"""Utilities for downloading and initializing model weights.""" import fnmatch import glob import json import os from collections import defaultdict from typing import Any, Iterable, Iterator, List, Optional, Tuple import filelock import huggingface_hub.constants import numpy as np import torch from huggingface_hub import HfFileSystem, snapshot_download from loguru import logger from safetensors.torch import load_file, safe_open, save_file from tqdm.auto import tqdm from transformers import PretrainedConfig, AutoModelForCausalLM from aphrodite.common.config import ModelConfig from aphrodite.common.gguf import (GGUFReader, get_tensor_name_map, MODEL_ARCH_NAMES) from aphrodite.common.logger import get_loading_progress_bar from aphrodite.modeling.layers.quantization import (QuantizationConfig, get_quantization_config) from aphrodite.modeling.layers.quantization.schema import QuantParamSchema _xdg_cache_home = os.getenv("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) _aphrodite_filelocks_path = os.path.join(_xdg_cache_home, "aphrodite/locks/") def enable_hf_transfer(): """automatically activates hf_transfer """ if "HF_HUB_ENABLE_HF_TRANSFER" not in os.environ: try: # enable hf hub transfer if available import hf_transfer # type: ignore # noqa huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True except ImportError: pass enable_hf_transfer() class Disabledtqdm(tqdm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, disable=True) def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None): lock_dir = cache_dir if cache_dir is not None else _aphrodite_filelocks_path os.makedirs(os.path.dirname(lock_dir), exist_ok=True) lock_file_name = model_name_or_path.replace("/", "-") + ".lock" lock = filelock.SoftFileLock(os.path.join(lock_dir, lock_file_name)) return lock def _shared_pointers(tensors): ptrs = defaultdict(list) for k, v in tensors.items(): ptrs[v.data_ptr()].append(k) failing = [] for _, names in ptrs.items(): if len(names) > 1: failing.append(names) return failing def convert_bin_to_safetensor_file( pt_filename: str, sf_filename: str, ) -> None: loaded = torch.load(pt_filename, map_location="cpu") if "state_dict" in loaded: loaded = loaded["state_dict"] shared = _shared_pointers(loaded) for shared_weights in shared: for name in shared_weights[1:]: loaded.pop(name) # For tensors to be contiguous loaded = {k: v.contiguous() for k, v in loaded.items()} dirname = os.path.dirname(sf_filename) os.makedirs(dirname, exist_ok=True) save_file(loaded, sf_filename, metadata={"format": "pt"}) # check file size sf_size = os.stat(sf_filename).st_size pt_size = os.stat(pt_filename).st_size if (sf_size - pt_size) / pt_size > 0.01: raise RuntimeError(f"""The file size different is more than 1%: - {sf_filename}: {sf_size} - {pt_filename}: {pt_size} """) # check if the tensors are the same reloaded = load_file(sf_filename) for k in loaded: pt_tensor = loaded[k] sf_tensor = reloaded[k] if not torch.equal(pt_tensor, sf_tensor): raise RuntimeError(f"The output tensors do not match for key {k}") # TODO: Move this to another place. def get_quant_config(model_config: ModelConfig) -> QuantizationConfig: quant_cls = get_quantization_config(model_config.quantization) # Read the quantization config from the HF model config, if available. # if the quantization if "gguf", we skip and return quant_cls() if model_config.quantization in ["exl2", "gguf"]: return quant_cls() hf_quant_config = getattr(model_config.hf_config, "quantization_config", None) if hf_quant_config is not None: return quant_cls.from_config(hf_quant_config) model_name_or_path = model_config.model is_local = os.path.isdir(model_name_or_path) if not is_local: # Download the config files. with get_lock(model_name_or_path, model_config.download_dir): hf_folder = snapshot_download( model_name_or_path, revision=model_config.revision, allow_patterns="*.json", cache_dir=model_config.download_dir, tqdm_class=Disabledtqdm, ) else: hf_folder = model_name_or_path config_files = glob.glob(os.path.join(hf_folder, "*.json")) quant_config_files = [ f for f in config_files if any( f.endswith(x) for x in quant_cls.get_config_filenames()) ] if len(quant_config_files) == 0: raise ValueError( f"Cannot find the config file for {model_config.quantization}") if len(quant_config_files) > 1: raise ValueError( f"Found multiple config files for {model_config.quantization}: " f"{quant_config_files}") quant_config_file = quant_config_files[0] with open(quant_config_file, "r") as f: config = json.load(f) return quant_cls.from_config(config) def prepare_hf_model_weights( model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", fall_back_to_pt: bool = True, revision: Optional[str] = None, ) -> Tuple[str, List[str], bool]: # Download model weights from huggingface. is_local = os.path.isdir(model_name_or_path) use_safetensors = False # Some quantized models use .pt files for storing the weights. if load_format == "auto": allow_patterns = ["*.safetensors", "*.bin"] elif load_format == "safetensors": use_safetensors = True allow_patterns = ["*.safetensors"] elif load_format == "pt": allow_patterns = ["*.pt"] elif load_format == "npcache": allow_patterns = ["*.bin"] else: raise ValueError(f"Unknown load_format: {load_format}") if fall_back_to_pt: allow_patterns += ["*.pt"] if not is_local: # Before we download we look at that is available: fs = HfFileSystem() file_list = fs.ls(model_name_or_path, detail=False, revision=revision) # depending on what is available we download different things for pattern in allow_patterns: matching = fnmatch.filter(file_list, pattern) if len(matching) > 0: allow_patterns = [pattern] break logger.info(f"Using model weights format {allow_patterns}") # Use file lock to prevent multiple processes from # downloading the same model weights at the same time. with get_lock(model_name_or_path, cache_dir): hf_folder = snapshot_download( model_name_or_path, allow_patterns=allow_patterns, cache_dir=cache_dir, tqdm_class=Disabledtqdm, revision=revision, ) else: hf_folder = model_name_or_path hf_weights_files: List[str] = [] for pattern in allow_patterns: hf_weights_files += glob.glob(os.path.join(hf_folder, pattern)) if len(hf_weights_files) > 0: if pattern == "*.safetensors": use_safetensors = True break if not use_safetensors: # Exclude files that are not needed for inference. # https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/trainer.py#L227-L233 blacklist = [ "training_args.bin", "optimizer.bin", "optimizer.pt", "scheduler.pt", "scaler.pt", "trainer_state.json", "hidden_states.safetensors", # exllamav2 ] hf_weights_files = [ f for f in hf_weights_files if not any(f.endswith(x) for x in blacklist) ] if len(hf_weights_files) == 0: raise RuntimeError( f"Cannot find any model weights with `{model_name_or_path}`") return hf_folder, hf_weights_files, use_safetensors def convert_gguf_to_state_dict(checkpoint, config): model_type = config.model_type # hack: ggufs have a different name than transformers if model_type == "cohere": model_type = "command-r" arch = None for key, value in MODEL_ARCH_NAMES.items(): if value == model_type: arch = key break if arch is None: raise RuntimeError(f"Unknown model_type: {model_type}") num_layers = config.num_hidden_layers name_map = get_tensor_name_map(arch, num_layers) with torch.device("meta"): dummy_model = AutoModelForCausalLM.from_config(config) state_dict = dummy_model.state_dict() gguf_to_hf_name_map = {} keys_to_remove = [] for hf_name in state_dict: name, suffix = hf_name.rsplit(".", 1) gguf_name = name_map.get_name(name) if gguf_name: gguf_to_hf_name_map[f"{gguf_name}.{suffix}"] = hf_name elif name == "lm_head": keys_to_remove.append(hf_name) logger.warning( f"GGUF tensor name for {hf_name} not found, " "this is normal if the model uses tie word embeddings.") else: logger.warning( f"GGUF tensor name for {hf_name} in hf state_dict not found.") for key in keys_to_remove: state_dict.pop(key) if os.path.isfile(checkpoint): results = [GGUFReader(checkpoint)] elif os.path.isdir(checkpoint): results = [ GGUFReader(os.path.join(checkpoint, file)) for file in os.listdir(checkpoint) if os.path.splitext(file)[-1].lower() == ".gguf" ] else: raise RuntimeError( f"Cannot find any model weights with `{checkpoint}`") with get_loading_progress_bar() as progress: task = progress.add_task( "[cyan]Converting GGUF tensors to PyTorch...", total=sum([len(result.tensors) for result in results]), ) for result in results: for ts in result.tensors: try: hf_name = gguf_to_hf_name_map[ts.name] except KeyError: logger.warning( f"hf tensor name for {ts.name} in GGUF not found.") continue data = torch.tensor(ts.data) if state_dict[hf_name].dim() == 2: data = data.view(state_dict[hf_name].shape[0], -1) state_dict[hf_name] = data weight_type = torch.tensor(int(ts.tensor_type), dtype=torch.int) if weight_type > 1: state_dict[hf_name.replace("weight", "weight_type")] = weight_type progress.update(task, advance=1) return state_dict def hf_model_weights_iterator( model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None, config: Optional[PretrainedConfig] = None, fall_back_to_pt: Optional[bool] = True, ) -> Iterator[Tuple[str, torch.Tensor]]: if model_name_or_path.endswith("gguf"): for name, param