"""The model container class for ExLlamaV2 models.""" import asyncio import gc import math import pathlib import traceback import torch import uuid from exllamav2 import ( ExLlamaV2, ExLlamaV2Config, ExLlamaV2Cache, ExLlamaV2Cache_Q4, ExLlamaV2Cache_Q6, ExLlamaV2Cache_Q8, ExLlamaV2Tokenizer, ExLlamaV2Lora, ) from exllamav2.generator import ( ExLlamaV2Sampler, ExLlamaV2DynamicGeneratorAsync, ExLlamaV2DynamicJobAsync, ) from itertools import zip_longest from loguru import logger from typing import List, Optional, Union from backends.exllamav2.grammar import ( ExLlamaV2Grammar, clear_grammar_func_cache, ) from backends.exllamav2.utils import ( exllama_disabled_flash_attn, hardware_supports_flash_attn, supports_paged_attn, ) from common.concurrency import iterate_in_threadpool from common.gen_logging import ( log_generation_params, log_metrics, log_prompt, log_response, ) from common.templating import ( PromptTemplate, TemplateLoadError, find_template_from_model, ) from common.transformers_utils import GenerationConfig, HuggingFaceConfig from common.utils import coalesce, unwrap class ExllamaV2Container: """The model container class for ExLlamaV2 models.""" # Exl2 vars config: Optional[ExLlamaV2Config] = None draft_config: Optional[ExLlamaV2Config] = None model: Optional[ExLlamaV2] = None draft_model: Optional[ExLlamaV2] = None cache: Optional[ExLlamaV2Cache] = None draft_cache: Optional[ExLlamaV2Cache] = None tokenizer: Optional[ExLlamaV2Tokenizer] = None generator: Optional[ExLlamaV2DynamicGeneratorAsync] = None prompt_template: Optional[PromptTemplate] = None paged: bool = True # Internal config vars cache_size: int = None cache_mode: str = "FP16" draft_cache_mode: str = "FP16" max_batch_size: Optional[int] = None generation_config: Optional[GenerationConfig] = None hf_config: Optional[HuggingFaceConfig] = None # GPU split vars gpu_split: Optional[list] = None gpu_split_auto: bool = True autosplit_reserve: List[float] = [96 * 1024**2] # Load state model_is_loading: bool = False model_loaded: bool = False # Load synchronization # The lock keeps load tasks sequential # The condition notifies any waiting tasks load_lock: asyncio.Lock = asyncio.Lock() load_condition: asyncio.Condition = asyncio.Condition() def __init__(self, model_directory: pathlib.Path, quiet=False, **kwargs): """ Create model container Args: model_dir (int): Model directory containing config.json, tokenizer.model etc. quiet (bool): Suppress console output load_progress_callback (function, optional): A function to call for each module loaded. Prototype: def progress(loaded_modules: int, total_modules: int, loading_draft: bool) **kwargs: `cache_mode` (str): Sets cache mode: "FP16"/"Q8"/"Q6"/"Q4" (default: "FP16") 'max_seq_len' (int): Override model's default max sequence length (default: 4096) 'cache_size' (int): Num of tokens to allocate space for in the k/v cache (default: max_seq_len) 'rope_scale' (float): Set RoPE scaling factor for model (default: 1.0) 'rope_alpha' (float): Set RoPE alpha (NTK) factor for model (default: 1.0) 'prompt_template' (str): Manually sets the prompt template for this model (default: None) 'chunk_size' (int): Sets the maximum chunk size for the model (default: 2048) Inferencing in chunks reduces overall VRAM overhead by processing very long sequences in smaller batches. This limits the size of temporary buffers needed for the hidden state and attention weights. 'draft_model_dir' (str): Draft model directory 'draft_rope_scale' (float): Set RoPE scaling factor for draft model (default: 1.0) 'draft_rope_alpha' (float): RoPE alpha (NTK) factor for draft model. By default, the draft model's alpha value is calculated automatically to scale to the size of the full model. 'draft_cache_mode' (str): Sets draft cache mode: "FP16"/"Q8"/"Q6"/"Q4" (default: "FP16") 'lora_dir' (str): LoRA directory 'loras' (list[dict]): List of loras to be loaded, consisting of 'name' and 'scaling' 'gpu_split_auto' (bool): Automatically split model across available devices (default: True) 'gpu_split' (list[float]): Allocation for weights and (some) tensors, per device """ self.quiet = quiet self.cache_mode = unwrap(kwargs.get("cache_mode"), "FP16") # Turn off GPU split if the user is using 1 GPU gpu_count = torch.cuda.device_count() gpu_split_auto = unwrap(kwargs.get("gpu_split_auto"), True) gpu_device_list = list(range(0, gpu_count)) if gpu_count > 1 and gpu_split_auto: # Auto