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Remove non-HF ExLlamaV2 loader (#5431)
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oobabooga
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b6077b02e4
commit
cde000d478
@ -12,7 +12,7 @@ from modules.models import reload_model
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def add_lora_to_model(lora_names):
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if 'GPTQForCausalLM' in shared.model.__class__.__name__ or shared.args.loader == 'AutoGPTQ':
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add_lora_autogptq(lora_names)
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elif shared.model.__class__.__name__ in ['Exllamav2Model', 'Exllamav2HF'] or shared.args.loader == ['ExLlamav2', 'ExLlamav2_HF']:
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elif shared.model.__class__.__name__ in ['Exllamav2HF'] or shared.args.loader == ['ExLlamav2_HF']:
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add_lora_exllamav2(lora_names)
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else:
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add_lora_transformers(lora_names)
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@ -39,11 +39,7 @@ def add_lora_exllamav2(lora_names):
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shared.model.loras = []
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for lora_name in lora_names:
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lora_path = get_lora_path(lora_name)
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if shared.model.__class__.__name__ == 'Exllamav2Model':
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lora = ExLlamaV2Lora.from_directory(shared.model.model, str(lora_path))
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else:
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lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path))
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lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path))
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shared.model.loras.append(lora)
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shared.lora_names = lora_names
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@ -1,149 +0,0 @@
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import traceback
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from pathlib import Path
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import torch
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from exllamav2 import (
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ExLlamaV2,
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ExLlamaV2Cache,
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ExLlamaV2Cache_8bit,
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ExLlamaV2Config,
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ExLlamaV2Tokenizer
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)
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from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator
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from modules import shared
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from modules.logging_colors import logger
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from modules.text_generation import get_max_prompt_length
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try:
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import flash_attn
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except ModuleNotFoundError:
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logger.warning(
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'You are running ExLlamaV2 without flash-attention. This will cause the VRAM usage '
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'to be a lot higher than it could be.\n'
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'Try installing flash-attention following the instructions here: '
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'https://github.com/Dao-AILab/flash-attention#installation-and-features'
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)
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pass
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except Exception:
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logger.warning('Failed to load flash-attention due to the following error:\n')
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traceback.print_exc()
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class Exllamav2Model:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path_to_model):
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path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
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config = ExLlamaV2Config()
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config.model_dir = str(path_to_model)
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config.prepare()
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config.max_seq_len = shared.args.max_seq_len
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config.scale_pos_emb = shared.args.compress_pos_emb
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config.scale_alpha_value = shared.args.alpha_value
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config.no_flash_attn = shared.args.no_flash_attn
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config.num_experts_per_token = int(shared.args.num_experts_per_token)
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model = ExLlamaV2(config)
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split = None
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if shared.args.gpu_split:
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split = [float(alloc) for alloc in shared.args.gpu_split.split(",")]
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model.load(split)
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tokenizer = ExLlamaV2Tokenizer(config)
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if shared.args.cache_8bit:
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cache = ExLlamaV2Cache_8bit(model)
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else:
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cache = ExLlamaV2Cache(model)
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generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)
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result = self()
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result.model = model
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result.cache = cache
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result.tokenizer = tokenizer
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result.generator = generator
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result.loras = None
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return result, result
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def encode(self, string, **kwargs):
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return self.tokenizer.encode(string, add_bos=True, encode_special_tokens=True)
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def decode(self, ids, **kwargs):
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if isinstance(ids, list):
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ids = torch.tensor([ids])
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elif isinstance(ids, torch.Tensor) and ids.numel() == 1:
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ids = ids.view(1, -1)
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return self.tokenizer.decode(ids, decode_special_tokens=True)[0]
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def get_logits(self, token_ids, **kwargs):
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self.cache.current_seq_len = 0
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if token_ids.shape[-1] > 1:
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self.model.forward(token_ids[:, :-1], self.cache, input_mask=None, preprocess_only=True, loras=self.loras)
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return self.model.forward(token_ids[:, -1:], self.cache, input_mask=None, loras=self.loras, **kwargs).float().cpu()
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def generate_with_streaming(self, prompt, state):
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settings = ExLlamaV2Sampler.Settings()
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settings.token_repetition_penalty = state['repetition_penalty']
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settings.token_repetition_range = -1 if state['repetition_penalty_range'] <= 0 else state['repetition_penalty_range']
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settings.token_frequency_penalty = state['frequency_penalty']
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settings.token_presence_penalty = state['presence_penalty']
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settings.temperature = state['temperature']
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settings.top_k = state['top_k']
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settings.top_p = state['top_p']
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settings.top_a = state['top_a']
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settings.min_p = state['min_p']
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settings.tfs = state['tfs']
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settings.typical = state['typical_p']
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settings.temperature_last = state['temperature_last']
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settings.mirostat = state['mirostat_mode'] == 2
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settings.mirostat_tau = state['mirostat_tau']
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settings.mirostat_eta = state['mirostat_eta']
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if state['ban_eos_token']:
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settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
