Skip to main content

llama_cpp_4/model/
params.rs

1//! A safe wrapper around `llama_model_params`.
2
3use crate::model::params::kv_overrides::KvOverrides;
4use std::ffi::{c_char, CStr};
5use std::fmt::{Debug, Formatter};
6use std::pin::Pin;
7use std::ptr::null;
8
9pub mod kv_overrides;
10
11/// Exact model-file loading strategy exposed by llama.cpp.
12///
13/// The `llama_load_mode` constants are `u32` under the Itanium ABI (Linux/macOS)
14/// but `i32` under MSVC, so each discriminant uses `as _` to coerce to the
15/// `#[repr(u32)]` type on every target (matching [`token_type`](crate::token_type)).
16#[derive(Debug, Clone, Copy, PartialEq, Eq)]
17#[repr(u32)]
18pub enum LlamaLoadMode {
19    /// No memory mapping, locking, or direct I/O.
20    None = llama_cpp_sys_4::LLAMA_LOAD_MODE_NONE as _,
21    /// Memory-map model files when supported.
22    Mmap = llama_cpp_sys_4::LLAMA_LOAD_MODE_MMAP as _,
23    /// Read model files normally and lock loaded pages in memory.
24    Mlock = llama_cpp_sys_4::LLAMA_LOAD_MODE_MLOCK as _,
25    /// Memory-map model files and lock mapped pages in memory.
26    MmapMlock = llama_cpp_sys_4::LLAMA_LOAD_MODE_MMAP_MLOCK as _,
27    /// Use direct I/O when supported.
28    DirectIo = llama_cpp_sys_4::LLAMA_LOAD_MODE_DIRECT_IO as _,
29}
30
31/// A safe wrapper around `llama_model_params`.
32#[allow(clippy::module_name_repetitions)]
33pub struct LlamaModelParams {
34    pub(crate) params: llama_cpp_sys_4::llama_model_params,
35    kv_overrides: Vec<llama_cpp_sys_4::llama_model_kv_override>,
36}
37
38impl Debug for LlamaModelParams {
39    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
40        f.debug_struct("LlamaModelParams")
41            .field("n_gpu_layers", &self.params.n_gpu_layers)
42            .field("main_gpu", &self.params.main_gpu)
43            .field("vocab_only", &self.params.vocab_only)
44            .field("load_mode", &self.load_mode())
45            .field("load_mtp", &self.load_mtp())
46            .field("kv_overrides", &"vec of kv_overrides")
47            .finish()
48    }
49}
50
51impl LlamaModelParams {
52    /// See [`KvOverrides`]
53    ///
54    /// # Examples
55    ///
56    /// ```rust
57    /// # use llama_cpp_4::model::params::LlamaModelParams;
58    /// let params = Box::pin(LlamaModelParams::default());
59    /// let kv_overrides = params.kv_overrides();
60    /// let count = kv_overrides.into_iter().count();
61    /// assert_eq!(count, 0);
62    /// ```
63    #[must_use]
64    pub fn kv_overrides(&self) -> KvOverrides<'_> {
65        KvOverrides::new(self)
66    }
67
68    /// Appends a key-value override to the model parameters. It must be pinned as this creates a self-referential struct.
69    ///
70    /// # Examples
71    ///
72    /// ```rust
73    /// # use std::ffi::{CStr, CString};
74    /// use std::pin::pin;
75    /// # use llama_cpp_4::model::params::LlamaModelParams;
76    /// # use llama_cpp_4::model::params::kv_overrides::ParamOverrideValue;
77    /// let mut params = pin!(LlamaModelParams::default());
78    /// let key = CString::new("key").expect("CString::new failed");
79    /// params.as_mut().append_kv_override(&key, ParamOverrideValue::Int(50));
80    ///
81    /// let kv_overrides = params.kv_overrides().into_iter().collect::<Vec<_>>();
82    /// assert_eq!(kv_overrides.len(), 1);
83    ///
84    /// let (k, v) = &kv_overrides[0];
85    /// assert_eq!(v, &ParamOverrideValue::Int(50));
86    ///
87    /// assert_eq!(k.to_bytes(), b"key", "expected key to be 'key', was {:?}", k);
88    /// ```
89    #[allow(clippy::missing_panics_doc)] // panics are just to enforce internal invariants, not user errors
90    pub fn append_kv_override(
91        mut self: Pin<&mut Self>,
92        key: &CStr,
93        value: kv_overrides::ParamOverrideValue,
94    ) {
95        let kv_override = self
96            .kv_overrides
97            .get_mut(0)
98            .expect("kv_overrides did not have a next allocated");
99
100        assert_eq!(kv_override.key[0], 0, "last kv_override was not empty");
101
102        // There should be some way to do this without iterating over everything.
