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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