Remove non-HF ExLlamaV2 loader (#5431)

modules/LoRA.py

@@ -12,7 +12,7 @@ from modules.models import reload_model
 def add_lora_to_model(lora_names):
     if 'GPTQForCausalLM' in shared.model.__class__.__name__ or shared.args.loader == 'AutoGPTQ':
         add_lora_autogptq(lora_names)
-    elif shared.model.__class__.__name__ in ['Exllamav2Model', 'Exllamav2HF'] or shared.args.loader == ['ExLlamav2', 'ExLlamav2_HF']:
+    elif shared.model.__class__.__name__ in ['Exllamav2HF'] or shared.args.loader == ['ExLlamav2_HF']:
         add_lora_exllamav2(lora_names)
     else:
         add_lora_transformers(lora_names)
@@ -39,11 +39,7 @@ def add_lora_exllamav2(lora_names):
         shared.model.loras = []
         for lora_name in lora_names:
             lora_path = get_lora_path(lora_name)
-            if shared.model.__class__.__name__ == 'Exllamav2Model':
+            lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path))
-                lora = ExLlamaV2Lora.from_directory(shared.model.model, str(lora_path))
-            else:
-                lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path))
-
             shared.model.loras.append(lora)
 
         shared.lora_names = lora_names

modules/exllamav2.py

@@ -1,149 +0,0 @@
-import traceback
-from pathlib import Path
-
-import torch
-from exllamav2 import (
-    ExLlamaV2,
-    ExLlamaV2Cache,
-    ExLlamaV2Cache_8bit,
-    ExLlamaV2Config,
-    ExLlamaV2Tokenizer
-)
-from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator
-
-from modules import shared
-from modules.logging_colors import logger
-from modules.text_generation import get_max_prompt_length
-
-try:
-    import flash_attn
-except ModuleNotFoundError:
-    logger.warning(
-        'You are running ExLlamaV2 without flash-attention. This will cause the VRAM usage '
-        'to be a lot higher than it could be.\n'
-        'Try installing flash-attention following the instructions here: '
-        'https://github.com/Dao-AILab/flash-attention#installation-and-features'
-    )
-    pass
-except Exception:
-    logger.warning('Failed to load flash-attention due to the following error:\n')
-    traceback.print_exc()
-
-
-class Exllamav2Model:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path_to_model):
-
-        path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
-
-        config = ExLlamaV2Config()
-        config.model_dir = str(path_to_model)
-        config.prepare()
-
-        config.max_seq_len = shared.args.max_seq_len
-        config.scale_pos_emb = shared.args.compress_pos_emb
-        config.scale_alpha_value = shared.args.alpha_value
-        config.no_flash_attn = shared.args.no_flash_attn
-        config.num_experts_per_token = int(shared.args.num_experts_per_token)
-
-        model = ExLlamaV2(config)
-
-        split = None
-        if shared.args.gpu_split:
-            split = [float(alloc) for alloc in shared.args.gpu_split.split(",")]
-
-        model.load(split)
-
-        tokenizer = ExLlamaV2Tokenizer(config)
-        if shared.args.cache_8bit:
-            cache = ExLlamaV2Cache_8bit(model)
-        else:
-            cache = ExLlamaV2Cache(model)
-
-        generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)
-
-        result = self()
-        result.model = model
-        result.cache = cache
-        result.tokenizer = tokenizer
-        result.generator = generator
-        result.loras = None
-        return result, result
-
-    def encode(self, string, **kwargs):
-        return self.tokenizer.encode(string, add_bos=True, encode_special_tokens=True)
-
-    def decode(self, ids, **kwargs):
-        if isinstance(ids, list):
-            ids = torch.tensor([ids])
-        elif isinstance(ids, torch.Tensor) and ids.numel() == 1:
-            ids = ids.view(1, -1)
-
-        return self.tokenizer.decode(ids, decode_special_tokens=True)[0]
-
-    def get_logits(self, token_ids, **kwargs):
-        self.cache.current_seq_len = 0
-        if token_ids.shape[-1] > 1:
-            self.model.forward(token_ids[:, :-1], self.cache, input_mask=None, preprocess_only=True, loras=self.loras)
-
-        return self.model.forward(token_ids[:, -1:], self.cache, input_mask=None, loras=self.loras, **kwargs).float().cpu()
-
-    def generate_with_streaming(self, prompt, state):
-        settings = ExLlamaV2Sampler.Settings()
-
-        settings.token_repetition_penalty = state['repetition_penalty']
-        settings.token_repetition_range = -1 if state['repetition_penalty_range'] <= 0 else state['repetition_penalty_range']
-
-        settings.token_frequency_penalty = state['frequency_penalty']
-        settings.token_presence_penalty = state['presence_penalty']
-
-        settings.temperature = state['temperature']
-        settings.top_k = state['top_k']
-        settings.top_p = state['top_p']
-        settings.top_a = state['top_a']
-        settings.min_p = state['min_p']
-        settings.tfs = state['tfs']
-        settings.typical = state['typical_p']
-
-        settings.temperature_last = state['temperature_last']
-
-        settings.mirostat = state['mirostat_mode'] == 2
-        settings.mirostat_tau = state['mirostat_tau']
-        settings.mirostat_eta = state['mirostat_eta']
-
-        if state['ban_eos_token']:
-            settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
-
-        if state['custom_token_bans']:
-            to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
-            if len(to_ban) > 0:
-                settings.disallow_tokens(self.tokenizer, to_ban)
-
-        ids = self.tokenizer.encode(prompt, add_bos=state['add_bos_token'], encode_special_tokens=True)
-        ids = ids[:, -get_max_prompt_length(state):]
-
-        if state['auto_max_new_tokens']:
-            max_new_tokens = state['truncation_length'] - ids.shape[-1]
-        else:
-            max_new_tokens = state['max_new_tokens']
-
-        self.generator.begin_stream(ids, settings, loras=self.loras)
-
-        decoded_text = ''
-        for i in range(max_new_tokens):
-            chunk, eos, _ = self.generator.stream()
-            if eos or shared.stop_everything:
-                break
-
-            decoded_text += chunk
-            yield decoded_text
-
-    def generate(self, prompt, state):
-        output = ''
-        for output in self.generate_with_streaming(prompt, state):
-            pass
-
-        return output