GPU split parameters self.gpu_split_auto = gpu_split_auto autosplit_reserve_megabytes = unwrap(kwargs.get("autosplit_reserve"), [96]) self.autosplit_reserve = [ int(math.ceil(value * 1024**2)) for value in autosplit_reserve_megabytes ] elif gpu_count > 1: # Manual GPU split self.gpu_split = kwargs.get("gpu_split") self.gpu_split_auto = False gpu_device_list = [ device_idx for device_idx, memory in enumerate(self.gpu_split) if memory > 0 ] else: # One GPU setup self.gpu_split_auto = False logger.info("Disabling GPU split because one GPU is in use.") self.config = ExLlamaV2Config() self.config.model_dir = str(model_directory.resolve()) # Make the max seq len 4096 before preparing the config # This is a better default than 2048 self.config.max_seq_len = 4096 # Hardcode max output length to 16 self.config.max_output_len = 16 self.config.prepare() # Check if the model arch is compatible with various exl2 features try: self.config.arch_compat_overrides() except AttributeError: pass # Create the hf_config self.hf_config = HuggingFaceConfig.from_file(model_directory) # Then override the base_seq_len if present override_base_seq_len = kwargs.get("override_base_seq_len") if override_base_seq_len: self.config.max_seq_len = override_base_seq_len # Grab the base model's sequence length before overrides for # rope calculations base_seq_len = self.config.max_seq_len # Set the target seq len if present target_max_seq_len = kwargs.get("max_seq_len") if target_max_seq_len: self.config.max_seq_len = target_max_seq_len # Set the rope scale self.config.scale_pos_emb = unwrap( kwargs.get("rope_scale"), self.config.scale_pos_emb ) # Automatically calculate rope alpha self.config.scale_alpha_value = unwrap( kwargs.get("rope_alpha"), self.calculate_rope_alpha(base_seq_len) ) # Enable fasttensors loading if present self.config.fasttensors = unwrap(kwargs.get("fasttensors"), False) # Set max batch size to the config override self.max_batch_size = unwrap(kwargs.get("max_batch_size")) # Check whether the user's configuration supports flash/paged attention # Also check if exl2 has disabled flash attention if ( exllama_disabled_flash_attn(self.config.no_flash_attn) or not hardware_supports_flash_attn(gpu_device_list) or not supports_paged_attn() ): self.config.no_flash_attn = True self.paged = False self.max_batch_size = 1 torch.backends.cuda.enable_flash_sdp(False) # Set k/v cache size # cache_size is only relevant when paged mode is enabled if self.paged: cache_size = unwrap(kwargs.get("cache_size"), self.config.max_seq_len) if cache_size < self.config.max_seq_len: logger.warning( f"The given cache_size ({cache_size}) is smaller than the " "desired context length.\n" "Overriding cache_size to max_seq_len. " ) cache_size = self.config.max_seq_len # Enforce a multiple of 256 for cache size # Overestimate to ensure that the cache isn't below max_seq_len cache_remainder = cache_size % 256 if cache_remainder != 0: rounded_cache_size = int( 256 * ((cache_size - cache_remainder) / 256 + 1) ) logger.warning( f"The given cache size ({cache_size}) is " "not a multiple of 256.\n" "Overriding cache_size with an overestimated value of " f"{rounded_cache_size} tokens." ) cache_size = rounded_cache_size # Warn user if cache size may be inadequate for CFG if cache_size < 2 * self.config.max_seq_len: logger.warning( f"The given cache_size ({cache_size}) is less than 2 * max_seq_len " "and may be too small for requests using CFG. \n" "Ignore this warning if you do not plan on using CFG." ) self.cache_size = cache_size else: self.cache_size = self.config.max_seq_len # Load generation config overrides generation_config_path = model_directory / "generation_config.json" if generation_config_path.exists(): try: self.generation_config = GenerationConfig.from_file( generation_config_path.parent ) except Exception: logger.error(traceback.format_exc()) logger.warning( "Skipping generation config load because of an unexpected error." ) # Try to set prompt template self.prompt_template = self.find_prompt_template( kwargs.get("prompt_template"), model_directory ) # Catch all for template lookup errors if self.prompt_template: logger.info( f'Using template "{self.prompt_template.name}" for chat completions.' ) else: logger.warning( "Chat completions are disabled because a prompt " "template wasn't provided or auto-detected." ) # Set num of experts per token if provided num_experts_override = kwargs.get("num_experts_per_token") if num_experts_override: self.config.num_experts_per_token = kwargs.get("num_experts_per_token") # Make sure chunk size is >= 16 and <= max seq length user_chunk_size = unwrap(kwargs.get("chunk_size"), 2048) chunk_size = sorted((16, user_chunk_size, self.config.max_seq_len))[1] self.config.max_input_len = chunk_size self.config.max_attention_size = chunk_size**2 draft_args = unwrap(kwargs.get("draft"), {}) draft_model_name = draft_args.get("draft_model_name") enable_draft = draft_args and draft_model_name # Always disable draft if params are incorrectly configured if draft_args and draft_model_name is None: logger.warning( "Draft model is disabled because a model name " "wasn't provided. Please check your config.yml!" ) enable_draft = False if enable_draft: self.draft_config = ExLlamaV2Config() self.draft_config.no_flash_attn = self.config.no_flash_attn draft_model_path = pathlib.Path( unwrap(draft_args.get("draft_model_dir"), "models") ) draft_model_path = draft_model_path / draft_model_name self.draft_config.model_dir = str(draft_model_path.resolve()) self.draft_config.prepare() self.draft_config.scale_pos_emb = unwrap( draft_args.get("draft_rope_scale"), 1.0 ) # Automatically calculate draft rope alpha self.draft_config.scale_alpha_value = unwrap( draft_args.get("draft_rope_alpha"), self.calculate_rope_alpha(self.draft_config.max_seq_len), ) self.draft_config.max_seq_len = self.config.max_seq_len self.draft_cache_mode = unwrap(draft_args.get("draft_cache_mode"), "FP16") if chunk_size: self.draft_config.max_input_len = chunk_size self.draft_config.max_attention_size = chunk_size**2 def find_prompt_template(self, prompt_template_name, model_directory): """Tries to find a prompt template using various methods""" logger.info("Attempting to load a prompt template if present.") find_template_functions = [ lambda: PromptTemplate.from_model_json( pathlib.Path(self.config.model_dir) / "tokenizer_config.json", "chat_template", ), lambda: PromptTemplate.from_file(find_template_from_model(model_directory)), ] # Add lookup from prompt template name if provided if prompt_template_name: find_template_functions[:0] = [ lambda: PromptTemplate.from_file(prompt_template_name), lambda: PromptTemplate.from_model_json( pathlib.Path(self.config.model_dir) / "tokenizer_config.json", "chat_template", prompt_template_name, ), ] # Continue on exception since functions are tried as they fail for template_func in find_template_functions: try: prompt_template = template_func() if prompt_template is not None: return prompt_template except TemplateLoadError as e: logger.warning(f"TemplateLoadError: {str(e)}") continue except Exception: logger.error(traceback.format_exc()) logger.warning( "An unexpected error happened when trying to load the template. " "Trying other methods." ) continue def calculate_rope_alpha(self, base_seq_len): """Calculate the rope alpha value for a given sequence length.""" ratio = self.config.max_seq_len / base_seq_len # Default to a 1 alpha if the sequence length is ever less # than or equal to 1 if ratio <= 1.0: alpha = 1 else: alpha = -0.13436 + 0.80541 * ratio + 0.28833 * ratio**2 return alpha def get_model_path(self, is_draft: bool = False): """Get the path for this model.""" if is_draft and not self.draft_config: return None model_path = pathlib.Path( self.draft_config.model_dir if is_draft else self.config.model_dir ) return model_path def get_model_parameters(self): model_params = { "name": self.get_model_path().name, "rope_scale": self.config.scale_pos_emb, "rope_alpha": self.config.scale_alpha_value, "max_seq_len": self.config.max_seq_len, "cache_size": self.cache_size, "cache_mode": self.cache_mode, "chunk_size": self.config.max_input_len, "num_experts_per_token": self.config.num_experts_per_token, "prompt_template": self.prompt_template.name if self.prompt_template else None, } if self.draft_config: draft_model_params = { "name": self.get_model_path(is_draft=True).name, "rope_scale": self.draft_config.scale_pos_emb, "rope_alpha": self.draft_config.scale_alpha_value, "max_seq_len": self.draft_config.max_seq_len, "cache_mode": self.draft_cache_mode, } model_params["draft"] = draft_model_params return model_params async def wait_for_jobs(self, skip_wait: bool = False): """Polling mechanism to wait for pending generation jobs.""" if not self.generator: return # Immediately abort all jobs if asked if skip_wait: logger.warning( "Immediately terminating all jobs. " "Clients will have their requests cancelled.