Remove AutoAWQ as a standalone loader

(it works better through transformers)

modules/LoRA.py

@@ -72,8 +72,6 @@ def add_lora_autogptq(lora_names):
     else:
         if len(lora_names) > 1:
             logger.warning('AutoGPTQ can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
-        if not shared.args.no_inject_fused_attention:
-            logger.warning('Fused Attention + AutoGPTQ may break Lora loading. Disable it.')
 
         peft_config = GPTQLoraConfig(
             inference_mode=True,

modules/loaders.py

@@ -127,15 +127,6 @@ loaders_and_params = OrderedDict({
         'no_use_fast',
         'autogptq_info',
     ],
-    'AutoAWQ': [
-        'cpu_memory',
-        'gpu_memory',
-        'auto_devices',
-        'max_seq_len',
-        'no_inject_fused_attention',
-        'trust_remote_code',
-        'no_use_fast',
-    ],
     'HQQ': [
         'hqq_backend',
         'trust_remote_code',
@@ -200,7 +191,6 @@ def transformers_samplers():
 loaders_samplers = {
     'Transformers': transformers_samplers(),
     'AutoGPTQ': transformers_samplers(),
-    'AutoAWQ': transformers_samplers(),
     'HQQ': transformers_samplers(),
     'ExLlamav2': {
         'temperature',

modules/models.py

@@ -75,7 +75,6 @@ def load_model(model_name, loader=None):
         'llamacpp_HF': llamacpp_HF_loader,
         'ExLlamav2': ExLlamav2_loader,
         'ExLlamav2_HF': ExLlamav2_HF_loader,
-        'AutoAWQ': AutoAWQ_loader,
         'HQQ': HQQ_loader,
         'TensorRT-LLM': TensorRT_LLM_loader,
     }
@@ -292,24 +291,6 @@ def llamacpp_HF_loader(model_name):
     return model
 
 
-def AutoAWQ_loader(model_name):
-    from awq import AutoAWQForCausalLM
-
-    model_dir = Path(f'{shared.args.model_dir}/{model_name}')
-
-    model = AutoAWQForCausalLM.from_quantized(
-        quant_path=model_dir,
-        max_new_tokens=shared.args.max_seq_len,
-        trust_remote_code=shared.args.trust_remote_code,
-        fuse_layers=not shared.args.no_inject_fused_attention,
-        max_memory=get_max_memory_dict(),
-        batch_size=1,
-        safetensors=any(model_dir.glob('*.safetensors')),
-    )
-
-    return model
-
-
 def AutoGPTQ_loader(model_name):
     import modules.AutoGPTQ_loader
 

modules/models_settings.py

@@ -180,8 +180,6 @@ def infer_loader(model_name, model_settings):
         loader = None
     elif (path_to_model / 'quantize_config.json').exists() or ('wbits' in model_settings and isinstance(model_settings['wbits'], int) and model_settings['wbits'] > 0):
         loader = 'ExLlamav2_HF'
-    elif (path_to_model / 'quant_config.json').exists() or re.match(r'.*-awq', model_name.lower()):
-        loader = 'AutoAWQ'
     elif len(list(path_to_model.glob('*.gguf'))) > 0 and path_to_model.is_dir() and (path_to_model / 'tokenizer_config.json').exists():
         loader = 'llamacpp_HF'
     elif len(list(path_to_model.glob('*.gguf'))) > 0:

modules/shared.py

@@ -89,7 +89,7 @@ group.add_argument('--idle-timeout', type=int, default=0, help='Unload model aft
 
 # 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.')
+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.')
 
 # Transformers/Accelerate
 group = parser.add_argument_group('Transformers/Accelerate')
@@ -160,10 +160,6 @@ group.add_argument('--disable_exllamav2', action='store_true', help='Disable ExL
 group.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
 group.add_argument('--groupsize', type=int, default=-1, help='Group size.')
 
-# AutoAWQ
-group = parser.add_argument_group('AutoAWQ')
-group.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.')
-
 # HQQ
 group = parser.add_argument_group('HQQ')
 group.add_argument('--hqq-backend', type=str, default='PYTORCH_COMPILE', help='Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.')
@@ -217,6 +213,7 @@ group.add_argument('--model_type', type=str, help='DEPRECATED')
 group.add_argument('--pre_layer', type=int, nargs='+', help='DEPRECATED')
 group.add_argument('--checkpoint', type=str, help='DEPRECATED')
 group.add_argument('--monkey-patch', action='store_true', help='DEPRECATED')
+group.add_argument('--no_inject_fused_attention', action='store_true', help='DEPRECATED')
 
 args = parser.parse_args()
 args_defaults = parser.parse_args([])
@@ -267,8 +264,6 @@ def fix_loader_name(name):
         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 ['autoawq', 'awq', 'auto-awq']:
-        return 'AutoAWQ'
     elif name in ['hqq']:
         return 'HQQ'
     elif name in ['tensorrt', 'tensorrtllm', 'tensorrt_llm', 'tensorrt-llm', 'tensort', 'tensortllm']:

modules/ui.py

@@ -78,7 +78,6 @@ def list_model_elements():
         'groupsize',
         'triton',
         'desc_act',
-        'no_inject_fused_attention',
         'no_inject_fused_mlp',
         'no_use_cuda_fp16',
         'disable_exllama',

modules/ui_model_menu.py

@@ -127,7 +127,6 @@ def create_ui():
                             shared.gradio['no_offload_kqv'] = gr.Checkbox(label="no_offload_kqv", value=shared.args.no_offload_kqv, info='Do not offload the  K, Q, V to the GPU. This saves VRAM but reduces the performance.')
                             shared.gradio['no_mul_mat_q'] = gr.Checkbox(label="no_mul_mat_q", value=shared.args.no_mul_mat_q, info='Disable the mulmat kernels.')
                             shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
-                            shared.gradio['no_inject_fused_attention'] = gr.Checkbox(label="no_inject_fused_attention", value=shared.args.no_inject_fused_attention, info='Disable fused attention. Fused attention improves inference performance but uses more VRAM. Fuses layers for AutoAWQ. Disable if running low on VRAM.')
                             shared.gradio['no_inject_fused_mlp'] = gr.Checkbox(label="no_inject_fused_mlp", value=shared.args.no_inject_fused_mlp, info='Affects Triton only. Disable fused MLP. Fused MLP improves performance but uses more VRAM. Disable if running low on VRAM.')
                             shared.gradio['no_use_cuda_fp16'] = gr.Checkbox(label="no_use_cuda_fp16", value=shared.args.no_use_cuda_fp16, info='This can make models faster on some systems.')
                             shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.')