level: int = 0, hyde_template: str = None, hyde_show_only_final: bool = False, hyde_show_intermediate_in_accordion: bool = True, map_reduce_show_intermediate_in_accordion: bool = True, doc_json_mode: bool = False, metadata_in_context: Union[str, list] = 'auto', auto_reduce_chunks: bool = True, max_chunks: int = 100, headsize: int = 50, n_jobs: int = -1, n_gpus: int = None, clear_torch_cache_level: int = 1, # urls use_unstructured: bool = True, use_playwright: bool = False, use_selenium: bool = False, use_scrapeplaywright: bool = False, use_scrapehttp: bool = False, # pdfs use_pymupdf: Union[bool, str] = 'auto', use_unstructured_pdf: Union[bool, str] = 'auto', use_pypdf: Union[bool, str] = 'auto', enable_pdf_ocr: Union[bool, str] = 'auto', enable_pdf_doctr: Union[bool, str] = 'auto', try_pdf_as_html: Union[bool, str] = 'auto', # images enable_ocr: bool = False, enable_doctr: bool = True, enable_pix2struct: bool = False, enable_captions: bool = True, enable_llava: bool = True, enable_transcriptions: bool = True, pre_load_image_audio_models: bool = False, caption_gpu: bool = True, caption_gpu_id: Union[int, str] = 'auto', captions_model: str = "microsoft/Florence-2-base", doctr_gpu: bool = True, doctr_gpu_id: Union[int, str] = 'auto', llava_model: str = None, llava_prompt: str = 'auto', image_file: str = None, image_control: str = None, images_num_max: int = None, image_resolution: tuple = None, image_format: str = None, rotate_align_resize_image: bool = None, video_frame_period: int = None, image_batch_image_prompt: str = None, image_batch_final_prompt: str = None, image_batch_stream: bool = False, visible_vision_models: Union[str, int, list] = None, video_file: str = None, response_format: str = 'text', guided_json: Union[str, dict] = '', guided_regex: str = '', guided_choice: typing.List[str] = None, guided_grammar: str = '', guided_whitespace_pattern: str = None, asr_model: str = "openai/whisper-medium", asr_gpu: bool = True, asr_gpu_id: Union[int, str] = 'auto', asr_use_better: bool = True, asr_use_faster: bool = False, enable_stt: Union[str, bool] = 'auto', stt_model: str = "openai/whisper-base.en", stt_gpu: bool = True, stt_gpu_id: Union[int, str] = 'auto', stt_continue_mode: int = 1, enable_tts: Union[str, bool] = 'auto', tts_gpu: bool = True, tts_gpu_id: Union[int, str] = 'auto', tts_model: str = 'microsoft/speecht5_tts', tts_gan_model: str = 'microsoft/speecht5_hifigan', tts_coquiai_deepspeed: bool = False, tts_coquiai_roles: dict = None, chatbot_role: str = "None", # "Female AI Assistant", speaker: str = "None", # "SLT (female)", tts_language: str = 'autodetect', tts_speed: float = 1.0, tts_action_phrases: typing.List[str] = [], # ['Nimbus'], tts_stop_phrases: typing.List[str] = [], # ['Yonder'], sst_floor: float = 100, enable_image: bool = False, visible_image_models: typing.List[str] = [], image_size: str = image_size_default, image_quality: str = 'standard', image_guidance_scale: float = 3.0, image_num_inference_steps: int = 30, image_gpu_ids: typing.List[Union[str, int]] = None, enable_llava_chat: bool = False, # json jq_schema='.[]', extract_frames: int = 10, max_quality: bool = False, enable_heap_analytics: bool = True, heap_app_id: str = "1680123994", client_metadata: str = '', cert_lookup_directory: str = "/etc/ssl/more-certs", ): """ :param load_8bit: load model in 8-bit using bitsandbytes :param load_4bit: load model in 4-bit using bitsandbytes :param low_bit_mode: 0: no quantization config 1: change compute 2: nf4 3: double quant 4: 2 and 3 See: https://huggingface.co/docs/transformers/main_classes/quantization If using older bitsandbytes or transformers, 0 is required :param load_half: load model in float16 (None means auto, which means True unless t5 based model) otherwise specify bool :param use_flash_attention_2: Whether to try to use flash attention 2 if available when loading HF models Warning: We have seen nans and type mismatches with flash-attn==2.3.4 installed and this enabled, even for other models like embedding model that is unrelated to primary models. :param load_gptq: to load model with GPTQ, put model_basename here, e.g. 'model' for TheBloke models :param use_autogptq: whether to use AutoGPTQ (True) or HF Transformers (False) Some models are only supported by one or the other :param load_awq: load model with AWQ, e.g. 'model' for TheBloke models :param load_exllama: whether to use exllama (only applicable to LLaMa1/2 models with 16-bit or GPTQ :param use_safetensors: to use safetensors version (assumes file/HF points to safe tensors version) :param revision: Which HF revision to use :param use_gpu_id: whether to control devices with gpu_id. If False, then spread across GPUs :param base_model: model HF-type name. If use --base_model to preload model, cannot unload in gradio in models tab :param display_name: display name for model (used in UI and API to access) :param tokenizer_base_model: tokenizer HF-type name. Usually not required, inferred from base_model. If model is private or doesn't exist as HF model, can use "tiktoken" and pass max_seq_len and (if different) max_output_seq_len For inference servers like OpenAI etc. if have model name, we use tiktoken with known input/output sequence lengths. :param lora_weights: LORA weights path/HF link :param gpu_id: if use_gpu_id, then use gpu_id for cuda device ID, or auto mode if gpu_id != -1 :param compile_model Whether to compile the model :param use_cache: Whether to use caching in model (some models fail when multiple threads use) :param inference_server: Consume base_model as type of model at this address Address can be text-generation-server hosting that base_model e.g. python generate.py --inference_server="http://192.168.1.46:6112" --base_model=HuggingFaceH4/zephyr-7b-beta For a gradio server, use same as TGI server. We infer if it's TGI or Gradio. e.g. python generate.py --inference_server="http://192.168.1.46:7860" --base_model=HuggingFaceH4/zephyr-7b-beta For auth protected gradio, do: e.g. python generate.py --inference_server="http://192.168.1.46:7860:user:password" --base_model=HuggingFaceH4/zephyr-7b-beta If don't want to specify port, do: e.g. python generate.py --inference_server="https://gpt.h2o.ai:None:user:password" --base_model=HuggingFaceH4/zephyr-7b-beta Or Address can be "openai_chat" or "openai" for OpenAI API Or Address can be "openai_azure_chat" or "openai_azure" for Azure OpenAI API e.g. python generate.py --inference_server="openai_chat" --base_model=gpt-3.5-turbo e.g. python generate.py --inference_server="openai" --base_model=text-davinci-003 e.g. python generate.py --inference_server="openai_azure_chat::::" --base_model=gpt-3.5-turbo e.g. python generate.py --inference_server="openai_azure::::" --base_model=text-davinci-003 Optionals (Replace with None or just leave empty but keep :) of some deployment name : e.g. ".openai.azure.com" for some without https:// of some api, e.g. 2023-05-15 Or Address can be for vLLM: Use: "vllm:IP:port" for OpenAI-compliant vLLM endpoint Use: "vllm_chat:IP:port" for OpenAI-Chat-compliant vLLM endpoint Use: "vllm:http://IP:port/v1" for OpenAI-compliant vLLM endpoint Use: "vllm_chat:http://IP:port/v1" for OpenAI-Chat-compliant vLLM endpoint Use: "vllm:https://IP/v1" for OpenAI-compliant vLLM endpoint Use: "vllm_chat:https://IP/v1" for OpenAI-Chat-compliant vLLM endpoint For example, for standard URL and API key for vllm, one would do: vllm_chat:https://vllm.h2o.ai:None:/v1:1234ABCD or for non-standard URL: vllm_chat:https://vllm.h2o.ai:None:/1b1219f7-4bb4-43e9-881f-fa8fa9fe6e04/v1:1234ABCD where vllm.h2o.ai is the DNS name of the IP, None means no extra port, so will be dropped from base_url when using API, /1b1219f7-4bb4-43e9-881f-fa8fa9fe6e04/v1 is the url of the "page" to access, and 1234ABCD is the api key Or for example: vllm_chat:https://vllm.h2o.ai:5001:/1b1219f7-4bb4-43e9-881f-fa8fa9fe6e04/v1:1234ABCD where vllm.h2o.ai is the DNS name of the IP, 5001 is the port, /1b1219f7-4bb4-43e9-881f-fa8fa9fe6e04/v1 is the url of the "page" to access, and 1234ABCD is the api key If you have any other OpenAI compatible chat completion endpoint, you should use vllm_chat way. E.g. llama.cpp http server: https://github.com/ggerganov/llama.cpp/tree/master/examples/server For sglang, text models are supported via OpenAI API and can use vllm_chat or vllm as usual. For sglang and vision models, need to specify sglang so we use http requests API via generate endpoint. Use "sglang" prefix and otherwise it is like vllm endpoint Currently it's not clear how to make an API key work: https://github.com/sgl-project/sglang/issues/466, so one should rely upon firewalls One should also pass the name of the python module used for conversation, e.g. for python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --tokenizer-path lmms-lab/llama3-llava-next-8b-tokenizer --port=30000 --host="0.0.0.0" --tp-size=1 --random-seed=1234 --context-length=8192 One should use: sglang:conv_llava_llama_3:http://IP:port For together.ai that is OpenAI compliant, use: vllm_chat:https://api.together.xyz:None:/v1:1234ABCD Or for groq, can use OpenAI API like: GROQ IS BROKEN FOR OPENAI API: vllm:https://api.groq.com/openai:None:/v1:' with: other model_lock or CLI options: {'inference_server': 'vllm:https://api.groq.com/openai:None:/v1:', 'base_model':'mixtral-8x7b-32768', 'visible_models':'mixtral-8x7b-32768', 'max_seq_len': 31744, 'prompt_type':'plain'} i.e.ensure to use 'plain' prompt, not mixtral. For groq: groq and ensures set env GROQ_API_KEY or groq: with: other model_lock or CLI options: {'inference_server': 'groq:', 'base_model':'mixtral-8x7b-32768', 'visible_models':'mixtral-8x7b-32768', 'max_seq_len': 31744, 'prompt_type':'plain'} Or Address can be replicate: Use: --inference_server=replicate: will use a Replicate server, requiring a Replicate key. e.g. looks like "a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5" Or Address can be for AWS SageMaker: Use: "sagemaker_chat:" for chat models that AWS sets up as dialog Use: "sagemaker:" for foundation models that AWS only text as inputs Or Address can be for Anthropic Claude. Ensure key is set in env ANTHROPIC_API_KEY Use: "anthropic E.g. --base_model=claude-2.1 --inference_server=anthropic Or Address can be for Google Gemini. Ensure key is set in env GOOGLE_API_KEY Use: "google" E.g. --base_model=gemini-pro --inference_server=google Or Address can be for MistralAI. Ensure key is set in env MISTRAL_API_KEY Use: "mistralai" E.g. --base_model=mistral-medium --inference_server=mistralai :param regenerate_clients: Whether to regenerate client every LLM call or use start-up version Benefit of doing each LLM call is timeout can be controlled to max_time in expert settings, else we use default of 600s. Maybe risky, some lack of thread safety: https://github.com/encode/httpx/discussions/3043, so disabled Because gradio clients take long time to start-up, we don't ever regenerate them each time (including llava models) :param regenerate_gradio_clients: Whether to also regenerate gradio clients (slow) :param validate_clients: Whether to validate clients, and if invalid, do not add them to list (e.g. if OpenAI API key is invalid, then just report in logs, do not hard fail, but do not add the model to model list) Currently only done for OpenAI or vLLM endpoints :param fail_if_invalid_client: Whether to fail hard if any client fails validation :param prompt_type: type of prompt, usually matched to fine-tuned model or plain for foundational model :param prompt_dict: If prompt_type=custom, then expects (some) items returned by get_prompt(..., return_dict=True) :param chat_template: jinja HF transformers chat_template to use. '' or None means no change to template Sometimes hard to pass string with proper escapes etc. So string can be base64 encoded with base64_encode_jinja_template() :param system_prompt: Universal system prompt to use if model supports, like LLaMa2, regardless of prompt_type definition. Useful for langchain case to control behavior, or OpenAI and Replicate. If None, 'None', or 'auto', then for LLaMa or other models that internally have system_prompt, will use default for each model If '', then no system prompt (no empty template given to model either, just no system part added at all) If some string not in ['None', 'auto'], then use that as system prompt Default is '', no system_prompt, because often it hurts performance/accuracy :param allow_chat_system_prompt: Whether to use conversation_history to pre-append system prompt :param llamacpp_path: Location to store downloaded gguf or load list of models from Note HF models go into hf cache folder, and gpt4all models go into their own cache folder Can override with ENV LLAMACPP_PATH :param llamacpp_dict: n_gpu_layers: for llama.cpp based models, number of GPU layers to offload (default is all by using large value) use_mlock: when using `llama.cpp` based CPU models, for computers with low system RAM or slow CPUs, recommended False n_batch: Can make smaller to 128 for slower low-memory CPU systems n_gqa: Required to be 8 for LLaMa 70B ... etc. anything that could be passed to llama.cpp or GPT4All models e.g. python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path --llamacpp_dict="{'n_gpu_layers':25,'n_batch':128}" :param model_path_llama: model path or URL (for auto-download) :param model_name_gptj: model path or URL (for auto-download) :param model_name_gpt4all_llama: model path or URL (for auto-download) :param model_name_exllama_if_no_config: exllama model's full path for model, tokenizer, generator for use when no HuggingFace config :param exllama_dict for setting various things for Exllama class E.g. compress_pos_emb, set_auto_map, gpu_peer_fix, alpha_value, matmul_recons_thd, fused_mlp_thd sdp_thd fused_attn matmul_fused_remap rmsnorm_no_half2 rope_no_half2 matmul_no_half2 silu_no_half2 concurrent_streams E.g. to set memory to be split across 2 GPUs, use --exllama_dict="{'set_auto_map':20,20}" :param gptq_dict: Choices for AutoGPTQ, e.g. one can change defaults to these non-defaults: inject_fused_attention=False disable_exllama=True use_triton=True :param attention_sinks: Whether to enable attention sinks. :param sink_dict: dict of options for attention sinks E.g. {'window_length': 1024, 'num_sink_tokens': 4} Default is window length same size as max_input_tokens (max_seq_len if max_input_tokens not set) :param hf_model_dict: dict of options for HF models using transformers :param truncation_generation: Whether (for torch) to terminate generation once reach context length of model. For some models, perplexity becomes critically large beyond context For other models like Mistral, one can generate beyond max_seq_len set to 4096 or 8192 without issue, since based upon 32k embeddings codellama can also generate beyond its 16k context length So default is off, but for simpler/older models True may be wise to avoid bad generations :param model_lock: Lock models to specific combinations, for ease of use and extending to many models Only used if gradio = True List of dicts, each dict has base_model, tokenizer_base_model, lora_weights, inference_server, prompt_type, and prompt_dict If all models have same prompt_type, and prompt_dict, can still specify that once in CLI outside model_lock as default for dict Can specify model_lock instead of those items on CLI As with CLI itself, base_model can infer prompt_type and prompt_dict if in prompter.py. Also, tokenizer_base_model and lora_weights are optional. Also, inference_server is optional if loading model from local system. All models provided will automatically appear in compare model mode Model loading-unloading and related choices will be disabled. Model/lora/server adding will be disabled :param model_lock_columns: How many columns to show if locking models (and so showing all at once) If None, then defaults to up to 3 if -1, then all goes into 1 row Maximum value is 4 due to non-dynamic gradio rendering elements :param model_lock_layout_based_upon_initial_visible: Whether to base any layout upon visible models (True) or upon all possible models. gradio does not allow dynamic objects, so all layouts are preset, and these are two reasonable options. False is best when there are many models and user excludes middle ones as being visible. :param fail_if_cannot_connect: if doing model locking (e.g. with many models), fail if True. Otherwise ignore. Useful when many endpoints and want to just see what works, but still have to wait for timeout. :param temperature: generation temperature :param top_p: generation top_p :param top_k: generation top_k :param penalty_alpha: penalty_alpha>0 and top_k>1 enables contrastive search (not all models support) :param num_beams: generation number of beams :param repetition_penalty: generation repetition penalty :param num_return_sequences: generation number of sequences (1 forced for chat) :param do_sample: generation sample. Enable for sampling for given temperature, top_p, top_k, else greedy decoding and then temperature, top_p, top_k not used. https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig.do_sample https://txt.cohere.com/llm-parameters-best-outputs-language-ai/ https://medium.com/@daniel.puenteviejo/the-science-of-control-how-temperature-top-p-and-top-k-shape-large-language-models-853cb0480dae :param seed: seed (0 means random seed, >0 uses that seed for sampling so reproducible even for sampling). None becomes 0. :param max_new_tokens: generation max new tokens :param min_new_tokens: generation min tokens :param early_stopping: generation early stopping :param max_time: maximum time to allow for generation :param memory_restriction_level: 0 = no restriction to tokens or model, 1 = some restrictions on token 2 = HF like restriction 3 = very low memory case :param debug: enable debug mode :param save_dir: directory chat data is saved to :param local_files_only: whether to only use local files instead of doing to HF for models :param resume_download: whether to resume downloads from HF for models :param use_auth_token: whether to use HF auth token (requires CLI did huggingface-cli login before) :param admin_pass: Administrator password :param trust_remote_code: whether to use trust any code needed for HF model :param rope_scaling: For HF transformers model: scaling for rope-based models. For long context models that have been tuned for a specific size, you have to only use that specific size by setting the `--rope_scaling` exactly correctly e.g. --rope_scaling="{'type':'dynamic', 'factor':4}" e.g. --rope_scaling="{'type':'linear', 'factor':4}" e.g. python generate.py --rope_scaling="{'type':'linear','factor':4}" --base_model=lmsys/vicuna-13b-v1.5-16k --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 --load_8bit=True --langchain_mode=UserData --user_path=user_path --prompt_type=vicuna11 --h2ocolors=False For exllama model: --rope_scaling="{'alpha_value':4}" . This automatically scales max_seq_len for exllama :param max_seq_len: Manually set maximum sequence length for the LLM :param max_output_seq_len: Manually set maximum output length for the LLM :param offload_folder: path for spilling model onto disk :param src_lang: source languages to include if doing translation (None = all) :param tgt_lang: target languages to include if doing translation (None = all) :param prepare_offline_level: Whether to just prepare for offline use, do not go into cli, eval, or gradio run modes 0 : no prep 1: prepare just h2oGPT with exact same setup as passed to CLI and ensure all artifacts for h2oGPT alone added to ~/.cache/ 2: prepare h2oGPT + all inference servers so h2oGPT+inference servers can use the ~/.cache/ :param cli: whether to use CLI (non-gradio) interface. :param eval: whether to run evals :param cli_loop: whether to loop for CLI (False usually only for testing) :param gradio: whether to enable gradio, or to enable benchmark mode :param function: whether to run function mode to just return locals for function server :param force_streaming_on_to_handle_timeouts: whether to force streaming internally even if UI/API doesn't do it, so can handle timeouts and avoid blocking calls. :param openai_server: whether to launch OpenAI proxy server for local gradio server Disabled if API is disabled :param openai_port: port for OpenAI proxy server :param openai_workers: number of workers for OpenAI (1 means 1 worker, 0 means all physical cores, else choose) :param function_server: whether to launch Function server to handle document loading offloading to separate thread or forks :param function_server_port: port for OpenAI proxy server :param function_server_workers: number of workers for Function Server (1 means 1 worker, 0 means all physical cores, else choose) :param function_api_key: API key for function server, auto-set if not provided, uses first key like OpenAI proxy server does as well :param agent_server: whether to launch Agent proxy server Disabled if API is disabled :param agent_port: port for Agent proxy server :param agent_workers: number of workers for Agent Server (1 means 1 worker, 0 means all physical cores, else choose) :param multiple_workers_gunicorn: whether to use gunicorn (True) or uvicorn (False) for multiple workers :param gradio_offline_level: > 0, then change fonts so full offline == 1 means backend won't need internet for fonts, but front-end UI might if font not cached == 2 means backend and frontend don't need internet to download any fonts. Note: Some things always disabled include HF telemetry, gradio telemetry, chromadb posthog that involve uploading. This option further disables google fonts for downloading, which is less intrusive than uploading, but still required in air-gapped case. The fonts don't look as nice as google fonts, but ensure full offline behavior. Also set --share=False to avoid sharing a gradio live link. :param server_name: IP to use. In linux 0.0.0.0 is good choice so exposed to outside host, else for only local use 127.0.0.1. For windows/MAC 0.0.0.0 or 127.0.0.1 will work, but may need to specify actual LAN IP address for other LAN clients to see. :param share: whether to share the gradio app with sharable URL :param open_browser: whether to automatically open browser tab with gradio UI :param close_button: Whether to show close button in system tab (if not public) :param shutdown_via_api: Whether to allow shutdown via API :param root_path: The root path (or "mount point") of the application, if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", the `root_path` should be set to "/myapp". :param ssl_verify: passed go gradio launch :param ssl_keyfile: passed go gradio launch :param ssl_certfile: passed go gradio launch :param ssl_keyfile_password: passed go gradio launch :param chat: whether to enable chat mode with chat history :param chat_conversation: list of tuples of (human, bot) conversation pre-appended to existing chat when using instruct/chat models Requires also add_chat_history_to_context = True It does *not* require chat=True, so works with nochat_api etc. :param text_context_list: List of strings to add to context for non-database version of document Q/A for faster handling via API etc. Forces LangChain code path and uses as many entries in list as possible given max_seq_len, with first assumed to be most relevant and to go near prompt. :param stream_output: whether to stream output :param enable_caching: whether to enable caching (Only for anthropic) :param async_output: Whether to do asyncio handling For summarization Applicable to HF TGI server Only if stream_output=False in CLI, UI, or API :param num_async: Number of simultaneously allowed asyncio calls to make for async_output Too many will overload inference server, too few will be too slow :param stream_map: Whether to stream map_reduce fully even while doing async (if async, then only first map in any group map will be streamed) Experimental, not working fully. :param show_examples: whether to show clickable examples in gradio :param verbose: whether to show verbose prints :param h2ocolors: whether to use H2O.ai theme :param dark: whether to use dark mode for UI by default (still controlled in UI) :param height: height of chat window :param render_markdown: Whether to render markdown in chatbot UI. In some cases this distorts the rendering. https://github.com/gradio-app/gradio/issues/4344#issuecomment-1771963021 :param show_lora: whether to show LORA options in UI (expert so can be hard to understand) :param show_llama: whether to show LLaMa.cpp/GPT4All options in UI (only likely useful if have weak GPUs) :param show_gpt4all: whether to show GPT4All models in UI (not often useful, llama.cpp models best) :param login_mode_if_model0: set to True to load --base_model after client logs in, to be able to free GPU memory when model is swapped :param block_gradio_exit: whether to block gradio exit (used for testing) :param concurrency_count: gradio concurrency count (1 is optimal for local LLMs to avoid sharing cache that messes up models, else 64 is used if hosting remote inference servers only) :param api_open: If False, don't let API calls skip gradio queue :param allow_api: whether to allow API calls at all to gradio server :param input_lines: how many input lines to show for chat box (>1 forces shift-enter for submit, else enter is submit) :param gradio_size: Overall size of text and spaces: "xsmall", "small", "medium", "large". Small useful for many chatbots in model_lock mode :param show_copy_button: Whether to show copy button for chatbots :param large_file_count_mode: Whether to force manual update to UI of drop-downs, good idea if millions of chunks or documents :param gradio_ui_stream_chunk_size: Number of characters to wait before pushing text to ui. None is default, which is 0 when not doing model lock. Else 20 by default. 