mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-12-01 11:54:04 +01:00
114 lines
4.2 KiB
Python
114 lines
4.2 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 shared
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from modules.logging_colors import logger
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if torch.cuda.is_available() and not torch.version.hip:
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try:
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from llama_cpp_cuda import Llama
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except:
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from llama_cpp import Llama
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else:
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from llama_cpp import Llama
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class LlamacppHF(PreTrainedModel):
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def __init__(self, model):
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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.cache = None
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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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@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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# TODO: Some decoding methods (such as Contrastive Search) may not work at this time
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assert len(args) == 0, 'no *args should be passed to forward'
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use_cache = kwargs.get('use_cache', True)
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labels = kwargs.get('labels', None)
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seq = kwargs['input_ids'][0].tolist()
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cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
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# Make the forward call
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seq_tensor = torch.tensor(seq)
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if labels is None:
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if self.cache is None or not torch.equal(self.cache, seq_tensor[:-1]):
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self.model.reset()
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self.model.eval(seq)
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else:
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self.model.eval([seq[-1]])
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logits = torch.tensor(self.model.eval_logits[-1]).view(1, 1, -1).to(kwargs['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(kwargs['input_ids'].device)
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self.cache = seq_tensor
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# Based on transformers/models/llama/modeling_llama.py
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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=cache 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('*ggml*.bin'))[0]
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logger.info(f"llama.cpp weights detected: {model_file}\n")
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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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'seed': int(shared.args.llama_cpp_seed),
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'n_threads': shared.args.threads 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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'low_vram': shared.args.low_vram,
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'n_gpu_layers': shared.args.n_gpu_layers,
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'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.),
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'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
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'logits_all': True,
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}
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model = Llama(**params)
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return LlamacppHF(model)
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