\n" ) # Requires a copy to avoid errors during iteration jobs_copy = self.generator.jobs.copy() for job in jobs_copy.values(): await job.cancel() while self.generator.jobs: await asyncio.sleep(0.01) async def load(self, progress_callback=None): """ Load model Args: progress_callback (function, optional): A function to call for each module loaded. Prototype: def progress(loaded_modules: int, total_modules: int) """ async for _ in self.load_gen(progress_callback): pass async def load_gen(self, progress_callback=None, **kwargs): """Loads a model and streams progress via a generator.""" # Indicate that model load has started # Do this operation under the load lock's context try: await self.load_lock.acquire() self.model_is_loading = True # Wait for existing generation jobs to finish await self.wait_for_jobs(kwargs.get("skip_wait")) # Streaming gen for model load progress model_load_generator = self.load_model_sync(progress_callback) async for value in iterate_in_threadpool(model_load_generator): yield value # Create async generator await self.create_generator() # Clean up any extra vram usage from torch and cuda # (Helps reduce VRAM bottlenecking on Windows) gc.collect() torch.cuda.empty_cache() # Cleanup and update model load state self.model_loaded = True logger.info("Model successfully loaded.") finally: self.load_lock.release() self.model_is_loading = False async with self.load_condition: self.load_condition.notify_all() @torch.inference_mode() def load_model_sync(self, progress_callback=None): """ Load model, generator function Args: progress_callback (function, optional): A function to call for each module loaded. Prototype: def progress(loaded_modules: int, total_modules: int) Runs under a shared inference mode context. """ # Reset tokenizer namespace vars and create a tokenizer ExLlamaV2Tokenizer.unspecial_piece_to_id = {} ExLlamaV2Tokenizer.unspecial_id_to_piece = {} ExLlamaV2Tokenizer.extended_id_to_piece = {} ExLlamaV2Tokenizer.extended_piece_to_id = {} self.tokenizer = ExLlamaV2Tokenizer(self.config) # Calculate autosplit reserve for all GPUs gpu_count = torch.cuda.device_count() autosplit_reserve = self.autosplit_reserve + [0] * ( gpu_count - len(self.autosplit_reserve) ) # Load draft model if a config is present if self.draft_config: self.draft_model = ExLlamaV2(self.draft_config) if not self.quiet: logger.info("Loading draft model: " + self.draft_config.model_dir) if self.draft_cache_mode == "Q4": self.draft_cache = ExLlamaV2Cache_Q4( self.draft_model, max_seq_len=self.cache_size, lazy=True, ) elif self.draft_cache_mode == "Q6": self.draft_cache = ExLlamaV2Cache_Q6( self.draft_model, max_seq_len=self.cache_size, lazy=True, ) elif self.draft_cache_mode == "Q8": self.draft_cache = ExLlamaV2Cache_Q8( self.draft_model, max_seq_len=self.cache_size, lazy=True, ) else: self.draft_cache = ExLlamaV2Cache( self.draft_model, max_seq_len=self.cache_size, lazy=True, ) for value in self.draft_model.load_autosplit_gen( self.draft_cache, reserve_vram=autosplit_reserve, last_id_only=True, callback_gen=progress_callback, ): if value: yield value # Test VRAM allocation with a full-length forward pass input_ids = torch.zeros((1, self.config.max_input_len), dtype=torch.long) self.draft_model.forward(input_ids, cache=self.cache, preprocess_only=True) self.model = ExLlamaV2(self.config) if not self.quiet: logger.info("Loading model: " + self.config.model_dir) # Load model with manual split # Entrypoint for single GPU users if not self.gpu_split_auto: logger.info("Loading with a manual GPU split (or a one GPU setup)") for value in self.model.load_gen( self.gpu_split, callback_gen=progress_callback, ): if value: yield value if self.cache_mode == "Q4": self.cache = ExLlamaV2Cache_Q4( self.model, max_seq_len=self.cache_size, lazy=self.gpu_split_auto, batch_size=1, ) elif self.cache_mode == "Q6": self.cache = ExLlamaV2Cache_Q6( self.model, max_seq_len=self.cache_size, lazy=self.gpu_split_auto, batch_size=1, ) elif self.cache_mode == "Q8": self.cache = ExLlamaV2Cache_Q8( self.model, max_seq_len=self.cache_size, lazy=self.gpu_split_auto, batch_size=1, ) else: self.cache = ExLlamaV2Cache( self.model, max_seq_len=self.cache_size, lazy=self.gpu_split_auto, batch_size=1, ) # Load model with autosplit if self.gpu_split_auto: logger.info("Loading with