Add cache_8bit option

README.md

@@ -337,6 +337,7 @@ Optionally, you can use the following command-line flags:
 |`--max_seq_len MAX_SEQ_LEN`           | Maximum sequence length. |
 |`--cfg-cache`                         | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
 |`--no_flash_attn`                     | Force flash-attention to not be used. |
+|`--cache_8bit`                        | Use 8-bit cache to save VRAM. |
 
 #### AutoGPTQ
 

modules/exllamav2.py

@@ -6,6 +6,7 @@ import torch
 from exllamav2 import (
     ExLlamaV2,
     ExLlamaV2Cache,
+    ExLlamaV2Cache_8bit,
     ExLlamaV2Config,
     ExLlamaV2Tokenizer
 )
@@ -57,7 +58,11 @@ class Exllamav2Model:
         model.load(split)
 
         tokenizer = ExLlamaV2Tokenizer(config)
-        cache = ExLlamaV2Cache(model)
+        if shared.args.cache_8bit:
+            cache = ExLlamaV2Cache_8bit(model)
+        else:
+            cache = ExLlamaV2Cache(model)
+
         generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)
 
         result = self()

modules/exllamav2_hf.py

@@ -4,7 +4,12 @@ from pathlib import Path
 from typing import Any, Dict, Optional, Union
 
 import torch
-from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Config
+from exllamav2 import (
+    ExLlamaV2,
+    ExLlamaV2Cache,
+    ExLlamaV2Cache_8bit,
+    ExLlamaV2Config
+)
 from torch.nn import CrossEntropyLoss
 from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
 from transformers.modeling_outputs import CausalLMOutputWithPast
@@ -40,11 +45,18 @@ class Exllamav2HF(PreTrainedModel):
         self.generation_config = GenerationConfig()
         self.loras = None
 
-        self.ex_cache = ExLlamaV2Cache(self.ex_model)
+        if shared.args.cache_8bit:
-        self.past_seq = None
+            self.ex_cache = ExLlamaV2Cache_8bit(self.ex_model)
+        else:
+            self.ex_cache = ExLlamaV2Cache(self.ex_model)
 
+        self.past_seq = None
         if shared.args.cfg_cache:
-            self.ex_cache_negative = ExLlamaV2Cache(self.ex_model)
+            if shared.args.cache_8bit:
+                self.ex_cache_negative = ExLlamaV2Cache_8bit(self.ex_model)
+            else:
+                self.ex_cache_negative = ExLlamaV2Cache(self.ex_model)
+
             self.past_seq_negative = None
 
     def _validate_model_class(self):

modules/loaders.py

@@ -41,6 +41,8 @@ loaders_and_params = OrderedDict({
         'gpu_split',
         'max_seq_len',
         'cfg_cache',
+        'no_flash_attn',
+        'cache_8bit',
         'alpha_value',
         'compress_pos_emb',
         'use_fast',
@@ -56,6 +58,8 @@ loaders_and_params = OrderedDict({
     'ExLlamav2': [
         'gpu_split',
         'max_seq_len',
+        'no_flash_attn',
+        'cache_8bit',
         'alpha_value',
         'compress_pos_emb',
     ],

modules/shared.py

@@ -118,6 +118,7 @@ parser.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM
 parser.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
 parser.add_argument('--cfg-cache', action='store_true', help='ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.')
 parser.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
+parser.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
 
 # AutoGPTQ
 parser.add_argument('--triton', action='store_true', help='Use triton.')

modules/ui.py

@@ -68,6 +68,8 @@ def list_model_elements():
         'no_use_cuda_fp16',
         'disable_exllama',
         'cfg_cache',
+        'no_flash_attn',
+        'cache_8bit',
         'threads',
         'threads_batch',
         'n_batch',

modules/ui_model_menu.py

@@ -125,6 +125,8 @@ def create_ui():
                             shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='To enable this option, start the web UI with the --trust-remote-code flag. It is necessary for some models.', interactive=shared.args.trust_remote_code)
                             shared.gradio['use_fast'] = gr.Checkbox(label="use_fast", value=shared.args.use_fast, info='Set use_fast=True while loading the tokenizer. May trigger a conversion that takes several minutes.')
                             shared.gradio['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel.')
+                            shared.gradio['no_flash_attn'] = gr.Checkbox(label="no_flash_attn", value=shared.args.no_flash_attn, info='Force flash-attention to not be used.')
+                            shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit cache to save VRAM.')
                             shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa support is currently only kept for compatibility with older GPUs. AutoGPTQ or ExLlama is preferred when compatible. GPTQ-for-LLaMa is installed by default with the webui on supported systems. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
                             shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
                             shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')