103        for (i, &c) in key.to_bytes_with_nul().iter().enumerate() {
104            kv_override.key[i] = c_char::try_from(c).expect("invalid character in key");
105        }
106
107        kv_override.tag = value.tag();
108        kv_override.__bindgen_anon_1 = value.value();
109
110        // set to null pointer for panic safety (as push may move the vector, invalidating the pointer)
111        self.params.kv_overrides = null();
112
113        // push the next one to ensure we maintain the iterator invariant of ending with a 0
114        self.kv_overrides
115            .push(llama_cpp_sys_4::llama_model_kv_override {
116                key: [0; 128],
117                tag: 0,
118                __bindgen_anon_1: llama_cpp_sys_4::llama_model_kv_override__bindgen_ty_1 {
119                    val_i64: 0,
120                },
121            });
122
123        // set the pointer to the (potentially) new vector
124        self.params.kv_overrides = self.kv_overrides.as_ptr();
125
126        eprintln!("saved ptr: {:?}", self.params.kv_overrides);
127    }
128}
129
130impl LlamaModelParams {
131    /// Get the number of layers to offload to the GPU.
132    #[must_use]
133    pub fn n_gpu_layers(&self) -> i32 {
134        self.params.n_gpu_layers
135    }
136
137    /// The GPU that is used for scratch and small tensors
138    #[must_use]
139    pub fn main_gpu(&self) -> i32 {
140        self.params.main_gpu
141    }
142
143    /// only load the vocabulary, no weights
144    #[must_use]
145    pub fn vocab_only(&self) -> bool {
146        self.params.vocab_only
147    }
148
149    /// Returns the exact model-file loading strategy.
150    #[must_use]
151    pub fn load_mode(&self) -> LlamaLoadMode {
152        match self.params.load_mode {
153            llama_cpp_sys_4::LLAMA_LOAD_MODE_MMAP => LlamaLoadMode::Mmap,
154            llama_cpp_sys_4::LLAMA_LOAD_MODE_MLOCK => LlamaLoadMode::Mlock,
155            llama_cpp_sys_4::LLAMA_LOAD_MODE_MMAP_MLOCK => LlamaLoadMode::MmapMlock,
156            llama_cpp_sys_4::LLAMA_LOAD_MODE_DIRECT_IO => LlamaLoadMode::DirectIo,
157            _ => LlamaLoadMode::None,
158        }
159    }
160
161    /// Whether the model's MTP (multi-token prediction) layers will be loaded.
162    ///
163    /// MTP layers drive multi-token-prediction speculative decoding for models
164    /// that ship them (e.g. `DeepSeek V4`). Once loaded, the speculative state is
165    /// captured and restored through [`crate::speculative`]. Defaults to `false`
166    /// because most models carry no MTP weights.
167    #[must_use]
168    pub fn load_mtp(&self) -> bool {
169        self.params.load_mtp
170    }
171
172    /// use mmap if possible
173    #[must_use]
174    pub fn use_mmap(&self) -> bool {
175        matches!(
176            self.load_mode(),
177            LlamaLoadMode::Mmap | LlamaLoadMode::MmapMlock
178        )
179    }
180
181    /// force system to keep model in RAM
182    #[must_use]
183    pub fn use_mlock(&self) -> bool {
184        matches!(
185            self.load_mode(),
186            LlamaLoadMode::Mlock | LlamaLoadMode::MmapMlock
187        )
188    }
189
190    /// sets the number of gpu layers to offload to the GPU.
