Enable NUMA feature for llama_cpp_python (#4040)

README.md

@@ -295,14 +295,15 @@ Optionally, you can use the following command-line flags:
 
 | Flag          | Description |
 |---------------|---------------|
+| `--mul_mat_q` | Activate new mulmat kernels. |
+| `--tensor_split TENSOR_SPLIT`       | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
+| `--llama_cpp_seed SEED`             | Seed for llama-cpp models. Default 0 (random). |
+| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
+|`--cfg-cache`                        | llamacpp_HF: Create an additional cache for CFG negative prompts. |
 | `--no-mmap`   | Prevent mmap from being used. |
 | `--mlock`     | Force the system to keep the model in RAM. |
-| `--mul_mat_q` | Activate new mulmat kernels. |
-| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
-| `--tensor_split TENSOR_SPLIT`  | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
-| `--llama_cpp_seed SEED`        | Seed for llama-cpp models. Default 0 (random). |
-| `--cpu`                        | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
-|`--cfg-cache`                   | llamacpp_HF: Create an additional cache for CFG negative prompts. |
+| `--numa`      | Activate NUMA task allocation for llama.cpp |
+| `--cpu`       | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
 
 #### ctransformers
 

modules/llamacpp_hf.py

@@ -172,6 +172,7 @@ class LlamacppHF(PreTrainedModel):
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
         assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
+
         if isinstance(pretrained_model_name_or_path, str):
             pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
 
@@ -198,6 +199,7 @@ class LlamacppHF(PreTrainedModel):
             'use_mlock': shared.args.mlock,
             'mul_mat_q': shared.args.mul_mat_q,
             'low_vram': shared.args.low_vram,
+            'numa': shared.args.numa,
             'n_gpu_layers': shared.args.n_gpu_layers,
             'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
             'tensor_split': tensor_split_list,

modules/llamacpp_model.py

@@ -81,6 +81,7 @@ class LlamaCppModel:
             'use_mlock': shared.args.mlock,
             'mul_mat_q': shared.args.mul_mat_q,
             'low_vram': shared.args.low_vram,
+            'numa': shared.args.numa,
             'n_gpu_layers': shared.args.n_gpu_layers,
             'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
             'tensor_split': tensor_split_list,

modules/loaders.py

@@ -100,6 +100,7 @@ loaders_and_params = OrderedDict({
         'rope_freq_base',
         'compress_pos_emb',
         'cpu',
+        'numa',
     ],
     'llamacpp_HF': [
         'n_ctx',
@@ -115,6 +116,7 @@ loaders_and_params = OrderedDict({
         'rope_freq_base',
         'compress_pos_emb',
         'cpu',
+        'numa',
         'cfg_cache',
         'use_fast',
         'llamacpp_HF_info',

modules/shared.py

@@ -125,6 +125,7 @@ parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layer
 parser.add_argument('--tensor_split', type=str, default=None, help="Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17")
 parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.')
 parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default 0 (random)')
+parser.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp')
 
 # GPTQ
 parser.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.')

modules/ui.py

@@ -82,7 +82,8 @@ def list_model_elements():
         'max_seq_len',
         'compress_pos_emb',
         'alpha_value',
-        'rope_freq_base'
+        'rope_freq_base',
+        'numa',
     ]
 
     for i in range(torch.cuda.device_count()):

modules/ui_model_menu.py

@@ -102,6 +102,12 @@ def create_ui():
                             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.')
+                            shared.gradio['mul_mat_q'] = gr.Checkbox(label="mul_mat_q", value=shared.args.mul_mat_q, info='Recommended in most cases. Improves generation speed by 10-20%.')
+                            shared.gradio['cfg_cache'] = gr.Checkbox(label="cfg-cache", value=shared.args.cfg_cache, info='Create an additional cache for CFG negative prompts.')
+                            shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
+                            shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
+                            shared.gradio['numa'] = gr.Checkbox(label="numa", value=shared.args.numa, info='NUMA support can help on some systems with non-uniform memory access.')
+                            shared.gradio['low_vram'] = gr.Checkbox(label="low-vram", value=shared.args.low_vram)
                             shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
                             shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
                             shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
@@ -109,11 +115,6 @@ def create_ui():
                             shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
                             shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit)
                             shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant)
-                            shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
-                            shared.gradio['low_vram'] = gr.Checkbox(label="low-vram", value=shared.args.low_vram)
-                            shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
-                            shared.gradio['mul_mat_q'] = gr.Checkbox(label="mul_mat_q", value=shared.args.mul_mat_q, info='Recommended in most cases. Improves generation speed by 10-20%.')
-                            shared.gradio['cfg_cache'] = gr.Checkbox(label="cfg-cache", value=shared.args.cfg_cache, info='Create an additional cache for CFG negative prompts.')
                             shared.gradio['tensor_split'] = gr.Textbox(label='tensor_split', info='Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17')
                             shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed)
                             shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')