Untitled
unknown
plain_text
a year ago
8.8 kB
2
Indexable
Never
import torch from transformers import AutoTokenizer, AutoModelForCausalLM from huggingface_hub import hf_hub_download, snapshot_download from accelerate.utils import load_and_quantize_model import gc from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = { "syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json", } class LongLlamaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LongLLaMA-7B. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the LongLLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LongLlamaModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings mem_layers (`List[int]`, defaults to `[]`): Layers with memory mem_positionals (`bool`, *optional*, defaults to `True`): Whether to use positional embeddings in memory layers mem_dtype (`str`, *optional*, defaults to `"bfloat16"`): Type for keys and values stored in memory mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`): One can trade speed for memory by performing attention in memory layers sequentially. When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once. That is at most 4*2048 queries at once. torch_attention (`bool`, *optional*, defaults to `False`): Whether to use torch scaled_dot_product_attention gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`): When gradient checkpointing is enabled checkpoint every ith layer Example: ```python >>> from transformers import LongLlamaModel, LongLlamaConfig >>> # Initializing a LongLLaMA longllama-7b style configuration >>> configuration = LongLlamaConfig() >>> # Initializing a model from the longllama-7b style configuration >>> model = LongLlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "longllama" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, last_context_length=1024, mem_layers=[], mem_positionals=True, mem_dtype="bfloat16", mem_attention_grouping=None, torch_attention=False, gradient_checkpoint_every_ith=1, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.last_context_length = last_context_length self.mem_layers = mem_layers self.mem_positionals = mem_positionals self.mem_dtype = mem_dtype self.mem_attention_grouping = mem_attention_grouping self.torch_attention = torch_attention self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith self._rope_scaling_validation() super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) def _rope_scaling_validation(self): """ Validate the `rope_scaling` configuration. """ if self.rope_scaling is not None: raise ValueError("LongLLaMA does not use rope_scaling") def load_and_quantize_llama_model(model_path: str, tokenizer_path: str, device: torch.device, quantized: bool = True, num_bit: int = 8): """ Wczytuje i kwantyzuje model LongLLaMA. Returns: model (LongLlamaForCausalLM): Wczytany i skwantyzowany model. tokenizer (AutoTokenizer): Tokenizator dla modelu. """ try: tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) if not quantized: model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True, mem_attention_grouping=(1, 1024), ) model.eval() else: model = _quantized_model_loading(num_bit, model_path, device) return model, tokenizer except Exception as e: raise RuntimeError(f"Błąd podczas wczytywania modelu: {str(e)}") def _quantized_model_loading(num_bit, model_path, device): """ Procedura ładująca skwantyzowany model LongLLaMA. """ print(f"!!!!!WARNING!!!!! Model będzie kwantyzowany do {num_bit} bitów!\n" "Może to wpłynąć na wydajność modelu!") cfg = LongLlamaConfig.from_pretrained(model_path) cfg.mem_attention_grouping = (1, 1024) with init_empty_weights(): empty_model = LongLlamaForCausalLM(cfg) gc.collect() if num_bit == 8: weights_loc = hf_hub_download(repo_id=model_path, filename="quantized/pytorch_model_8bit.bin") bnb_quantization_config = BnbQuantizationConfig(load_in_8bit=True, llm_int8_threshold=6) elif num_bit == 4: weights_loc = snapshot_download(model_path) bnb_quantization_config = BnbQuantizationConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) else: raise ValueError(f"Kwantyzacja {num_bit} nie jest obsługiwana.") gc.collect() model = transformers_load_and_quantize_model(empty_model, weights_location=weights_loc, bnb_quantization_config=bnb_quantization_config, device_map="auto") model.eval() return model