autosplit") for value in self.model.load_autosplit_gen( self.cache, reserve_vram=autosplit_reserve, last_id_only=True, callback_gen=progress_callback, ): if value: yield value # Test VRAM allocation with a full-length forward pass input_ids = torch.zeros((1, self.config.max_input_len), dtype=torch.long) self.model.forward(input_ids, cache=self.cache, preprocess_only=True) async def create_generator(self): try: # Don't acquire locks unless a model is loaded if self.model_loaded: await self.load_lock.acquire() # Immediately cancel all jobs await self.wait_for_jobs(skip_wait=True) # Create new generator self.generator = ExLlamaV2DynamicGeneratorAsync( model=self.model, cache=self.cache, draft_model=self.draft_model, draft_cache=self.draft_cache, tokenizer=self.tokenizer, max_batch_size=self.max_batch_size, paged=self.paged, ) finally: # This means the generator is being recreated # The load lock is already released in the load function if self.model_loaded: self.load_lock.release() async with self.load_condition: self.load_condition.notify_all() def get_loras(self): """Convenience function to get all loras.""" return unwrap(self.generator.generator.current_loras, []) async def load_loras(self, lora_directory: pathlib.Path, **kwargs): """ Load loras """ loras = unwrap(kwargs.get("loras"), []) try: await self.load_lock.acquire() # Wait for existing generation jobs to finish await self.wait_for_jobs(kwargs.get("skip_wait")) loras_to_load: List[ExLlamaV2Lora] = [] success: List[str] = [] failure: List[str] = [] for lora in loras: lora_name = lora.get("name") lora_scaling = unwrap(lora.get("scaling"), 1.0) if lora_name is None: logger.warning( "One of your loras does not have a name. Please check your " "config.yml! Skipping lora load." ) failure.append(lora_name) continue logger.info(f"Adding lora: {lora_name} at scaling {lora_scaling}") lora_path = lora_directory / lora_name loras_to_load.append( ExLlamaV2Lora.from_directory(self.model, lora_path, lora_scaling) ) logger.info(f"Lora successfully added: {lora_name}") success.append(lora_name) self.generator.generator.set_loras(loras_to_load) logger.info("All loras successfully loaded") # Return success and failure names return {"success": success, "failure": failure} finally: self.load_lock.release() async with self.load_condition: self.load_condition.notify_all() async def unload(self, loras_only: bool = False, **kwargs): """ Free all VRAM resources used by this model """ # Shutdown immediately unloads and bypasses all locks do_shutdown = kwargs.get("shutdown") try: if not do_shutdown: await self.load_lock.acquire() # Wait for other jobs to finish await self.wait_for_jobs(kwargs.get("skip_wait")) # Delete references held in the grammar module clear_grammar_func_cache() # Unload LoRAs if self.generator and self.generator.generator.current_loras: for lora in self.generator.generator.current_loras: lora.unload() self.generator.generator.set_loras([]) # Unload the entire model if not just unloading loras if not loras_only: if self.model: self.model.unload() self.model = None if self.draft_model: self.draft_model.unload() self.draft_model = None self.config = None self.cache = None self.tokenizer = None # Cleanup the generator from any pending jobs if self.generator is not None: await self.generator.close() self.generator = None # Set all model state variables to False self.model_is_loading = False self.model_loaded = False gc.collect() torch.cuda.empty_cache() logger.info("Loras unloaded." if loras_only else "Model unloaded.") finally: if not do_shutdown: self.load_lock.release() async with self.load_condition: self.load_condition.notify_all() def encode_tokens(self, text: str, **kwargs): """Wrapper to encode tokens from a text string""" return ( self.tokenizer.encode( text, add_bos=unwrap(kwargs.get("add_bos_token"), True), encode_special_tokens=unwrap(kwargs.get("encode_special_tokens"), True), ) .flatten() .tolist() ) def decode_tokens(self, ids: List[int], **kwargs): """Wrapper to decode tokens from a list of IDs""" ids = torch.tensor([ids]) return self.tokenizer.decode( ids, decode_special_tokens=unwrap(kwargs.get("decode_special_tokens"), True), )[0] # TODO: Maybe support generation_config for eos_token def get_special_tokens( self, add_bos_token: bool = True, ban_eos_token: bool = False ): return { "bos_token": self.tokenizer.bos_token if add_bos_token else "", "eos_token": self.tokenizer.eos_token