20 is reasonable value for fast models and fast systems when handling several models at once Choose 0 to disable (this disables use of gradio_ui_stream_chunk_min_seconds and gradio_ui_stream_chunk_seconds too) Work around for these bugs that lead to UI being overwhelmed under various cases https://github.com/gradio-app/gradio/issues/5914 https://github.com/gradio-app/gradio/issues/6609 :param gradio_ui_stream_chunk_min_seconds: Number of seconds before allow yield to avoid spamming yields at rate user would not care about, regardless of chunk_size :param gradio_ui_stream_chunk_seconds: Number of seconds to yield regardless of reaching gradio_ui_stream_chunk_size as long as something to yield Helps case when streaming is slow and want to see progress at least every couple seconds :param gradio_api_use_same_stream_limits: Whether to use same streaming limits as UI for API :param gradio_upload_to_chatbot: Whether to show upload in chatbots :param gradio_upload_to_chatbot_num_max: Max number of things to add to chatbot :param gradio_errors_to_chatbot: Whether to show errors in Accordion in chatbot or just in exceptions in each tab :param pre_load_embedding_model: Whether to preload embedding model for shared use across DBs and users (multi-thread safe only) :param embedding_gpu_id: which GPU to place embedding model on. Only used if preloading embedding model. If 'auto', then use first device as is default If 'cpu' or some other string like 'mps', then use that as device name. :param auth: gradio auth for launcher in form [(user1, pass1), (user2, pass2), ...] e.g. --auth=[('jon','password')] with no spaces e.g. --auth="[('jon', 'password)())(')]" so any special characters can be used e.g. --auth=auth.db to specify persisted state file with name auth.db (auth_filename then not required) e.g. --auth='' will use default auth.db as file name for persisted state file (auth_filename good idea to control location) e.g. --auth=None will use no auth, but still keep track of auth state, just not from logins :param auth_filename: Set auth filename, used only if --auth= was passed list of user/passwords If use auth.db will use sqlite3 database for auth for faster access for large number of users If you had .json and want to use faster .db, just pass filename with .db instead of .json and at startup it will be migrated automatically to .db and used. :param auth_access: 'open': Allow new users to be added 'closed': Stick to existing users :param auth_freeze: whether freeze authentication based upon current file, no longer update file :param auth_message: Message to show if having users login, fixed if passed, else dynamic internally :param google_auth: Whether to use google auth :param guest_name: guess name if using auth and have open access. If '', then no guest allowed even if open access, then all databases for each user always persisted If None, then set to 'guest' for open access, or '' for closed access For open or closed access, if guest_name is set, that forms prefix of actual internal userID apart from authentication and can serve as way to access UI or API freshly via auth with fixed password with no document persistence beyond that single session. :param enforce_h2ogpt_api_key: Whether to enforce h2oGPT token usage for API :param enforce_h2ogpt_ui_key: Whether to enforce h2oGPT token usage for UI (same keys as API assumed) :param h2ogpt_api_keys: list of tokens allowed for API access or file accessed on demand for json of list of keys :param h2ogpt_key: E.g. can be set when accessing gradio h2oGPT server from local gradio h2oGPT server that acts as client to that inference server Only applied for API at runtime when API accesses using gradio inference_server are made :param extra_allowed_paths: List of strings for extra allowed paths users could access for file viewing/downloading. '.' can be used but be careful what that exposes. Note by default all paths in langchain_mode_paths given at startup are allowed :param blocked_paths: Any blocked paths to add for gradio access for file viewing/downloading. :param max_max_time: Maximum max_time for gradio slider :param max_max_new_tokens: Maximum max_new_tokens for gradio slider :param min_max_new_tokens: Minimum of max_new_tokens, when auto-scaling down to handle more docs/prompt, but still let generation have some tokens :param max_input_tokens: Max input tokens to place into model context for each LLM call -1 means auto, fully fill context for query, and fill by original document chunk for summarization >=0 means use that to limit context filling to that many tokens :param max_total_input_tokens: like max_input_tokens but instead of per LLM call, applies across all LLM calls for single summarization/extraction action :param docs_token_handling: 'chunk' means fill context with top_k_docs (limited by max_input_tokens or model_max_len) chunks for query or top_k_docs original document chunks summarization None or 'split_or_merge' means same as 'chunk' for query, while for summarization merges documents to fill up to max_input_tokens or model_max_len tokens :param docs_joiner: string to join lists of text when doing split_or_merge. None means '\n\n' :param hyde_level: HYDE level for HYDE approach (https://arxiv.org/abs/2212.10496) 0: No HYDE 1: Use non-document-based LLM response and original query for embedding query 2: Use document-based LLM response and original query for embedding query 3+: Continue iterations of embedding prior answer and getting new response :param hyde_template: None, 'None', 'auto' uses internal value and enable '{query}' is minimal template one can pass :param hyde_show_only_final: Whether to show only last result of HYDE, not intermediate steps :param hyde_show_intermediate_in_accordion: Whether to show intermediate HYDE, but inside HTML accordion :param map_reduce_show_intermediate_in_accordion: Whether to show intermediate map_reduce, but inside HTML accordion :param visible_models: Which models in model_lock list to show by default Takes integers of position in model_lock (model_states) list or strings of base_model names Ignored if model_lock not used For nochat API, this is single item within a list for model by name or by index in model_lock If None, then just use first model in model_lock list If model_lock not set, use model selected by CLI --base_model etc. Note that unlike h2ogpt_key, this visible_models only applies to this running h2oGPT server, and the value is not used to access the inference server. If need a visible_models for an inference server, then use --model_lock and group together. :param max_visible_models: maximum visible models to allow to select in UI :param visible_ask_anything_high: Whether ask anything block goes near top or near bottom of UI Chat :param visible_visible_models: Whether visible models drop-down is visible in UI :param visible_submit_buttons: whether submit buttons are visible when UI first comes up :param visible_side_bar: whether left side bar is visible when UI first comes up :param visible_document_subset: whether document subset is visible when UI first comes up :param visible_max_quality: whether max quality is visible when UI first comes up :param visible_add_doc_to_chat: whether add document to chat is visible when UI first comes up :param visible_chat_history: whether chat history being choosable is visible when UI first comes up :param visible_doc_track: whether left side bar's document tracking is visible when UI first comes up :param visible_chat_tab: "" for chat tab :param visible_doc_selection_tab: "" for doc selection tab :param visible_doc_view_tab: "" for doc view tab :param visible_chat_history_tab: "" for chat history tab :param visible_expert_tab: "" for expert tab :param visible_models_tab: "" for models tab :param visible_system_tab: "" for system tab :param visible_tos_tab: "" for ToS tab :param visible_login_tab: "" for Login tab (needed for persistence or to enter key for UI access to models and ingestion) :param visible_hosts_tab: "" for hosts tab :param visible_langchain_action_radio: "" for action radio :param visible_langchain_purge: for purge option :param chat_tabless: Just show Chat as block without tab (useful if want only chat view) :param visible_h2ogpt_links: Whether github stars, URL are visible :param visible_h2ogpt_qrcode: Whether QR code is visible :param visible_h2ogpt_logo: Whether central logo is visible :param visible_chatbot_label: Whether to show label in chatbot (e.g. if only one model for own purpose, then can set to False) :param visible_all_prompter_models: Whether to show all prompt_type_to_model_name items or just curated ones :param visible_curated_models: Whether to show curated models (useful to see few good options) :param actions_in_sidebar: Whether to show sidebar with actions in old style :param document_choice_in_sidebar: Whether to show document choices in sidebar Useful if often changing picking specific document(s) :param enable_add_models_to_list_ui: Whether to show add model, lora, server to dropdown list Disabled by default since clutters Models tab in UI, and can just add custom item directly in dropdown :param max_raw_chunks: Maximum number of chunks to show in UI when asking for raw DB text from documents/collection :param pdf_height: Height of PDF viewer in UI :param avatars: Whether to show avatars in chatbot :param add_disk_models_to_ui: Whether to add HF cache models and llama.cpp models to UI :param page_title: Title of the web page, default is h2oGPT :param favicon_path: Path to favicon, default is h2oGPT favicon :param visible_ratings: Whether full review is visible, else just likable chatbots :param reviews_file: File to store reviews, set to `reviews.csv` if visible_ratings=True if this isn't set :param sanitize_user_prompt: whether to remove profanity from user input (slows down input processing) Requires optional packages: pip install alt-profanity-check==1.2.2 better-profanity==0.7.0 :param sanitize_bot_response: whether to remove profanity and repeat lines from bot output (about 2x slower generation for long streaming cases due to better_profanity being slow) :param extra_model_options: extra models to show in list in gradio :param extra_lora_options: extra LORA to show in list in gradio :param extra_server_options: extra servers to show in list in gradio :param score_model: which model to score responses None: no response scoring 'auto': auto mode, '' (no model) for CPU or 1 GPU, 'OpenAssistant/reward-model-deberta-v3-large-v2' for >=2 GPUs, because on CPU takes too much compute just for scoring response :param verifier_model: model for verifier :param verifier_tokenizer_base_model: tokenizer server for verifier (if empty/None, infer from model) :param verifier_inference_server: inference server for verifier :param eval_filename: json file to use for evaluation, if None is sharegpt :param eval_prompts_only_num: for no gradio benchmark, if using eval_filename prompts for eval instead of examples :param eval_prompts_only_seed: for no gradio benchmark, seed for eval_filename sampling :param eval_as_output: for no gradio benchmark, whether to test eval_filename output itself :param langchain_mode: Data source to include. Choose "UserData" to only consume files from make_db.py. None: auto mode, check if langchain package exists, at least do LLM if so, else Disabled If not passed, then chosen to be first langchain_modes, else langchain_mode->Disabled is set if no langchain_modes either WARNING: wiki_full requires extra data processing via read_wiki_full.py and requires really good workstation to generate db, unless already present. :param user_path: user path to glob from to generate db for vector search, for 'UserData' langchain mode. If already have db, any new/changed files are added automatically if path set, does not have to be same path used for prior db sources :param langchain_modes: dbs to generate at launch to be ready for LLM Apart from additional user-defined collections, can include ['wiki', 'wiki_full', 'UserData', 'MyData', 'github h2oGPT', 'DriverlessAI docs'] But wiki_full is expensive and requires preparation To allow personal space only live in session, add 'MyData' to list Default: If only want to consume local files, e.g. prepared by make_db.py, only include ['UserData'] If have own user modes, need to add these here or add in UI. :param langchain_mode_paths: dict of langchain_mode keys and disk path values to use for source of documents E.g. "{'UserData2': 'userpath2'}" A disk path be None, e.g. --langchain_mode_paths="{'UserData2': None}" even if existing DB, to avoid new documents being added from that path, source links that are on disk still work. If `--user_path` was passed, that path is used for 'UserData' instead of the value in this dict :param langchain_mode_types: dict of langchain_mode keys and database types E.g. python generate.py --base_model=llama --langchain_modes=['TestData'] --langchain_mode_types="{'TestData':'shared'}" The type is attempted to be inferred if directory already exists, then don't have to pass this :param detect_user_path_changes_every_query: whether to detect if any files changed or added every similarity search (by file hashes). Expensive for large number of files, so not done by default. By default only detect changes during db loading. :param update_selection_state_from_cli: whether to update all user options (during login) with CLI options for langchain_modes, langchain_mode_paths, langchain_mode_types If want user auth state to always be used regardless of changes to CLI options, then set False :param langchain_action: Mode langchain operations in on documents. Query: Make query of document(s) Summarize or Summarize_map_reduce: Summarize document(s) via map_reduce Summarize_all: Summarize document(s) using entire document at once Summarize_refine: Summarize document(s) using entire document, and try to refine before returning summary Extract: Extract information from document(s) via map (no reduce) Currently enabled is Query, Summarize, and Extract. Summarize is a "map reduce" and extraction is "map". That is, map returns a text output (roughly) per input item, while reduce reduces all maps down to single text output. The "roughly" refers to fact that if one has docs_token_handling='split_or_merge' then we split or merge chunks, so you will get a map for some optimal-sized chunks given the model size. If you choose docs_token_handling='chunk', then you get back a map for each chunk you give, but you should ensure the model token limit is not exceeded yourself. Summarize is useful when wanting to reduce down to single text, while Extract is useful when want to operate the prompt on blocks of data and get back a result per block. :param langchain_agents: Which agents to use 'search': Use Web Search as context for LLM response, e.g. SERP if have SERPAPI_API_KEY in env :param force_langchain_evaluate: Whether to force langchain LLM use even if not doing langchain, mostly for testing. :param visible_langchain_actions: Which actions to allow :param visible_langchain_agents: Which agents to allow :param document_subset: Default document choice when taking subset of collection :param document_choice: Chosen document(s) by internal name, 'All' means use all docs e.g. --document_choice="['file2.pdf']" or --document_choice="['file2.pdf', 'file3.pdf']" :param document_source_substrings: substrings in list to search in source names in metadata for chroma dbs :param document_source_substrings_op: 'and or 'or' for source search words :param document_content_substrings: substrings in list to search in content for chroma dbs :param document_content_substrings_op: 'and or 'or' for content search words :param use_llm_if_no_docs: Whether to use LLM even if no documents, when langchain_mode=UserData or MyData or custom :param load_db_if_exists: Whether to load chroma db if exists or re-generate db :param keep_sources_in_context: Whether to keep url sources in context, not helpful usually :param db_type: 'faiss' for in-memory 'chroma' (for chroma >= 0.4) 'chroma_old' (for chroma < 0.4) -- recommended for large collections 'weaviate' for persisted on disk 'qdrant' for a Qdrant server or an in-memory instance :param use_openai_embedding: Whether to use OpenAI embeddings for vector db :param use_openai_model: Whether to use OpenAI model for use with vector db :param hf_embedding_model: Which HF embedding model to use for vector db Default is instructor-large with 768 parameters per embedding if have GPUs, else all-MiniLM-L6-v2 if no GPUs Can also choose simpler model with 384 parameters per embedding: "sentence-transformers/all-MiniLM-L6-v2" A better choice is: 'BAAI/bge-large-en-v1.5' For multilingual can use intfloat/multilingual-e5-large We support automatically changing of embeddings for chroma, with a backup of db made if this is done :param migrate_embedding_model: whether to use hf_embedding_model embedding even if database already had an embedding set. used to migrate all embeddings to a new one, but will take time to re-embed. Default (False) is to use the prior embedding for existing databases, and only use hf_embedding_model for new databases If had old database without embedding saved, then hf_embedding_model is also used. :param cut_distance: Distance to cut off references with larger distances when showing references. 1.64 is good to avoid dropping references for all-MiniLM-L6-v2, but instructor-large will always show excessive references. For all-MiniLM-L6-v2, a value of 1.5 can push out even more references, or a large value of 100 can avoid any loss of references. :param answer_with_sources: Whether to determine (and return) sources :param append_sources_to_answer: Whether to place source information in chat response (ignored by LLM). Always disabled for API. :param append_sources_to_chat: Whether to place sources information in chat response but in separate chat turn (ignored by LLM). Always disabled for API. :param sources_show_text_in_accordion: whether to show accordion for document references in chatbot UI :param top_k_docs_max_show: Max number of docs to show in UI for sources If web search is enabled, then this is modified to be max(top_k_docs_max_show, number of links used in search) :param show_link_in_sources: Whether to show URL link to source document in references :param langchain_instruct_mode: Whether to have langchain operate in instruct mode (True) or few-shot mode (False) Normally this might be decidable from --prompt_type=plain, but in some cases (like vllm_chat) we want inference server to handle all prompting, so need to tell h2oGPT to use plain prompting, but don't want to change langchain behavior :param pre_prompt_query: prompt before documents to query, if None then use internal defaults :param prompt_query: prompt after documents to query, if None then use internal defaults :param pre_prompt_summary: prompt before documents to summarize/extract from, if None then use internal defaults :param prompt_summary: prompt after documents to summarize/extract from, if None then use internal defaults For summarize/extract, normal to have empty query (nothing added in ask anything in UI or empty string in API) If pass query, template is "Focusing on %s, %s" % (query, prompt_summary) If pass query and iinput, template is "Focusing on %s, %s, %s" % (query, iinput, prompt_summary) For query, prompt template is: "{pre_prompt_query} \"\"\" {fstring} \"\"\" {prompt_query}{instruction}" For summarization or extraction, for some internal document part fstring, the template looks like: "{pre_prompt_summary} \"\"\" {fstring} \"\"\" {prompt_summary}" If added instruction for summarization or extraction, prompt template is "{pre_prompt_summary} \"\"\" {fstring} \"\"\" Focusing on {instruction}, {prompt_summary}" {fstring} is some document chunks separated by {docs_joiner} :param hyde_llm_prompt: hyde prompt for first step when using LLM :param all_docs_start_prompt: Prompt before all documents :param all_docs_finish_prompt: Prompt after all documents :param user_prompt_for_fake_system_prompt: user part of pre-conversation if LLM doesn't handle system prompt :param json_object_prompt: prompt for getting LLM to do JSON object :param json_object_prompt_simpler: simpler of "" for MistralAI :param json_code_prompt: prompt for getting LLm to do JSON in code block :param json_code_prompt_if_no_schema: prompt part for LLM if not schema, but need good keys etc. for JSON (e.g. due to Claude-3 limitations) :param json_schema_instruction: prompt for LLM to use schema :param json_preserve_system_prompt: whether to preserve system_prompt for JSON mode :param json_object_post_prompt_reminder: json object reminder about JSON :param json_code_post_prompt_reminder: json code w/ schema reminder about JSON :param json_code2_post_prompt_reminder: json code wo/ schema reminder about JSON :param doc_json_mode: Use system prompting approach with JSON input and output, e.g. for codellama or GPT-4 :param metadata_in_context: Keys of metadata to include in LLM context for Query 'all': Include all metadata 'auto': Includes these keys: ['date', 'file_path', 'input_type', 'keywords', 'chunk_id', 'page', 'source', 'title', 'total_pages'] ['key1', 'key2', ...]: Include only these keys NOTE: not all parsers have all keys, only keys that exist are added to each document chunk. Example key-values that some PDF parsers make: author = Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori, Yusuke Noda, Paul Tang, Ehsan Adeli, Shrinidhi Kowshika Lakshmikanth, Kevin Schulman, Arnold Milstein, Demetri Terzopoulos, Ade Famoti, Noboru Kuno, Ashley Llorens, Hoi Vo, Katsu Ikeuchi, Li Fei-Fei, Jianfeng Gao, Naoki Wake, Qiuyuan Huang chunk_id = 21 creationDate = D:20240209020045Z creator = LaTeX with hyperref date = 2024-02-11 23:58:11.929155 doc_hash = 5db1d548-7 file_path = /tmp/gradio/15ac25af8610f21b9ab55252f1944841727ba157/2402.05929.pdf format = PDF 1.5 hashid = 3cfb31cea127c745c72554f4714105dd head = An Interactive Agent Foundation Model Figure 2. We input_type = .pdf keywords = Machine Learning, ICML modDate = D:20240209020045Z order_id = 2 page = 2 parser = PyMuPDFLoader producer = pdfTeX-1.40.25 source = /tmp/gradio/15ac25af8610f21b9ab55252f1944841727ba157/2402.05929.pdf subject = Proceedings of the International Conference on Machine Learning 2024 time = 1707724691.929157 title = An Interactive Agent Foundation Model total_pages = 22 :param add_chat_history_to_context: Include chat context when performing action Not supported when using CLI mode :param add_search_to_context: Include web search in context as augmented prompt :param context: Default context to use (for system pre-context in gradio UI) context comes before chat_conversation and any document Q/A from text_context_list :param iinput: Default input for instruction-based prompts :param allow_upload_to_user_data: Whether to allow file uploads to update shared vector db (UserData or custom user dbs) Ensure pass user_path for the files uploaded to be moved to this location for linking. :param reload_langchain_state: Whether to reload langchain_modes.pkl file that contains any new user collections. :param allow_upload_to_my_data: Whether to allow file uploads to update personal vector db :param enable_url_upload: Whether to allow upload from URL :param enable_text_upload: Whether to allow upload of text :param enable_sources_list: Whether to allow list (or download for non-shared db) of list of sources for chosen db :param chunk: Whether to chunk data (True unless know data is already optimally chunked) :param chunk_size: Size of chunks, with typically top-4 passed to LLM, so needs to be in context length :param top_k_docs: For langchain_action query: number of chunks to give LLM -1 : auto-fills context up to max_seq_len For langchain_action summarize/extract: number of document parts, like pages for PDF. There's no such thing as chunks for summarization. -1 : auto-fills context up to max_seq_len :param docs_ordering_type: Type of ordering of docs. 'best_first': Order by score so score is worst match near prompt 'best_near_prompt' or 'reverse_sort' : reverse docs order so most relevant is closest to question. Best choice for sufficiently smart model, and truncation occurs for oldest context, so best then too. But smaller 6_9 models fail to use newest context and can get stuck on old information. '' or None (i.e. default) or 'reverse_ucurve_sort' : Sort so most relevant is either near start or near end Best to avoid "lost in middle" as well as avoid hallucinating off starting content that LLM focuses on alot. :param auto_reduce_chunks: Whether to automatically reduce top_k_docs to fit context given prompt :param max_chunks: If top_k_docs=-1, maximum number of chunks to allow :param headsize: Maximum number of characters for head of document document for UI to show :param n_jobs: Number of processors to use when consuming documents (-1 = all, is default) :param n_gpus: Number of GPUs (None = autodetect) :param clear_torch_cache_level: 0: never clear except where critically required 1: clear critical 2: clear aggressively and clear periodically every 20s to free-up GPU memory (may lead to lag in response) :param use_unstructured: Enable unstructured URL loader :param use_playwright: Enable PlayWright URL loader :param use_selenium: Enable Selenium URL loader :param use_scrapeplaywright: Enable Scrape PlayWright URL loader :param use_scrapehttp: Enable Scrape HTTP URL loader using aiohttp :param use_pymupdf: enable PyMUPDF 'auto' means use first, use others if they are 'auto' if no result :param use_unstructured_pdf: enable Unstructured PDF loader, 'auto' means use if pymupdf fails to get doc result :param use_pypdf: enable PyPDF loader 'auto' means use if unstructured fails to get doc result :param enable_pdf_ocr: 'auto' means only use OCR if normal text extraction fails. Useful for pure image-based PDFs with text. if enable_pdf_doctr == 'on' then don't do. 