in convert_gguf_to_state_dict(model_name_or_path, config).items(): yield name, param return hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights( model_name_or_path, cache_dir=cache_dir, load_format=load_format, fall_back_to_pt=fall_back_to_pt, revision=revision, ) if load_format == "npcache": # Currently np_cache only support *.bin checkpoints assert use_safetensors is False # Convert the model weights from torch tensors to numpy arrays for # faster loading. np_folder = os.path.join(hf_folder, "np") os.makedirs(np_folder, exist_ok=True) weight_names_file = os.path.join(np_folder, "weight_names.json") # Use file lock to prevent multiple processes from # dumping the same model weights to numpy at the same time. with get_lock(model_name_or_path, cache_dir): if not os.path.exists(weight_names_file): weight_names = [] for bin_file in hf_weights_files: state = torch.load(bin_file, map_location="cpu") for name, param in state.items(): param_path = os.path.join(np_folder, name) with open(param_path, "wb") as f: np.save(f, param.cpu().detach().numpy()) weight_names.append(name) with open(weight_names_file, "w") as f: json.dump(weight_names, f) with open(weight_names_file, "r") as f: weight_names = json.load(f) for name in weight_names: param_path = os.path.join(np_folder, name) with open(param_path, "rb") as f: param = np.load(f) yield name, torch.from_numpy(param) elif use_safetensors: for st_file in hf_weights_files: with safe_open(st_file, framework="pt") as f: for name in f.keys(): # noqa: SIM118 param = f.get_tensor(name) yield name, param else: for bin_file in hf_weights_files: state = torch.load(bin_file, map_location="cpu") for name, param in state.items(): yield name, param del state torch.cuda.empty_cache() def kv_cache_scales_loader( filename: str, tp_rank: int, tp_size: int, num_hidden_layers: int, model_type: Optional[str]) -> Iterable[Tuple[int, float]]: """ A simple utility to read in KV cache scaling factors that have been previously serialized to disk. Used by the model to populate the appropriate KV cache scaling factors. The serialization should represent a dictionary whose keys are the TP ranks and values are another dictionary mapping layers to their KV cache scaling factors. Keep this function in sync with the output of examples/fp8/extract_scales.py """ try: with open(filename) as f: context = { "model_type": model_type, "num_hidden_layers": num_hidden_layers, "tp_rank": tp_rank, "tp_size": tp_size, } schema_dct = json.load(f) schema = QuantParamSchema.model_validate(schema_dct, context=context) layer_scales_map = schema.kv_cache.scaling_factor[tp_rank] return layer_scales_map.items() except FileNotFoundError: logger.error(f"File or directory '{filename}' not found.") except json.JSONDecodeError: logger.error(f"Error decoding JSON in file '{filename}'.") except Exception as e: logger.error(f"An error occurred while reading '{filename}': {e}") # This section is reached if and only if any of the excepts are hit # Return an empty iterable (list) => no KV cache scales are loaded # which ultimately defaults to 1.0 scales logger.warning("Defaulting to KV cache scaling factors = 1.0 " f"for all layers in TP rank {tp_rank} " "as an error occurred during loading.") return [] def convert_pyslice_to_tensor(x: Any) -> torch.Tensor: """convert PySafeSlice object from safetensors to torch.Tensor PySafeSlice object supports indexing, which is done before loading the actual tensor and can reduce the amount of memory being read into the memory. However, it does not support more advanced functionalities like `.view()` or `.t()`. Therefore, if we need to modify the loaded tensor with these more complicated operators, we need to convert to tensor first. """ if not isinstance(x, torch.Tensor): x = x[:] return x def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Default weight loader.""" if isinstance(param, torch.nn.parameter.UninitializedParameter): param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype) assert param.size() == loaded_weight.size() param.data.copy_(loaded_weight) def initialize_dummy_weights( model: torch.nn.Module, low: float = -1e-3, high: float = 1e-3, ) -> None: """Initialize model weights with random values. The model weights must be randomly initialized for accurate performance measurements. Additionally, the model weights should not cause NaNs in the forward pass. We empirically found that initializing the weights with values between -1e-3 and 1e-3 works well for most models. """ for param in model.state_dict().values(): if torch.is_floating_point(param): param.data.uniform_(low, high)