Merge branch 'oobabooga:main' into main

README.md

@@ -60,7 +60,9 @@ pip3 install torch torchvision torchaudio --extra-index-url https://download.pyt
 conda install pytorch torchvision torchaudio git -c pytorch
 ```
 
-See also: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
+> **Note**
+> 1. If you are on Windows, it may be easier to run the commands above in a WSL environment. The performance may also be better.
+> 2. For a more detailed, user-contributed guide, see: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
 
 ## Installation option 2: one-click installers
 
@@ -140,8 +142,9 @@ Optionally, you can use the following command-line flags:
 | `--cai-chat`  | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. |
 | `--cpu`       | Use the CPU to generate text.|
 | `--load-in-8bit`  | Load the model with 8-bit precision.|
-| `--load-in-4bit`  | Load the model with 4-bit precision. Currently only works with LLaMA.|
+| `--load-in-4bit`  | DEPRECATED: use `--gptq-bits 4` instead. |
-| `--gptq-bits GPTQ_BITS`  |  Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA. |
+| `--gptq-bits GPTQ_BITS`  |  Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
+| `--gptq-model-type MODEL_TYPE`  |  Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
 | `--bf16`  | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
 | `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
 | `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |

modules/GPTQ_loader.py

@@ -7,28 +7,40 @@ import torch
 import modules.shared as shared
 
 sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
-from llama import load_quant
+import llama
+import opt
 
 
-# 4-bit LLaMA
+def load_quantized(model_name):
-def load_quantized_LLaMA(model_name):
+    if not shared.args.gptq_model_type:
-    if shared.args.load_in_4bit:
+        # Try to determine model type from model name
-        bits = 4
+        model_type = model_name.split('-')[0].lower()
+        if model_type not in ('llama', 'opt'):
+            print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
+                  "argument")
+            exit()
     else:
-        bits = shared.args.gptq_bits
+        model_type = shared.args.gptq_model_type.lower()
+
+    if model_type == 'llama':
+        load_quant = llama.load_quant
+    elif model_type == 'opt':
+        load_quant = opt.load_quant
+    else:
+        print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
+        exit()
 
     path_to_model = Path(f'models/{model_name}')
-    pt_model = ''
     if path_to_model.name.lower().startswith('llama-7b'):
-        pt_model = f'llama-7b-{bits}bit.pt'
+        pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
     elif path_to_model.name.lower().startswith('llama-13b'):
-        pt_model = f'llama-13b-{bits}bit.pt'
+        pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
     elif path_to_model.name.lower().startswith('llama-30b'):
-        pt_model = f'llama-30b-{bits}bit.pt'
+        pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
     elif path_to_model.name.lower().startswith('llama-65b'):
-        pt_model = f'llama-65b-{bits}bit.pt'
+        pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
     else:
-        pt_model = f'{model_name}-{bits}bit.pt'
+        pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
 
     # Try to find the .pt both in models/ and in the subfolder
     pt_path = None
@@ -40,7 +52,7 @@ def load_quantized_LLaMA(model_name):
         print(f"Could not find {pt_model}, exiting...")
         exit()
 
-    model = load_quant(str(path_to_model), str(pt_path), bits)
+    model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
 
     # Multiple GPUs or GPU+CPU
     if shared.args.gpu_memory:

modules/models.py

@@ -1,6 +1,5 @@
 import json
 import os
-import sys
 import time
 import zipfile
 from pathlib import Path
@@ -35,6 +34,7 @@ if shared.args.deepspeed:
     ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
     dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
 
+
 def load_model(model_name):
     print(f"Loading {model_name}...")
     t0 = time.time()
@@ -42,7 +42,7 @@ def load_model(model_name):
     shared.is_RWKV = model_name.lower().startswith('rwkv-')
 
     # Default settings
-    if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.gptq_bits > 0, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
+    if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
         if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
         else:
@@ -87,11 +87,11 @@ def load_model(model_name):
 
         return model, tokenizer
 
-    # 4-bit LLaMA
+    # Quantized model
-    elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit:
+    elif shared.args.gptq_bits > 0:
-        from modules.quantized_LLaMA import load_quantized_LLaMA
+        from modules.GPTQ_loader import load_quantized
 
-        model = load_quantized_LLaMA(model_name)
+        model = load_quantized(model_name)
 
     # Custom
     else:

modules/shared.py

@@ -69,8 +69,9 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
 parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
 parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
 parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
-parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision. Currently only works with LLaMA.')
+parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
-parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA.')
+parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
+parser.add_argument('--gptq-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
 parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
 parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
 parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
@@ -95,3 +96,8 @@ parser.add_argument('--share', action='store_true', help='Create a public URL. T
 parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
 parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
 args = parser.parse_args()
+
+# Provisional, this will be deleted later
+if args.load_in_4bit:
+    print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
+    args.gptq_bits = 4

modules/text_generation.py

@@ -122,7 +122,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
     input_ids = encode(question, max_new_tokens)
     original_input_ids = input_ids
     output = input_ids[0]
-    cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
+    cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
     eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
     if eos_token is not None:
         eos_token_ids.append(int(encode(eos_token)[0][-1]))
@@ -132,45 +132,48 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
         t = encode(stopping_string, 0, add_special_tokens=False)
         stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
 
+    generate_params = {}
     if not shared.args.flexgen:
-        generate_params = [
+        generate_params.update({
-            f"max_new_tokens=max_new_tokens",
+            "max_new_tokens": max_new_tokens,
-            f"eos_token_id={eos_token_ids}",
+            "eos_token_id": eos_token_ids,
-            f"stopping_criteria=stopping_criteria_list",
+            "stopping_criteria": stopping_criteria_list,
-            f"do_sample={do_sample}",
+            "do_sample": do_sample,
-            f"temperature={temperature}",
+            "temperature": temperature,
-            f"top_p={top_p}",
+            "top_p": top_p,
-            f"typical_p={typical_p}",
+            "typical_p": typical_p,
-            f"repetition_penalty={repetition_penalty}",
+            "repetition_penalty": repetition_penalty,
-            f"top_k={top_k}",
+            "top_k": top_k,
-            f"min_length={min_length if shared.args.no_stream else 0}",
+            "min_length": min_length if shared.args.no_stream else 0,
-            f"no_repeat_ngram_size={no_repeat_ngram_size}",
+            "no_repeat_ngram_size": no_repeat_ngram_size,
-            f"num_beams={num_beams}",
+            "num_beams": num_beams,
-            f"penalty_alpha={penalty_alpha}",
+            "penalty_alpha": penalty_alpha,
-            f"length_penalty={length_penalty}",
+            "length_penalty": length_penalty,
-            f"early_stopping={early_stopping}",
+            "early_stopping": early_stopping,
-        ]
+        })
     else:
-        generate_params = [
+        generate_params.update({
-            f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
+            "max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
-            f"do_sample={do_sample}",
+            "do_sample": do_sample,
-            f"temperature={temperature}",
+            "temperature": temperature,
-            f"stop={eos_token_ids[-1]}",
+            "stop": eos_token_ids[-1],
-        ]
+        })
     if shared.args.deepspeed:
-        generate_params.append("synced_gpus=True")
+        generate_params.update({"synced_gpus": True})
     if shared.soft_prompt:
         inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-        generate_params.insert(0, "inputs_embeds=inputs_embeds")
+        generate_params.update({"inputs_embeds": inputs_embeds})
-        generate_params.insert(0, "inputs=filler_input_ids")
+        generate_params.update({"inputs": filler_input_ids})
     else:
-        generate_params.insert(0, "inputs=input_ids")
+        generate_params.update({"inputs": input_ids})
 
     try:
         # Generate the entire reply at once.
         if shared.args.no_stream:
             with torch.no_grad():
-                output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
+                output = shared.model.generate(**generate_params)[0]
+                if cuda:
+                    output = output.cuda()
             if shared.soft_prompt:
                 output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
 
@@ -194,7 +197,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
                 return Iteratorize(generate_with_callback, kwargs, callback=None)
 
             yield formatted_outputs(original_question, shared.model_name)
-            with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
+            with generate_with_streaming(**generate_params) as generator:
                 for output in generator:
                     if shared.soft_prompt:
                         output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -214,7 +217,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             for i in range(max_new_tokens//8+1):
                 clear_torch_cache()
                 with torch.no_grad():
-                    output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
+                    output = shared.model.generate(**generate_params)[0]
                 if shared.soft_prompt:
                     output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
                 reply = decode(output)

server.py

@@ -269,7 +269,7 @@ if shared.args.chat or shared.args.cai_chat:
 
         function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
 
-        gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen'))
+        gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
         gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
         gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
         gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))