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llama.cpp: allow ctx_size=0 for auto context via --fit

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oobabooga 2026-03-04 19:33:20 -08:00
parent fbfcd59fe0
commit 69fa4dd0b1
6 changed files with 13 additions and 9 deletions
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7
modules/llama_cpp_server.py
View file

@ -339,7 +339,6 @@ class LlamaServer:
cmd = [
self.server_path,
"--model", self.model_path,
"--ctx-size", str(shared.args.ctx_size),
"--batch-size", str(shared.args.batch_size),
"--ubatch-size", str(shared.args.ubatch_size),
"--port", str(self.port),
@ -347,6 +346,9 @@ class LlamaServer:
"--flash-attn", "on",
]
if shared.args.ctx_size > 0:
cmd += ["--ctx-size", str(shared.args.ctx_size)]
if shared.args.gpu_layers >= 0:
cmd += ["--gpu-layers", str(shared.args.gpu_layers), "--fit", "off"]
else:
@ -449,7 +451,8 @@ class LlamaServer:
print()
gpu_layers_str = "auto" if shared.args.gpu_layers < 0 else str(shared.args.gpu_layers)
logger.info(f"Using gpu_layers={gpu_layers_str} | ctx_size={shared.args.ctx_size} | cache_type={cache_type}")
ctx_size_str = "auto" if shared.args.ctx_size == 0 else str(shared.args.ctx_size)
logger.info(f"Using gpu_layers={gpu_layers_str} | ctx_size={ctx_size_str} | cache_type={cache_type}")
# Start the server with pipes for output
self.process = subprocess.Popen(
cmd,

3
modules/models.py
View file

@ -54,7 +54,8 @@ def load_model(model_name, loader=None):
shared.settings.update({k: v for k, v in metadata.items() if k in shared.settings})
if loader.lower().startswith('exllama') or loader.lower().startswith('tensorrt') or loader == 'llama.cpp':
shared.settings['truncation_length'] = shared.args.ctx_size
if shared.args.ctx_size > 0:
shared.settings['truncation_length'] = shared.args.ctx_size
shared.is_multimodal = False
if loader.lower() in ('exllamav3', 'llama.cpp') and hasattr(model, 'is_multimodal'):

2
modules/models_settings.py
View file

@ -418,7 +418,7 @@ def update_gpu_layers_and_vram(loader, model, gpu_layers, ctx_size, cache_type):
Compute the estimated VRAM usage for the given GPU layers and return
an HTML string for the UI display.
"""
if loader != 'llama.cpp' or model in ["None", None] or not model.endswith(".gguf") or gpu_layers < 0:
if loader != 'llama.cpp' or model in ["None", None] or not model.endswith(".gguf") or gpu_layers < 0 or ctx_size == 0:
return "<div id=\"vram-info\"'>Estimated VRAM to load the model:</div>"
vram_usage = estimate_vram(model, gpu_layers, ctx_size, cache_type)

4
modules/shared.py
View file

@ -71,7 +71,7 @@ group.add_argument('--loader', type=str, help='Choose the model loader manually,
# Cache
group = parser.add_argument_group('Context and cache')
group.add_argument('--ctx-size', '--n_ctx', '--max_seq_len', type=int, default=8192, metavar='N', help='Context size in tokens.')
group.add_argument('--ctx-size', '--n_ctx', '--max_seq_len', type=int, default=8192, metavar='N', help='Context size in tokens. llama.cpp: 0 = auto if gpu-layers is also -1.')
group.add_argument('--cache-type', '--cache_type', type=str, default='fp16', metavar='N', help='KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV2 - fp16, fp8, q8, q6, q4; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).')
# Speculative decoding
@ -88,7 +88,7 @@ group.add_argument('--spec-ngram-min-hits', type=int, default=1, help='Minimum n
# llama.cpp
group = parser.add_argument_group('llama.cpp')
group.add_argument('--gpu-layers', '--n-gpu-layers', type=int, default=-1, metavar='N', help='Number of layers to offload to the GPU. Set to -1 for auto mode, where llama.cpp decides via --fit.')
group.add_argument('--gpu-layers', '--n-gpu-layers', type=int, default=-1, metavar='N', help='Number of layers to offload to the GPU. -1 = auto.')
group.add_argument('--cpu-moe', action='store_true', help='Move the experts to the CPU (for MoE models).')
group.add_argument('--mmproj', type=str, default=None, help='Path to the mmproj file for vision models.')
group.add_argument('--streaming-llm', action='store_true', help='Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.')

4
modules/ui_model_menu.py
View file

@ -41,8 +41,8 @@ def create_ui():
gr.Markdown("## Main options")
with gr.Row():
with gr.Column():
shared.gradio['gpu_layers'] = gr.Slider(label="gpu-layers", minimum=-1, maximum=get_initial_gpu_layers_max(), step=1, value=shared.args.gpu_layers, info='Number of layers to offload to the GPU. -1 = auto (llama.cpp decides via --fit).')
shared.gradio['ctx_size'] = gr.Slider(label='ctx-size', minimum=256, maximum=131072, step=256, value=shared.args.ctx_size, info='Context length. Common values: 4096, 8192, 16384, 32768, 65536, 131072.')
shared.gradio['gpu_layers'] = gr.Slider(label="gpu-layers", minimum=-1, maximum=get_initial_gpu_layers_max(), step=1, value=shared.args.gpu_layers, info='Number of layers to offload to the GPU. -1 = auto.')
shared.gradio['ctx_size'] = gr.Slider(label='ctx-size', minimum=0, maximum=131072, step=256, value=shared.args.ctx_size, info='Context length. llama.cpp: 0 = auto if gpu-layers is also -1. Common values: 4096, 8192, 16384, 32768, 65536, 131072.')
shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
shared.gradio['attn_implementation'] = gr.Dropdown(label="attn-implementation", choices=['sdpa', 'eager', 'flash_attention_2'], value=shared.args.attn_implementation, info='Attention implementation.')
shared.gradio['cache_type'] = gr.Dropdown(label="cache-type", choices=['fp16', 'q8_0', 'q4_0', 'fp8', 'q8', 'q7', 'q6', 'q5', 'q4', 'q3', 'q2'], value=shared.args.cache_type, allow_custom_value=True, info='Valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV2 - fp16, fp8, q8, q6, q4; ExLlamaV3 - fp16, q2 to q8. For ExLlamaV3, you can type custom combinations for separate k/v bits (e.g. q4_q8).')

2
modules/ui_parameters.py
View file

@ -128,7 +128,7 @@ def create_event_handlers():
def get_truncation_length():
if 'ctx_size' in shared.provided_arguments or shared.args.ctx_size != shared.args_defaults.ctx_size:
if shared.args.ctx_size > 0 and ('ctx_size' in shared.provided_arguments or shared.args.ctx_size != shared.args_defaults.ctx_size):
return shared.args.ctx_size
else:
return shared.settings['truncation_length']