191    /// ```
192    /// # use llama_cpp_4::model::params::LlamaModelParams;
193    /// let params = LlamaModelParams::default();
194    /// let params = params.with_n_gpu_layers(1);
195    /// assert_eq!(params.n_gpu_layers(), 1);
196    /// ```
197    #[must_use]
198    pub fn with_n_gpu_layers(mut self, n_gpu_layers: u32) -> Self {
199        // The only way this conversion can fail is if u32 overflows the i32 - in which case we set
200        // to MAX
201        let n_gpu_layers = i32::try_from(n_gpu_layers).unwrap_or(i32::MAX);
202        self.params.n_gpu_layers = n_gpu_layers;
203        self
204    }
205
206    /// sets the main GPU
207    #[must_use]
208    pub fn with_main_gpu(mut self, main_gpu: i32) -> Self {
209        self.params.main_gpu = main_gpu;
210        self
211    }
212
213    /// sets `vocab_only`
214    #[must_use]
215    pub fn with_vocab_only(mut self, vocab_only: bool) -> Self {
216        self.params.vocab_only = vocab_only;
217        self
218    }
219
220    /// Sets the exact model-file loading strategy.
221    #[must_use]
222    pub fn with_load_mode(mut self, load_mode: LlamaLoadMode) -> Self {
223        self.params.load_mode = load_mode as llama_cpp_sys_4::llama_load_mode;
224        self
225    }
226
227    /// Sets whether to load the model's MTP (multi-token prediction) layers.
228    ///
229    /// Enable this for models that ship MTP weights (e.g. `DeepSeek V4`) when you
230    /// intend to use MTP-based speculative decoding, then drive the speculative
231    /// state via [`crate::speculative`]. For models without MTP layers the flag
232    /// has no effect. Corresponds to `llama_model_params.load_mtp`, added
233    /// upstream in llama.cpp PR #25784 (`DeepSeek V4` MTP + `DSpark`).
234    ///
235    /// ```
236    /// # use llama_cpp_4::model::params::LlamaModelParams;
237    /// let params = LlamaModelParams::default().with_load_mtp(true);
238    /// assert!(params.load_mtp());
239    /// ```
240    #[must_use]
241    pub fn with_load_mtp(mut self, load_mtp: bool) -> Self {
242        self.params.load_mtp = load_mtp;
243        self
244    }
245
246    /// sets `use_mlock`
247    #[must_use]
248    pub fn with_use_mlock(mut self, use_mlock: bool) -> Self {
249        let load_mode = match (self.use_mmap(), use_mlock) {
250            (true, true) => LlamaLoadMode::MmapMlock,
251            (true, false) => LlamaLoadMode::Mmap,
252            (false, true) => LlamaLoadMode::Mlock,
253            (false, false) => LlamaLoadMode::None,
254        };
255        self.params.load_mode = load_mode as llama_cpp_sys_4::llama_load_mode;
256        self
257    }
258}
259
260/// Default parameters for `LlamaModel`. (as defined in llama.cpp by `llama_model_default_params`)
261/// ```
262/// # use llama_cpp_4::model::params::LlamaModelParams;
263/// let params = LlamaModelParams::default();
264/// assert_eq!(params.n_gpu_layers(), -1, "n_gpu_layers should be -1 (all layers)");
265/// assert_eq!(params.main_gpu(), 0, "main_gpu should be 0");
266/// assert_eq!(params.vocab_only(), false, "vocab_only should be false");
267/// assert_eq!(params.use_mmap(), true, "use_mmap should be true");
268/// assert_eq!(params.use_mlock(), false, "use_mlock should be false");
269/// ```
270impl Default for LlamaModelParams {
271    fn default() -> Self {
272        let default_params = unsafe { llama_cpp_sys_4::llama_model_default_params() };
273        LlamaModelParams {
274            params: default_params,
275            // push the next one to ensure we maintain the iterator invariant of ending with a 0
276            kv_overrides: vec![llama_cpp_sys_4::llama_model_kv_override {
277                key: [0; 128],
278                tag: 0,
279                __bindgen_anon_1: llama_cpp_sys_4::llama_model_kv_override__bindgen_ty_1 {
280                    val_i64: 0,
281                },
282            }],
283        }
284    }
285}