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if state['custom_token_bans']:
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to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
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if len(to_ban) > 0:
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settings.disallow_tokens(self.tokenizer, to_ban)
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ids = self.tokenizer.encode(prompt, add_bos=state['add_bos_token'], encode_special_tokens=True)
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ids = ids[:, -get_max_prompt_length(state):]
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if state['auto_max_new_tokens']:
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max_new_tokens = state['truncation_length'] - ids.shape[-1]
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else:
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max_new_tokens = state['max_new_tokens']
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self.generator.begin_stream(ids, settings, loras=self.loras)
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decoded_text = ''
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for i in range(max_new_tokens):
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chunk, eos, _ = self.generator.stream()
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if eos or shared.stop_everything:
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break
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decoded_text += chunk
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yield decoded_text
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def generate(self, prompt, state):
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output = ''
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for output in self.generate_with_streaming(prompt, state):
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pass
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return output
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@ -81,16 +81,6 @@ loaders_and_params = OrderedDict({
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'trust_remote_code',
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'no_use_fast',
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],
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'ExLlamav2': [
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'gpu_split',
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'max_seq_len',
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'no_flash_attn',
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'num_experts_per_token',
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'cache_8bit',
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'alpha_value',
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'compress_pos_emb',
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'exllamav2_info',
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],
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'AutoGPTQ': [
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'triton',
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'no_inject_fused_attention',
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@ -204,29 +194,6 @@ loaders_samplers = {
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'AutoAWQ': transformers_samplers(),
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'QuIP#': transformers_samplers(),
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'HQQ': transformers_samplers(),
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'ExLlamav2': {
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'temperature',
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'temperature_last',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'tfs',
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'top_a',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'repetition_penalty_range',
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'seed',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'ban_eos_token',
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'add_bos_token',
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'custom_token_bans',
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'skip_special_tokens',
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'auto_max_new_tokens',
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},
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'ExLlamav2_HF': {
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'temperature',
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'temperature_last',
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@ -13,11 +13,10 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
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logger.error("No model is loaded! Select one in the Model tab.")
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return 'Error: No model is loaded1 Select one in the Model tab.', previous
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is_non_hf_exllamav2 = shared.model.__class__.__name__ == 'Exllamav2Model'
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is_non_hf_llamacpp = shared.model.__class__.__name__ == 'LlamaCppModel'
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if use_samplers:
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if any([is_non_hf_exllamav2, is_non_hf_llamacpp]):
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if is_non_hf_llamacpp:
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logger.error("Sampler hijacking is not supported non-Huggingface loaders.")
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# sampling is all done in c for exllama, so it is really hard to hijack
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# it should be possible to hijack llamacpp sampler by hijacking all their sampling methods,
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@ -31,13 +30,7 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
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scores = sampler_hijack.global_scores[-1]
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else:
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if is_non_hf_exllamav2:
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if is_torch_xpu_available():
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tokens = shared.tokenizer.encode(prompt).to("xpu:0")
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else:
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tokens = shared.tokenizer.encode(prompt).cuda()
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scores = shared.model.get_logits(tokens)[-1][-1]
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elif is_non_hf_llamacpp:
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if is_non_hf_llamacpp:
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tokens = shared.tokenizer.encode(prompt)
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scores = shared.model.get_logits(tokens)[-1][-1]
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else:
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@ -45,6 +38,7 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
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tokens = shared.tokenizer.encode(prompt, return_tensors='pt').to("xpu:0")
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else:
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tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
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output = shared.model(input_ids=tokens)
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scores = output['logits'][-1][-1]
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@ -65,7 +65,6 @@ def load_model(model_name, loader=None):
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'GPTQ-for-LLaMa': GPTQ_loader,
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'llama.cpp': llamacpp_loader,
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'llamacpp_HF': llamacpp_HF_loader,
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'ExLlamav2': ExLlamav2_loader,
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'ExLlamav2_HF': ExLlamav2_HF_loader,
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'ctransformers': ctransformers_loader,
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'AutoAWQ': AutoAWQ_loader,
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@ -376,13 +375,6 @@ def AutoGPTQ_loader(model_name):
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return modules.AutoGPTQ_loader.load_quantized(model_name)
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def ExLlamav2_loader(model_name):
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from modules.exllamav2 import Exllamav2Model
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model, tokenizer = Exllamav2Model.from_pretrained(model_name)
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return model, tokenizer
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def ExLlamav2_HF_loader(model_name):
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from modules.exllamav2_hf import Exllamav2HF
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@ -141,6 +141,8 @@ def get_model_metadata(model):
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if re.match(pat.lower(), model.lower()):
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for k in settings[pat]:
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model_settings[k] = settings[pat][k]
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if k == 'loader' and settings[pat][k] == 'ExLlamav2':
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model_settings[k] = 'ExLlamav2_HF'
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return model_settings
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@ -88,7 +88,7 @@ group.add_argument('--chat-buttons', action='store_true', help='Show buttons on
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# Model loader
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group = parser.add_argument_group('Model loader')
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group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, ctransformers, QuIP#.')