if not ban_eos_token else "", "pad_token": self.tokenizer.pad_token, "unk_token": self.tokenizer.unk_token, } def get_logprobs(self, token_ids: torch.Tensor, token_probs: torch.Tensor): top_tokens = [ self.tokenizer.extended_id_to_piece.get( index, self.tokenizer.id_to_piece[index] ) for index in token_ids.flatten().tolist() ] top_values = torch.log(token_probs).flatten().tolist() # Cannot return -inf in JSON cleaned_values = [ -1000 if value == float("-inf") else value for value in top_values ] return dict(zip_longest(top_tokens, cleaned_values)) async def generate( self, prompt: str, request_id: str, abort_event: asyncio.Event = None, **kwargs ): """Generate a response to a prompt""" generations = [] async for generation in self.generate_gen( prompt, request_id, abort_event, **kwargs ): generations.append(generation) joined_generation = { "text": "", "prompt_tokens": 0, "generation_tokens": 0, "offset": [], "token_probs": {}, "logprobs": [], } if generations: # Get finish_reason first and then shift where -1 points to if "finish_reason" in generations[-1]: finish_reason_gen = generations.pop() joined_generation["finish_reason"] = finish_reason_gen.get( "finish_reason" ) else: joined_generation["finish_reason"] = "stop" if len(generations) > 0: for generation in generations: joined_generation["text"] += unwrap(generation.get("text"), "") joined_generation["offset"].append(unwrap(generation.get("offset"), -1)) joined_generation["token_probs"].update( unwrap(generation.get("token_probs"), {}) ) # Include empty logprob dicts for index preservation joined_generation["logprobs"].append( unwrap(generation.get("logprobs"), {}) ) joined_generation["prompt_tokens"] = unwrap( generations[-1].get("prompt_tokens"), 0 ) joined_generation["generated_tokens"] = unwrap( generations[-1].get("generated_tokens"), 0 ) return joined_generation def check_unsupported_settings(self, **kwargs): """Check and warn the user if a sampler is unsupported. Meant for dev wheels!""" return kwargs async def generate_gen( self, prompt: str, request_id: str, abort_event: Optional[asyncio.Event] = None, **kwargs, ): """ Create generator function for prompt completion. for kwargs, check common/sampling.py """ # Wait for load lock to be freed before processing async with self.load_condition: await self.load_condition.wait_for(lambda: not self.load_lock.locked()) prompts = [prompt] token_healing = unwrap(kwargs.get("token_healing"), False) generate_window = max( unwrap(kwargs.get("generate_window"), 512), self.config.max_seq_len // 8 ) # Sampler settings gen_settings = ExLlamaV2Sampler.Settings() # Check unsupported settings for dev wheels kwargs = self.check_unsupported_settings(**kwargs) # Apply settings gen_settings.temperature = unwrap(kwargs.get("temperature"), 1.0) gen_settings.temperature_last = unwrap(kwargs.get("temperature_last"), False) gen_settings.smoothing_factor = unwrap(kwargs.get("smoothing_factor"), 0.0) gen_settings.top_k = unwrap(kwargs.get("top_k"), 0) gen_settings.top_p = unwrap(kwargs.get("top_p"), 1.0) gen_settings.top_a = unwrap(kwargs.get("top_a"), 0.0) gen_settings.min_p = unwrap(kwargs.get("min_p"), 0.0) gen_settings.tfs = unwrap(kwargs.get("tfs"), 1.0) gen_settings.typical = unwrap(kwargs.get("typical"), 1.0) gen_settings.mirostat = unwrap(kwargs.get("mirostat"), False) gen_settings.skew = unwrap(kwargs.get("skew"), 0) # DynaTemp settings max_temp = unwrap(kwargs.get("max_temp"), 1.0) min_temp = unwrap(kwargs.get("min_temp"), 1.0) if max_temp > min_temp: gen_settings.max_temp = max_temp gen_settings.min_temp = min_temp gen_settings.temp_exponent = unwrap(kwargs.get("temp_exponent"), 1.0) else: # Force to default values gen_settings.max_temp = 1.0 gen_settings.min_temp = 1.0 gen_settings.temp_exponent = 1.0 # Warn if max/min temp values are > 0 # and if they're less than or equal to each other if max_temp < min_temp or ( 1 not in {min_temp, max_temp} and max_temp == min_temp ): logger.warning( "Max temp is less than or equal to min temp, skipping DynaTemp." ) # Default tau and eta fallbacks don't matter if mirostat is off gen_settings.mirostat_tau = unwrap(kwargs.get("mirostat_tau"), 1.5) gen_settings.mirostat_eta = unwrap(kwargs.get("mirostat_eta"), 0.1) # Set CFG scale and negative prompt cfg_scale = unwrap(kwargs.get("cfg_scale"), 1.0) negative_prompt = None if cfg_scale not in [None, 1.0]: if self.paged: gen_settings.cfg_scale = cfg_scale # If the negative prompt is empty, use the BOS token negative_prompt = unwrap( kwargs.get("negative_prompt"), self.tokenizer.bos_token ) prompts.append(negative_prompt) else: logger.warning( "CFG is currently disabled because paged mode is disabled. " "Please use an ampere (30 series) or higher GPU for CFG support." ) gen_settings.token_repetition_penalty = unwrap( kwargs.get("repetition_penalty"), 1.0 ) gen_settings.token_frequency_penalty = unwrap( kwargs.get("frequency_penalty"), 0.0 ) gen_settings.token_presence_penalty = unwrap( kwargs.get("presence_penalty"), 0.0 ) # Applies for all penalties despite being called token_repetition_range gen_settings.token_repetition_range = unwrap( kwargs.get("penalty_range"), self.config.max_seq_len ) # Dynamically scale penalty range to output tokens # Only do this if freq/pres pen is enabled # and the repetition range is -1 auto_scale_penalty_range = ( gen_settings.token_frequency_penalty != 0 or gen_settings.token_presence_penalty != 0 ) and gen_settings.token_repetition_range == -1 # Always make sure the fallback is 0 if range < 0 # It's technically fine to use -1, but this just validates the passed # fallback # Always default to 0 if something goes wrong if gen_settings.token_repetition_range < 0: fallback_decay = 0 else: fallback_decay = gen_settings.token_repetition_range gen_settings.token_repetition_decay = coalesce( kwargs.get("repetition_decay"), fallback_decay, 0 ) stop_conditions: List[Union[str, int]] = unwrap(kwargs.get("stop"), []) banned_strings: List[str] = unwrap(kwargs.get("banned_strings"), []) add_bos_token = unwrap(kwargs.get("add_bos_token"), True) ban_eos_token = unwrap(kwargs.get("ban_eos_token"), False) logit_bias = kwargs.get("logit_bias") # Logprobs request_logprobs = unwrap(kwargs.get("logprobs"), 0) # Speculative Ngram self.generator.speculative_ngram = unwrap( kwargs.get("speculative_ngram"), False ) # Override sampler settings for temp = 0 if gen_settings.temperature == 0: gen_settings.temperature = 1.0 gen_settings.top_k = 1 gen_settings.top_p = 0 gen_settings.typical = 0 # Store the gen settings for logging purposes gen_settings_log_dict = vars(gen_settings) # Set banned tokens banned_tokens = unwrap(kwargs.get("banned_tokens"), []) if banned_tokens: gen_settings.disallow_tokens(self.tokenizer, banned_tokens) # Set logit bias if logit_bias: # Create a vocab tensor if it doesn't exist for token biasing if gen_settings.token_bias is None: padding = -self.tokenizer.config.vocab_size % 32 gen_settings.token_bias = torch.zeros( (self.tokenizer.config.vocab_size + padding,), dtype=torch.float, ) # Map logits to the tensor with their biases for token_id, bias in logit_bias.items(): if 0 <= token_id < len(self.tokenizer.id_to_piece): gen_settings.token_bias[token_id] = bias else: logger.warning( f"Logit bias: Token {token_id} not present " "in the model's vocab. Skipping." ) # Initialize grammar handler grammar_handler = ExLlamaV2Grammar() # Add JSON schema filter if it exists json_schema = unwrap(kwargs.get("json_schema")) if json_schema: grammar_handler.add_json_schema_filter( json_schema, self.model, self.tokenizer ) # Add regex filter if it exists regex_pattern = unwrap(kwargs.get("regex_pattern")) if regex_pattern: grammar_handler.add_regex_filter(regex_pattern, self.tokenizer) # Add EBNF filter if it exists grammar_string = unwrap(kwargs.get("grammar_string")) if grammar_string: grammar_handler.add_ebnf_filter(grammar_string, self.model, self.tokenizer) # Fetch EOS tokens from generation_config if they exist eos_tokens = ( self.generation_config.eos_tokens() if self.generation_config else [self.tokenizer.eos_token_id] ) # Ban the EOS token if specified. If not, append to stop conditions # as well. # Set this below logging to avoid polluting the stop strings array if ban_eos_token: gen_settings.disallow_tokens(self.tokenizer, eos_tokens) else: stop_conditions += eos_tokens # Encode both positive and negative prompts input_ids = [ self.tokenizer.encode( prompt, add_bos=add_bos_token, encode_special_tokens=True ) for prompt in prompts ] # The first index will always be the positive prompt context_len = input_ids[0].size(dim=-1) if context_len > self.config.max_seq_len: logger.warning( f"Context length {context_len} is greater than max_seq_len " f"{self.config.max_seq_len}. Generation is truncated and " "metrics may not be accurate." ) # Automatically set max_tokens