llama-cpp-4 0.2.44

//! A safe wrapper around `llama_context_params`.
use std::fmt::Debug;
use std::num::NonZeroU32;

/// A rusty wrapper around `rope_scaling_type`.
#[repr(i8)]
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
pub enum RopeScalingType {
    /// The scaling type is unspecified
    Unspecified = -1,
    /// No scaling
    None = 0,
    /// Linear scaling
    Linear = 1,
    /// Yarn scaling
    Yarn = 2,
}

/// Create a `RopeScalingType` from a `c_int` - returns `RopeScalingType::ScalingUnspecified` if
/// the value is not recognized.
impl From<i32> for RopeScalingType {
    fn from(value: i32) -> Self {
        match value {
            0 => Self::None,
            1 => Self::Linear,
            2 => Self::Yarn,
            _ => Self::Unspecified,
        }
    }
}

/// Create a `c_int` from a `RopeScalingType`.
impl From<RopeScalingType> for i32 {
    fn from(value: RopeScalingType) -> Self {
        match value {
            RopeScalingType::None => 0,
            RopeScalingType::Linear => 1,
            RopeScalingType::Yarn => 2,
            RopeScalingType::Unspecified => -1,
        }
    }
}

/// A rusty wrapper around `LLAMA_POOLING_TYPE`.
#[repr(i8)]
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
pub enum LlamaPoolingType {
    /// The pooling type is unspecified
    Unspecified = -1,
    /// No pooling    
    None = 0,
    /// Mean pooling
    Mean = 1,
    /// CLS pooling
    Cls = 2,
    /// Last pooling
    Last = 3,
}

/// Create a `LlamaPoolingType` from a `c_int` - returns `LlamaPoolingType::Unspecified` if
/// the value is not recognized.
impl From<i32> for LlamaPoolingType {
    fn from(value: i32) -> Self {
        match value {
            0 => Self::None,
            1 => Self::Mean,
            2 => Self::Cls,
            3 => Self::Last,
            _ => Self::Unspecified,
        }
    }
}

/// Create a `c_int` from a `LlamaPoolingType`.
impl From<LlamaPoolingType> for i32 {
    fn from(value: LlamaPoolingType) -> Self {
        match value {
            LlamaPoolingType::None => 0,
            LlamaPoolingType::Mean => 1,
            LlamaPoolingType::Cls => 2,
            LlamaPoolingType::Last => 3,
            LlamaPoolingType::Unspecified => -1,
        }
    }
}

/// A safe wrapper around `llama_context_params`.
///
/// Generally this should be created with [`Default::default()`] and then modified with `with_*` methods.
///
/// # Examples
///
/// ```rust
/// # use std::num::NonZeroU32;
/// use llama_cpp_4::context::params::LlamaContextParams;
///
/// let ctx_params = LlamaContextParams::default()
///     .with_n_ctx(NonZeroU32::new(2048));
///
/// assert_eq!(ctx_params.n_ctx(), NonZeroU32::new(2048));
/// ```
#[derive(Debug, Clone)]
#[allow(
    missing_docs,
    clippy::struct_excessive_bools,
    clippy::module_name_repetitions
)]
pub struct LlamaContextParams {
    pub(crate) context_params: llama_cpp_sys_4::llama_context_params,
    /// When `true`, the TurboQuant attention rotation (PR #21038) will be
    /// disabled for any context created from these params.
    pub(crate) attn_rot_disabled: bool,
}

/// SAFETY: we do not currently allow setting or reading the pointers that cause this to not be automatically send or sync.
unsafe impl Send for LlamaContextParams {}
unsafe impl Sync for LlamaContextParams {}

impl LlamaContextParams {
    /// Set the side of the context
    ///
    /// # Examples
    ///
    /// ```rust
    /// # use std::num::NonZeroU32;
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// let params = params.with_n_ctx(NonZeroU32::new(2048));
    /// assert_eq!(params.n_ctx(), NonZeroU32::new(2048));
    /// ```
    #[must_use]
    pub fn with_n_ctx(mut self, n_ctx: Option<NonZeroU32>) -> Self {
        self.context_params.n_ctx = n_ctx.map_or(0, std::num::NonZeroU32::get);
        self
    }

    /// Get the size of the context.
    ///
    /// [`None`] if the context size is specified by the model and not the context.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.n_ctx(), std::num::NonZeroU32::new(512));
    #[must_use]
    pub fn n_ctx(&self) -> Option<NonZeroU32> {
        NonZeroU32::new(self.context_params.n_ctx)
    }

    /// Set the `n_batch`
    ///
    /// # Examples
    ///
    /// ```rust
    /// # use std::num::NonZeroU32;
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///     .with_n_batch(2048);
    /// assert_eq!(params.n_batch(), 2048);
    /// ```
    #[must_use]
    pub fn with_n_batch(mut self, n_batch: u32) -> Self {
        self.context_params.n_batch = n_batch;
        self
    }

