mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
227 lines
8.0 KiB
Python
227 lines
8.0 KiB
Python
import os
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from pathlib import Path
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from typing import Any, Dict, Optional, Union
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import torch
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from torch.nn import CrossEntropyLoss
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from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from modules import RoPE, llama_cpp_python_hijack, shared
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from modules.logging_colors import logger
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try:
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import llama_cpp
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except:
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llama_cpp = None
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try:
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import llama_cpp_cuda
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except:
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llama_cpp_cuda = None
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try:
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import llama_cpp_cuda_tensorcores
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except:
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llama_cpp_cuda_tensorcores = None
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def llama_cpp_lib():
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if shared.args.cpu and llama_cpp is not None:
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return llama_cpp
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elif shared.args.tensorcores and llama_cpp_cuda_tensorcores is not None:
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return llama_cpp_cuda_tensorcores
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elif llama_cpp_cuda is not None:
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return llama_cpp_cuda
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else:
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return llama_cpp
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class LlamacppHF(PreTrainedModel):
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def __init__(self, model, path):
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super().__init__(PretrainedConfig())
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self.model = model
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self.generation_config = GenerationConfig()
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self.past_seq = None
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self.llamacpp_cache = {
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'n_tokens': self.model.n_tokens,
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'input_ids': self.model.input_ids,
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'scores': self.model.scores,
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'ctx': self.model._ctx
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}
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if shared.args.cfg_cache:
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self.past_seq_negative = None
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self.llamacpp_cache_negative = {
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'n_tokens': self.model.n_tokens,
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'input_ids': self.model.input_ids.copy(),
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'scores': self.model.scores.copy(),
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'ctx': llama_cpp_lib()._internals._LlamaContext(model=model._model, params=model.context_params)
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}
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def _validate_model_class(self):
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pass
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def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
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pass
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def prepare_inputs_for_generation(self, input_ids, **kwargs):
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return {'input_ids': input_ids, **kwargs}
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def save_cache(self):
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self.llamacpp_cache.update({
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'n_tokens': self.model.n_tokens,
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'input_ids': self.model.input_ids,
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'scores': self.model.scores,
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'ctx': self.model._ctx
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})
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def save_negative_cache(self):
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self.llamacpp_cache_negative.update({
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'n_tokens': self.model.n_tokens,
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'input_ids': self.model.input_ids,
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'scores': self.model.scores,
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'ctx': self.model._ctx
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})
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def load_cache(self):
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self.model.n_tokens = self.llamacpp_cache['n_tokens']
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self.model.input_ids = self.llamacpp_cache['input_ids']
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self.model.scores = self.llamacpp_cache['scores']
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self.model._ctx = self.llamacpp_cache['ctx']
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def load_negative_cache(self):
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self.model.n_tokens = self.llamacpp_cache_negative['n_tokens']
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self.model.input_ids = self.llamacpp_cache_negative['input_ids']
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self.model.scores = self.llamacpp_cache_negative['scores']
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self.model._ctx = self.llamacpp_cache_negative['ctx']
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@property
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def device(self) -> torch.device:
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return torch.device(0)
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def __call__(self, *args, **kwargs):
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use_cache = kwargs.get('use_cache', True)
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labels = kwargs.get('labels', None)
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past_key_values = kwargs.get('past_key_values', None)
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if len(args) > 0:
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if not shared.args.cfg_cache:
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logger.error("Please enable the cfg-cache option to use CFG with llamacpp_HF.")
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return
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input_ids = args[0]
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is_negative = True
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past_seq = self.past_seq_negative
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self.load_negative_cache()
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else:
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input_ids = kwargs['input_ids']
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is_negative = False
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past_seq = self.past_seq
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self.load_cache()
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seq = input_ids[0].tolist()
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if is_negative and past_key_values is not None:
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seq = past_key_values + seq
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seq_tensor = torch.tensor(seq)
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reset = True
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# Make the forward call. The prefix-match code has been adapted from
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# https://github.com/abetlen/llama-cpp-python/commit/f4090a0bb2a2a25acfe28d31c82cc1aa273bedee
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if labels is None:
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if past_seq is not None:
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min_length = min(past_seq.shape[0], seq_tensor.shape[0])
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indices = torch.nonzero(~torch.eq(past_seq[:min_length], seq_tensor[:min_length]))
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if len(indices) > 0:
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longest_prefix = indices[0].item()
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else:
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longest_prefix = min_length
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if longest_prefix > 0:
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reset = False
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self.model.n_tokens = longest_prefix
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if len(seq_tensor) - longest_prefix > 0:
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self.model.eval(seq[longest_prefix:])
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else:
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self.model.n_tokens -= 1
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self.model.eval([seq[-1]])
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if reset:
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self.model.reset()
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self.model.eval(seq)
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logits = torch.tensor(self.model.scores[self.model.n_tokens - 1, :]).view(1, 1, -1).to(input_ids.device)
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else:
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self.model.reset()
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self.model.eval(seq)
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logits = torch.tensor(self.model.eval_logits)
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logits = logits.view(1, logits.shape[0], logits.shape[1]).to(input_ids.device)
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if is_negative:
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self.save_negative_cache()
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self.past_seq_negative = seq_tensor
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else:
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self.save_cache()
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self.past_seq = seq_tensor
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, logits.shape[-1])
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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loss = loss_fct(shift_logits, shift_labels)
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return CausalLMOutputWithPast(logits=logits, past_key_values=seq if use_cache else None, loss=loss)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
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assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
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if isinstance(pretrained_model_name_or_path, str):
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pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
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path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path)
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if path.is_file():
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model_file = path
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else:
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model_file = list(path.glob('*.gguf'))[0]
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logger.info(f"llama.cpp weights detected: {model_file}\n")
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if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
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tensor_split_list = None
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else:
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tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
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params = {
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'model_path': str(model_file),
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'n_ctx': shared.args.n_ctx,
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'n_threads': shared.args.threads or None,
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'n_threads_batch': shared.args.threads_batch or None,
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'n_batch': shared.args.n_batch,
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'use_mmap': not shared.args.no_mmap,
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'use_mlock': shared.args.mlock,
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'mul_mat_q': not shared.args.no_mul_mat_q,
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'numa': shared.args.numa,
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'n_gpu_layers': shared.args.n_gpu_layers,
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'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
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'tensor_split': tensor_split_list,
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'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
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'logits_all': shared.args.logits_all,
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'offload_kqv': not shared.args.no_offload_kqv,
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'split_mode': 1 if not shared.args.row_split else 2
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}
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Llama = llama_cpp_lib().Llama
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model = Llama(**params)
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return LlamacppHF(model, model_file)
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