Add AutoGPTQ support (basic) (#2132)

modules/AutoGPTQ_loader.py

@@ -0,0 +1,41 @@
+import logging
+from pathlib import Path
+
+from auto_gptq import AutoGPTQForCausalLM
+
+import modules.shared as shared
+from modules.models import get_max_memory_dict
+
+
+def load_quantized(model_name):
+    path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
+    pt_path = None
+    use_safetensors = False
+
+    # Find the model checkpoint
+    found_pts = list(path_to_model.glob("*.pt"))
+    found_safetensors = list(path_to_model.glob("*.safetensors"))
+    if len(found_safetensors) > 0:
+        if len(found_pts) > 1:
+            logging.warning('More than one .safetensors model has been found. The last one will be selected. It could be wrong.')
+
+        use_safetensors = True
+        pt_path = found_safetensors[-1]
+    elif len(found_pts) > 0:
+        if len(found_pts) > 1:
+            logging.warning('More than one .pt model has been found. The last one will be selected. It could be wrong.')
+
+        pt_path = found_pts[-1]
+
+    # Define the params for AutoGPTQForCausalLM.from_quantized
+    params = {
+        'model_basename': pt_path.stem,
+        'device': "cuda:0" if not shared.args.cpu else "cpu",
+        'use_triton': shared.args.triton,
+        'use_safetensors': use_safetensors,
+        'max_memory': get_max_memory_dict()
+    }
+
+    logging.warning(f"The AutoGPTQ params are: {params}")
+    model = AutoGPTQForCausalLM.from_quantized(path_to_model, **params)
+    return model

modules/models.py

@@ -72,7 +72,10 @@ def load_model(model_name):
 
     shared.model_type = find_model_type(model_name)
     if shared.args.wbits > 0:
-        load_func = GPTQ_loader
+        if shared.args.autogptq:
+            load_func = AutoGPTQ_loader
+        else:
+            load_func = GPTQ_loader
     elif shared.model_type == 'llamacpp':
         load_func = llamacpp_loader
     elif shared.model_type == 'rwkv':
@@ -261,6 +264,12 @@ def GPTQ_loader(model_name):
     return model
 
 
+def AutoGPTQ_loader(model_name):
+    from modules.AutoGPTQ_loader import load_quantized
+
+    return load_quantized(model_name)
+
+
 def get_max_memory_dict():
     max_memory = {}
     if shared.args.gpu_memory:
@@ -283,7 +292,7 @@ def get_max_memory_dict():
         logging.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.")
         max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
 
-    return max_memory
+    return max_memory if len(max_memory) > 0 else None
 
 
 def clear_torch_cache():

modules/shared.py

@@ -137,6 +137,10 @@ parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable q
 parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.')
 parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.')
 
+# AutoGPTQ
+parser.add_argument('--autogptq', action='store_true', help='Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader.')
+parser.add_argument('--triton', action='store_true', help='Use triton.')
+
 # FlexGen
 parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
 parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')