'on' means always do OCR as additional parsing of same documents 'off' means don't do OCR (e.g. because it's slow even if 'auto' only would trigger if nothing else worked) :param enable_pdf_doctr: Whether to support doctr on pdfs, 'auto' means use do if failed to get doc result so far :param try_pdf_as_html: Try "PDF" as if HTML file, in case web link has .pdf extension but really is just HTML :param enable_ocr: Whether to support OCR on images :param enable_doctr: Whether to support doctr on images (using OCR better than enable_ocr=True) :param enable_pix2struct: Whether to support pix2struct on images for captions :param enable_captions: Whether to support captions for image files as documents, then preloads that model if pre_load_image_audio_models=True :param enable_llava: If LLaVa IP port is set, whether to use response for image ingestion :param enable_transcriptions: Whether to enable audio transcriptions (youtube of from files) Preloaded if pre_load_image_audio_models=True :param pre_load_image_audio_models: Whether to preload caption model (True), or load after forking parallel doc loader (False) parallel loading disabled if preload and have images, to prevent deadlocking on cuda context Recommended if using larger caption model or doing production serving with many users to avoid GPU OOM if many would use model at same time Also applies to DocTR and ASR models :param captions_model: Which model to use for captions. captions_model: str = "microsoft/Florence-2-base", # fine captions_model: str = "microsoft/Florence-2-large", # quite good :param caption_gpu: If support caption, then use GPU if exists :param caption_gpu_id: Which GPU id to use, if 'auto' then select 0 :param doctr_gpu: If support doctr, then use GPU if exists :param doctr_gpu_id: Which GPU id to use, if 'auto' then select 0 :param llava_model: IP:port for h2oai version of LLaVa gradio server for hosted image chat E.g. http://192.168.1.46:7861 None means no such LLaVa support :param llava_prompt: Prompt passed to LLaVa for querying the image :param image_file: Initial image for UI (or actual image for CLI) Vision Q/A. Or list of images for some models :param image_control: Initial image for UI Image Control :param images_num_max: Maximum number of images in any LLM call. if None, then checks images_num_max and uses that value for defined models (assumes 80GB GPU), else uses 1 If set here or in model_lock, then that model uses the set value If set to 0, then won't use images even if image model and given images If set to -1, then always forces batching if any images, even if model could handle all images at once. The amount is inferred for each model This is useful because models do poorly when mixing images and text when text duplicates content of image information, LLM tends to just look at text not image even if image contains better information. If set to -2, -3, etc., then 1, 2, 3 images are used per batch :param image_resolution: Resolution of any images :param image_format: Preferred format of images, esp. for video output :param rotate_align_resize_image: Whether to apply rotation, alignment, resize before giving to LLM :param video_frame_period: Period of frames to use from video :param image_batch_image_prompt: Prompt used to query image only if doing batching of images :param image_batch_final_prompt: Prompt used to query result of batching of images :param image_batch_stream: Whether to stream batching of images. :param visible_vision_models: Model to use for vision, e.g. if base LLM has no vision :param video_file: Video file for gradio to start with :param response_format: text or json_object or json_code json_object means always try to use best mechanism to make JSON. json_code means use code block method, not guided_json or built-in json mode # https://github.com/vllm-project/vllm/blob/a3c226e7eb19b976a937e745f3867eb05f809278/vllm/entrypoints/openai/protocol.py#L117-L135 :param guided_json: str or dict of JSON schema :param guided_regex: :param guided_choice: list of strings to have LLM choose from :param guided_grammar: :param guided_whitespace_pattern: :param asr_model: Name of model for ASR, e.g. openai/whisper-medium or openai/whisper-large-v3 or distil-whisper/distil-large-v3 or microsoft/speecht5_asr whisper-medium uses about 5GB during processing, while whisper-large-v3 needs about 10GB during processing :param asr_gpu: Whether to use GPU for ASR model :param asr_gpu_id: Which GPU to put ASR model on (only used if preloading model) :param asr_use_better: Whether to use BetterTransformer :param asr_use_faster: Whether to use faster_whisper package and models (loads normal whisper then unloads it, to get this into pipeline) :param enable_stt: Whether to enable and show Speech-to-Text (STT) with microphone in UI Note STT model is always preloaded, but if stt_model=asr_model and pre_load_image_audio_models=True, then asr model is used as STT model. :param stt_model: Name of model for STT, can be same as asr_model, which will then use same model for conserving GPU :param stt_gpu: Whether to use gpu for STT model :param stt_gpu_id: If not using asr_model, then which GPU to go on if using cuda :param stt_continue_mode: How to continue speech with button control 0: Always append audio regardless of start/stop of recording, so always appends in STT model for full STT conversion Only can edit after hit stop and then submit, if hit record again edits are lost since using only audio stream for STT conversion 1: If hit stop, text made so far is saved and audio cleared, so next recording will be separate text conversion Can make edits on any text after hitting stop and they are preserved :param enable_tts: Whether to enable TTS :param tts_gpu: Whether to use GPU if present for TTS :param tts_gpu_id: Which GPU ID to use for TTS :param tts_model: Which model to use. For microsoft, use 'microsoft/speecht5_tts' For coqui.ai use one given by doing in python: ```python from tts_coqui import list_models list_models() ``` e.g. 'tts_models/multilingual/multi-dataset/xtts_v2' Note that coqui.ai models are better, but some have non-commercial research license, while microsoft models are MIT. So coqui.ai ones can be used for non-commercial activities only, and one should agree to their license, see: https://coqui.ai/cpml Commercial use of xtts_v2 should be obtained through their product offering at https://coqui.ai/ :param tts_gan_model: For microsoft model, which gan model to use, e.g. 'microsoft/speecht5_hifigan' :param tts_coquiai_deepspeed: For coqui.ai models, whether to use deepspeed for faster inference Disabled by default, saw compilation hang recently :param tts_coquiai_roles: role dictionary mapping name (key) to wave file (value) If None, then just use default from get_role_to_wave_map() :param chatbot_role: Default role for coqui models. If 'None', then don't by default speak when launching h2oGPT for coqui model choice. :param speaker: Default speaker for microsoft models If 'None', then don't by default speak when launching h2oGPT for microsoft model choice. :param tts_language: Default language for coqui models :param tts_speed: Default speed of TTS, < 1.0 (needs rubberband) for slower than normal, > 1.0 for faster. Tries to keep fixed pitch. :param tts_action_phrases: Phrases or words to use as action word to trigger click of Submit hands-free assistant style Set to None or empty list to avoid any special action words :param tts_stop_phrases: Like tts_action_phrases but to stop h2oGPT from speaking and generating NOTE: Action/Stop phrases should be rare but easy (phonetic) words for Whisper to recognize. E.g. asking GPT-4 a couple good ones are ['Nimbus'] and ['Yonder'], and one can help Whisper by saying "Nimbus Clouds" which still works as "stop word" as trigger. :param sst_floor: Floor in wave square amplitude below which ignores the chunk of audio This helps avoid long silence messing up the transcription. :param jq_schema: control json loader By default '.[]' ingests everything in brute-force way, but better to match your schema See: https://python.langchain.com/docs/modules/data_connection/document_loaders/json#using-jsonloader :param extract_frames: How many unique frames to extract from video (if 0, then just do audio if audio type file as well) :param enable_image: Whether to enable image generation model :param visible_image_models: Which image gen models to include :param image_size :param image_quality :param image_guidance_scale :param image_num_inference_steps :param image_gpu_ids: GPU ids to use for each visible image model :param enable_llava_chat: Whether to use LLaVa model to chat directly against instead of just for ingestion :param max_quality: Choose maximum quality ingestion with all available parsers Pro: Catches document when some default parsers would fail Pro: Enables DocTR that has much better OCR than Tesseract Con: Fills DB with results from all parsers, so similarity search gives redundant results :param enable_heap_analytics: Toggle telemetry. :param heap_app_id: App ID for Heap, change to your ID. :param cert_lookup_directory: Defines the directory containing the additional private certs to trust. :return: """ append_certificates(cert_lookup_directory) main_kwargs = locals().copy() if base_model is None: base_model = '' if tokenizer_base_model is None: tokenizer_base_model = '' if lora_weights is None: lora_weights = '' if inference_server is None: inference_server = '' # listen to env if set model_lock = os.getenv('model_lock', str(model_lock)) model_lock = ast.literal_eval(model_lock) chat_conversation = str_to_list(chat_conversation) text_context_list = str_to_list(text_context_list) llamacpp_dict = str_to_dict(llamacpp_dict) tts_coquiai_roles = str_to_dict(tts_coquiai_roles) roles_state0 = tts_coquiai_roles tts_action_phrases = str_to_list(tts_action_phrases) tts_stop_phrases = str_to_list(tts_stop_phrases) visible_image_models = str_to_list(visible_image_models) if not image_size: image_size = image_size_default image_gpu_ids = str_to_list(image_gpu_ids) document_choice = str_to_list(document_choice) visible_models = str_to_list(visible_models, allow_none=True) # None means first model visible_vision_models = str_to_list(visible_vision_models, allow_none=True) # None means first model if image_gpu_ids: assert len(image_gpu_ids) == len(visible_image_models) if isinstance(metadata_in_context, str) and metadata_in_context == 'None': metadata_in_context = [] if seed is None: seed = 0 if image_batch_image_prompt is None: image_batch_image_prompt = image_batch_image_prompt0 if image_batch_final_prompt is None: image_batch_final_prompt = image_batch_final_prompt0 assert response_format in response_formats, "Invalid response_format: %s, must be in %s" % ( response_format, response_formats) assert isinstance(guided_json, (str, dict, type(None))) assert isinstance(guided_regex, (type(None), str)) assert isinstance(guided_choice, (type(None), list)) assert isinstance(guided_grammar, (type(None), str)) assert isinstance(guided_whitespace_pattern, (type(None), str)) # defaults, but not keep around if not used so can use model_path_llama for prompt_type auto-setting # NOTE: avoid defaults for model_lock, require to be specified if base_model == 'llama': if not model_path_llama: model_path_llama = 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf?download=true' prompt_type = 'llama2' if not prompt_type: prompt_type = 'unknown' elif base_model == 'gptj' and not model_name_gptj: model_name_gptj = 'ggml-gpt4all-j-v1.3-groovy.bin' elif base_model == 'gpt4all_llama' and not model_name_gpt4all_llama: model_name_gpt4all_llama = 'ggml-wizardLM-7B.q4_2.bin' if load_exllama and not model_name_exllama_if_no_config: model_name_exllama_if_no_config = 'TheBloke/Nous-Hermes-Llama2-GPTQ' # switch-a-roo on base_model so can pass GGUF/GGML as base model base_model0 = base_model # for prompt infer base_model, model_path_llama, load_gptq, load_awq, llamacpp_dict['n_gqa'] = \ switch_a_roo_llama(base_model, model_path_llama, load_gptq, load_awq, llamacpp_dict.get('n_gqa', 0), llamacpp_path) # add others to single dict llamacpp_dict['model_path_llama'] = model_path_llama llamacpp_dict['model_name_gptj'] = model_name_gptj llamacpp_dict['model_name_gpt4all_llama'] = model_name_gpt4all_llama llamacpp_dict['model_name_exllama_if_no_config'] = model_name_exllama_if_no_config # ensure not used by accident del model_path_llama del model_name_gptj del model_name_gpt4all_llama del model_name_exllama_if_no_config # if user overrides but doesn't set these: if 'n_batch' not in llamacpp_dict: llamacpp_dict['n_batch'] = 128 if 'n_gpu_layers' not in llamacpp_dict: llamacpp_dict['n_gpu_layers'] = 100 if 'n_gqa' not in llamacpp_dict: llamacpp_dict['n_gqa'] = 0 exllama_dict = str_to_dict(exllama_dict) gptq_dict = str_to_dict(gptq_dict) sink_dict = str_to_dict(sink_dict) hf_model_dict = str_to_dict(hf_model_dict) enable_imagegen = enable_image and \ len(set(visible_image_models).difference(valid_imagegen_models)) < len(set(visible_image_models)) enable_imagechange = enable_image and \ len(set(visible_image_models).difference(valid_imagechange_models)) < len( set(visible_image_models)) enable_imagestyle = enable_image and \ len(set(visible_image_models).difference(valid_imagestyle_models)) < len( set(visible_image_models)) if agent_server and not have_autogen: print("Disabled Agent Server since no Agent package installed") agent_server = False if os.environ.get('SERPAPI_API_KEY') is None and \ LangChainAgent.SEARCH.value in visible_langchain_agents: visible_langchain_agents.remove(LangChainAgent.SEARCH.value) if (not have_diffusers or not enable_imagegen) and \ LangChainAction.IMAGE_GENERATE.value in visible_langchain_actions: visible_langchain_actions.remove(LangChainAction.IMAGE_GENERATE.value) if (not have_diffusers or not enable_imagechange) and \ LangChainAction.IMAGE_CHANGE.value in visible_langchain_actions: visible_langchain_actions.remove(LangChainAction.IMAGE_CHANGE.value) if (not have_diffusers or not enable_imagestyle) and \ LangChainAction.IMAGE_STYLE.value in visible_langchain_actions: visible_langchain_actions.remove(LangChainAction.IMAGE_STYLE.value) if (not llava_model or not enable_llava or not enable_llava_chat) and \ LangChainAction.IMAGE_QUERY.value in visible_langchain_actions: visible_langchain_actions.remove(LangChainAction.IMAGE_QUERY.value) if model_lock: assert gradio or function, "model_lock only supported for gradio=True or function=True" assert not cli, "model_lock only supported for cli=False" assert not (not cli and not (gradio or function)), "model_lock only supported for eval (cli=gradio=False)" assert not base_model, "Don't specify model_lock and base_model" assert not tokenizer_base_model, "Don't specify model_lock and tokenizer_base_model" assert not lora_weights, "Don't specify model_lock and lora_weights" assert not inference_server, "Don't specify model_lock and inference_server" # assert not prompt_type, "Don't specify model_lock and prompt_type" # assert not prompt_dict, "Don't specify model_lock and prompt_dict" if gradio_ui_stream_chunk_size is None: gradio_ui_stream_chunk_size = 20 else: # for faster default feel of speed if gradio_ui_stream_chunk_size is None: gradio_ui_stream_chunk_size = 0 n_jobs = int(os.getenv('n_jobs', str(n_jobs))) is_hf = bool(int(os.getenv("HUGGINGFACE_SPACES", '0'))) is_gpth2oai = bool(int(os.getenv("GPT_H2O_AI", '0'))) is_public = is_hf or is_gpth2oai # multi-user case with fixed model and disclaimer if enforce_h2ogpt_ui_key is None: # nominally allow UI access public or not enforce_h2ogpt_ui_key = False if is_public: if max_visible_models is None and (gradio or function): is_gradio_h2oai = get_is_gradio_h2oai() max_visible_models = 4 if is_gradio_h2oai else None visible_hosts_tab = False visible_tos_tab = True if enforce_h2ogpt_api_key is None: enforce_h2ogpt_api_key = True else: if enforce_h2ogpt_api_key is None: enforce_h2ogpt_api_key = False if isinstance(h2ogpt_api_keys, str) and not os.path.isfile(h2ogpt_api_keys): h2ogpt_api_keys = str_to_list(h2ogpt_api_keys) os.environ['H2OGPT_H2OGPT_API_KEYS'] = str(h2ogpt_api_keys) if isinstance(extra_allowed_paths, str): extra_allowed_paths = str_to_list(extra_allowed_paths) if memory_restriction_level is None: memory_restriction_level = 2 if is_hf else 0 # 2 assumes run on 24GB consumer GPU else: assert 0 <= memory_restriction_level <= 3, "Bad memory_restriction_level=%s" % memory_restriction_level if n_jobs == -1: # if -1, assume hypercores, don't use, force user to pass n_jobs to be specific if not standard cores n_jobs = max(1, os.cpu_count() // 2) if is_public and os.getenv('n_jobs') is None: n_jobs = min(n_jobs, max(1, min(os.cpu_count() // 2, 8))) if is_public: gradio_upload_to_chatbot_num_max = 1 if admin_pass is None: admin_pass = os.getenv("ADMIN_PASS") # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result # but becomes unrecoverable sometimes if raise, so just be silent for now raise_generate_gpu_exceptions = True rope_scaling = str_to_dict(rope_scaling) if isinstance(auth, str): if auth.strip().startswith('['): auth = str_to_list(auth) if isinstance(auth, str) and auth: auth_filename = auth if not auth_filename: auth_filename = "auth.db" assert isinstance(auth, (str, list, tuple, type(None))), "Unknown type %s for auth=%s" % (type(auth), auth) if auth_filename.endswith('.db'): # this migrates json to db assert fetch_user(auth_filename, '', verbose=verbose) == {} if guest_name is None: if auth_access == 'closed': # ensure, but should be protected inside anyways guest_name = '' elif auth_access == 'open': guest_name = "guest" h2ogpt_pid = os.getpid() if close_button and not is_public else None # allow set token directly if not use_auth_token: use_auth_token = os.environ.get("HUGGING_FACE_HUB_TOKEN", use_auth_token) if isinstance(use_auth_token, str) and use_auth_token: os.environ['HUGGING_FACE_HUB_TOKEN'] = use_auth_token allow_upload_to_user_data = bool( int(os.environ.get("allow_upload_to_user_data", str(int(allow_upload_to_user_data))))) allow_upload_to_my_data = bool(int(os.environ.get("allow_upload_to_my_data", str(int(allow_upload_to_my_data))))) height = int(os.environ.get("HEIGHT", height)) h2ocolors = bool(int(os.getenv('h2ocolors', h2ocolors))) # allow enabling langchain via ENV # FIRST PLACE where LangChain referenced, but no imports related to it langchain_modes = ast.literal_eval(os.environ.get("langchain_modes", str(langchain_modes))) if not isinstance(langchain_modes, list): langchain_modes = [] # always allow DISABLED if LangChainMode.DISABLED.value not in langchain_modes: langchain_modes.append(LangChainMode.DISABLED.value) if not have_langchain: # only allow disabled, not even LLM that is langchain related langchain_mode = LangChainMode.DISABLED.value langchain_modes = [langchain_mode] # update langchain_mode_paths = str_to_dict(langchain_mode_paths) langchain_mode_types = str_to_dict(langchain_mode_types) for lmode in [LangChainMode.GITHUB_H2OGPT.value, LangChainMode.H2O_DAI_DOCS.value, LangChainMode.WIKI.value, LangChainMode.WIKI_FULL.value, ]: if lmode not in langchain_mode_types: langchain_mode_types[lmode] = 'shared' if lmode not in langchain_mode_paths: langchain_mode_types[lmode] = '' if user_path: user_path = makedirs(user_path, use_base=True) langchain_mode_paths['UserData'] = user_path langchain_mode_paths['UserData'] = LangChainTypes.SHARED.value if llamacpp_path: llamacpp_path = makedirs(llamacpp_path, use_base=True) if is_public: allow_upload_to_user_data = False if LangChainMode.USER_DATA.value in langchain_modes: langchain_modes.remove(LangChainMode.USER_DATA.value) if max_raw_chunks is None: max_raw_chunks = 30 if is_public else 1000000 # in-place, for non-scratch dbs if allow_upload_to_user_data: # always listen to CLI-passed user_path if passed if user_path: langchain_mode_paths['UserData'] = user_path assert langchain_action in langchain_actions, "Invalid langchain_action %s not in %s" % ( langchain_action, langchain_actions) assert len( set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents # auto-set langchain_mode langchain_mode = os.environ.get("LANGCHAIN_MODE", langchain_mode) if have_langchain and langchain_mode is None: # start in chat mode, in case just want to chat and don't want to get "No documents to query" by default. if LangChainMode.LLM.value in langchain_modes: langchain_mode = LangChainMode.LLM.value elif len(langchain_modes) >= 1: # infer even if don't pass which langchain_mode, just langchain_modes. langchain_mode = langchain_modes[0] if allow_upload_to_user_data and not is_public and langchain_mode_paths['UserData']: if verbose: print("Auto set langchain_mode=%s. Could use UserData instead." % langchain_mode, flush=True) elif allow_upload_to_my_data: if verbose: print("Auto set langchain_mode=%s. Could use MyData instead." " To allow UserData to pull files from disk," " set user_path or langchain_mode_paths, and ensure allow_upload_to_user_data=True" % langchain_mode, flush=True) else: raise RuntimeError("Please pass --langchain_mode= out of %s" % langchain_modes) if not have_langchain and langchain_mode not in [None, LangChainMode.DISABLED.value, LangChainMode.LLM.value]: raise RuntimeError("Asked for LangChain mode but langchain python package cannot be found.") if langchain_mode is None: # if not set yet, disable langchain_mode = LangChainMode.DISABLED.value print("Auto set langchain_mode=%s Have langchain package: %s" % (langchain_mode, have_langchain), flush=True) # go ahead and add if langchain_mode not in langchain_modes: langchain_modes.append(langchain_mode) if is_public: # See also get_minmax_top_k_docs() # as another restriction apart from top_k_docs and when using long context models # model will limit more if required max_input_tokens = max_input_tokens_public if max_input_tokens is None else max_input_tokens max_total_input_tokens = max_total_input_tokens_public if max_total_input_tokens is None else max_total_input_tokens allow_upload_to_user_data = False input_lines = 1 # ensure set, for ease of use temperature = 0.3 if temperature is None else temperature top_p = 1.0 if top_p is None else top_p top_k = 1 if top_k is None else top_k penalty_alpha = 0.0 if penalty_alpha is None else penalty_alpha if is_hf: do_sample = True if do_sample is None else do_sample top_k_docs = 3 if top_k_docs is None else top_k_docs else: # by default don't sample, too chatty do_sample = False if do_sample is None else do_sample # now 10 since also limiting total tokens, in case some pages (for summarization) are small top_k_docs = max_top_k_docs_public if top_k_docs is None else top_k_docs if memory_restriction_level == 2: if not base_model and not inference_server and not model_lock: base_model = 'h2oai/h2ogpt-oasst1-512-12b' # don't set load_8bit if passed base_model, doesn't always work so can't just override load_8bit = True load_4bit = False # FIXME - consider using 4-bit instead of 8-bit elif not inference_server: top_k_docs = max_top_k_docs_public if top_k_docs is None else top_k_docs if memory_restriction_level >= 2: load_8bit = True load_4bit = False # FIXME - consider using 4-bit instead of 8-bit if hf_embedding_model is None: hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2" top_k_docs = 3 if top_k_docs is None else top_k_docs if top_k_docs is None: top_k_docs = max_top_k_docs_default if max_input_tokens is None: max_input_tokens = -1 if max_total_input_tokens is None: max_total_input_tokens = -1 if is_public: if not max_time: max_time = 60 * 2 if not max_max_time: max_max_time = max_time if not max_new_tokens: max_new_tokens = 1024 if not max_max_new_tokens: max_max_new_tokens = 1024 else: if not max_max_time: max_max_time = 600 * 20 #changed by David from 1200 if not max_max_new_tokens: max_max_new_tokens = 1024 if is_hf: # must override share if in spaces share = False if not max_time: max_time = 60 * 1 if not max_max_time: max_max_time = max_time # HF accounted for later in get_max_max_new_tokens() save_dir = os.getenv('SAVE_DIR', save_dir) save_dir = makedirs(save_dir, exist_ok=True, tmp_ok=True, use_base=True) score_model = os.getenv('SCORE_MODEL', score_model) if str(score_model) == 'None': score_model = '' # prioritize verifier model to replace output if verifier_model: score_model = '' all_inference_server = inference_server or model_lock and all(x.get('inference_server') for x in model_lock) if inference_server == 'openai' and base_model in openai_gpts: # deprecate chat models with non-chat API inference_server = 'openai_chat' if os.getenv('CONCURRENCY_COUNT'): concurrency_count = int(os.getenv('CONCURRENCY_COUNT')) elif concurrency_count: pass else: if all_inference_server: concurrency_count = 64 else: # can't share LLM state across user requests due to k-v cache for LLMs # FIXME: In gradio 4 could use 1 for only LLM tasks, higher for rest concurrency_count = 1 if concurrency_count > 1 and not all_inference_server and base_model: # FIXME: Could use semaphore to manage each LLM concurrency, in case mix of local and remote raise ValueError( "Concurrency count > 1 will lead to mixup in cache use for local LLMs, disable this raise at own risk.") api_open = bool(int(os.getenv('API_OPEN', str(int(api_open))))) allow_api = bool(int(os.getenv('ALLOW_API', str(int(allow_api))))) if openai_server and not allow_api: print("Cannot enable OpenAI server when allow_api=False") openai_server = False if agent_server and not allow_api: print("Cannot enable Agent server when allow_api=False") agent_server = False if not os.getenv('CLEAR_CLEAR_TORCH'): if clear_torch_cache_level == 0: os.environ['CLEAR_CLEAR_TORCH'] = '0' elif clear_torch_cache_level == 1: os.environ['CLEAR_CLEAR_TORCH'] = '1' n_gpus1 = torch.cuda.device_count() if torch.cuda.is_available() else 0 n_gpus1, gpu_ids = cuda_vis_check(n_gpus1) if n_gpus is None: n_gpus = n_gpus1 if load_half is None and t5_type(base_model): load_half = False print("load_half=%s auto-set for %s to avoid bad generation" % (load_half, base_model), flush=True) if n_gpus == 0 or get_device(n_gpus=n_gpus) == "mps": # No CUDA GPUs usable if get_device(n_gpus=n_gpus) != "mps": print("No GPUs detected", flush=True) enable_captions = False gpu_id = None load_8bit = False load_4bit = False low_bit_mode = 1 if load_half is None: # wouldn't work if specified True, but respect load_half = False use_flash_attention_2 = False load_gptq = '' load_awq = '' load_exllama = False use_gpu_id = False if get_device(n_gpus=n_gpus) == "cuda": torch.backends.cudnn.benchmark = True torch.backends.cudnn.enabled = False torch.set_default_dtype(torch.float32) if is_public and not inference_server and not model_lock: # 12B uses ~94GB # 6.9B uses ~47GB base_model = 'h2oai/h2ogpt-oig-oasst1-512-6_9b' if not base_model else base_model if hf_embedding_model is None: # if no GPUs, use simpler embedding model to avoid cost in time hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2" if score_model == 'auto': score_model = '' else: if not have_flash_attention_2: use_flash_attention_2 = False if load_half is None: load_half = True # CUDA GPUs visible if score_model == 'auto': if n_gpus >= 2: # will by default place scoring model on last GPU # avoid