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group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, ctransformers, QuIP#.')
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# Transformers/Accelerate
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group = parser.add_argument_group('Transformers/Accelerate')
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@ -130,11 +130,11 @@ group.add_argument('--logits_all', action='store_true', help='Needs to be set fo
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group.add_argument('--no_offload_kqv', action='store_true', help='Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.')
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group.add_argument('--cache-capacity', type=str, help='Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
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# ExLlama
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group = parser.add_argument_group('ExLlama')
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# ExLlamaV2
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group = parser.add_argument_group('ExLlamaV2')
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group.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.')
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group.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
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group.add_argument('--cfg-cache', action='store_true', help='ExLlamav2_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
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group.add_argument('--cfg-cache', action='store_true', help='Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
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group.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
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group.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
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group.add_argument('--num_experts_per_token', type=int, default=2, help='Number of experts to use for generation. Applies to MoE models like Mixtral.')
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@ -248,11 +248,7 @@ def fix_loader_name(name):
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return 'AutoGPTQ'
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elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']:
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return 'GPTQ-for-LLaMa'
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elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']:
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return 'ExLlama'
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elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']:
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return 'ExLlamav2'
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elif name in ['exllamav2-hf', 'exllamav2_hf', 'exllama-v2-hf', 'exllama_v2_hf', 'exllama-v2_hf', 'exllama2-hf', 'exllama2_hf', 'exllama-2-hf', 'exllama_2_hf', 'exllama-2_hf']:
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elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2', 'exllama', 'ex-llama', 'ex_llama', 'exlama', 'exllamav2-hf', 'exllamav2_hf', 'exllama-v2-hf', 'exllama_v2_hf', 'exllama-v2_hf', 'exllama2-hf', 'exllama2_hf', 'exllama-2-hf', 'exllama_2_hf', 'exllama-2_hf']:
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return 'ExLlamav2_HF'
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elif name in ['ctransformers', 'ctranforemrs', 'ctransformer']:
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return 'ctransformers'
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@ -45,7 +45,7 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False, escap
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yield ''
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return
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel']:
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel']:
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generate_func = generate_reply_custom
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else:
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generate_func = generate_reply_HF
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@ -120,10 +120,11 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
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if shared.tokenizer is None:
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raise ValueError('No tokenizer is loaded')
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel', 'Exllamav2Model']:
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel']:
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input_ids = shared.tokenizer.encode(str(prompt))
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if shared.model.__class__.__name__ not in ['Exllamav2Model']:
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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# The step below is necessary for llama.cpp, but may not be
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# necessary for future loaders.
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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else:
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
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if not add_bos_token:
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@ -134,7 +135,7 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
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if truncation_length is not None:
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input_ids = input_ids[:, -truncation_length:]
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel'] or shared.args.cpu:
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel'] or shared.args.cpu:
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return input_ids
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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@ -135,7 +135,6 @@ def create_ui():
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shared.gradio['no_use_fast'] = gr.Checkbox(label="no_use_fast", value=shared.args.no_use_fast, info='Set use_fast=False while loading the tokenizer.')
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shared.gradio['num_experts_per_token'] = gr.Number(label="Number of experts per token", value=shared.args.num_experts_per_token, info='Only applies to MoE models like Mixtral.')
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shared.gradio['gptq_for_llama_info'] = gr.Markdown('Legacy loader for compatibility with older GPUs. ExLlamav2_HF or AutoGPTQ are preferred for GPTQ models when supported.')
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shared.gradio['exllamav2_info'] = gr.Markdown("ExLlamav2_HF is recommended over ExLlamav2 for better integration with extensions and more consistent sampling behavior across loaders.")
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shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF loads llama.cpp as a Transformers model. To use it, you need to download a tokenizer.\n\nOption 1 (recommended): place your .gguf in a subfolder of models/ along with these 4 files: special_tokens_map.json, tokenizer_config.json, tokenizer.json, tokenizer.model.\n\nOption 2: download `oobabooga/llama-tokenizer` under "Download model or LoRA". That\'s a default Llama tokenizer that will work for some (but not all) models.')
|
||||
|
||||
with gr.Column():
|
||||
|
||||
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Reference in New Issue
Block a user