mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
221 lines
7.6 KiB
Python
221 lines
7.6 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, shared
|
|
from modules.logging_colors import logger
|
|
from modules.utils import is_gguf
|
|
|
|
import llama_cpp
|
|
|
|
try:
|
|
import llama_cpp_ggml
|
|
except:
|
|
llama_cpp_ggml = llama_cpp
|
|
|
|
if torch.cuda.is_available() and not torch.version.hip:
|
|
try:
|
|
import llama_cpp_cuda
|
|
except:
|
|
llama_cpp_cuda = None
|
|
try:
|
|
import llama_cpp_ggml_cuda
|
|
except:
|
|
llama_cpp_ggml_cuda = llama_cpp_cuda
|
|
else:
|
|
llama_cpp_cuda = None
|
|
llama_cpp_ggml_cuda = None
|
|
|
|
|
|
def llama_cpp_lib(model_file: Union[str, Path] = None):
|
|
if model_file is not None:
|
|
gguf_model = is_gguf(model_file)
|
|
else:
|
|
gguf_model = True
|
|
|
|
if shared.args.cpu or llama_cpp_cuda is None:
|
|
return llama_cpp if gguf_model else llama_cpp_ggml
|
|
else:
|
|
return llama_cpp_cuda if gguf_model else llama_cpp_ggml_cuda
|
|
|
|
|
|
class LlamacppHF(PreTrainedModel):
|
|
def __init__(self, model):
|
|
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.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)
|
|
|
|
# Make the forward call
|
|
if labels is None:
|
|
if past_seq is None or not torch.equal(past_seq, seq_tensor[:-1]):
|
|
self.model.reset()
|
|
self.model.eval(seq)
|
|
else:
|
|
self.model.eval([seq[-1]])
|
|
|
|
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*')) + list(path.glob('*ggml*.bin')))[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,
|
|
'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,
|
|
'mul_mat_q': shared.args.mul_mat_q,
|
|
'low_vram': shared.args.low_vram,
|
|
'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': True,
|
|
}
|
|
|
|
if not is_gguf(model_file):
|
|
ggml_params = {
|
|
'n_gqa': shared.args.n_gqa or None,
|
|
'rms_norm_eps': shared.args.rms_norm_eps or None,
|
|
}
|
|
params = params | ggml_params
|
|
|
|
Llama = llama_cpp_lib(model_file).Llama
|
|
model = Llama(**params)
|
|
|
|
return LlamacppHF(model)
|