score model for now, not really useful # score_model = 'OpenAssistant/reward-model-deberta-v3-large-v2' score_model = '' else: score_model = '' if hf_embedding_model is None: # if still None, then set default hf_embedding_model = 'BAAI/bge-large-en-v1.5' # get defaults if base_model: model_lower = base_model.lower() model_lower0 = base_model0.lower() elif model_lock: assert len(model_lock) > 0 and model_lock[0]['base_model'], "model_lock: %s" % model_lock # set to '' so don't contaminate other models in lock with first one model_lower = '' model_lower0 = '' else: model_lower = '' model_lower0 = '' if not (gradio or function): # force, else not single response like want to look at stream_output = False # else prompt removal can mess up output chat = False if not stream_output: stream_map = False # hard-coded defaults first_para = False text_limit = None if offload_folder: offload_folder = makedirs(offload_folder, exist_ok=True, tmp_ok=True, use_base=True) # auto-set stt and tts. # Done early here for lg_to_gr() and preload of db to know what's enabled if cli or not (gradio or function): enable_stt = enable_tts = False if not (have_soundfile and have_librosa and have_wavio): if enable_stt == 'auto': print("soundfile, librosa, and wavio not installed, disabling STT", flush=True) enable_stt = False elif enable_stt is True: raise RuntimeError("STT packages (soundfile, librosa, wavio) not installed") elif enable_stt == 'auto': enable_stt = False if n_gpus != 0 and enable_stt: print("STT enabled, may use more GPU, set --enable_stt=False for low-memory systems", flush=True) if not (have_soundfile and have_librosa and have_wavio): if enable_tts == 'auto': print("soundfile, librosa, and wavio not installed, disabling TTS", flush=True) enable_tts = False elif enable_tts is True: raise RuntimeError("TTS packages (soundfile, librosa, wavio) not installed") elif enable_tts == 'auto': enable_tts = False if not have_langchain and enable_transcriptions: print("Must install langchain for transcription, disabling", flush=True) enable_transcriptions = False if not (have_soundfile and have_librosa and have_wavio) and enable_tts: enable_tts = False print("soundfile, librosa, and wavio not installed, disabling TTS", flush=True) if n_gpus != 0 and enable_tts: print("TTS enabled, may use more GPU, set --enable_tts=False for low-memory systems", flush=True) if n_gpus == 0: tts_gpu = False stt_gpu = False caption_gpu = False asr_gpu = False if n_gpus == 0 and get_device(n_gpus=n_gpus) != "mps": # if local DocTR, doesn't work on CPU enable_doctr = False enable_pdf_doctr = False if is_public: stt_model = 'distil-whisper/distil-large-v3' # defaults caption_loader = None doctr_loader = None pix2struct_loader = None asr_loader = None image_audio_loaders_options0, image_audio_loaders_options, \ pdf_loaders_options0, pdf_loaders_options, \ url_loaders_options0, url_loaders_options = lg_to_gr(**locals().copy()) jq_schema0 = jq_schema extract_frames0 = extract_frames guided_whitespace_pattern0 = guided_whitespace_pattern metadata_in_context0 = metadata_in_context # transcribe image_audio_loaders = image_audio_loaders_options0 pdf_loaders = pdf_loaders_options0 url_loaders = url_loaders_options0 placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, prompt_dict, chat_template, \ temperature, top_p, top_k, penalty_alpha, num_beams, \ max_new_tokens, min_new_tokens, early_stopping, max_time, \ repetition_penalty, num_return_sequences, \ do_sample, \ seed, \ src_lang, tgt_lang, \ examples, \ task_info = \ get_generate_params(model_lower, model_lower0, inference_server, llamacpp_dict, chat, stream_output, enable_caching, show_examples, prompt_type, prompt_dict, chat_template, system_prompt, pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, all_docs_start_prompt, all_docs_finish_prompt, user_prompt_for_fake_system_prompt, json_object_prompt, json_object_prompt_simpler, json_code_prompt, json_code_prompt_if_no_schema, json_schema_instruction, json_preserve_system_prompt, json_object_post_prompt_reminder, json_code_post_prompt_reminder, json_code2_post_prompt_reminder, temperature, top_p, top_k, penalty_alpha, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, seed, top_k_docs, chunk, chunk_size, image_audio_loaders, pdf_loaders, url_loaders, jq_schema, extract_frames, llava_prompt, docs_ordering_type, min_max_new_tokens, max_input_tokens, max_total_input_tokens, docs_token_handling, docs_joiner, hyde_level, hyde_template, hyde_show_only_final, doc_json_mode, metadata_in_context, chatbot_role, speaker, tts_language, tts_speed, image_file, image_control, images_num_max, image_resolution, image_format, rotate_align_resize_image, video_frame_period, image_batch_image_prompt, image_batch_final_prompt, image_batch_stream, visible_vision_models, video_file, response_format, guided_json, guided_regex, guided_choice, guided_grammar, guided_whitespace_pattern, client_metadata, verbose, ) git_hash = get_githash() locals_dict = locals().copy() locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()]) if verbose: print(f"Generating model with params:\n{locals_print}", flush=True) print("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), git_hash), flush=True) # PRELOAD if enable_captions: if pre_load_image_audio_models: from image_captions import H2OImageCaptionLoader caption_loader = H2OImageCaptionLoader(caption_gpu=caption_gpu, gpu_id=caption_gpu_id).load_model() else: caption_loader = 'gpu' if n_gpus > 0 and caption_gpu else 'cpu' else: caption_loader = False if not have_langchain and pre_load_embedding_model: print("Must install langchain for preloading embedding model, disabling", flush=True) pre_load_embedding_model = False if use_openai_embedding: # makes later code simpler hf_embedding_model = '' if pre_load_embedding_model and \ langchain_mode != LangChainMode.DISABLED.value and \ not use_openai_embedding: from gpt_langchain import get_embedding hf_embedding_model = dict(name=hf_embedding_model, model=get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model, preload=True, gpu_id=embedding_gpu_id)) if not (have_doctr and have_langchain) and enable_doctr: print("Must install DocTR and LangChain installed if enabled DocTR, disabling", flush=True) enable_doctr = False enable_pdf_ocr = 'off' if enable_doctr or enable_pdf_ocr in [True, 'auto', 'on']: if pre_load_image_audio_models: from image_doctr import H2OOCRLoader doctr_loader = H2OOCRLoader(layout_aware=True, gpu_id=doctr_gpu_id).load_model() else: doctr_loader = 'gpu' if n_gpus > 0 and caption_gpu else 'cpu' else: doctr_loader = False if enable_transcriptions: if pre_load_image_audio_models: from audio_langchain import H2OAudioCaptionLoader asr_loader = H2OAudioCaptionLoader(asr_gpu=asr_gpu, gpu_id=asr_gpu_id, asr_model=asr_model, use_better=asr_use_better, use_faster=asr_use_faster).load_model() else: asr_loader = 'gpu' if n_gpus > 0 and asr_gpu else 'cpu' else: asr_loader = False if enable_stt: from stt import transcribe if pre_load_image_audio_models and \ stt_model == asr_model: transcriber = asr_loader.model.pipe else: from stt import get_transcriber transcriber = get_transcriber(model=stt_model, use_gpu=stt_gpu, gpu_id=stt_gpu_id) transcriber_func = functools.partial(transcribe, transcriber=transcriber, debug=debug, max_chunks=30 if is_public else None, sst_floor=sst_floor, ) model_xtt, supported_languages_xtt = None, None predict_from_text_func = None generate_speech_func = None return_as_byte = True # outside conditional since used without other checks if enable_tts: # NOTE: required bytes for now for audio streaming to work, else untested combine_audios() if tts_model.startswith('microsoft'): from tts import predict_from_text, get_tts_model, generate_speech processor_tts, model_tts, vocoder_tts = \ get_tts_model(t5_model=tts_model, t5_gan_model=tts_gan_model, use_gpu=tts_gpu, gpu_id=tts_gpu_id, ) predict_from_text_func = functools.partial(predict_from_text, processor=processor_tts, model=model_tts, return_as_byte=return_as_byte, vocoder=vocoder_tts, verbose=verbose) generate_speech_func = functools.partial(generate_speech, processor=processor_tts, model=model_tts, vocoder=vocoder_tts, return_as_byte=return_as_byte, verbose=verbose) elif tts_model.startswith('tts_models/'): if not have_TTS: raise ImportError("Selected non-default Coqui models, but did not install TTS") if not have_deepspeed and tts_coquiai_deepspeed: tts_coquiai_deepspeed = False print("deepspeed not installed, disabling", flush=True) from tts_coqui import get_xtt, predict_from_text, generate_speech model_xtt, supported_languages_xtt = get_xtt(model_name=tts_model, deepspeed=tts_coquiai_deepspeed, use_gpu=tts_gpu, gpu_id=tts_gpu_id, ) predict_from_text_func = functools.partial(predict_from_text, model=model_xtt, supported_languages=supported_languages_xtt, return_as_byte=return_as_byte, verbose=verbose, ) generate_speech_func = functools.partial(generate_speech, model=model_xtt, supported_languages=supported_languages_xtt, return_as_byte=return_as_byte, verbose=verbose) # setup image models from vision.utils_vision import get_image_model_dict image_model_dict = get_image_model_dict(enable_image, visible_image_models, image_gpu_ids) visible_image_models_state0 = list(image_model_dict.keys()) # DB SETUP if langchain_mode != LangChainMode.DISABLED.value: # SECOND PLACE where LangChain referenced, but all imports are kept local so not required from gpt_langchain import prep_langchain, get_some_dbs_from_hf, get_persist_directory if is_hf: get_some_dbs_from_hf() dbs = {} for langchain_mode1 in langchain_modes: if langchain_mode1 in langchain_modes_intrinsic: # don't store intrinsic dbs in dbs if db, and don't worry about LLM/Disabled continue langchain_type = langchain_mode_types.get(langchain_mode1, LangChainTypes.EITHER.value) if langchain_type == LangChainTypes.PERSONAL.value: # shouldn't prepare per-user databases here continue persist_directory1, langchain_type = get_persist_directory(langchain_mode1, langchain_type=langchain_type) langchain_mode_types[langchain_mode1] = langchain_type if langchain_type == LangChainTypes.PERSONAL.value: # shouldn't prepare per-user databases here continue try: db = prep_langchain(persist_directory1, load_db_if_exists, db_type, use_openai_embedding, langchain_mode1, langchain_mode_paths, langchain_mode_types, hf_embedding_model, migrate_embedding_model, n_jobs=n_jobs, embedding_gpu_id=embedding_gpu_id, kwargs_make_db=locals().copy(), verbose=verbose) finally: # in case updated embeddings or created new embeddings clear_torch_cache(allow_skip=True) dbs[langchain_mode1] = db # remove None db's so can just rely upon k in dbs for if hav db dbs = {k: v for k, v in dbs.items() if v is not None} else: dbs = {} # import control if os.environ.get("TEST_LANGCHAIN_IMPORT"): assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have" assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have" # MODEL SETUP if attention_sinks: if use_cache is False: raise ValueError("attention sinks requires use_cache=True") else: use_cache = True # never truncate if using attention sinks truncation_generation = truncation_generation and not attention_sinks other_model_state_defaults = dict(load_8bit=load_8bit, load_4bit=load_4bit, low_bit_mode=low_bit_mode, load_half=load_half, use_flash_attention_2=use_flash_attention_2, load_gptq=load_gptq, load_awq=load_awq, load_exllama=load_exllama, use_safetensors=use_safetensors, revision=revision, use_gpu_id=use_gpu_id, gpu_id=gpu_id, compile_model=compile_model, use_cache=use_cache, llamacpp_dict=llamacpp_dict, rope_scaling=rope_scaling, max_seq_len=max_seq_len, max_output_seq_len=max_output_seq_len, exllama_dict=exllama_dict, gptq_dict=gptq_dict, attention_sinks=attention_sinks, sink_dict=sink_dict, truncation_generation=truncation_generation, hf_model_dict=hf_model_dict, force_seq2seq_type=force_seq2seq_type, force_t5_type=force_t5_type, trust_remote_code=trust_remote_code, ) assert list(other_model_state_defaults.keys()) == list(other_model_state_defaults0.keys()) model_state_none = model_state_none0.copy() model_state_none.update(other_model_state_defaults) # for allowing rest of eval_func_param_names for k in eval_func_param_names: if k not in model_state_none: model_state_none[k] = None selection_docs_state0 = dict(langchain_modes=langchain_modes, langchain_mode_paths=langchain_mode_paths, langchain_mode_types=langchain_mode_types) selection_docs_state = copy.deepcopy(selection_docs_state0) if cli or not (gradio or function): # initial state for query prompt model_name = base_model pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt = \ get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, ) # get score model score_model_state0 = dict(model=None, tokenizer=None, device=None, base_model=None, display_name=None, tokenizer_base_model='', lora_weights='', inference_server='', prompt_type='', prompt_dict='', chat_template=None, visible_models=None, h2ogpt_key=None, reward_model=None) if score_model: all_kwargs = locals().copy() smodel, stokenizer, sdevice = get_score_model(reward_type=True, **get_kwargs(get_score_model, exclude_names=['reward_type'], **all_kwargs)) score_model_state0.update(dict(model=smodel, tokenizer=stokenizer, device=sdevice, base_model=score_model, reward_model=True)) # get verifier model, replaces score_model if exists if verifier_model: score_model = verifier_model all_kwargs = locals().copy() all_kwargs.update(base_model=verifier_model, tokenizer_base_model=verifier_tokenizer_base_model, inference_server=verifier_inference_server, prompt_type=noop_prompt_type, prompt_dict={}, chat_template=None, visible_models=None, h2ogpt_key=None) smodel, stokenizer, sdevice = get_model_retry(reward_type=False, **get_kwargs(get_model, exclude_names=['reward_type'], **all_kwargs)) score_model_state0.update(dict(model=smodel, tokenizer=stokenizer, device=sdevice, base_model=verifier_model, tokenizer_base_model=verifier_tokenizer_base_model, inference_server=verifier_inference_server, prompt_type=noop_prompt_type, reward_model=False)) # get default model(s) model_states = [] model_state_base0 = {} model_state_base0.update(model_state_none) model_state_base0.update(dict(base_model=base_model, base_model0=base_model0, tokenizer_base_model=tokenizer_base_model, lora_weights=lora_weights, inference_server=inference_server, prompt_type=prompt_type, prompt_dict=prompt_dict, chat_template=chat_template, display_name=display_name)) model_state_base0.update(other_model_state_defaults) # for allowing rest of eval_func_param_names. We don't want to force CLI values always by default for k in eval_func_param_names: if k not in model_state_base0: model_state_base0[k] = None model_list = [model_state_base0] model_list0 = copy.deepcopy(model_list) # just strings, safe to deepcopy model_state0 = copy.deepcopy(model_state_none) assert len(model_state_none) == len(model_state0) have_model_lock = model_lock is not None and len(model_lock) > 0 if have_model_lock: model_list = copy.deepcopy(model_lock) kwargs_model_lock_to_state = locals().copy() kwargs_model_lock_to_state = {k: v for k, v in kwargs_model_lock_to_state.items() if isinstance(v, (str, dict, int, float, bool, type(None), list))} excluded_kwargs_model_lock_to_state_keys = [k for k in locals() if k not in kwargs_model_lock_to_state] if verbose: print('excluded_kwargs_model_lock_to_state_keys', excluded_kwargs_model_lock_to_state_keys) # do reverse, so first is default base_model etc., so some logic works in go_gradio() more easily for model_dict in reversed(model_list): model_dict.update({k: v for k, v in model_state_none.items() if k not in model_dict}) # use non-cache since accumulate model_lock and may have to dedup model_state_trial = model_lock_to_state(model_dict, cache_model_state=False, **kwargs_model_lock_to_state) if not model_state_trial: continue model_state0 = model_state_trial.copy() assert len(model_state_none) == len(model_state0) if have_model_lock: # last in iteration will be first model_states.insert(0, model_state_trial) # fill model_state0 so go_gradio() easier, manage model_states separately model_state0 = model_state_trial.copy() else: model_state0 = model_state_trial.copy() # begin prompt adjustments # get query prompt for (say) last base model if using model lock pre_prompt_query1, prompt_query1, pre_prompt_summary1, prompt_summary1, hyde_llm_prompt1 = ( get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, )) # if mixed setup, choose non-empty so best models best # FIXME: Make per model dict passed through to evaluate pre_prompt_query = pre_prompt_query if pre_prompt_query is not None else pre_prompt_query1 prompt_query = prompt_query if prompt_query is not None else prompt_query1 pre_prompt_summary = pre_prompt_summary if pre_prompt_summary is not None else pre_prompt_summary1 prompt_summary = prompt_summary if prompt_summary is not None else prompt_summary1 hyde_llm_prompt = hyde_llm_prompt if hyde_llm_prompt is not None else hyde_llm_prompt1 if all_docs_start_prompt == 'auto' or all_docs_finish_prompt == 'auto': all_docs_start_prompt = None all_docs_finish_prompt = None user_prompt_for_fake_system_prompt = user_prompt_for_fake_system_prompt or user_prompt_for_fake_system_prompt0 json_object_prompt = json_object_prompt or json_object_prompt0 json_object_prompt_simpler = json_object_prompt_simpler or json_object_prompt_simpler0 json_code_prompt = json_code_prompt or json_code_prompt0 json_code_prompt_if_no_schema = json_code_prompt_if_no_schema or json_code_prompt_if_no_schema0 json_schema_instruction = json_schema_instruction or json_schema_instruction0 json_object_post_prompt_reminder = json_object_post_prompt_reminder or json_object_post_prompt_reminder0 json_code_post_prompt_reminder = json_code_post_prompt_reminder or json_code_post_prompt_reminder0 json_code2_post_prompt_reminder = json_code2_post_prompt_reminder or json_code2_post_prompt_reminder0 image_batch_image_prompt = image_batch_image_prompt or image_batch_image_prompt0 image_batch_final_prompt = image_batch_final_prompt or image_batch_final_prompt0 # end prompt adjustments # get initial display name. Use user display name if set all_possible_display_names = [ x.get('base_model', xi) if x.get('base_model', '') != 'llama' or not x.get('llamacpp_dict').get('model_path_llama', '') else x.get('llamacpp_dict').get('model_path_llama', '') for xi, x in enumerate(model_states)] [x.update( dict(display_name=x.get('display_name', all_possible_display_names[xi]) or all_possible_display_names[xi])) for xi, x in enumerate(model_states)] # dedup display names all_possible_display_names = [x['display_name'] for x in model_states] display_names = deduplicate_names([x for x in all_possible_display_names]) all_possible_display_names = display_names # save display names [x.update(dict(display_name=display_names[xi])) for xi, x in enumerate(model_states)] visible_models_state0 = [x for xi, x in enumerate(all_possible_display_names) if visible_models is None or x in visible_models or xi in visible_models] # get list of visible vision models is_vision_models = [x.get('display_name') for x in model_states if x.get('is_vision_model')] all_possible_vision_display_names = [x for x in all_possible_display_names if is_vision_model(x) or x in is_vision_models] vision_display_names = deduplicate_names([x for x in all_possible_vision_display_names]) all_possible_vision_display_names = vision_display_names visible_vision_models_state0 = [x for xi, x in enumerate(all_possible_vision_display_names) if visible_vision_models is None or x in visible_vision_models or xi in visible_vision_models] if visible_vision_models_state0: # only single choice visible_vision_models_state0 = visible_vision_models_state0[0] else: visible_vision_models_state0 = '' # update to be consistent with what is passed from CLI and model chose # do after go over all models if multi-model, so don't contaminate # This is just so UI shows reasonable correct value, not 2048 dummy value if len(model_states) >= 1: max_seq_len = model_states[0]['tokenizer'].model_max_length elif model_state0 is not None and \ 'tokenizer' in model_state0 and \ hasattr(model_state0['tokenizer'], 'model_max_length'): max_seq_len = model_state0['tokenizer'].model_max_length local_kwargs = locals().copy() local_kwargs['my_db_state0'] = my_db_state0 # run if cli: from cli import run_cli return run_cli(**get_kwargs(run_cli, **local_kwargs)) elif eval: from eval import run_eval return run_eval(**get_kwargs(run_eval, **local_kwargs)) elif gradio or prepare_offline_level > 0: # imported here so don't require gradio to run generate from gradio_runner import go_gradio # assume gradio needs everything go_gradio(**local_kwargs) elif function: return local_kwargs def evaluate_fake(*args, **kwargs): if kwargs.get('langchain_action', LangChainAction.QUERY.value) == LangChainAction.EXTRACT.value: response = [invalid_key_msg] else: response = invalid_key_msg yield dict(response=response, sources=[], save_dict=dict(prompt='INVALID', extra_dict=dict(num_prompt_tokens=0, base_model='')), llm_answers=dict(response_raw=response), response_no_refs=response, sources_str='', audio=None, prompt_raw='INVALID', error=invalid_key_msg) return # keep in sync with H2oGPTParams def evaluate( model_state, my_db_state, selection_docs_state, requests_state, roles_state, # START NOTE: Examples must have same order of parameters instruction, iinput, context, stream_output, enable_caching, prompt_type, prompt_dict, chat_template, temperature, top_p, top_k, penalty_alpha, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, seed, chat, instruction_nochat, iinput_nochat, langchain_mode, add_chat_history_to_context, langchain_action, langchain_agents, top_k_docs, chunk, chunk_size, document_subset, document_choice, document_source_substrings, document_source_substrings_op, document_content_substrings, document_content_substrings_op, pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, all_docs_start_prompt, all_docs_finish_prompt, user_prompt_for_fake_system_prompt, json_object_prompt, json_object_prompt_simpler, json_code_prompt, json_code_prompt_if_no_schema, json_schema_instruction, json_preserve_system_prompt, json_object_post_prompt_reminder, json_code_post_prompt_reminder, json_code2_post_prompt_reminder, system_prompt, image_audio_loaders, pdf_loaders, url_loaders, jq_schema, extract_frames, llava_prompt, visible_models, visible_image_models, image_size, image_quality, image_guidance_scale, image_num_inference_steps, h2ogpt_key, add_search_to_context, chat_conversation, text_context_list, docs_ordering_type, min_max_new_tokens, max_input_tokens, max_total_input_tokens, docs_token_handling, docs_joiner, hyde_level, hyde_template, hyde_show_only_final, doc_json_mode, metadata_in_context, chatbot_role, speaker, tts_language, tts_speed, image_file, image_control, images_num_max, image_resolution, image_format, rotate_align_resize_image, video_frame_period, image_batch_image_prompt, image_batch_final_prompt, image_batch_stream, visible_vision_models, video_file, response_format, guided_json, guided_regex, guided_choice, guided_grammar, guided_whitespace_pattern, model_lock, # not really used by evaluate, just pure API client_metadata, # END NOTE: Examples must have same order of parameters captions_model=None, caption_loader=None, doctr_loader=None, pix2struct_loader=None, llava_model=None, image_model_dict=None, asr_model=None, asr_loader=None, async_output=None, num_async=None, src_lang=None, tgt_lang=None, debug=False, concurrency_count=None, save_dir=None, sanitize_bot_response=False, model_state0=None, use_auth_token=None, trust_remote_code=None, memory_restriction_level=None, max_max_new_tokens=None, is_public=None, from_ui=True, regenerate_clients=None, regenerate_gradio_clients=None, validate_clients=None, fail_if_invalid_client=None, max_max_time=None, raise_generate_gpu_exceptions=None, lora_weights=None, use_llm_if_no_docs=True, load_db_if_exists=True, dbs=None, detect_user_path_changes_every_query=None, use_openai_embedding=None, use_openai_model=None, hf_embedding_model=None, migrate_embedding_model=None, cut_distance=None, db_type=None, n_jobs=None, first_para=None, text_limit=None, sources_show_text_in_accordion=None, hyde_show_intermediate_in_accordion=None, map_reduce_show_intermediate_in_accordion=None, top_k_docs_max_show=None, show_link_in_sources=None, langchain_instruct_mode=None, verbose=False, gradio=True, force_streaming_on_to_handle_timeouts=True, cli=False, use_cache=None, auto_reduce_chunks=None, max_chunks=None, headsize=None, force_langchain_evaluate=None, model_state_none=None, llamacpp_path=None, llamacpp_dict=None, exllama_dict=None, gptq_dict=None, attention_sinks=None, sink_dict=None, truncation_generation=None, hf_model_dict=None, force_seq2seq_type=None, force_t5_type=None, load_exllama=None, answer_with_sources=None, append_sources_to_answer=None, append_sources_to_chat=None, image_audio_loaders_options0=None, pdf_loaders_options0=None, url_loaders_options0=None, jq_schema0=None, extract_frames0=None, guided_whitespace_pattern0=None, metadata_in_context0=None, keep_sources_in_context=None, gradio_errors_to_chatbot=None, allow_chat_system_prompt=None, # carry defaults to know what forced-off means use_pymupdf=None, use_unstructured_pdf=None, use_pypdf=None, enable_pdf_ocr=None, enable_pdf_doctr=None, try_pdf_as_html=None, load_awq=None, stream_map=None, ): if client_metadata: print(f"evaluate start client_metadata: {client_metadata}", flush=True) # ensure passed these assert concurrency_count is not None assert memory_restriction_level is not None assert raise_generate_gpu_exceptions is not None assert use_openai_embedding is not None assert use_openai_model is not None assert hf_embedding_model is not None assert migrate_embedding_model is not None assert db_type is not None assert top_k_docs is not None and isinstance(top_k_docs, int) assert chunk is not None and isinstance(chunk, bool) assert chunk_size is not None and isinstance(chunk_size, int) assert n_jobs is not None assert first_para is not None assert isinstance(add_chat_history_to_context, bool) assert isinstance(add_search_to_context, bool) assert load_exllama is not None # for lazy client (even chat client) if image_audio_loaders is None: image_audio_loaders = image_audio_loaders_options0 if pdf_loaders is None: pdf_loaders = pdf_loaders_options0 if url_loaders is None: url_loaders = url_loaders_options0 if jq_schema is None: jq_schema = jq_schema0 if extract_frames is None: extract_frames = extract_frames0 if seed is None: seed = 0 if guided_whitespace_pattern is None: if guided_whitespace_pattern0: guided_whitespace_pattern = guided_whitespace_pattern0 if guided_whitespace_pattern == '': # translate empty string to None guided_whitespace_pattern = None if metadata_in_context is None: metadata_in_context = metadata_in_context0 if response_format is None: response_format = response_formats[0] assert response_format in response_formats, "Invalid response_format: %s, must be in %s" % ( response_format, response_formats) if isinstance(langchain_agents, str): if langchain_agents.strip().startswith('['): # already list, but as string langchain_agents = str_to_list(langchain_agents) else: # just 1 item and make list langchain_agents = [langchain_agents] if langchain_agents is None: langchain_agents = [] chat_conversation = str_to_list(chat_conversation) text_context_list = str_to_list(text_context_list) if not image_size: imag_size = image_size_default langchain_modes = selection_docs_state['langchain_modes'] langchain_mode_paths = selection_docs_state['langchain_mode_paths'] langchain_mode_types = selection_docs_state['langchain_mode_types'] if debug: locals_dict = locals().copy() locals_dict.pop('model_state', None) locals_dict.pop('model_state0', None) locals_dict.pop('model_states', None) print(locals_dict) if langchain_action in LangChainAction.IMAGE_GENERATE.value: t_generate = time.time() if isinstance(visible_image_models, list): assert len(visible_image_models) > 0, "visible_image_models is empty" visible_image_models = visible_image_models[0] if visible_image_models == '' and image_model_dict: # choose first if nothing passed visible_image_models = list(image_model_dict.keys())[0] image_model_dict = image_model_dict[visible_image_models] pipe, make_image = image_model_dict['pipe'], image_model_dict['make_image'] filename_image = sanitize_filename("image_%s_%s.png" % (instruction, str(uuid.uuid4())), file_length_limit=50) gradio_tmp = get_gradio_tmp() image_file_gen = make_image(instruction, filename=os.path.join(gradio_tmp, filename_image), pipe=pipe, image_size=image_size, image_quality=image_quality, image_guidance_scale=float(image_guidance_scale), image_num_inference_steps=int(image_num_inference_steps), ) response = (image_file_gen,) # FIXME: Could run this through image model if was selected extra_dict = dict(t_generate=time.time() - t_generate, instruction=instruction, prompt_raw=instruction, prompt_type=prompt_type, base_model=LangChainAction.IMAGE_GENERATE.value) save_dict = dict(prompt=instruction, output=response, extra_dict=extra_dict) yield dict(response=response, sources=[], save_dict=save_dict, llm_answers=dict(response_raw=''), response_no_refs="Generated image for %s" % instruction, sources_str="", prompt_raw=instruction) if client_metadata: print(f"evaluate finish image client_metadata: {client_metadata}", flush=True) return no_model_msg = "Please choose a base model with --base_model (CLI) or load in Models Tab (gradio).