to fill up the context # This should be an OK default, but may be changed in the future max_tokens = unwrap( kwargs.get("max_tokens"), self.config.max_seq_len - context_len ) # Set min_tokens to generate while keeping EOS banned min_tokens = unwrap(kwargs.get("min_tokens"), 0) # This is an inverse of skip_special_tokens decode_special_tokens = unwrap(not kwargs.get("skip_special_tokens"), False) # Log prompt to console log_prompt(prompt, request_id, negative_prompt) # Create and add a new job # Don't use the request ID here as there can be multiple jobs per request job_id = uuid.uuid4().hex job = ExLlamaV2DynamicJobAsync( self.generator, input_ids=input_ids, max_new_tokens=max_tokens, min_new_tokens=min_tokens, gen_settings=gen_settings, stop_conditions=stop_conditions, decode_special_tokens=decode_special_tokens, filters=grammar_handler.filters, filter_prefer_eos=bool(grammar_handler.filters), return_probs=request_logprobs > 0, return_top_tokens=request_logprobs, return_logits=request_logprobs > 0, banned_strings=banned_strings, token_healing=token_healing, identifier=job_id, ) # Save generated tokens and full response # Copy over max seq len incase model is unloaded and stored jobs can complete # Full response is required for offset calculation max_seq_len = self.config.max_seq_len generated_tokens = 0 full_response = "" metrics_result = {} # Get the generation status once it's ready try: async for result in job: # Abort if the event is set while streaming if abort_event and abort_event.is_set(): await job.cancel() break stage = result.get("stage") result_id = result.get("identifier") if stage == "streaming" and result_id == job_id: chunk = unwrap(result.get("text"), "") full_response += chunk chunk_tokens = result.get("token_ids") if chunk_tokens is not None: generated_tokens += chunk_tokens.size(dim=0) generation = { "text": chunk, "prompt_tokens": context_len, "generated_tokens": generated_tokens, "offset": len(full_response), } if request_logprobs > 0: # Get top tokens and probs top_tokens = unwrap( result.get("top_k_tokens"), torch.empty((1, 0, 1), dtype=torch.long), ) top_probs = unwrap( result.get("top_k_probs"), torch.empty((1, 0, 1), dtype=torch.float), ) if top_tokens.numel() > 0 and top_probs.numel() > 0: logprobs = self.get_logprobs(top_tokens, top_probs) generation["logprobs"] = logprobs # The first logprob is the selected token prob generation["token_probs"] = { token: logprobs[token] for token in list(logprobs.keys())[:1] } yield generation # Second yield if eos is true if result.get("eos"): log_response(request_id, full_response) eos_reason = result.get("eos_reason") finish_reason = ( "length" if eos_reason == "max_new_tokens" else "stop" ) # Save the final result for metrics logging metrics_result = result # Remove the token text generation = { "prompt_tokens": generation.get("prompt_tokens"), "generated_tokens": generation.get("generated_tokens"), "finish_reason": finish_reason, } yield generation break except asyncio.CancelledError: await job.cancel() except Exception as ex: # Create a new generator since the current state is broken # No need to wait for this to finish logger.error( "FATAL ERROR with generation. " "Attempting to recreate the generator. " "If this fails, please restart the server.\n" ) asyncio.ensure_future(self.create_generator()) raise ex finally: # Log generation options to console # Some options are too large, so log the args instead log_generation_params( request_id=request_id, max_tokens=max_tokens, min_tokens=min_tokens, stream=kwargs.get("stream"), **gen_settings_log_dict, token_healing=token_healing, auto_scale_penalty_range=auto_scale_penalty_range, generate_window=generate_window, bos_token_id=self.tokenizer.bos_token_id, eos_token_id=eos_tokens, add_bos_token=add_bos_token, ban_eos_token=ban_eos_token, skip_special_tokens=not decode_special_tokens, speculative_ngram=self.generator.speculative_ngram, logprobs=request_logprobs, stop_conditions=stop_conditions, banned_tokens=banned_tokens, banned_strings=banned_strings, logit_bias=logit_bias, filters=grammar_handler.filters, ) # Log the metrics if present if metrics_result: log_metrics( request_id, metrics_result.get("time_enqueued"), metrics_result.get("prompt_tokens"), metrics_result.get("cached_tokens"), metrics_result.get("time_prefill"), metrics_result.get("new_tokens"), metrics_result.get("time_generate"), context_len, max_seq_len, )