text-generation-webui/modules/llamacpp_hf.py

110 lines
4.1 KiB
Python

import os
from pathlib import Path
from typing import Any, Dict, Optional, Union
import torch
from torch.nn import CrossEntropyLoss
from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from modules import shared
from modules.logging_colors import logger
if torch.cuda.is_available():
from llama_cpp_cuda import Llama
else:
from llama_cpp import Llama
class LlamacppHF(PreTrainedModel):
def __init__(self, model):
super().__init__(PretrainedConfig())
self.model = model
self.generation_config = GenerationConfig()
self.cache = None
def _validate_model_class(self):
pass
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
pass
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {'input_ids': input_ids, **kwargs}
@property
def device(self) -> torch.device:
return torch.device(0)
def __call__(self, *args, **kwargs):
# TODO: Some decoding methods (such as Contrastive Search) may not work at this time
assert len(args) == 0, 'no *args should be passed to forward'
use_cache = kwargs.get('use_cache', True)
labels = kwargs.get('labels', None)
seq = kwargs['input_ids'][0].tolist()
cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
# Make the forward call
seq_tensor = torch.tensor(seq)
if labels is None:
if self.cache is None or not torch.equal(self.cache, seq_tensor[:-1]):
self.model.reset()
self.model.eval(seq)
else:
self.model.eval([seq[-1]])
logits = torch.tensor(self.model.eval_logits[-1]).view(1, 1, -1).to(kwargs['input_ids'].device)
else:
self.model.reset()
self.model.eval(seq)
logits = torch.tensor(self.model.eval_logits)
logits = logits.view(1, logits.shape[0], logits.shape[1]).to(kwargs['input_ids'].device)
self.cache = seq_tensor
# Based on transformers/models/llama/modeling_llama.py
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, logits.shape[-1])
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
return CausalLMOutputWithPast(logits=logits, past_key_values=cache if use_cache else None, loss=loss)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
if isinstance(pretrained_model_name_or_path, str):
pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path)
if path.is_file():
model_file = path
else:
model_file = list(path.glob('*ggml*.bin'))[0]
logger.info(f"llama.cpp weights detected: {model_file}\n")
params = {
'model_path': str(model_file),
'n_ctx': shared.args.n_ctx,
'seed': int(shared.args.llama_cpp_seed),
'n_threads': shared.args.threads or None,
'n_batch': shared.args.n_batch,
'use_mmap': not shared.args.no_mmap,
'use_mlock': shared.args.mlock,
'low_vram': shared.args.low_vram,
'n_gpu_layers': shared.args.n_gpu_layers,
'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.),
'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
'logits_all': True,
}
model = Llama(**params)
return LlamacppHF(model)