\n" \ "Then start New Conversation" if model_state is None: model_state = model_state_none.copy() if model_state0 is None: # e.g. for no gradio case, set dummy value, else should be set model_state0 = model_state_none.copy() # model_state['model] is only 'model' if should use model_state0 # model could also be None have_fresh_model = model_state['model'] not in [None, 'model', no_model_str] have_cli_model = model_state0['model'] not in [None, 'model', no_model_str] no_llm_ok = langchain_action in [LangChainAction.IMAGE_GENERATE.value, LangChainAction.IMAGE_CHANGE.value, LangChainAction.IMAGE_QUERY.value, LangChainAction.IMAGE_STYLE.value, ] chosen_model_state = model_state0 if have_fresh_model: # USE FRESH MODEL chosen_model_state = model_state elif have_cli_model: # USE MODEL SETUP AT CLI assert isinstance(model_state['model'], (type(None), str)) # expect no fresh model elif not no_llm_ok: raise AssertionError(no_model_msg) # get variables model = chosen_model_state['model'] tokenizer = chosen_model_state['tokenizer'] device = chosen_model_state['device'] base_model = chosen_model_state['base_model'] display_name = chosen_model_state['display_name'] tokenizer_base_model = chosen_model_state['tokenizer_base_model'] lora_weights = chosen_model_state['lora_weights'] inference_server = chosen_model_state['inference_server'] visible_models = chosen_model_state['visible_models'] is_vision_model1 = chosen_model_state['is_vision_model'] is_actually_vision_model1 = chosen_model_state['is_actually_vision_model'] # use overall key if have, so key for this gradio and any inner gradio if chosen_model_state['h2ogpt_key'] is not None: h2ogpt_key = chosen_model_state['h2ogpt_key'] # prefer use input from API over model state prompt_type = prompt_type or chosen_model_state['prompt_type'] prompt_dict = prompt_dict or chosen_model_state['prompt_dict'] if prompt_type == unknown_prompt_type and chosen_model_state['prompt_type'] not in [None, '', unknown_prompt_type]: prompt_type = chosen_model_state['prompt_type'] prompt_dict = chosen_model_state['prompt_dict'] # prefer use input from API over model state (see prep_bot()) images_num_max = images_num_max or chosen_model_state['images_num_max'] if images_num_max is not None: # gradio 3 gr.Number issue images_num_max = int(images_num_max) if isinstance(image_resolution, str) and image_resolution.strip(): # from gradio was string of tuple image_resolution = ast.literal_eval(image_resolution.strip()) assert isinstance(image_resolution, (list, tuple)) image_resolution = image_resolution or chosen_model_state['image_resolution'] image_format = image_format or chosen_model_state['image_format'] video_frame_period = video_frame_period or chosen_model_state['video_frame_period'] if base_model is None and not no_llm_ok: raise AssertionError(no_model_msg) assert base_model.strip(), no_model_msg assert model is not None, "Model is missing" assert tokenizer is not None, "Tokenizer is missing" model_lower = base_model.lower() llamacpp_dict = str_to_dict(llamacpp_dict) if chat_template and hasattr(tokenizer, 'apply_chat_template'): try: tokenizer.chat_template = base64_decode_jinja_template(chat_template) messages_test = [dict(role='user', content='Hi'), dict(role='assistant', content='Hello! How can I help you today?')] test_prompt = tokenizer.apply_chat_template(messages_test, tokenize=False, add_generation_prompt=True) assert isinstance(test_prompt, str) except Exception as e: print("Could not overwrite %s template: %s" % (base_model, str(e))) # can't support chat_template = '' raise # choose chat or non-chat mode if not chat: if not instruction and instruction_nochat: instruction = instruction_nochat if not iinput and iinput_nochat: iinput = iinput_nochat # avoid instruction in chat_conversation itself, since always used as additional context to prompt in what follows if isinstance(chat_conversation, list) and \ len(chat_conversation) > 0 and \ len(chat_conversation[-1]) == 2 and \ chat_conversation[-1][0] == instruction and \ chat_conversation[-1][1] in [None, '']: chat_conversation = chat_conversation[:-1] if not add_chat_history_to_context: # make it easy to ignore without needing add_chat_history_to_context # some langchain or unit test may need to then handle more general case chat_conversation = [] # get ready for LLM chat_conversation = history_for_llm(chat_conversation) # Control generation hyperparameters # adjust for bad inputs, e.g. in case also come from API that doesn't get constrained by gradio sliders # below is for TGI server, not required for HF transformers # limits are chosen similar to gradio_runner.py sliders/numbers top_p = min(max(1e-3, top_p), 1.0) top_k = min(max(1, int(top_k)), 100) penalty_alpha = min(2.0, max(0.0, penalty_alpha)) if temperature == 0.0 and top_p == 1.0 and top_k == 1: do_sample = False if temperature > 0.0 or top_p < 1.0 or top_k > 1: do_sample = True if not do_sample: temperature = 0 top_p = 1.0 top_k = 1 seed = 1 if seed == 0 and do_sample: seed = randint(0, 32000) # Note: Could do below, but for now gradio way can control do_sample directly # elif temperature >= 0.01: # do_sample = True max_input_tokens = int(max_input_tokens) if max_input_tokens is not None else -1 max_total_input_tokens = int(max_total_input_tokens) if max_total_input_tokens is not None else -1 # FIXME: https://github.com/h2oai/h2ogpt/issues/106 num_beams = 1 if stream_output else num_beams # See max_beams in gradio_runner if model_lower == 'distilgpt2': # always truncate for certain models that totally fail otherwise truncation_generation = True if not inference_server: # can listen to truncation_generation pass else: # these don't support allowing going beyond total context truncation_generation = True max_max_new_tokens = get_max_max_new_tokens(chosen_model_state, memory_restriction_level=memory_restriction_level, max_new_tokens=max_new_tokens, attention_sinks=attention_sinks, max_max_new_tokens=max_max_new_tokens, truncation_generation=truncation_generation) if min_max_new_tokens is None: # default for nochat api min_max_new_tokens = 512 if max_input_tokens is None: max_input_tokens = -1 if max_total_input_tokens is None: max_total_input_tokens = -1 if docs_ordering_type is None: docs_ordering_type = docs_ordering_types_default if docs_token_handling is None: docs_token_handling = docs_token_handling_default if docs_joiner is None: docs_joiner = docs_joiner_default model_max_length = get_model_max_length(chosen_model_state) max_new_tokens = min(max(1, int(max_new_tokens)), max_max_new_tokens) min_max_new_tokens = min(min_max_new_tokens, max_new_tokens) min_new_tokens = min(max(0, int(min_new_tokens)), max_new_tokens) max_time = min(max(0, max_time), max_max_time) repetition_penalty = min(max(0.01, repetition_penalty), 3.0) num_return_sequences = 1 if chat else min(max(1, int(num_return_sequences)), 10) min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public, from_ui) # limit total tokens processed, e.g. for summarization, if public instance if is_public: # control API too for public case if from_ui: max_input_tokens = max_input_tokens_public else: max_input_tokens = max_input_tokens_public_api if from_ui: max_total_input_tokens = min(max_total_input_tokens, max_total_input_tokens_public) else: max_total_input_tokens = min(max_total_input_tokens, max_total_input_tokens_public_api) top_k_docs = min(max(min_top_k_docs, int(top_k_docs)), max_top_k_docs) chunk_size = min(max(128, int(chunk_size)), 2048) if not context: context = '' # NOTE!!!!!!!!!! Choice of developer. But only possible to force stream if num_beams=1 # stream if can, so can control task iteration and time of iteration # not required, but helpful for max_time control etc. stream_output0 = stream_output if force_streaming_on_to_handle_timeouts: stream_output = gradio and num_beams == 1 # https://platform.openai.com/docs/guides/reasoning/beta-limitations if base_model in ['o1-mini', 'o1-preview'] and os.getenv('O1STREAM', '0') == '0': stream_output = False from gradio_utils.grclient import GradioClient from gradio_client import Client gradio_server = inference_server.startswith('http') and ( isinstance(model, GradioClient) or isinstance(model, Client)) h2ogpt_gradio_server = gradio_server and not is_gradio_vision_model(base_model) if image_file and hasattr(tokenizer, 'chat_template') and isinstance(tokenizer.chat_template, str) and tokenizer.chat_template: if 'Prompting with images is incompatible with system messages' in tokenizer.chat_template: system_prompt_xml = f"""\n\n{system_prompt}\n\n""" if system_prompt else '' if instruction and system_prompt_xml: if '' not in instruction: instruction = system_prompt_xml + '\n\n' + instruction else: if system_prompt_xml: if '' not in prompt_query: prompt_query = system_prompt_xml + prompt_query if '' not in prompt_summary: prompt_summary = system_prompt_xml + prompt_summary system_prompt = '' if guided_json == '': guided_json = None if guided_regex == '': guided_regex = None if guided_grammar == '': guided_grammar = None if isinstance(guided_choice, str) and guided_choice: guided_choice = ast.literal_eval(guided_choice) assert isinstance(guided_choice, list), "Wrong type: guided_choice: %s" % guided_choice # don't repeat prompting if doing gradio server since inner prompting will handle json_vllm = chosen_model_state['json_vllm'] # for guided_choice etc. needs to be outside below conditional block json_schema_type = None if not h2ogpt_gradio_server and \ response_format in ['json_object', 'json_code']: json_object_prompt = json_object_prompt or json_object_prompt0 json_object_prompt = '\n' + json_object_prompt + '\n\n' json_object_prompt_simpler = json_object_prompt_simpler or json_object_prompt_simpler0 json_object_prompt_simpler = '\n' + json_object_prompt_simpler + '\n\n' json_code_prompt = json_code_prompt or json_code_prompt0 json_code_prompt = '\n' + json_code_prompt + '\n\n' json_code_prompt_if_no_schema = json_code_prompt_if_no_schema or json_code_prompt_if_no_schema0 json_code_prompt_if_no_schema = '\n' + json_code_prompt_if_no_schema + '\n\n' json_schema_instruction = json_schema_instruction or json_schema_instruction0 json_schema_instruction = '\n' + json_schema_instruction + '\n\n' json_object_post_prompt_reminder = json_object_post_prompt_reminder or json_object_post_prompt_reminder0 json_code_post_prompt_reminder = json_code_post_prompt_reminder or json_code_post_prompt_reminder0 json_code2_post_prompt_reminder = json_code2_post_prompt_reminder or json_code2_post_prompt_reminder0 if isinstance(guided_json, str): try: guided_json = guided_json_properties = json.loads(guided_json) except (json.decoder.JSONDecodeError, TypeError): try: guided_json = guided_json_properties = ast.literal_eval(guided_json) except: guided_json = guided_json_properties = {} else: guided_json = guided_json_properties = guided_json or {} assert isinstance(guided_json_properties, dict), "guided_json_properties must be dict by now" if 'properties' in guided_json_properties: guided_json_properties = guided_json_properties['properties'] # back to string, so e.g. do not get ' in prompt but " for quotes etc. gemma messes that up. guided_json_properties_json = json.dumps(guided_json_properties) if guided_json_properties_json.startswith('{'): json_schema_type = 'object' elif guided_json_properties_json.startswith('['): json_schema_type = 'array' elif guided_json_properties_json.startswith('"'): json_schema_type = 'string' elif guided_json_properties_json.startswith('true') or guided_json_properties_json.startswith('false'): json_schema_type = 'boolean' elif guided_json_properties_json.startswith('null'): json_schema_type = 'null' elif guided_json_properties_json.isdigit(): json_schema_type = 'number' schema_instruction = json_schema_instruction.format(properties_schema=guided_json_properties_json) pre_instruction = '' post_instruction = '' supports_schema = get_supports_schema(inference_server, base_model, response_format, guided_json=guided_json, json_vllm=json_vllm) if supports_schema: # for vLLM or claude-3, support schema if given # can't give schema both in prompt and tool/guided_json, messes model up if json_vllm: # e.g. for llama2-13b https://github.com/vllm-project/vllm/issues/4093 pre_instruction = schema_instruction elif is_json_model(base_model, inference_server, json_vllm=json_vllm) and \ response_format == 'json_object' and \ not (json_vllm and not guided_json): # these models don't support schema if given if inference_server and inference_server.startswith('mistral'): # mistral-large gets confused with extra info, and not required # updates, things changed, revise again # https://docs.mistral.ai/capabilities/json_mode/ json_object_prompt = json_object_prompt_simpler # shouldn't have to tell to use json, but should tell schema if guided_json_properties: # FIXME: Do function calling if can instead pre_instruction = json_object_prompt + schema_instruction else: # OpenAI requires "json" to appear somewhere in messages pre_instruction = json_object_prompt # often models need reminder to do it in actual JSON post_instruction = json_object_post_prompt_reminder else: # json_code way # have to tell to use json and give schema if present if guided_json_properties: pre_instruction = json_code_prompt + schema_instruction post_instruction = json_code_post_prompt_reminder else: pre_instruction = json_code_prompt + json_code_prompt_if_no_schema post_instruction = json_code2_post_prompt_reminder # ignore these, make no sense for JSON mode if not json_preserve_system_prompt: system_prompt = '' # can mess up the model, e.g. 70b if pre_instruction: if True or base_model and base_model in anthropic_mapping: # NOTE: enabled generally for now, seems to help generally pre_instruction = '\n\n' + \ pre_instruction + \ '\n\n\n' else: pre_instruction = 'Begin response format instructions:\n###\n' + \ pre_instruction + \ '\n###\nEnd response format instructions\n\n' if instruction: # avoid duplication, assuming instruction will be in final prompt after prompt_query or prompt_summary if pre_instruction: instruction = pre_instruction + '\n\n' + instruction if post_instruction: instruction = instruction + '\n\n' + post_instruction pre_prompt_query = '' pre_prompt_summary = '' else: pre_prompt_query = '' pre_prompt_summary = '' if pre_instruction: prompt_query = pre_instruction + prompt_query prompt_summary = pre_instruction + prompt_summary if post_instruction: # '' allowed, but don't add extra \n\n if such prompt_query = prompt_query + '\n\n' + post_instruction if prompt_query else post_instruction prompt_summary = prompt_summary + '\n\n' + post_instruction if prompt_summary else post_instruction ############### # prompt_type and prompter setup if inference_server.startswith('openai_chat') or inference_server.startswith('openai_azure_chat'): # no extra LLM prompting prompt_type = 'openai_chat' elif inference_server.startswith('vllm_chat'): # no extra LLM prompting prompt_type = unknown_prompt_type # in some cases, like lean nochat API, don't want to force sending prompt_type, allow default choice # This doesn't do switch-a-roo, assume already done, so might be wrong model and can't infer if prompt_type in ['', None, unknown_prompt_type] and prompt_type != 'custom': prompt_type_trial = model_name_to_prompt_type(base_model, inference_server, llamacpp_dict=llamacpp_dict, tokenizer=tokenizer) if prompt_type_trial: prompt_type = prompt_type_trial if verbose: print("Auto-selecting prompt_type=%s for %s" % (prompt_type, base_model), flush=True) assert prompt_type is not None, "prompt_type was None" # get prompter prompter = Prompter(prompt_type, prompt_dict, debug=debug, stream_output=stream_output, system_prompt=system_prompt, tokenizer=tokenizer, base_model=base_model) # THIRD PLACE where LangChain referenced, but imports only occur if enabled and have db to use assert langchain_mode in langchain_modes, "Invalid langchain_mode %s not in %s" % (langchain_mode, langchain_modes) assert langchain_action in langchain_actions, "Invalid langchain_action %s not in %s" % ( langchain_action, langchain_actions) assert len( set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents # get db, but also fill db state so return already has my_db_state and dbs filled so faster next query if langchain_mode != LangChainMode.DISABLED.value: from gpt_langchain import get_any_db db = get_any_db(my_db_state, langchain_mode, langchain_mode_paths, langchain_mode_types, dbs=dbs, load_db_if_exists=load_db_if_exists, db_type=db_type, use_openai_embedding=use_openai_embedding, hf_embedding_model=hf_embedding_model, migrate_embedding_model=migrate_embedding_model, for_sources_list=True, verbose=verbose, n_jobs=n_jobs, ) else: db = None t_generate = time.time() langchain_only_model = base_model in non_hf_types or \ load_exllama or \ inference_server.startswith('replicate') or \ inference_server.startswith('sagemaker') or \ inference_server.startswith('openai_azure_chat') or \ inference_server.startswith('openai_azure') or \ inference_server.startswith('anthropic') or \ inference_server.startswith('google') or \ inference_server.startswith('mistralai') or \ inference_server.startswith('groq') or \ (image_file or image_control) and (not gradio_server) do_langchain_path = langchain_mode not in [False, 'Disabled', 'LLM'] or \ langchain_only_model or \ force_langchain_evaluate or \ len(text_context_list) > 0 if len(langchain_agents) > 0: do_langchain_path = True if add_search_to_context: # easier to manage prompt etc. by doing full langchain path do_langchain_path = True gen_hyper_dict = dict(do_sample=do_sample, seed=seed, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p, top_k=top_k, penalty_alpha=penalty_alpha, num_beams=num_beams, min_new_tokens=min_new_tokens, max_new_tokens=max_new_tokens, early_stopping=early_stopping, max_time=max_time, num_return_sequences=num_return_sequences, ) extra_dict = gen_hyper_dict.copy() extra_dict.update(dict(base_model=base_model, display_name=display_name, prompt_type=prompt_type, inference_server=inference_server, langchain_mode=langchain_mode, langchain_action=langchain_action, langchain_agents=langchain_agents, document_subset=document_subset, document_choice=document_choice, document_source_substrings=document_source_substrings, document_source_substrings_op=document_source_substrings_op, document_content_substrings=document_content_substrings, document_content_substrings_op=document_content_substrings_op, add_search_to_context=add_search_to_context, instruction=instruction, iinput=iinput, context=context, ntokens=None, tokens_persecond=None, llamacpp_dict=llamacpp_dict, )) save_dict = dict(base_model=base_model, display_name=display_name, save_dir=save_dir, extra_dict=extra_dict) if do_langchain_path: text = '' sources = [] sources_str = '' response = '' response_raw = '' response_no_refs = '' prompt_raw = '' # use smaller cut_distance for wiki_full since so many matches could be obtained, and often irrelevant unless close from gpt_langchain import run_qa_db loaders_dict, captions_model, asr_model = gr_to_lg(image_audio_loaders, pdf_loaders, url_loaders, use_pymupdf=use_pymupdf, use_unstructured_pdf=use_unstructured_pdf, use_pypdf=use_pypdf, enable_pdf_ocr=enable_pdf_ocr, enable_pdf_doctr=enable_pdf_doctr, try_pdf_as_html=try_pdf_as_html, captions_model=captions_model, asr_model=asr_model, ) loaders_dict.update(dict(captions_model=captions_model, caption_loader=caption_loader, doctr_loader=doctr_loader, pix2struct_loader=pix2struct_loader, llava_model=llava_model, asr_model=asr_model, asr_loader=asr_loader, jq_schema=jq_schema, extract_frames=extract_frames, llava_prompt=llava_prompt, )) data_point = dict(context=context, instruction=instruction, input=iinput) # no longer stuff chat history directly into context this early prompt_basic = prompter.generate_prompt(data_point, context_from_history=False, image_file=image_file) prompt = prompt_basic num_prompt_tokens = 0 ntokens = None llm_answers = {} for r in run_qa_db( inference_server=inference_server, regenerate_clients=regenerate_clients, regenerate_gradio_clients=regenerate_gradio_clients, validate_clients=validate_clients, fail_if_invalid_client=fail_if_invalid_client, model_name=base_model, model=model, tokenizer=tokenizer, langchain_only_model=langchain_only_model, load_awq=load_awq, async_output=async_output, num_async=num_async, prompter=prompter, use_llm_if_no_docs=use_llm_if_no_docs, load_db_if_exists=load_db_if_exists, db=db, langchain_mode_paths=langchain_mode_paths, langchain_mode_types=langchain_mode_types, detect_user_path_changes_every_query=detect_user_path_changes_every_query, cut_distance=1.1 if langchain_mode in ['wiki_full'] else cut_distance, answer_with_sources=answer_with_sources, append_sources_to_answer=append_sources_to_answer, append_sources_to_chat=append_sources_to_chat, add_chat_history_to_context=add_chat_history_to_context, add_search_to_context=add_search_to_context, keep_sources_in_context=keep_sources_in_context, gradio_errors_to_chatbot=gradio_errors_to_chatbot, memory_restriction_level=memory_restriction_level, system_prompt=system_prompt, allow_chat_system_prompt=allow_chat_system_prompt, use_openai_embedding=use_openai_embedding, use_openai_model=use_openai_model, hf_embedding_model=hf_embedding_model, migrate_embedding_model=migrate_embedding_model, first_para=first_para, text_limit=text_limit, sources_show_text_in_accordion=sources_show_text_in_accordion, hyde_show_intermediate_in_accordion=hyde_show_intermediate_in_accordion, map_reduce_show_intermediate_in_accordion=map_reduce_show_intermediate_in_accordion, top_k_docs_max_show=top_k_docs_max_show, show_link_in_sources=show_link_in_sources, langchain_instruct_mode=langchain_instruct_mode, # evaluate args items