modules/loaders.py

@@ -81,16 +81,6 @@ loaders_and_params = OrderedDict({
         'trust_remote_code',
         'no_use_fast',
     ],
-    'ExLlamav2': [
-        'gpu_split',
-        'max_seq_len',
-        'no_flash_attn',
-        'num_experts_per_token',
-        'cache_8bit',
-        'alpha_value',
-        'compress_pos_emb',
-        'exllamav2_info',
-    ],
     'AutoGPTQ': [
         'triton',
         'no_inject_fused_attention',
@@ -204,29 +194,6 @@ loaders_samplers = {
     'AutoAWQ': transformers_samplers(),
     'QuIP#': transformers_samplers(),
     'HQQ': transformers_samplers(),
-    'ExLlamav2': {
-        'temperature',
-        'temperature_last',
-        'top_p',
-        'min_p',
-        'top_k',
-        'typical_p',
-        'tfs',
-        'top_a',
-        'repetition_penalty',
-        'presence_penalty',
-        'frequency_penalty',
-        'repetition_penalty_range',
-        'seed',
-        'mirostat_mode',
-        'mirostat_tau',
-        'mirostat_eta',
-        'ban_eos_token',
-        'add_bos_token',
-        'custom_token_bans',
-        'skip_special_tokens',
-        'auto_max_new_tokens',
-    },
     'ExLlamav2_HF': {
         'temperature',
         'temperature_last',

modules/logits.py

@@ -13,11 +13,10 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
         logger.error("No model is loaded! Select one in the Model tab.")
         return 'Error: No model is loaded1 Select one in the Model tab.', previous
 
-    is_non_hf_exllamav2 = shared.model.__class__.__name__ == 'Exllamav2Model'
     is_non_hf_llamacpp = shared.model.__class__.__name__ == 'LlamaCppModel'
 
     if use_samplers:
-        if any([is_non_hf_exllamav2, is_non_hf_llamacpp]):
+        if is_non_hf_llamacpp:
             logger.error("Sampler hijacking is not supported non-Huggingface loaders.")
             # sampling is all done in c for exllama, so it is really hard to hijack
             # it should be possible to hijack llamacpp sampler by hijacking all their sampling methods,
@@ -31,13 +30,7 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
 
         scores = sampler_hijack.global_scores[-1]
     else:
-        if is_non_hf_exllamav2:
+        if is_non_hf_llamacpp:
-            if is_torch_xpu_available():
-                tokens = shared.tokenizer.encode(prompt).to("xpu:0")
-            else:
-                tokens = shared.tokenizer.encode(prompt).cuda()
-            scores = shared.model.get_logits(tokens)[-1][-1]
-        elif is_non_hf_llamacpp:
             tokens = shared.tokenizer.encode(prompt)
             scores = shared.model.get_logits(tokens)[-1][-1]
         else:
@@ -45,6 +38,7 @@ def get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return
                 tokens = shared.tokenizer.encode(prompt, return_tensors='pt').to("xpu:0")
             else:
                 tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
+
             output = shared.model(input_ids=tokens)
             scores = output['logits'][-1][-1]
 

