text-generation-webui/modules/llamacpp_hf.py

227 lines
8.0 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 RoPE, llama_cpp_python_hijack, shared
from modules.logging_colors import logger
try:
import llama_cpp
except:
llama_cpp = None
try:
import llama_cpp_cuda
except:
llama_cpp_cuda = None
try:
import llama_cpp_cuda_tensorcores
except:
llama_cpp_cuda_tensorcores = None
def llama_cpp_lib():
if shared.args.cpu and llama_cpp is not None:
return llama_cpp
elif shared.args.tensorcores and llama_cpp_cuda_tensorcores is not None:
return llama_cpp_cuda_tensorcores
elif llama_cpp_cuda is not None:
return llama_cpp_cuda
else:
return llama_cpp
class LlamacppHF(PreTrainedModel):
def __init__(self, model, path):
super().__init__(PretrainedConfig())
self.model = model
self.generation_config = GenerationConfig()
self.past_seq = None
self.llamacpp_cache = {
'n_tokens': self.model.n_tokens,
'input_ids': self.model.input_ids,
'scores': self.model.scores,
'ctx': self.model._ctx
}
if shared.args.cfg_cache:
self.past_seq_negative = None
self.llamacpp_cache_negative = {
'n_tokens': self.model.n_tokens,
'input_ids': self.model.input_ids.copy(),
'scores': self.model.scores.copy(),
'ctx': llama_cpp_lib().llama_new_context_with_model(model.model, model.context_params)
}
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}
def save_cache(self):
self.llamacpp_cache.update({
'n_tokens': self.model.n_tokens,
'input_ids': self.model.input_ids,
'scores': self.model.scores,
'ctx': self.model._ctx
})
def save_negative_cache(self):
self.llamacpp_cache_negative.update({
'n_tokens': self.model.n_tokens,
'input_ids': self.model.input_ids,
'scores': self.model.scores,
'ctx': self.model._ctx
})
def load_cache(self):
self.model.n_tokens = self.llamacpp_cache['n_tokens']
self.model.input_ids = self.llamacpp_cache['input_ids']
self.model.scores = self.llamacpp_cache['scores']
self.model._ctx = self.llamacpp_cache['ctx']
def load_negative_cache(self):
self.model.n_tokens = self.llamacpp_cache_negative['n_tokens']
self.model.input_ids = self.llamacpp_cache_negative['input_ids']
self.model.scores = self.llamacpp_cache_negative['scores']
self.model._ctx = self.llamacpp_cache_negative['ctx']
@property
def device(self) -> torch.device:
return torch.device(0)
def __call__(self, *args, **kwargs):
use_cache = kwargs.get('use_cache', True)
labels = kwargs.get('labels', None)
past_key_values = kwargs.get('past_key_values', None)
if len(args) > 0:
if not shared.args.cfg_cache:
logger.error("Please enable the cfg-cache option to use CFG with llamacpp_HF.")
return
input_ids = args[0]
is_negative = True
past_seq = self.past_seq_negative
self.load_negative_cache()
else:
input_ids = kwargs['input_ids']
is_negative = False
past_seq = self.past_seq
self.load_cache()
seq = input_ids[0].tolist()
if is_negative and past_key_values is not None:
seq = past_key_values + seq
seq_tensor = torch.tensor(seq)
reset = True
# Make the forward call. The prefix-match code has been adapted from
# https://github.com/abetlen/llama-cpp-python/commit/f4090a0bb2a2a25acfe28d31c82cc1aa273bedee
if labels is None:
if past_seq is not None:
min_length = min(past_seq.shape[0], seq_tensor.shape[0])
indices = torch.nonzero(~torch.eq(past_seq[:min_length], seq_tensor[:min_length]))
if len(indices) > 0:
longest_prefix = indices[0].item()
else:
longest_prefix = min_length
if longest_prefix > 0:
reset = False
self.model.n_tokens = longest_prefix
if len(seq_tensor) - longest_prefix > 0:
self.model.eval(seq[longest_prefix:])
else:
self.model.n_tokens -= 1
self.model.eval([seq[-1]])
if reset:
self.model.reset()
self.model.eval(seq)
logits = torch.tensor(self.model.scores[self.model.n_tokens - 1, :]).view(1, 1, -1).to(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(input_ids.device)
if is_negative:
self.save_negative_cache()
self.past_seq_negative = seq_tensor
else:
self.save_cache()
self.past_seq = seq_tensor
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=seq 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('*.gguf'))[0]
logger.info(f"llama.cpp weights detected: {model_file}\n")
if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
tensor_split_list = None
else:
tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
params = {
'model_path': str(model_file),
'n_ctx': shared.args.n_ctx,
'n_threads': shared.args.threads or None,
'n_threads_batch': shared.args.threads_batch or None,
'n_batch': shared.args.n_batch,
'use_mmap': not shared.args.no_mmap,
'use_mlock': shared.args.mlock,
'mul_mat_q': not shared.args.no_mul_mat_q,
'numa': shared.args.numa,
'n_gpu_layers': shared.args.n_gpu_layers,
'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
'tensor_split': tensor_split_list,
'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
'logits_all': shared.args.logits_all,
'offload_kqv': not shared.args.no_offload_kqv,
'split_mode': 1 if not shared.args.row_split else 2
}
Llama = llama_cpp_lib().Llama
model = Llama(**params)
return LlamacppHF(model, model_file)