query=instruction, iinput=iinput, context=context, stream_output0=stream_output0, stream_output=stream_output, enable_caching=enable_caching, chunk=chunk, chunk_size=chunk_size, **loaders_dict, langchain_mode=langchain_mode, langchain_action=langchain_action, langchain_agents=langchain_agents, document_subset=document_subset, document_choice=document_choice, document_source_substrings=document_source_substrings, document_source_substrings_op=document_source_substrings_op, document_content_substrings=document_content_substrings, document_content_substrings_op=document_content_substrings_op, top_k_docs=top_k_docs, prompt_type=prompt_type, prompt_dict=prompt_dict, chat_template=chat_template, pre_prompt_query=pre_prompt_query, prompt_query=prompt_query, pre_prompt_summary=pre_prompt_summary, prompt_summary=prompt_summary, hyde_llm_prompt=hyde_llm_prompt, all_docs_start_prompt=all_docs_start_prompt, all_docs_finish_prompt=all_docs_finish_prompt, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt, json_object_prompt=json_object_prompt, json_object_prompt_simpler=json_object_prompt_simpler, json_code_prompt=json_code_prompt, json_code_prompt_if_no_schema=json_code_prompt_if_no_schema, json_schema_instruction=json_schema_instruction, json_preserve_system_prompt=json_preserve_system_prompt, json_object_post_prompt_reminder=json_object_post_prompt_reminder, json_code_post_prompt_reminder=json_code_post_prompt_reminder, json_code2_post_prompt_reminder=json_code2_post_prompt_reminder, text_context_list=text_context_list, chat_conversation=chat_conversation, visible_models=visible_models, h2ogpt_key=h2ogpt_key, docs_ordering_type=docs_ordering_type, min_max_new_tokens=min_max_new_tokens, max_input_tokens=max_input_tokens, max_total_input_tokens=max_total_input_tokens, docs_token_handling=docs_token_handling, docs_joiner=docs_joiner, hyde_level=hyde_level, hyde_template=hyde_template, hyde_show_only_final=hyde_show_only_final, doc_json_mode=doc_json_mode, metadata_in_context=metadata_in_context, **gen_hyper_dict, db_type=db_type, n_jobs=n_jobs, verbose=verbose, cli=cli, sanitize_bot_response=sanitize_bot_response, lora_weights=lora_weights, llamacpp_path=llamacpp_path, llamacpp_dict=llamacpp_dict, exllama_dict=exllama_dict, gptq_dict=gptq_dict, attention_sinks=attention_sinks, sink_dict=sink_dict, truncation_generation=truncation_generation, hf_model_dict=hf_model_dict, force_seq2seq_type=force_seq2seq_type, force_t5_type=force_t5_type, auto_reduce_chunks=auto_reduce_chunks, max_chunks=max_chunks, headsize=headsize, image_file=image_file, image_control=image_control, images_num_max=images_num_max, image_resolution=image_resolution, image_format=image_format, rotate_align_resize_image=rotate_align_resize_image, video_frame_period=video_frame_period, image_batch_image_prompt=image_batch_image_prompt, image_batch_final_prompt=image_batch_final_prompt, image_batch_stream=image_batch_stream, visible_vision_models=visible_vision_models, video_file=video_file, response_format=response_format, guided_json=guided_json, guided_regex=guided_regex, guided_choice=guided_choice, guided_grammar=guided_grammar, guided_whitespace_pattern=guided_whitespace_pattern, client_metadata=client_metadata, json_vllm=json_vllm, from_ui=from_ui, stream_map=stream_map, is_vision_model1=is_vision_model1, is_actually_vision_model1=is_actually_vision_model1, ): # doesn't accumulate, new answer every yield, so only save that full answer response = r['response'] if response_format in ['json_object', 'json_code']: response_raw = response # this can get expensive if long, so only do if small, else do only at end if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) sources = r['sources'] num_prompt_tokens = r['num_prompt_tokens'] ntokens = r.get('ntokens') llm_answers = r['llm_answers'] llm_answers['response_raw'] = response_raw response_no_refs = r['response_no_refs'] sources_str = r['sources_str'] prompt_raw = str(r['prompt_raw']) if stream_output: yield dict(response=response, sources=[], save_dict={}, llm_answers=llm_answers, response_no_refs=response_no_refs, sources_str='', prompt_raw='') extra_dict.update(dict(num_prompt_tokens=num_prompt_tokens, t_generate=time.time() - t_generate, # tokens_persecond computed in save_generate_output sources_str=sources_str, sources=sources, ntokens=ntokens, )) if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) save_dict.update(dict(prompt=prompt, output=response, where_from="run_qa_db", extra_dict=extra_dict)) yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers=llm_answers, response_no_refs=response_no_refs, sources_str=sources_str, prompt_raw=prompt_raw) if verbose: print( 'Post-Generate Langchain: %s decoded_output: %s' % (str(datetime.now()), len(response) if response else -1), flush=True) if response or sources or langchain_only_model: # if got no response (e.g. not showing sources and got no sources, # so nothing to give to LLM), then slip through and ask LLM # Or if llama/gptj, then just return since they had no response and can't go down below code path # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it if client_metadata: print(f"evaluate finish run_qa_db client_metadata: {client_metadata}", flush=True) return if client_metadata: print(f"evaluate middle non-langchain client_metadata: {client_metadata}", flush=True) # NOT LANGCHAIN PATH, raw LLM # restrict instruction + , typically what has large input prompt, \ instruction, iinput, context, \ num_prompt_tokens, max_new_tokens, num_prompt_tokens0, num_prompt_tokens_actual, \ history_to_use_final, external_handle_chat_conversation, \ top_k_docs_trial, one_doc_size, truncation_generation, system_prompt, _, _ = \ get_limited_prompt(instruction, iinput, tokenizer, prompter=prompter, base_model=base_model, inference_server=inference_server, # prompt_type=prompt_type, # use prompter # prompt_dict=prompt_dict, # use prompter # chat=chat, # use prompter max_new_tokens=max_new_tokens, # system_prompt=system_prompt, # use prompter allow_chat_system_prompt=allow_chat_system_prompt, context=context, chat_conversation=chat_conversation, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt, keep_sources_in_context=keep_sources_in_context, model_max_length=model_max_length, memory_restriction_level=memory_restriction_level, langchain_mode=langchain_mode, add_chat_history_to_context=add_chat_history_to_context, min_max_new_tokens=min_max_new_tokens, max_input_tokens=max_input_tokens, max_total_input_tokens=max_total_input_tokens, truncation_generation=truncation_generation, gradio_server=gradio_server, attention_sinks=attention_sinks, hyde_level=hyde_level, gradio_errors_to_chatbot=gradio_errors_to_chatbot, # gradio is pass through, we don't make prompt with images here image_file=image_file if not gradio_server else [], is_actually_vision_model=is_actually_vision_model1, ) if inference_server.startswith('vllm') or \ inference_server.startswith('sglang') or \ inference_server.startswith('openai') or \ inference_server.startswith('http'): text = '' gen_server_kwargs = {} if inference_server.startswith('vllm') or \ inference_server.startswith('sglang') or \ inference_server.startswith('openai'): # sglang reaches here only for text mode assert not inference_server.startswith('openai_azure_chat'), "Not fo Azure, use langchain path" assert not inference_server.startswith('openai_azure'), "Not for Azure, use langchain path" if isinstance(model, dict): openai_client, openai_async_client, inf_type = model['client'], model['async_client'], model['inf_type'] else: openai_client, openai_async_client, \ inf_type, _, _, _, _ = set_openai(inference_server, model_name=base_model) where_from = inf_type responses = None terminate_response = prompter.terminate_response or [] stop_sequences = list(set(terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] # OpenAI will complain if ask for too many new tokens, takes it as min in some sense, wrongly so. max_new_tokens_openai = min(max_new_tokens, model_max_length - num_prompt_tokens_actual) gen_server_kwargs = dict(temperature=temperature if do_sample else 0, max_tokens=max_new_tokens_openai, top_p=top_p if do_sample else 1, frequency_penalty=0, seed=seed, n=num_return_sequences, presence_penalty=(repetition_penalty - 1.0) * 2.0 + 0.0, # so good default ) if base_model in ['o1-mini', 'o1-preview']: gen_server_kwargs['max_completion_tokens'] = gen_server_kwargs.pop('max_tokens') max_reasoning_tokens = int(os.getenv("MAX_REASONING_TOKENS", 25000)) gen_server_kwargs['max_completion_tokens'] = max_reasoning_tokens + max(100, gen_server_kwargs[ 'max_completion_tokens']) gen_server_kwargs['temperature'] = 1.0 gen_server_kwargs.pop('presence_penalty', None) gen_server_kwargs.pop('n', None) gen_server_kwargs.pop('frequency_penalty', None) gen_server_kwargs.pop('top_p', None) try: if inf_type in ['vllm', 'vllm_chat'] and chosen_model_state['json_vllm']: response_format_real = response_format if not ( guided_json or guided_regex or guided_choice or guided_grammar) else 'text' vllm_extra_dict = get_vllm_extra_dict(tokenizer, stop_sequences=stop_sequences, response_format=response_format_real, guided_json=guided_json, guided_regex=guided_regex, guided_choice=guided_choice, guided_grammar=guided_grammar, guided_whitespace_pattern=guided_whitespace_pattern, # repetition_penalty=repetition_penalty, # could pass ) else: vllm_extra_dict = {} if inf_type in ['vllm', 'sglang', 'openai']: other_dict = dict(timeout=max_time) responses = openai_client.completions.create( model=base_model, # response_format=dict(type=response_format), Text Completions API can't handle prompt=prompt, **gen_server_kwargs, stop=stop_sequences, **vllm_extra_dict, stream=stream_output, **other_dict, ) text = '' sources = [] response = '' response_raw = '' if not stream_output: text = responses.choices[0].text if hasattr(responses, 'usage'): print(f"Usage by {base_model}: {responses.usage}") response = prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response) if response_format in ['json_object', 'json_code']: response = get_json(response, json_schema_type=json_schema_type) else: collected_events = [] tgen0 = time.time() for event in responses: collected_events.append(event) # save the event response delta = event.choices[0].text if event.choices else None # extract the text if delta: text += delta # append the text response = prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response) if response_format in ['json_object', 'json_code']: response_raw = response if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') if time.time() - tgen0 > max_time: if verbose: print("Took too long for OpenAI or VLLM: %s" % (time.time() - tgen0), flush=True) break time.sleep(0.005) if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') elif inf_type in ['vllm_chat', 'openai_chat']: other_dict = dict(timeout=max_time) if system_prompt in [None, 'None', 'auto']: openai_system_prompt = "You are a helpful assistant." else: openai_system_prompt = system_prompt messages0 = [] if openai_system_prompt: if prompter.can_handle_system_prompt: messages0.append({"role": "system", "content": openai_system_prompt}) else: messages0.append({"role": "user", "content": user_prompt_for_fake_system_prompt or \ user_prompt_for_fake_system_prompt0}) messages0.append({"role": "assistant", "content": openai_system_prompt}) if chat_conversation and add_chat_history_to_context: assert external_handle_chat_conversation, "Should be handling only externally" # history_to_use_final handles token counting issues for message1 in history_to_use_final: if len(message1) == 2 and (message1[0] is None or message1[1] is None): # then not really part of LLM, internal, so avoid continue if len(message1) == 2: if message1[0]: messages0.append( {'role': 'user', 'content': gradio_to_llm(message1[0], bot=False)}) if message1[1]: messages0.append( {'role': 'assistant', 'content': gradio_to_llm(message1[1], bot=True)}) if instruction: messages0.append({'role': 'user', 'content': instruction}) if response_format == 'json_object' and inf_type == 'openai_chat': other_dict.update(dict(response_format=dict(type=response_format))) # JSON: https://platform.openai.com/docs/guides/text-generation/json-mode if inf_type == 'vllm_chat': model_name = get_model_name(base_model, openai_client) else: model_name = base_model responses = openai_client.chat.completions.create( model=model_name, messages=messages0, stream=stream_output, **gen_server_kwargs, **vllm_extra_dict, **other_dict, ) text = '' sources = [] response = '' response_raw = '' if not stream_output: if responses.choices is None and responses.model_extra: raise RuntimeError("OpenAI Chat failed: %s" % responses.model_extra) text = responses.choices[0].message.content response = text if response_format in ['json_object', 'json_code']: response_raw = response if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) else: # NOTE: If some stream failure like wrong model, don't get back response and no failure tgen0 = time.time() for chunk in responses: delta = chunk.choices[0].delta.content if chunk.choices else None if delta: text += delta response = text if response_format in ['json_object', 'json_code']: response_raw = response response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') if time.time() - tgen0 > max_time: if verbose: print("Took too long for OpenAI or VLLM Chat: %s" % (time.time() - tgen0), flush=True) break if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') else: raise RuntimeError("No such OpenAI mode: %s" % inference_server) finally: if responses is not None: try: responses.close() except Exception as e: print("Failed to close OpenAI response: %s" % str(e), flush=True) if regenerate_clients and openai_client is not None: try: openai_client.close() except Exception as e: print("Failed to close OpenAI client: %s" % str(e), flush=True) elif inference_server.startswith('http'): sources = [] inference_server0 = inference_server inference_server, _, _, _ = get_hf_server(inference_server) from text_generation import Client as HFClient if isinstance(model, GradioClient) and not regenerate_gradio_clients: gr_client = model.clone() hf_client = None elif isinstance(model, Client) and not regenerate_gradio_clients: gr_client = model hf_client = None elif isinstance(model, HFClient) and not regenerate_gradio_clients: gr_client = None hf_client = model else: inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server0, base_model=base_model, validate_clients=validate_clients, fail_if_invalid_client=fail_if_invalid_client, verbose=verbose) llava_direct_gradio = gr_client is not None and '/textbox_api_submit' in [x.api_name for x in gr_client.endpoints] if is_gradio_vision_model(base_model) and llava_direct_gradio: where_from = "gr_client for llava" # NOTE: llava doesn't handle context or system prompt directly from image_utils import get_image_file # comes out as list img_file = get_image_file(image_file, image_control, document_choice, base_model=base_model, images_num_max=images_num_max, image_resolution=image_resolution, image_format=image_format) # if images_num_max is None img_file = img_file[:llava_num_max] num_prompt_tokens += 1500 * len(img_file) # estimate for single image llava_kwargs = dict(file=img_file, llava_model=inference_server, # prompt=instruction, prompt=prompt, # prepared prompt with chat history etc. chat_conversation=chat_conversation, allow_prompt_auto=False, image_model=base_model, temperature=temperature, top_p=top_p, max_new_tokens=max_new_tokens, min_max_new_tokens=min_max_new_tokens, tokenizer=tokenizer, client=gr_client if not regenerate_gradio_clients else None, verbose=verbose, ) response = '' response_raw = '' if not stream_output and img_file == 1: from vision.utils_vision import get_llava_response response, _ = get_llava_response(**llava_kwargs) if response_format in ['json_object', 'json_code']: response_raw = response response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=[], save_dict={}, error='', llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') else: tgen0 = time.time() from vision.utils_vision import get_llava_stream for response1 in get_llava_stream(**llava_kwargs): if response_format in ['json_object', 'json_code']: response_raw = response1 if len(str(response)) < max_stream_string_for_json: response = get_json(response1, json_schema_type=json_schema_type) else: response = response1 yield dict(response=response, sources=[], save_dict={}, error='', llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') if time.time() - tgen0 > max_time: if verbose: print("Took too long for TGI: %s" % (time.time() - tgen0), flush=True) break if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') else: if gr_client is not None: # Note: h2oGPT gradio server could handle input token size issues for prompt, # but best to handle here so send less data to server chat_client = chat where_from = "gr_client" client_langchain_mode = LangChainMode.LLM.value client_add_chat_history_to_context = add_chat_history_to_context client_add_search_to_context = False client_langchain_action = LangChainAction.QUERY.value client_langchain_agents = [] gen_server_kwargs = dict(temperature=temperature, top_p=top_p, top_k=top_k, penalty_alpha=penalty_alpha, num_beams=num_beams, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, early_stopping=early_stopping, max_time=max_time, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, do_sample=do_sample, seed=seed, chat=chat_client, ) # account for gradio into gradio that handles prompting, avoid duplicating prompter prompt injection if prompt_type in [None, '', PromptType.plain.name, PromptType.plain.value, str(PromptType.plain.value)]: # if our prompt is plain, assume either correct or gradio server knows different prompt type, # so pass empty prompt_Type gr_prompt_type = '' gr_prompt_dict = '' gr_prompt = prompt # already prepared prompt gr_context = '' gr_iinput = '' gr_chat_template = None else: # if already have prompt_type that is not plain, None, or '', then already applied some prompting # But assume server can handle prompting, and need to avoid double-up. # Also assume server can do better job of using stopping.py to stop early, so avoid local prompting, let server handle # So avoid "prompt" and let gradio server reconstruct from prompt_type we passed # Note it's ok that prompter.get_response() has prompt+text, prompt=prompt passed, # because just means extra processing and removal of prompt, but that has no human-bot prompting doesn't matter # since those won't appear gr_context = context gr_prompt = instruction gr_iinput = iinput gr_prompt_type = prompt_type gr_prompt_dict = prompt_dict gr_chat_template = chat_template # ensure image in correct format from image_utils import get_image_file img_file = get_image_file(image_file, image_control, document_choice, base_model=base_model, images_num_max=images_num_max, image_resolution=image_resolution, image_format=image_format, convert=True) # comes out as list client_kwargs = dict(instruction=gr_prompt if chat_client else '', # only for chat=True iinput=gr_iinput, # only for chat=True context=gr_context, # streaming output is supported, loops over and outputs each generation in streaming mode # but leave stream_output=False for simple input/output mode stream_output=stream_output, enable_caching=enable_caching, **gen_server_kwargs, prompt_type=gr_prompt_type, prompt_dict=gr_prompt_dict, chat_template=gr_chat_template, instruction_nochat=gr_prompt if not chat_client else '', iinput_nochat=gr_iinput, # only for chat=False langchain_mode=client_langchain_mode, add_chat_history_to_context=client_add_chat_history_to_context, chat_conversation=chat_conversation, text_context_list=text_context_list, chatbot_role=chatbot_role, speaker=speaker, tts_language=tts_language, tts_speed=tts_speed, langchain_action=client_langchain_action, langchain_agents=client_langchain_agents, top_k_docs=top_k_docs, chunk=chunk, chunk_size=chunk_size, document_subset=DocumentSubset.Relevant.name, document_choice=[DocumentChoice.ALL.value], document_source_substrings=[], document_source_substrings_op='and', document_content_substrings=[], document_content_substrings_op='and', pre_prompt_query=pre_prompt_query, prompt_query=prompt_query, pre_prompt_summary=pre_prompt_summary, prompt_summary=prompt_summary, hyde_llm_prompt=hyde_llm_prompt, all_docs_start_prompt=all_docs_start_prompt, all_docs_finish_prompt=all_docs_finish_prompt, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt, json_object_prompt=json_object_prompt, json_object_prompt_simpler=json_object_prompt_simpler, json_code_prompt=json_code_prompt, json_code_prompt_if_no_schema=json_code_prompt_if_no_schema, json_schema_instruction=json_schema_instruction, json_preserve_system_prompt=json_preserve_system_prompt, json_object_post_prompt_reminder=json_object_post_prompt_reminder, json_code_post_prompt_reminder=json_code_post_prompt_reminder, json_code2_post_prompt_reminder=json_code2_post_prompt_reminder, system_prompt=system_prompt, image_audio_loaders=image_audio_loaders, pdf_loaders=pdf_loaders, url_loaders=url_loaders, jq_schema=jq_schema, extract_frames=extract_frames, llava_prompt=llava_prompt, visible_models=visible_models, visible_image_models=visible_image_models, image_size=image_size, image_quality=image_quality, image_guidance_scale=image_guidance_scale, image_num_inference_steps=image_num_inference_steps, h2ogpt_key=h2ogpt_key, add_search_to_context=client_add_search_to_context, docs_ordering_type=docs_ordering_type, min_max_new_tokens=min_max_new_tokens, max_input_tokens=max_input_tokens, max_total_input_tokens=max_total_input_tokens, docs_token_handling=docs_token_handling, docs_joiner=docs_joiner, hyde_level=hyde_level, hyde_template=hyde_template, hyde_show_only_final=hyde_show_only_final, doc_json_mode=doc_json_mode, metadata_in_context=metadata_in_context, image_file=img_file, image_control=None, # already stuffed into image_file images_num_max=images_num_max, image_resolution=None, # already changed image_format=None, # already changed rotate_align_resize_image=None, # already changed video_frame_period=None, # already changed image_batch_image_prompt=image_batch_image_prompt, image_batch_final_prompt=image_batch_final_prompt, image_batch_stream=image_batch_stream, visible_vision_models=visible_vision_models, video_file=video_file, response_format=response_format, guided_json=guided_json, guided_regex=guided_regex, guided_choice=guided_choice, guided_grammar=guided_grammar, guided_whitespace_pattern=guided_whitespace_pattern, model_lock=None, # already set client_metadata=client_metadata, ) assert len(set(list(client_kwargs.keys())).symmetric_difference(eval_func_param_names)) == 0 api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing response = '' response_raw = '' text = '' sources = [] strex = '' if not stream_output: res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name) res_dict = ast.literal_eval(res) GradioClient.check_error(res_dict) text = res_dict['response'] sources = res_dict['sources'] response = prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response) else: new_stream = False # hanging for many chatbots gr_stream_kwargs = dict(client_kwargs=client_kwargs, api_name=api_name, prompt=prompt, prompter=prompter, sanitize_bot_response=sanitize_bot_response, max_time=max_time, is_public=is_public, verbose=verbose) if new_stream: gener = gr_client.stream(**gr_stream_kwargs) else: gener = gr_client.simple_stream(**gr_stream_kwargs) response = '' response_raw = '' res_dict = {} for res_dict1 in gener: if 'response' in res_dict1: response = res_dict1['response'] if response_format in ['json_object', 'json_code']: response_raw = response if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) res_dict1['response'] = response res_dict1['llm_answers'] = res_dict1.get('llm_answers', {}) res_dict1['llm_answers']['response_raw'] = response_raw res_dict = res_dict1 yield res_dict1 if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) res_dict['response'] = response res_dict['llm_answers'] = res_dict.get('llm_answers', {}) res_dict['llm_answers']['response_raw'] = response_raw yield res_dict # listen to inner gradio num_prompt_tokens += res_dict.get('save_dict', {}).get('extra_dict', {}).get('num_prompt_tokens', num_prompt_tokens) prompt = res_dict.get('prompt_raw', prompt) elif hf_client: # quick sanity check to avoid long timeouts, just see if can reach server requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10'))) # HF inference server needs control over input tokens where_from = "hf_client" response = '' response_raw = '' sources = [] # prompt must include all human-bot like tokens, already added by prompt # https://github.com/huggingface/text-generation-inference/tree/main/clients/python#types terminate_response = prompter.terminate_response or [] stop_sequences = list(set(terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] gen_server_kwargs = dict(do_sample=do_sample, max_new_tokens=max_new_tokens, # best_of=None, repetition_penalty=repetition_penalty, return_full_text=False, seed=seed, stop_sequences=stop_sequences, temperature=max(1e-2, temperature), top_k=top_k, top_p=min(max(1e-2, top_p), 1.0 - 1e-3), # truncate=False, # behaves oddly # typical_p=top_p, # watermark=False, # decoder_input_details=False, ) # work-around for timeout at constructor time, will be issue if multi-threading, # so just do something reasonable or max_time if larger # lower bound because client is re-used if multi-threading hf_client.timeout = max(300, max_time) if not stream_output: text = hf_client.generate(prompt, **gen_server_kwargs).generated_text response = prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response) if response_format in ['json_object', 'json_code']: response_raw = response response = get_json(response, json_schema_type=json_schema_type) else: tgen0 = time.time() text = "" for responses in hf_client.generate_stream(prompt, **gen_server_kwargs): if not responses.token.special: # stop_sequences text_chunk = responses.token.text text += text_chunk response = prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=sanitize_bot_response) sources = [] if response_format in ['json_object', 'json_code']: response_raw = response if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') time.sleep(0.005) if time.time() - tgen0 > max_time: if verbose: print("Took too long for TGI: %s" % (time.time() - tgen0), flush=True) break if response_format in ['json_object', 'json_code']: # always do at end, in case didn't before due to length response = get_json(response, json_schema_type=json_schema_type) yield dict(response=response, sources=sources, save_dict={}, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw='') else: raise RuntimeError("Failed to get client: %s" % inference_server) if isinstance(model, GradioClient) and not regenerate_gradio_clients and gr_client is not None: if gr_client.server_hash != model.server_hash: with filelock.FileLock(os.path.join('locks', 'gradio_client.lock')): model.refresh_client() else: raise RuntimeError("No such inference_server %s" % inference_server) # only return yield with save_dict and prompt_raw here to keep streaming light extra_dict.update(gen_server_kwargs) ntokens = extra_dict.get('ntokens', None) extra_dict.update(dict(inference_server=inference_server, # changes in some cases num_prompt_tokens=num_prompt_tokens, t_generate=time.time() - t_generate, ntokens=ntokens, prompt_type=prompt_type, tokens_persecond=None, )) save_dict.update(dict(prompt=prompt, output=text, where_from=where_from, extra_dict=extra_dict)) # if not streaming, only place yield should be done yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw=prompt) if client_metadata: print(f"evaluate finish inference server client_metadata: {client_metadata}", flush=True) return else: assert not inference_server, "inference_server=%s not supported" % inference_server if isinstance(tokenizer, str): # pipeline if tokenizer == "summarization": key = 'summary_text' else: raise RuntimeError("No such task type %s" % tokenizer) # NOTE: uses max_length only sources = [] response = model(prompt, max_length=max_new_tokens)[0][key] response_raw = '' yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw=prompt) return if 'mbart-' in base_model.lower(): assert src_lang is not None tokenizer.src_lang = languages_covered()[src_lang] stopping_criteria = get_stopping(prompt_type, prompt_dict, tokenizer, device, base_model, model_max_length=model_max_length, prompter=prompter, truncation_generation=truncation_generation) inputs = tokenizer(prompt, return_tensors="pt") if debug and len(inputs["input_ids"]) > 0: print('input_ids length', len(inputs["input_ids"][0]), flush=True) input_ids = inputs["input_ids"].to(device) # CRITICAL LIMIT else will fail max_max_tokens = int(tokenizer.model_max_length) max_input_tokens_default = max(0, int(max_max_tokens - min_new_tokens)) if max_input_tokens >= 0: max_input_tokens = min(max_input_tokens_default, max_input_tokens) else: max_input_tokens = max_input_tokens_default # NOTE: Don't limit up front due to max_new_tokens, let go up to max or reach max_max_tokens in stopping.py assert isinstance(max_input_tokens, int), "Bad type for max_input_tokens=%s %s" % ( max_input_tokens, type(max_input_tokens)) input_ids = input_ids[:, -max_input_tokens:] # required for falcon if multiple threads or asyncio accesses to model during generation if use_cache is None: use_cache = False if 'falcon' in base_model else True if attention_sinks: assert use_cache, "attention sinks requires use_cache=True" bad_word_ids = [tokenizer.eos_token_id] gen_config_kwargs = dict(num_beams=num_beams, do_sample=do_sample, seed=seed, repetition_penalty=float(repetition_penalty), num_return_sequences=num_return_sequences, renormalize_logits=True, remove_invalid_values=True, use_cache=use_cache, max_new_tokens=max_new_tokens, # unsure if required here token=use_auth_token, trust_remote_code=trust_remote_code, ) if do_sample: gen_config_kwargs.update(dict(temperature=float(temperature), top_p=float(top_p), top_k=top_k)) if penalty_alpha > 0: gen_config_kwargs.update(dict(penalty_alpha=penalty_alpha)) if True: # unclear impact, some odd things going on inside # leads to: # The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. # Setting `pad_token_id` to `eos_token_id`:2 for open-end generation. # or leads to: # Using cls_token, but it is not set yet. # Using mask_token, but it is not set yet. # Using pad_token, but it is not set yet. # Using sep_token, but it is not set yet. token_ids = ['eos_token_id', 'pad_token_id', 'bos_token_id', 'cls_token_id', 'sep_token_id'] for token_id in token_ids: if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None: gen_config_kwargs.update({token_id: getattr(tokenizer, token_id)}) generation_config = GenerationConfig(**gen_config_kwargs) gen_kwargs = dict(input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=max_new_tokens, # prompt + new min_new_tokens=min_new_tokens, # prompt + new early_stopping=early_stopping, # False, True, "never" max_time=max_time, stopping_criteria=stopping_criteria, ) if use_cache and attention_sinks: from transformers import SinkCache sink_dict['window_length'] = sink_dict.get('window_length', max_input_tokens) sink_dict['num_sink_tokens'] = sink_dict.get('num_sink_tokens', 4) cache = SinkCache(**sink_dict) gen_kwargs.update(dict(past_key_values=cache)) if 'gpt2' in base_model.lower(): gen_kwargs.update(dict(bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.eos_token_id)) elif 'mbart-' in base_model.lower(): assert tgt_lang is not None tgt_lang = languages_covered()[tgt_lang] gen_kwargs.update(dict(forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang])) else: token_ids = ['eos_token_id', 'bos_token_id', 'pad_token_id'] for token_id in token_ids: if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None: gen_kwargs.update({token_id: getattr(tokenizer, token_id)}) decoder_kwargs = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True) decoder = functools.partial(tokenizer.decode, **decoder_kwargs ) with torch.no_grad(): have_lora_weights = lora_weights not in [no_lora_str, '', None] context_class_cast = NullContext if device == 'cpu' or have_lora_weights or device == 'mps' else torch.autocast if t5_type(base_model): # issues when casting to float16, can mess up t5 model, e.g. only when not streaming, or other odd behaviors context_class_cast = NullContext with context_class_cast(device): # protection for gradio not keeping track of closed users, # else hit bitsandbytes lack of thread safety: # https://github.com/h2oai/h2ogpt/issues/104 # but only makes sense if concurrency_count == 1 context_class = NullContext # if concurrency_count > 1 else filelock.FileLock if verbose: print('Pre-Generate: %s' % str(datetime.now()), flush=True) decoded_output = '' response = '' response_raw = '' with context_class("generate.lock"): if verbose: print('Generate: %s' % str(datetime.now()), flush=True) always_use_streaming_method = True # to deal with complex parsing of prompt vs. generation due to odd tokenizing if stream_output or always_use_streaming_method: skip_prompt = True # True means first output excludes prompt streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False, **decoder_kwargs) gen_kwargs.update(dict(streamer=streamer)) target = wrapped_partial(generate_with_exceptions, model.generate, raise_generate_gpu_exceptions=raise_generate_gpu_exceptions, **gen_kwargs) bucket = queue.Queue() thread = EThread(target=target, streamer=streamer, bucket=bucket) thread.start() ret = dict(response='', sources='', save_dict=dict(), llm_answers=dict(response_raw=response_raw), response_no_refs='', sources_str='', prompt_raw=prompt) outputs = "" sources = [] tgen0 = time.time() try: for new_text in streamer: if bucket.qsize() > 0 or thread.exc: thread.join() outputs += new_text response = prompter.get_response(outputs, prompt=None, only_new_text=True, sanitize_bot_response=sanitize_bot_response) if response_format in ['json_object', 'json_code']: response_raw = response if len(str(response)) < max_stream_string_for_json: response = get_json(response, json_schema_type=json_schema_type) ret = dict(response=response, sources=sources, save_dict=save_dict, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw=prompt) if stream_output: yield ret if time.time() - tgen0 > max_time: if verbose: print("Took too long for Torch: %s" % (time.time() - tgen0), flush=True) break if response_format in ['json_object', 'json_code']: response = get_json(response, json_schema_type=json_schema_type) ret = dict(response=response, sources=sources, save_dict=save_dict, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw=prompt) if stream_output: # will yield at end if required # yield if anything left over as can happen (FIXME: Understand better) yield ret except BaseException: # if any exception, raise that exception if was from thread, first if thread.exc: raise thread.exc raise finally: # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it # in case no exception and didn't join with thread yet, then join if not thread.exc: thread.join() # in case raise StopIteration or broke queue loop in streamer, but still have exception if thread.exc: raise thread.exc decoded_output = outputs ntokens = len(outputs) // 4 # hack for now else: # below length removal doesn't work in general, because encoding does not match internal of model generation input_ids_len = gen_kwargs['input_ids'][0].shape[0] try: outputs = model.generate(**gen_kwargs) finally: pass # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it # skip first IDs ntokens = sum([len(s) - input_ids_len for s in outputs.sequences]) if save_dir else -1 outputs = [decoder(s[input_ids_len:]) for s in outputs.sequences] sources = [] response = prompter.get_response(outputs, prompt=None, only_new_text=True, sanitize_bot_response=sanitize_bot_response) if response_format in ['json_object', 'json_code']: response_raw = response response = get_json(response, json_schema_type=json_schema_type) if outputs and len(outputs) >= 1: decoded_output = prompt + outputs[0] # full return with save_dict and prompt_raw # if not streaming, only place yield should be extra_dict.update(gen_config_kwargs) extra_dict.update(dict(num_prompt_tokens=num_prompt_tokens, t_generate=time.time() - t_generate, sources_str='', ntokens=ntokens, tokens_persecond=ntokens / (time.time() - t_generate), )) save_dict.update(dict(prompt=prompt, output=decoded_output, where_from="evaluate_%s" % str(stream_output), extra_dict=extra_dict)) yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers=dict(response_raw=response_raw), response_no_refs=response, sources_str='', prompt_raw=prompt) if torch.cuda.is_available() and device not in ['cpu', 'mps']: torch.cuda.empty_cache() if hasattr(model, 'memory') and hasattr(model.memory, 'reset'): model.memory.reset() if verbose: print('Post-Generate: %s decoded_output: %s' % ( str(datetime.now()), len(decoded_output) if decoded_output else -1), flush=True) if client_metadata: print(f"evaluate HF finish client_metadata: {client_metadata}", flush=True) inputs_list_names = list(inspect.signature(evaluate).parameters) state_names = input_args_list.copy() # doesn't have to be the same, but state_names must match evaluate() and how filled then inputs_kwargs_list = [x for x in inputs_list_names if x not in eval_func_param_names + state_names] def get_cutoffs(memory_restriction_level, for_context=False, model_max_length=2048, min_max_new_tokens=512): # help to avoid errors like: # RuntimeError: The size of tensor a (2048) must match the size of tensor b (2049) at non-singleton dimension 3 # RuntimeError: expected scalar type Half but found Float # with - 256 if memory_restriction_level > 0: max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256 else: # at least give room for 1 paragraph output max_length_tokenize = model_max_length - min_max_new_tokens cutoff_len = max_length_tokenize * 4 # if reaches limit, then can't generate new tokens output_smallest = 30 * 4 max_prompt_length = cutoff_len - output_smallest if for_context: # then lower even more to avoid later chop, since just estimate tokens in context bot max_prompt_length = max(64, int(max_prompt_length * 0.8)) return cutoff_len, output_smallest, max_length_tokenize, max_prompt_length class H2OTextIteratorStreamer(TextIteratorStreamer): """ normally, timeout required for now to handle exceptions, else get() but with H2O version of TextIteratorStreamer, loop over block to handle """ def __init__(self, tokenizer, skip_prompt: bool = False, timeout: typing.Optional[float] = None, block=True, **decode_kwargs): super().__init__(tokenizer, skip_prompt, **decode_kwargs) self.text_queue = queue.Queue() self.stop_signal = None self.do_stop = False self.timeout = timeout self.block = block def on_finalized_text(self, text: str, stream_end: bool = False): """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.""" self.text_queue.put(text, timeout=self.timeout) if stream_end: self.text_queue.put(self.stop_signal, timeout=self.timeout) def __iter__(self): return self def __next__(self): while True: try: value = self.stop_signal # value looks unused in pycharm, not true if self.do_stop: print("hit stop", flush=True) # could raise or break, maybe best to raise and make parent see if any exception in thread self.clear_queue() self.do_stop = False raise StopIteration() # break value = self.text_queue.get(block=self.block, timeout=self.timeout) break except queue.Empty: time.sleep(0.005) if value == self.stop_signal: self.clear_queue() self.do_stop = False raise StopIteration() else: return value def clear_queue(self): # make sure streamer is reusable after stop hit with self.text_queue.mutex: self.text_queue.queue.clear() def put(self, value): """ Receives tokens, decodes them, and prints them to stdout as soon as they form entire words. # same as base class, except remove hack w.r.t. text.rfind(" ") that ruins LLaMa2 """ if len(value.shape) > 1 and value.shape[0] > 1: raise ValueError("TextStreamer only supports batch size 1") elif len(value.shape) > 1: value = value[0] if self.skip_prompt and self.next_tokens_are_prompt: self.next_tokens_are_prompt = False return # Add the new token to the cache and decodes the entire thing. self.token_cache.extend(value.tolist()) text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs) # After the symbol for a new line, we flush the cache. if text.endswith("\n"): printable_text = text[self.print_len:] self.token_cache = [] self.print_len = 0 # If the last token is a CJK character, we print the characters. elif len(text) > 0 and self._is_chinese_char(ord(text[-1])): printable_text = text[self.print_len:] self.print_len += len(printable_text) # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words, # which may change with the subsequent token -- there are probably smarter ways to do this!) elif len(text) > 0 and text[-1] == '�': printable_text = text[self.print_len: text.rfind(" ") + 1] self.print_len += len(printable_text) else: printable_text = text[self.print_len:] self.print_len += len(printable_text) self.on_finalized_text(printable_text) def generate_with_exceptions(func, *args, raise_generate_gpu_exceptions=True, **kwargs): try: func(*args, **kwargs) except torch.cuda.OutOfMemoryError as e: print("GPU OOM 2: exception: %s" % str(e), flush=True) if 'input_ids' in kwargs: if kwargs['input_ids'] is not None: kwargs['input_ids'].cpu() kwargs['input_ids'] = None traceback.print_exc() clear_torch_cache() return except (Exception, RuntimeError) as e: if 'Expected all tensors to be on the same device' in str(e) or \ 'expected scalar type Half but found Float' in str(e) or \ 'probability tensor contains either' in str(e) or \ 'cublasLt ran into an error!' in str(e) or \ 'mat1 and mat2 shapes cannot be multiplied' in str(e): print( "GPU Error: exception: %s" % str(e), flush=True) traceback.print_exc() clear_torch_cache() if raise_generate_gpu_exceptions: raise return else: clear_torch_cache() if raise_generate_gpu_exceptions: raise def get_generate_params(model_lower, model_lower0, inference_server, llamacpp_dict, chat, stream_output, enable_caching, show_examples, prompt_type, prompt_dict, chat_template, system_prompt, pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, all_docs_start_prompt, all_docs_finish_prompt, user_prompt_for_fake_system_prompt, json_object_prompt, json_object_prompt_simpler, json_code_prompt, json_code_prompt_if_no_schema, json_schema_instruction, json_preserve_system_prompt, json_object_post_prompt_reminder, json_code_post_prompt_reminder, json_code2_post_prompt_reminder, temperature, top_p, top_k, penalty_alpha, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, seed, top_k_docs, chunk, chunk_size, image_audio_loaders, pdf_loaders, url_loaders, jq_schema, extract_frames, llava_prompt, docs_ordering_type, min_max_new_tokens, max_input_tokens, max_total_input_tokens, docs_token_handling, docs_joiner, hyde_level, hyde_template, hyde_show_only_final, doc_json_mode, metadata_in_context, chatbot_role, speaker, tts_language, tts_speed, image_file, image_control, images_num_max, image_resolution, image_format, rotate_align_resize_image, video_frame_period, image_batch_image_prompt, image_batch_final_prompt, image_batch_stream, visible_vision_models, video_file, response_format, guided_json, guided_regex, guided_choice, guided_grammar, guided_whitespace_pattern, client_metadata, verbose, ): use_defaults = False use_default_examples = True examples = [] task_info = 'LLM' if model_lower: print(f"Using Model {model_lower}", flush=True) else: if verbose: print("No model defined yet", flush=True) min_new_tokens = min_new_tokens if min_new_tokens is not None else 0 early_stopping = early_stopping if early_stopping is not None else False max_time_defaults = 60 * 10 max_time = max_time if max_time is not None else max_time_defaults if prompt_type in ['', None, unknown_prompt_type] and prompt_type != 'custom': prompt_type_trial = model_name_to_prompt_type(model_lower, inference_server, model_name0=model_lower0, llamacpp_dict=llamacpp_dict) if prompt_type_trial: prompt_type = prompt_type_trial if verbose: print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True) # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end if show_examples is None: if chat: show_examples = False else: show_examples = False summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and how can I get started? Jeff: where can I find documentation? Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face""" use_placeholder_instruction_as_example = False if 'bart-large-cnn-samsum' in model_lower or 'flan-t5-base-samsum' in model_lower: placeholder_instruction = summarize_example1 placeholder_input = "" use_defaults = True use_default_examples = False use_placeholder_instruction_as_example = True task_info = "Summarization" elif 't5-' in model_lower or 't5' == model_lower or 'flan-' in model_lower: placeholder_instruction = "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" placeholder_input = "" use_defaults = True use_default_examples = True task_info = "Multi-Task: Q/A, translation, Chain-of-Thought, Logical Reasoning, Summarization, etc. Best to use task prefix as trained on, e.g. `translate English to German: ` (space after colon)" elif 'mbart-' in model_lower: placeholder_instruction = "The girl has long hair." placeholder_input = "" use_defaults = True use_default_examples = False use_placeholder_instruction_as_example = True elif 'gpt2' in model_lower: placeholder_instruction = "The sky is" placeholder_input = "" prompt_type = prompt_type or noop_prompt_type use_default_examples = True # some will be odd "continuations" but can be ok use_placeholder_instruction_as_example = True task_info = "Auto-complete phrase, code, etc." use_defaults = True else: if chat: placeholder_instruction = "" else: placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter." placeholder_input = "" if prompt_type in ['', None, unknown_prompt_type] and prompt_type != 'custom': prompt_type_trial = model_name_to_prompt_type(model_lower, inference_server, model_name0=model_lower0, llamacpp_dict=llamacpp_dict) if prompt_type_trial: prompt_type = prompt_type_trial # default is unknown, because might rely upon trust_remote_code to handle prompting if model_lower: prompt_type = prompt_type or unknown_prompt_type task_info = "No task" if prompt_type == 'instruct': task_info = "Answer question or follow imperative as instruction with optionally input." elif prompt_type in [empty_prompt_type, noop_prompt_type, unknown_prompt_type]: task_info = "Auto-complete phrase, code, etc." elif prompt_type == 'human_bot': if chat: task_info = "Chat (Shift-Enter to give question/imperative, input concatenated with instruction)" else: task_info = "Ask question/imperative (input concatenated with instruction)" # revert to plain if still nothing if model_lower: prompt_type = prompt_type or unknown_prompt_type else: prompt_type = prompt_type or unknown_prompt_type if use_defaults: temperature = 0.0 if temperature is None else temperature top_p = 1.0 if top_p is None else top_p top_k = 1 if top_k is None else top_k penalty_alpha = 0 if penalty_alpha is None else penalty_alpha num_beams = num_beams or 1 max_new_tokens = max_new_tokens or 512 repetition_penalty = repetition_penalty or 1.0 # 1.07 causes issues still with more repetition num_return_sequences = min(num_beams, num_return_sequences or 1) do_sample = False if do_sample is None else do_sample else: temperature = 0.0 if temperature is None else temperature top_p = 1.0 if top_p is None else top_p top_k = 1 if top_k is None else top_k penalty_alpha = 0 if penalty_alpha is None else penalty_alpha num_beams = num_beams or 1 max_new_tokens = max_new_tokens or 1024 repetition_penalty = repetition_penalty or 1.0 # 1.07 causes issues still with more repetition num_return_sequences = min(num_beams, num_return_sequences or 1) do_sample = False if do_sample is None else do_sample # doesn't include chat, instruction_nochat, iinput_nochat, added later params_list = ["", stream_output, enable_caching, prompt_type, prompt_dict, chat_template, temperature, top_p, top_k, penalty_alpha, num_beams, max_new_tokens, min_new_tokens, early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample, seed] if use_placeholder_instruction_as_example: examples += [[placeholder_instruction, ''] + params_list] if use_default_examples: examples += [ ["Translate English to French", "Good morning"] + params_list, ["Give detailed answer for whether Einstein or Newton is smarter.", ''] + params_list, ["Explain in detailed list, all the best practices for coding in python.", ''] + params_list, [ "Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.", ''] + params_list, ['Translate to German: My name is Arthur', ''] + params_list, ["Please answer to the following question. Who is going to be the next Ballon d'or?", ''] + params_list, ['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.', ''] + params_list, ['Please answer the following question. What is the boiling point of Nitrogen?', ''] + params_list, ['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', ''] + params_list, ["Simplify the following expression: (False or False and True). Explain your answer.", ''] + params_list, [ "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?", ''] + params_list, ['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', ''] + params_list, [ 'Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?', ''] + params_list, ["""def area_of_rectangle(a: float, b: float): \"\"\"Return the area of the rectangle.\"\"\"""", ''] + params_list, ["""# a function in native python: def mean(a): return sum(a)/len(a) # the same function using numpy: import numpy as np def mean(a):""", ''] + params_list, ["""X = np.random.randn(100, 100) y = np.random.randint(0, 1, 100) # fit random forest classifier with 20 estimators""", ''] + params_list, ] # add summary example examples += [ [summarize_example1, 'Summarize' if prompt_type not in [noop_prompt_type, 'instruct_simple'] else ''] + params_list] src_lang = "English" tgt_lang = "Russian" # move to correct position for example in examples: example += [chat, '', '', LangChainMode.DISABLED.value, True, LangChainAction.QUERY.value, [], top_k_docs, chunk, chunk_size, DocumentSubset.Relevant.name, [], [], 'and', [], 'and', pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, all_docs_start_prompt, all_docs_finish_prompt, user_prompt_for_fake_system_prompt, json_object_prompt, json_object_prompt_simpler, json_code_prompt, json_code_prompt_if_no_schema, json_schema_instruction, json_preserve_system_prompt, json_object_post_prompt_reminder, json_code_post_prompt_reminder, json_code2_post_prompt_reminder, system_prompt, image_audio_loaders, pdf_loaders, url_loaders, jq_schema, extract_frames, llava_prompt, None, # visible_models None, # visible_image_models image_size_default, # image_size image_quality_choices[0], # image_quality 3.0, # image_guidance_scale 30, # image_num_inference_steps None, # h2ogpt_key False, # add_search_to_context None, # chat_conversation None, # text_context_list docs_ordering_type, min_max_new_tokens, max_input_tokens, max_total_input_tokens, docs_token_handling, docs_joiner, hyde_level, hyde_template, hyde_show_only_final, doc_json_mode, metadata_in_context, chatbot_role, speaker, tts_language, tts_speed, image_file, image_control, images_num_max, image_resolution, image_format, rotate_align_resize_image, video_frame_period, image_batch_image_prompt, image_batch_final_prompt, image_batch_stream, visible_vision_models, video_file, response_format, guided_json, guided_regex, guided_choice, guided_grammar, guided_whitespace_pattern, None, # model_lock, only client, don't need default value client_metadata, ] # adjust examples if non-chat mode if not chat: example[eval_func_param_names.index('instruction_nochat')] = example[ eval_func_param_names.index('instruction')] example[eval_func_param_names.index('instruction')] = '' example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')] example[eval_func_param_names.index('iinput')] = '' assert len(example) == len(eval_func_param_names), "Wrong example: %s %s" % ( len(example), len(eval_func_param_names)) if prompt_type == PromptType.custom.name and not prompt_dict: raise ValueError("Unexpected to get non-empty prompt_dict=%s for prompt_type=%s" % (prompt_dict, prompt_type)) # get prompt_dict from prompt_type, so user can see in UI etc., or for custom do nothing except check format if prompt_type: prompt_dict, error0 = get_prompt(prompt_type, prompt_dict, context='', reduced=False, making_context=False, return_dict=True, system_prompt=system_prompt) if error0: raise RuntimeError("Prompt wrong: %s" % error0) return placeholder_instruction, placeholder_input, \ stream_output, show_examples, \ prompt_type, prompt_dict, chat_template, \ temperature, top_p, top_k, penalty_alpha, num_beams, \ max_new_tokens, min_new_tokens, early_stopping, max_time, \ repetition_penalty, num_return_sequences, \ do_sample, \ seed, \ src_lang, tgt_lang, \ examples, \ task_info def languages_covered(): # https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered covered = """Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)""" covered = covered.split(', ') covered = {x.split(' ')[0]: x.split(' ')[1].replace(')', '').replace('(', '') for x in covered} return covered def score_qa(smodel, stokenizer, question, answer, memory_restriction_level=0): if memory_restriction_level > 0: max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256 elif hasattr(stokenizer, 'model_max_length'): max_length_tokenize = stokenizer.model_max_length else: # limit to 1024, not worth OOMing on reward score max_length_tokenize = 2048 - 1024 cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM question = question[-cutoff_len:] answer = answer[-cutoff_len:] inputs = stokenizer(question, answer, return_tensors="pt", truncation=True, max_length=max_length_tokenize).to(smodel.device) try: score = torch.sigmoid(smodel(**inputs.to(smodel.device)).logits[0].float()).cpu().detach().numpy()[0] except torch.cuda.OutOfMemoryError as e: score = 0.0 print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True) del inputs traceback.print_exc() clear_torch_cache() return 'Response Score: GPU OOM' except (Exception, RuntimeError) as e: score = 0.0 if 'Expected all tensors to be on the same device' in str(e) or \ 'expected scalar type Half but found Float' in str(e) or \ 'probability tensor contains either' in str(e) or \ 'cublasLt ran into an error!' in str(e) or \ 'device-side assert triggered' in str(e): print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True) traceback.print_exc() clear_torch_cache() return 'Response Score: GPU Error' else: raise os.environ['TOKENIZERS_PARALLELISM'] = 'true' return score def check_locals(**kwargs): # ensure everything in evaluate is here can_skip_because_locally_generated = no_default_param_names + [ # get_model: 'reward_type' ] missing1 = [] for k in eval_func_param_names: if k in can_skip_because_locally_generated: continue if k not in kwargs: missing1.append(k) assert not missing1, "Missing %s" % missing1 missing2 = [] for k in inputs_kwargs_list: if k in can_skip_because_locally_generated: continue if k not in kwargs: missing2.append(k) assert not missing2, "Missing %s" % missing2 def get_model_max_length(model_state): if not isinstance(model_state['tokenizer'], (str, type(None))): return model_state['tokenizer'].model_max_length else: return 2048 def get_model_max_length_from_tokenizer(tokenizer): if hasattr(tokenizer, 'model_max_length'): return int(tokenizer.model_max_length) else: return 2048 def get_max_max_new_tokens(model_state, **kwargs): if not isinstance(model_state['tokenizer'], (str, type(None))) or not kwargs.get('truncation_generation', False): if hasattr(model_state['tokenizer'], 'max_output_len'): max_max_new_tokens = model_state['tokenizer'].max_output_len elif hasattr(model_state['tokenizer'], 'model_max_length'): max_max_new_tokens = model_state['tokenizer'].model_max_length else: # e.g. fast up, no model max_max_new_tokens = None else: max_max_new_tokens = None if kwargs['max_max_new_tokens'] is not None and max_max_new_tokens is not None: if kwargs.get('truncation_generation', False): return min(max_max_new_tokens, kwargs['max_max_new_tokens']) else: # listen to max_max_new_tokens, ignore model limit return max(max_max_new_tokens, kwargs['max_max_new_tokens']) elif kwargs['max_max_new_tokens'] is not None: return kwargs['max_max_new_tokens'] elif kwargs['memory_restriction_level'] == 1: return 768 elif kwargs['memory_restriction_level'] == 2: return 512 elif kwargs['memory_restriction_level'] >= 3: return 256 else: # FIXME: Need to update after new model loaded, so user can control with slider return 2048 def get_minmax_top_k_docs(is_public, from_ui): label_top_k_docs = "Number of document chunks (query) or pages/parts (summarize)" if is_public: min_top_k_docs = 1 if from_ui: max_top_k_docs = max_top_k_docs_public else: max_top_k_docs = max_top_k_docs_public_api else: min_top_k_docs = -1 max_top_k_docs = 1000 label_top_k_docs = label_top_k_docs + " (-1 = auto fill model context, all pages/docs for summarize)" return min_top_k_docs, max_top_k_docs, label_top_k_docs def remove_refs(text, keep_sources_in_context, langchain_mode, hyde_level, gradio_errors_to_chatbot): # md -> back to text, maybe not super important if model trained enough if not keep_sources_in_context and \ langchain_mode != 'Disabled' and \ text.find(super_source_prefix) >= 0: # FIXME: This is relatively slow even for small amount of text, like 0.3s each history item import re text = re.sub(f'{re.escape(super_source_prefix)}.*?{re.escape(super_source_postfix)}', '', text, flags=re.DOTALL) if text.endswith('\n