modules/models.py

@@ -65,7 +65,6 @@ def load_model(model_name, loader=None):
         'GPTQ-for-LLaMa': GPTQ_loader,
         'llama.cpp': llamacpp_loader,
         'llamacpp_HF': llamacpp_HF_loader,
-        'ExLlamav2': ExLlamav2_loader,
         'ExLlamav2_HF': ExLlamav2_HF_loader,
         'ctransformers': ctransformers_loader,
         'AutoAWQ': AutoAWQ_loader,
@@ -376,13 +375,6 @@ def AutoGPTQ_loader(model_name):
     return modules.AutoGPTQ_loader.load_quantized(model_name)
 
 
-def ExLlamav2_loader(model_name):
-    from modules.exllamav2 import Exllamav2Model
-
-    model, tokenizer = Exllamav2Model.from_pretrained(model_name)
-    return model, tokenizer
-
-
 def ExLlamav2_HF_loader(model_name):
     from modules.exllamav2_hf import Exllamav2HF
 

modules/models_settings.py

@@ -141,6 +141,8 @@ def get_model_metadata(model):
         if re.match(pat.lower(), model.lower()):
             for k in settings[pat]:
                 model_settings[k] = settings[pat][k]
+                if k == 'loader' and settings[pat][k] == 'ExLlamav2':
+                    model_settings[k] = 'ExLlamav2_HF'
 
     return model_settings
 

modules/shared.py

@@ -88,7 +88,7 @@ group.add_argument('--chat-buttons', action='store_true', help='Show buttons on
 
 # Model loader
 group = parser.add_argument_group('Model loader')
-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#.')
+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#.')
 
 # Transformers/Accelerate
 group = parser.add_argument_group('Transformers/Accelerate')
@@ -130,11 +130,11 @@ group.add_argument('--logits_all', action='store_true', help='Needs to be set fo
 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.')
 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.')
 
-# ExLlama
+# ExLlamaV2
-group = parser.add_argument_group('ExLlama')
+group = parser.add_argument_group('ExLlamaV2')
 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.')
 group.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
-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.')
+group.add_argument('--cfg-cache', action='store_true', help='Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
 group.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
 group.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
 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.')
@@ -248,11 +248,7 @@ def fix_loader_name(name):
         return 'AutoGPTQ'
     elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']:
         return 'GPTQ-for-LLaMa'
-    elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']:
+    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']:
-        return 'ExLlama'
-    elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']:
-        return 'ExLlamav2'
-    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']:
         return 'ExLlamav2_HF'
     elif name in ['ctransformers', 'ctranforemrs', 'ctransformer']:
         return 'ctransformers'

modules/text_generation.py

@@ -45,7 +45,7 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False, escap
             yield ''
             return
 
-        if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel']:
+        if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel']:
             generate_func = generate_reply_custom
         else:
             generate_func = generate_reply_HF
@@ -120,10 +120,11 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
     if shared.tokenizer is None:
         raise ValueError('No tokenizer is loaded')
 
-    if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel', 'Exllamav2Model']:
+    if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel']:
         input_ids = shared.tokenizer.encode(str(prompt))
-        if shared.model.__class__.__name__ not in ['Exllamav2Model']:
+        # The step below is necessary for llama.cpp, but may not be
-            input_ids = np.array(input_ids).reshape(1, len(input_ids))
+        # necessary for future loaders.
+        input_ids = np.array(input_ids).reshape(1, len(input_ids))
     else:
         input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
         if not add_bos_token:
@@ -134,7 +135,7 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
     if truncation_length is not None:
         input_ids = input_ids[:, -truncation_length:]
 
-    if shared.model.__class__.__name__ in ['LlamaCppModel', 'Exllamav2Model', 'CtransformersModel'] or shared.args.cpu:
+    if shared.model.__class__.__name__ in ['LlamaCppModel', 'CtransformersModel'] or shared.args.cpu:
         return input_ids
     elif shared.args.deepspeed:
         return input_ids.to(device=local_rank)

modules/ui_model_menu.py

@@ -135,7 +135,6 @@ def create_ui():
                             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.')
                             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.')
                             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.')
-                            shared.gradio['exllamav2_info'] = gr.Markdown("ExLlamav2_HF is recommended over ExLlamav2 for better integration with extensions and more consistent sampling behavior across loaders.")
                             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():