"""The model container class for ExLlamaV2 models.""" import gc import pathlib import time import torch from exllamav2 import ( ExLlamaV2, ExLlamaV2Config, ExLlamaV2Cache, ExLlamaV2Cache_8bit, ExLlamaV2Tokenizer, ExLlamaV2Lora, ) from exllamav2.generator import ExLlamaV2StreamingGenerator, ExLlamaV2Sampler from gen_logging import log_generation_params, log_prompt, log_response from typing import List, Optional, Union from templating import ( PromptTemplate, find_template_from_model, get_template_from_model_json, get_template_from_file, ) from utils import coalesce, unwrap # Bytes to reserve on first device when loading with auto split AUTO_SPLIT_RESERVE_BYTES = 96 * 1024**2 class ModelContainer: """The model container class for ExLlamaV2 models.""" 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[ExLlamaV2StreamingGenerator] = None prompt_template: Optional[PromptTemplate] = None cache_fp8: bool = False gpu_split_auto: bool = True gpu_split: Optional[list] = None active_loras: List[ExLlamaV2Lora] = [] 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" or "FP8" (defaulf: "FP16") 'max_seq_len' (int): Override model's default max sequence length (default: 4096) '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. '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 'no_flash_attn' (bool): Turns off flash attention (increases vram usage) (default: False) """ self.quiet = quiet self.cache_fp8 = "cache_mode" in kwargs and kwargs["cache_mode"] == "FP8" self.gpu_split = kwargs.get("gpu_split") self.gpu_split_auto = unwrap(kwargs.get("gpu_split_auto"), True) 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 2038 self.config.max_seq_len = 4096 self.config.prepare() # 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"), 1.0) # Automatically calculate rope alpha self.config.scale_alpha_value = unwrap( kwargs.get("rope_alpha"), self.calculate_rope_alpha(base_seq_len) ) # Turn off flash attention? self.config.no_flash_attn = unwrap(kwargs.get("no_flash_attention"), False) # low_mem is currently broken in exllamav2. Don't use it until it's # fixed. """ if "low_mem" in kwargs and kwargs["low_mem"]: self.config.set_low_mem() """ # Set prompt template override if provided prompt_template_name = kwargs.get("prompt_template") if prompt_template_name: print( "Attempting to load prompt template with name", {prompt_template_name}, ) # Read the template self.prompt_template = get_template_from_file(prompt_template_name) else: # Then try finding the template from the tokenizer_config.json self.prompt_template = get_template_from_model_json( pathlib.Path(self.config.model_dir) / "tokenizer_config.json", "chat_template", "from_tokenizer_config", ) # Try finding the chat template from the model's config.json # TODO: This may not even be used with huggingface models, # mark for removal. if self.prompt_template is None: self.prompt_template = get_template_from_model_json( pathlib.Path(self.config.model_config), "chat_template", "from_model_config", ) # If that fails, attempt fetching from model name if self.prompt_template is None: template_match = find_template_from_model(model_directory) if template_match: self.prompt_template = get_template_from_file(template_match) # Catch all for template lookup errors if self.prompt_template: print( f"Using template {self.prompt_template.name} for chat " "completions." ) else: print( "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: if hasattr(self.config, "num_experts_per_token"): self.config.num_experts_per_token = num_experts_override else: print( " !! Warning: Currently installed ExLlamaV2 does not " "support overriding MoE experts" ) chunk_size = min( unwrap(kwargs.get("chunk_size"), 2048), self.config.max_seq_len ) self.config.max_input_len = chunk_size self.config.max_attn_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: print( "A draft config was found but a model name was not given. " "Please check your config.yml! Skipping draft load." ) enable_draft = False if enable_draft: self.draft_config = ExLlamaV2Config() 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 if "chunk_size" in kwargs: self.draft_config.max_input_len = kwargs["chunk_size"] self.draft_config.max_attn_size = kwargs["chunk_size"] ** 2 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.""" model_path = pathlib.Path( self.draft_config.model_dir if is_draft else self.config.model_dir ) return model_path 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) """ for _ in self.load_gen(progress_callback): pass def load_loras(self, lora_directory: pathlib.Path, **kwargs): """ Load loras """ loras = unwrap(kwargs.get("loras"), []) 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: print( "One of your loras does not have a name. Please check your " "config.yml! Skipping lora load." ) failure.append(lora_name) continue print(f"Loading lora: {lora_name} at scaling {lora_scaling}") lora_path = lora_directory / lora_name # FIXME(alpin): Does self.model need to be passed here? self.active_loras.append( ExLlamaV2Lora.from_directory(self.model, lora_path, lora_scaling) ) print("Lora successfully loaded.") success.append(lora_name) # Return success and failure names return {"success": success, "failure": failure} def load_gen(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) """ # Load tokenizer self.tokenizer = ExLlamaV2Tokenizer(self.config) # Load draft model if a config is present if self.draft_config: self.draft_model = ExLlamaV2(self.draft_config) if not self.quiet: print("Loading draft model: " + self.draft_config.model_dir) self.draft_cache = ExLlamaV2Cache(self.draft_model, lazy=True) reserve = [AUTO_SPLIT_RESERVE_BYTES] + [0] * 16 yield from self.draft_model.load_autosplit_gen( self.draft_cache, reserve_vram=reserve, last_id_only=True, callback_gen=progress_callback, ) # 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) # Load model self.model = ExLlamaV2(self.config) if not self.quiet: print("Loading model: " + self.config.model_dir) if not self.gpu_split_auto: for value in self.model.load_gen( self.gpu_split, callback_gen=progress_callback ): if isinstance(value, str): yield value if self.cache_fp8: self.cache = ExLlamaV2Cache_8bit(self.model, lazy=self.gpu_split_auto) else: self.cache = ExLlamaV2Cache(self.model, lazy=self.gpu_split_auto) if self.gpu_split_auto: reserve = [AUTO_SPLIT_RESERVE_BYTES] + [0] * 16 yield from self.model.load_autosplit_gen( self.cache, reserve_vram=reserve, last_id_only=True, callback_gen=progress_callback, ) # 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) # Create generator self.generator = ExLlamaV2StreamingGenerator( self.model, self.cache, self.tokenizer, self.draft_model, self.draft_cache, ) print("Model successfully loaded.") def unload(self, loras_only: bool = False): """ Free all VRAM resources used by this model """ for lora in self.active_loras: lora.unload() self.active_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 self.generator = None gc.collect() torch.cuda.empty_cache() def get_tokens(self, text: Optional[str], ids: Optional[List[int]], **kwargs): """Common function for token operations""" if text: # Assume token encoding return self.tokenizer.encode( text, add_bos=unwrap(kwargs.get("add_bos_token"), True), encode_special_tokens=unwrap(kwargs.get("encode_special_tokens"), True), ) if ids: # Assume token decoding ids = torch.tensor([ids]) return self.tokenizer.decode( ids, decode_special_tokens=unwrap(kwargs.get("decode_special_tokens"), True), )[0] return None def get_special_tokens(self, add_bos_token: bool, ban_eos_token: bool): 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 generate(self, prompt: str, **kwargs): """Generate a response to a prompt""" generation = list(self.generate_gen(prompt, **kwargs)) if generation: response = "".join(map(lambda chunk: chunk[0], generation)) return response, generation[-1][1], generation[-1][2] return "", 0, 0 # pylint: disable=too-many-locals,too-many-branches,too-many-statements def generate_gen(self, prompt: str, **kwargs): """ Create generator function for prompt completion Args: prompt (str): Input prompt **kwargs: 'token_healing' (bool): Use token healing (default: False) 'temperature' (float): Sampling temperature (default: 1.0) 'temperature_last' (bool): Apply temperature after all other samplers (default: False) 'top_k' (int): Sampling top-K (default: 0) 'top_p' (float): Sampling top-P (default: 1.0) 'min_p' (float): Sampling min-P (default: 0.0) 'tfs' (float): Tail-free sampling (default: 0.0) 'typical' (float): Sampling typical (default: 0.0) 'mirostat' (bool): Use Mirostat (default: False) 'mirostat_tau' (float) Mirostat tau parameter (default: 1.5) 'mirostat_eta' (float) Mirostat eta parameter (default: 0.1) 'repetition_penalty' (float): Token repetition/presence penalty (default: 1.15) 'repetition_range' (int): Repetition penalty range (default: whole context) 'repetition_decay' (int): Repetition penalty range (default: same as range) 'stop' (List[Union[str, int]]): List of stop strings/tokens to end response (default: [EOS]) 'max_tokens' (int): Max no. tokens in response (default: 150) 'add_bos_token' (bool): Adds the BOS token to the start of the prompt (default: True) 'ban_eos_token' (bool): Bans the EOS token from generation (default: False) 'logit_bias' (Dict[int, float]): Biases specific tokens to either show up more or less (default: None) 'stream_interval' (float): Interval in seconds between each output chunk (default: immediate) 'generate_window' (int): Space to reserve at the end of the model's context when generating. Rolls context window by the same amount if context length is exceeded to allow generating pastthe models max_seq_len. """ token_healing = unwrap(kwargs.get("token_healing"), False) max_tokens = unwrap(kwargs.get("max_tokens"), 150) stream_interval = unwrap(kwargs.get("stream_interval"), 0) generate_window = min(unwrap(kwargs.get("generate_window"), 512), max_tokens) # Sampler settings gen_settings = ExLlamaV2Sampler.Settings() # Warn of unsupported settings if the setting is enabled if (unwrap(kwargs.get("mirostat"), False)) and not hasattr( gen_settings, "mirostat" ): print( " !! Warning: Currently installed ExLlamaV2 does not support " "Mirostat sampling" ) if (unwrap(kwargs.get("min_p"), 0.0)) not in [0.0, 1.0] and not hasattr( gen_settings, "min_p" ): print( " !! Warning: Currently installed ExLlamaV2 does not " "support min-P sampling" ) if (unwrap(kwargs.get("tfs"), 0.0)) not in [0.0, 1.0] and not hasattr( gen_settings, "tfs" ): print( " !! Warning: Currently installed ExLlamaV2 does not support " "tail-free sampling (TFS)" ) if (unwrap(kwargs.get("temperature_last"), False)) and not hasattr( gen_settings, "temperature_last" ): print( " !! Warning: Currently installed ExLlamaV2 does not support " "temperature_last" ) # Apply settings gen_settings.temperature = unwrap(kwargs.get("temperature"), 1.0) gen_settings.temperature_last = unwrap(kwargs.get("temperature_last"), False) gen_settings.top_k = unwrap(kwargs.get("top_k"), 0) gen_settings.top_p = unwrap(kwargs.get("top_p"), 1.0) 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) # 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) gen_settings.token_repetition_penalty = unwrap( kwargs.get("repetition_penalty"), 1.0 ) gen_settings.token_repetition_range = unwrap( kwargs.get("repetition_range"), self.config.max_seq_len ) # 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"), []) 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") # 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 # Log generation options to console # Some options are too large, so log the args instead log_generation_params( max_tokens=max_tokens, **vars(gen_settings), token_healing=token_healing, add_bos_token=add_bos_token, ban_eos_token=ban_eos_token, stop_conditions=stop_conditions, logit_bias=logit_bias, ) # Log prompt to console log_prompt(prompt) # 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, bias in logit_bias.items(): gen_settings.token_bias[token] = bias # 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, [self.tokenizer.eos_token_id]) else: stop_conditions.append(self.tokenizer.eos_token_id) # Stop conditions self.generator.set_stop_conditions(stop_conditions) # Tokenized context ids = self.tokenizer.encode( prompt, add_bos=add_bos_token, encode_special_tokens=True ) context_len = len(ids[0]) if context_len > self.config.max_seq_len: print( f"WARNING: The context length {context_len} is greater than " f"the max_seq_len {self.config.max_seq_len}.", "Generation is truncated and metrics may not be accurate.", ) prompt_tokens = ids.shape[-1] # Begin generated_tokens = 0 full_response = "" start_time = time.time() last_chunk_time = start_time save_tokens = torch.empty((1, 0), dtype=torch.bool) chunk_buffer = "" chunk_tokens = 0 while True: # Ingest prompt if chunk_tokens == 0: ids = torch.cat((ids, save_tokens), dim=-1) save_tokens = torch.empty((1, 0), dtype=torch.bool) overflow = ids.shape[-1] + generate_window - self.config.max_seq_len active_ids = ids[:, max(0, overflow) :] chunk_tokens = self.config.max_seq_len - active_ids.shape[-1] self.generator.begin_stream( active_ids, gen_settings, token_healing=token_healing, loras=self.active_loras, ) # Generate chunk, eos, tokens = self.generator.stream() if token_healing: # Extract healed token ids[:, -1] = self.generator.sequence_ids[:, -2] token_healing = False save_tokens = torch.cat((save_tokens, tokens), dim=-1) chunk_buffer += chunk generated_tokens += 1 chunk_tokens -= 1 # Yield output now = time.time() elapsed = now - last_chunk_time if chunk_buffer != "" and ( elapsed > stream_interval or eos or generated_tokens == max_tokens ): yield chunk_buffer, prompt_tokens, generated_tokens full_response += chunk_buffer chunk_buffer = "" last_chunk_time = now if eos or generated_tokens == max_tokens: break # Print response log_response(full_response) elapsed_time = last_chunk_time - start_time initial_response = ( f"Metrics: {generated_tokens} tokens generated in " f"{round(elapsed_time, 2)} seconds" ) itemization = [] extra_parts = [] # Add tokens per second tokens_per_second = ( "Indeterminate" if elapsed_time == 0 else round(generated_tokens / elapsed_time, 2) ) itemization.append(f"{tokens_per_second} T/s") # Add context (original token count) if ids is not None: itemization.append(f"context {context_len} tokens") if context_len > self.config.max_seq_len: extra_parts.append("<-- Not accurate (truncated)") # Print output print( initial_response + " (" + ", ".join(itemization) + ") " + " ".join(extra_parts) )