'): text = text[:-4] # HYDE in_generic_chat = gradio_errors_to_chatbot or \ (hyde_level is None or hyde_level > 0) and \ not keep_sources_in_context and \ langchain_mode != 'Disabled' if in_generic_chat and text.find(generic_prefix) >= 0: # FIXME: This is relatively slow even for small amount of text, like 0.3s each history item import re text = re.sub(f'{re.escape(generic_prefix)}.*?{re.escape(generic_postfix)}', '', text, flags=re.DOTALL) if text.endswith('\n

'): text = text[:-4] return text def history_to_context(history, langchain_mode=None, add_chat_history_to_context=None, prompt_type=None, prompt_dict=None, model_max_length=None, memory_restriction_level=None, keep_sources_in_context=None, system_prompt=None, chat_conversation=None, hyde_level=None, gradio_errors_to_chatbot=None, min_max_new_tokens=512): """ Consumes all history up to (but not including) the latest history item that is presumed to be an [instruction, None] pair. :param history: :param langchain_mode: :param add_chat_history_to_context: :param prompt_type: :param prompt_dict: :param model_max_length: :param memory_restriction_level: :param keep_sources_in_context: :param system_prompt: :param chat_conversation: :param min_max_new_tokens: :return: """ history = merge_chat_conversation_history(chat_conversation, history) len_history = len(history) # Ensure output will be unique to models _, _, _, max_prompt_length = get_cutoffs(memory_restriction_level, for_context=True, model_max_length=model_max_length, min_max_new_tokens=min_max_new_tokens) # Account for the system prompt length if system_prompt: system_prompt_length = len(system_prompt) max_prompt_length -= system_prompt_length context1 = '' final_history = [] if max_prompt_length is not None and add_chat_history_to_context: # Compute terminate_response, chat_sep, chat_turn_sep once _, pre_response, terminate_response, chat_sep, chat_turn_sep = \ generate_prompt({}, prompt_type, prompt_dict, reduced=True, making_context=True, system_prompt=system_prompt, histi=-1) for histi in range(len_history - 1, -1, -1): # Iterate in reverse order user = history[histi][0] bot = history[histi][1] if user is None: # Used to indicate was error or something similar put into chatbot stream continue instruction = gradio_to_llm(user, bot=False) output = gradio_to_llm(bot, bot=True) if bot is not None else '' data_point = dict(instruction=instruction, input='', output=output) prompt, _, _, _, _ = \ generate_prompt(data_point, prompt_type, prompt_dict, reduced=True, making_context=True, system_prompt=system_prompt, histi=histi) prompt = remove_refs(prompt, keep_sources_in_context, langchain_mode, hyde_level, gradio_errors_to_chatbot) prompt = prompt.replace('
', chat_turn_sep) if not prompt.endswith(chat_turn_sep): prompt += chat_turn_sep if len(prompt + context1) > max_prompt_length: remaining_length = max_prompt_length - len(context1) if len(instruction) > len(output): if len(output) >= remaining_length: truncated_instruction = '' truncated_output = output[:remaining_length] else: truncated_output = output truncated_instruction = instruction[:remaining_length - len(output)] else: if len(instruction) >= remaining_length: truncated_instruction = instruction[:remaining_length] truncated_output = '' else: truncated_instruction = instruction truncated_output = output[:remaining_length - len(instruction)] data_point = dict(instruction=truncated_instruction, input='', output=truncated_output) truncated_prompt, _, _, _, _ = \ generate_prompt(data_point, prompt_type, prompt_dict, reduced=True, making_context=True, system_prompt=system_prompt, histi=histi) truncated_prompt = remove_refs(truncated_prompt, keep_sources_in_context, langchain_mode, hyde_level, gradio_errors_to_chatbot) truncated_prompt = truncated_prompt.replace('
', chat_turn_sep) if not truncated_prompt.endswith(chat_turn_sep): truncated_prompt += chat_turn_sep if bot is not None: context1 = truncated_prompt + context1 final_history.insert(0, (truncated_instruction, truncated_output)) break if bot is not None: context1 = prompt + context1 final_history.insert(0, (instruction, output)) if context1 and not context1.endswith(chat_turn_sep): context1 += chat_turn_sep # Ensure if terminates abruptly, then human continues on next line return context1, final_history def get_relaxed_max_new_tokens(prompt, tokenizer=None, max_new_tokens=None, max_new_tokens0=None): # check if can relax max_new_tokens for this specific prompt if max_new_tokens0 is not None and \ hasattr(tokenizer, 'model_max_len') and \ isinstance(tokenizer.model_max_len, (float, int)): max_new_tokens = int(tokenizer.model_max_length) - get_token_count(prompt, tokenizer) if max_new_tokens is not None: return min(max_new_tokens0, max_new_tokens) else: return max_new_tokens0 return max_new_tokens def get_limited_prompt(instruction, iinput, tokenizer, template_text='', prompter=None, base_model=None, inference_server=None, prompt_type=None, prompt_dict=None, max_new_tokens=None, system_prompt='', allow_chat_system_prompt=None, context='', chat_conversation=None, user_prompt_for_fake_system_prompt=None, text_context_list=None, keep_sources_in_context=False, gradio_errors_to_chatbot=True, model_max_length=None, memory_restriction_level=0, langchain_mode=None, add_chat_history_to_context=True, verbose=False, doc_importance=0.5, hyde_level=None, min_max_new_tokens=512, max_input_tokens=-1, max_total_input_tokens=-1, truncation_generation=False, gradio_server=False, attention_sinks=False, doing_grounding=False, image_file=[], lang_pre_prompt='', lang_prompt='', is_actually_vision_model=False, ): """ Take instruction (estimated_instruction for counting token purposes), iinput, system_prompt, context, chat_conversation, text_context_list as inputs and return a prompt and other items accounting for (if required) a balanced truncation of these outputs to avoid going over the token limits """ if gradio_server or not inference_server: # can listen to truncation_generation pass else: # these don't support allowing going beyond total context truncation_generation = True if chat_conversation is None: chat_conversation = [] if not attention_sinks: if max_input_tokens >= 0: # max_input_tokens is used to runtime (via client/UI) to control actual filling of context max_input_tokens = min(model_max_length - min_max_new_tokens, max_input_tokens) else: max_input_tokens = model_max_length - min_max_new_tokens else: if max_input_tokens < 0: max_input_tokens = model_max_length if is_actually_vision_model: max_input_tokens -= tokens_per_image(base_model) * len(image_file) if prompter: prompt_type = prompter.prompt_type prompt_dict = prompter.prompt_dict stream_output = prompter.stream_output system_prompt = prompter.system_prompt can_handle_system_prompt = prompter.can_handle_system_prompt else: can_handle_system_prompt = True # assume can so no extra conversation added if don't know generate_prompt_type = prompt_type external_handle_chat_conversation = False if inference_server and (any( inference_server.startswith(x) for x in ['openai_chat', 'openai_azure_chat', 'vllm_chat', 'anthropic', 'google'])) or gradio_server: # Chat APIs do not take prompting # Replicate does not need prompting if no chat history, but in general can take prompting # if using prompter, prompter.system_prompt will already be filled with automatic (e.g. from llama-2), # so if replicate final prompt with system prompt still correct because only access prompter.system_prompt that was already set # below already true for openai, # but not vllm by default as that can be any model and handled by FastChat API inside vLLM itself # claude is unique also, by not allowing system prompt, but as conversation # Also in list above, because get_limited_prompt called too late for it in gpt_langchain.py # So needs to be added directly in the get_llm for anthropic there, so used in ExtraChat generate_prompt_type = noop_prompt_type # Chat APIs don't handle chat history via single prompt, but in messages, assumed to be handled outside this function # but we will need to compute good history for external use external_handle_chat_conversation = True # not if plain prompt, only if unknown or unset use_chat_template = get_use_chat_template(tokenizer, prompt_type=prompt_type) if is_gradio_vision_model(base_model): use_chat_template = False if use_chat_template: # see if chat template handles system prompt if system_prompt in apply_chat_template("Test", system_prompt, [], tokenizer, image_file=[], test_only=True, user_prompt_for_fake_system_prompt=None): can_handle_system_prompt = True base_size = len(apply_chat_template("Test", None, [], tokenizer, image_file=[], test_only=True, user_prompt_for_fake_system_prompt=None)) else: base_size = 0 max_input_tokens -= base_size context1 = context if context1 is None: context1 = '' from h2oai_pipeline import H2OTextGenerationPipeline template_tokens = get_token_count(template_text, tokenizer) max_input_tokens -= template_tokens ########################### # leave bit for instruction regardless of system prompt system_prompt0 = system_prompt system_prompt, num_system_tokens = H2OTextGenerationPipeline.limit_prompt(system_prompt, tokenizer, max_prompt_length=int( max_input_tokens * 0.9)) num_system_tokens0 = num_system_tokens max_input_tokens -= num_system_tokens if prompter: prompter.system_prompt = system_prompt lang_prompt, num_system_tokens_a = H2OTextGenerationPipeline.limit_prompt(lang_prompt, tokenizer, max_prompt_length=int( max_input_tokens * 0.45)) max_input_tokens -= num_system_tokens_a lang_pre_prompt, num_system_tokens_b = H2OTextGenerationPipeline.limit_prompt(lang_pre_prompt, tokenizer, max_prompt_length=int( max_input_tokens * 0.45)) max_input_tokens -= num_system_tokens_b # get actual instruction, limited by template limitation instruction, num_instruction_tokens = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer, max_prompt_length=max_input_tokens) max_input_tokens -= num_instruction_tokens context1, num_context1_tokens = H2OTextGenerationPipeline.limit_prompt(context1, tokenizer, max_prompt_length=max_input_tokens) max_input_tokens -= num_context1_tokens iinput, num_iinput_tokens = H2OTextGenerationPipeline.limit_prompt(iinput, tokenizer, max_prompt_length=max_input_tokens) max_input_tokens -= num_iinput_tokens chat_system_prompt = not external_handle_chat_conversation and \ not can_handle_system_prompt and \ allow_chat_system_prompt if chat_system_prompt and system_prompt: user_prompt_for_fake_system_prompt = user_prompt_for_fake_system_prompt or user_prompt_for_fake_system_prompt0 chat_conversation_system_prompt = [[user_prompt_for_fake_system_prompt, system_prompt]] # nuke system prompt else will double-up system_prompt = '' else: chat_conversation_system_prompt = [] if not gradio_server: # else inner calls will handle LLM prompting and system prompt, so don't double up chat_conversation = chat_conversation_system_prompt + chat_conversation ########################### # merge handles if chat_conversation is None history = merge_chat_conversation_history(chat_conversation, []) history_to_context_func = functools.partial(history_to_context, langchain_mode=langchain_mode, add_chat_history_to_context=add_chat_history_to_context, prompt_type=generate_prompt_type, prompt_dict=prompt_dict, # still model_max_length because subtraction done again inside history_to_context model_max_length=model_max_length, memory_restriction_level=memory_restriction_level, keep_sources_in_context=keep_sources_in_context, # hyde_level=hyde_level, gradio_errors_to_chatbot=gradio_errors_to_chatbot, min_max_new_tokens=min_max_new_tokens) ########################### # get context2 without history or system_prompt if use_chat_template: context2 = apply_chat_template(instruction, '', [], tokenizer, image_file=image_file, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt) iinput = '' context1 = '' num_context1_tokens = 0 num_context2_tokens = get_token_count(context2, tokenizer) num_instruction_tokens0 = num_instruction_tokens num_instruction_tokens = 0 prompt_just_template_tokens = 0 else: context2, _ = history_to_context_func([], system_prompt='') context2, num_context2_tokens = H2OTextGenerationPipeline.limit_prompt(context2, tokenizer, max_prompt_length=max_input_tokens) # get template size data_point = dict(context=' ', instruction=' ', input=' ') context_from_history = len(history) > 0 # if used history -> context2, then already have (if exists) system prompt etc., just get rest of reduced prompt reduced = context_from_history psave = prompter.system_prompt prompter.system__prompt = ' ' prompt_just_template = prompter.generate_prompt(data_point, context_from_history=context_from_history, reduced=reduced, image_file=image_file) prompter.system_prompt = psave prompt_just_template_tokens = get_token_count(prompt_just_template, tokenizer) if system_prompt in prompt_just_template: prompt_just_template_tokens -= num_system_tokens num_context2_tokens += prompt_just_template_tokens if text_context_list is None: text_context_list = [] num_doc_overhead_tokens = count_overhead_tokens(tokenizer, doing_grounding=doing_grounding) if doing_grounding: docs_joiner = "Document xx" else: docs_joiner = docs_joiner_default # handle overhead by lowering locally max input tokens, since not removable max_input_tokens -= num_doc_overhead_tokens num_doc_tokens0 = sum([get_token_count(x + docs_joiner, tokenizer) for x in text_context_list]) num_prompt_tokens0 = (num_system_tokens or 0) + \ (num_system_tokens_a or 0) + \ (num_system_tokens_b or 0) + \ (num_instruction_tokens or 0) + \ (num_context1_tokens or 0) + \ (num_context2_tokens or 0) + \ (num_iinput_tokens or 0) + \ (num_doc_tokens0 or 0) # go down to no less than 256, about 1 paragraph # use max_new_tokens before use num_prompt_tokens0 else would be negative or ~0 min_max_new_tokens = min(min_max_new_tokens, max_new_tokens) ########################### # reduce docs # leave bit for history top_k_docs, one_doc_size, num_doc_tokens = get_docs_tokens(tokenizer, text_context_list=text_context_list, max_input_tokens=int(max_input_tokens * 0.9)) max_input_tokens -= num_doc_tokens ########################### # reduce history given rest of reductions history_to_use_final = [] low, high = 0, len(history) - 1 best_index = -1 # Keep track of the best index that satisfies the condition chat_index = 0 while low <= high: chat_index = (low + high) // 2 # Find the middle index if chat_system_prompt and history: # should always have history[0] but just protection in case # Don't ever lose system prompt if putting into chat history_to_use = [history[0]] + history[1 + chat_index:] else: history_to_use = history[0 + chat_index:] if use_chat_template: context2 = apply_chat_template(instruction, system_prompt, history_to_use, tokenizer, image_file=image_file, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt) else: context2, history_to_use = history_to_context_func(history_to_use, system_prompt=system_prompt) num_context2_tokens = get_token_count(context2, tokenizer) + prompt_just_template_tokens diff1 = max_input_tokens - ( num_system_tokens + num_system_tokens_a + num_system_tokens_b + num_instruction_tokens + num_context1_tokens + num_context2_tokens) if diff1 > 0: best_index = chat_index # Update best index # Condition met, try to find if there's a smaller history that still meets the condition history_to_use_final = history_to_use.copy() high = chat_index - 1 else: # Condition not met, need to include more history low = chat_index + 1 # i.e. if chat_index == len(history), then nothing can be consumed if best_index != -1: chat_index = best_index if chat_system_prompt and history: history_to_use_final = [history[0]] + history[1 + best_index:] else: history_to_use_final = history[0 + best_index:] else: chat_index = -1 # can't fit any history history_to_use_final = [] ########################### # get final context2 if use_chat_template: context2 = apply_chat_template(instruction, system_prompt, history_to_use_final, tokenizer, image_file=image_file, user_prompt_for_fake_system_prompt=user_prompt_for_fake_system_prompt) # now context2 has system tokens num_system_tokens = 0 else: context2, history_to_use_final = history_to_context_func(history_to_use_final, system_prompt=system_prompt) num_context2_tokens = get_token_count(context2, tokenizer) + prompt_just_template_tokens if verbose: print("chat_conversation used %d entries out of %d" % (chat_index + 1, len(history)), flush=True) # update full context # avoid including chat_conversation if handled externally, only used above for computations of prompt context = context1 + context2 if not external_handle_chat_conversation else context1 # update token counts (docs + non-docs, all tokens) num_prompt_tokens = (num_system_tokens or 0) + \ (num_system_tokens_a or 0) + \ (num_system_tokens_b or 0) + \ (num_instruction_tokens or 0) + \ (num_context1_tokens or 0) + \ (num_context2_tokens or 0) + \ (num_iinput_tokens or 0) + \ (num_doc_tokens or 0) # update max_new_tokens # limit so max_new_tokens = prompt + new < max # otherwise model can fail etc. e.g. for distilgpt2 asking for 1024 tokens is enough to fail if prompt=1 token if not attention_sinks: max_new_tokens = max(1, min(max_new_tokens, model_max_length - num_prompt_tokens)) if max_new_tokens < min_max_new_tokens - 30: # FIXME: fudge factor if os.getenv('HARD_ASSERTS'): raise ValueError("Invalid max_new_tokens=%s" % max_new_tokens) else: max_new_tokens = max(32, max_new_tokens) if prompter is None: # get prompter debug = False stream_output = False # doesn't matter prompter = Prompter(prompt_type, prompt_dict, debug=debug, stream_output=stream_output, system_prompt=system_prompt, tokenizer=tokenizer, base_model=base_model) if prompt_type != generate_prompt_type: # override just this attribute, keep system_prompt etc. from original prompt_type prompter.prompt_type = generate_prompt_type if not use_chat_template: data_point = dict(context=context, instruction=instruction, input=iinput) # handle promptA/promptB addition if really from history. # if not from history, then reduced=False inside correct # if mixed, then no specific correct thing to do, so treat like history and promptA/B will come first still context_from_history = len(history) > 0 # if used history -> context2, then already have (if exists) system prompt etc., just get rest of reduced prompt reduced = context_from_history prompt = prompter.generate_prompt(data_point, context_from_history=context_from_history, reduced=reduced, image_file=image_file) else: # assume inner gradio server handles. if we point to gradio server (i.e. gradio_server=True) then we just pass instruction prompt = instruction if gradio_server else context2 if gradio_server and not prompter.can_handle_system_prompt and system_prompt: # then must have added in pre-conversation, remove for inner gradio to handle, here we just wanted to count accurately if history_to_use_final and history_to_use_final[0][1] == system_prompt: # protection just in case logic isn't perfect history_to_use_final.pop(0) num_prompt_tokens_actual = get_token_count(prompt, tokenizer) if chat_system_prompt and system_prompt: system_prompt_return = system_prompt0 else: system_prompt_return = system_prompt return prompt, \ instruction, iinput, context, \ num_prompt_tokens, max_new_tokens, num_prompt_tokens0, num_prompt_tokens_actual, \ history_to_use_final, external_handle_chat_conversation, \ top_k_docs, one_doc_size, truncation_generation, \ system_prompt_return, lang_pre_prompt, lang_prompt def count_overhead_tokens(tokenizer, doing_grounding=False): if doing_grounding: from openai_server.backend_utils import structure_to_messages system_prompt = '' instruction = 'foo' chat_conversation = [] image_file = [] prompt = tokenizer.apply_grounded_generation_template( structure_to_messages(instruction, system_prompt if system_prompt not in [None, '', 'auto'] else None, chat_conversation, image_file), documents=[dict(text='foo')], citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) return get_token_count(prompt, tokenizer) else: return 0 def entrypoint_main(): """ Examples: WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B' python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B' # generate without lora weights, no prompt python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq' # OpenChatKit settings: python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False python generate.py --base_model='t5-large' --prompt_type='simple_instruct' python generate.py --base_model='philschmid/bart-large-cnn-samsum' python generate.py --base_model='philschmid/flan-t5-base-samsum' python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt' python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28' must have 4*48GB GPU and run without 8bit in order for sharding to work with use_gpu_id=False can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --use_gpu_id=False --prompt_type='human_bot' python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b """ H2O_Fire(main) def append_certificates(certs_dir): import certifi cert_bundle_path = certifi.where() default_bundle_from_helm = "/etc/ssl/certs/root-ca-bundle.crt" ssl_cache_dir = os.getenv("SSL_CACHE_DIR", ".cache/.ssl_cache") ssl_cache_dir = os.path.abspath(makedirs(ssl_cache_dir, exist_ok=True, tmp_ok=True, use_base=True)) output_file = os.path.join(ssl_cache_dir, "ca-bundle.pem") with open(cert_bundle_path, 'r') as bundle_file: bundle_content = bundle_file.read() combined_cert_content = bundle_content additional_certs_found = False if certs_dir: for root, _, files in os.walk(certs_dir): for file in files: if file.endswith(('.crt', '.pem')): cert_file_path = os.path.join(root, file) print(f"adding cert {os.path.abspath(cert_file_path)}") with open(cert_file_path, 'r') as cert: combined_cert_content += '\n' + cert.read() additional_certs_found = True if os.path.exists(default_bundle_from_helm) and os.path.isfile(default_bundle_from_helm): print(f"adding default helm cert {default_bundle_from_helm}") with open(default_bundle_from_helm, 'r') as cert: combined_cert_content += '\n' + cert.read() additional_certs_found = True if additional_certs_found: with open(output_file, 'w') as output: output.write(combined_cert_content) os.environ['SSL_CERT_FILE'] = output_file print(f"Combined certificate file created at: {output_file}") if __name__ == "__main__": entrypoint_main()