    /// Get the `n_batch`
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// assert_eq!(params.n_batch(), 2048);
    /// ```
    #[must_use]
    pub fn n_batch(&self) -> u32 {
        self.context_params.n_batch
    }

    /// Set the `n_ubatch`
    ///
    /// # Examples
    ///
    /// ```rust
    /// # use std::num::NonZeroU32;
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///     .with_n_ubatch(512);
    /// assert_eq!(params.n_ubatch(), 512);
    /// ```
    #[must_use]
    pub fn with_n_ubatch(mut self, n_ubatch: u32) -> Self {
        self.context_params.n_ubatch = n_ubatch;
        self
    }

    /// Get the `n_ubatch`
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// assert_eq!(params.n_ubatch(), 512);
    /// ```
    #[must_use]
    pub fn n_ubatch(&self) -> u32 {
        self.context_params.n_ubatch
    }

    /// Set the `flash_attention` parameter
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///     .with_flash_attention(true);
    /// assert_eq!(params.flash_attention(), true);
    /// ```
    #[must_use]
    pub fn with_flash_attention(mut self, enabled: bool) -> Self {
        self.context_params.flash_attn_type = if enabled {
            llama_cpp_sys_4::LLAMA_FLASH_ATTN_TYPE_ENABLED
        } else {
            llama_cpp_sys_4::LLAMA_FLASH_ATTN_TYPE_DISABLED
        };
        self
    }

    /// Get the `flash_attention` parameter
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// assert_eq!(params.flash_attention(), false);
    /// ```
    #[must_use]
    pub fn flash_attention(&self) -> bool {
        self.context_params.flash_attn_type == llama_cpp_sys_4::LLAMA_FLASH_ATTN_TYPE_ENABLED
    }

    /// Set the `offload_kqv` parameter to control offloading KV cache & KQV ops to GPU
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///     .with_offload_kqv(false);
    /// assert_eq!(params.offload_kqv(), false);
    /// ```
    #[must_use]
    pub fn with_offload_kqv(mut self, enabled: bool) -> Self {
        self.context_params.offload_kqv = enabled;
        self
    }

    /// Get the `offload_kqv` parameter
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// assert_eq!(params.offload_kqv(), true);
    /// ```
    #[must_use]
    pub fn offload_kqv(&self) -> bool {
        self.context_params.offload_kqv
    }

    /// Set the type of rope scaling.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::{LlamaContextParams, RopeScalingType};
    /// let params = LlamaContextParams::default()
    ///     .with_rope_scaling_type(RopeScalingType::Linear);
    /// assert_eq!(params.rope_scaling_type(), RopeScalingType::Linear);
    /// ```
    #[must_use]
    pub fn with_rope_scaling_type(mut self, rope_scaling_type: RopeScalingType) -> Self {
        self.context_params.rope_scaling_type = i32::from(rope_scaling_type);
        self
    }

    /// Get the type of rope scaling.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.rope_scaling_type(), llama_cpp_4::context::params::RopeScalingType::Unspecified);
    /// ```
    #[must_use]
    pub fn rope_scaling_type(&self) -> RopeScalingType {
        RopeScalingType::from(self.context_params.rope_scaling_type)
    }

    /// Set the rope frequency base.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///    .with_rope_freq_base(0.5);
    /// assert_eq!(params.rope_freq_base(), 0.5);
    /// ```
    #[must_use]
    pub fn with_rope_freq_base(mut self, rope_freq_base: f32) -> Self {
        self.context_params.rope_freq_base = rope_freq_base;
        self
    }

    /// Get the rope frequency base.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.rope_freq_base(), 0.0);
    /// ```
    #[must_use]
    pub fn rope_freq_base(&self) -> f32 {
        self.context_params.rope_freq_base
    }

    /// Set the rope frequency scale.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///   .with_rope_freq_scale(0.5);
    /// assert_eq!(params.rope_freq_scale(), 0.5);
    /// ```
    #[must_use]
    pub fn with_rope_freq_scale(mut self, rope_freq_scale: f32) -> Self {
        self.context_params.rope_freq_scale = rope_freq_scale;
        self
    }

    /// Get the rope frequency scale.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.rope_freq_scale(), 0.0);
    /// ```
    #[must_use]
    pub fn rope_freq_scale(&self) -> f32 {
        self.context_params.rope_freq_scale
    }

    /// Get the number of threads.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.n_threads(), 4);
    /// ```
    #[must_use]
    pub fn n_threads(&self) -> i32 {
        self.context_params.n_threads
    }

    /// Get the number of threads allocated for batches.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.n_threads_batch(), 4);
    /// ```
    #[must_use]
    pub fn n_threads_batch(&self) -> i32 {
        self.context_params.n_threads_batch
    }

    /// Set the number of threads.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///    .with_n_threads(8);
    /// assert_eq!(params.n_threads(), 8);
    /// ```
    #[must_use]
    pub fn with_n_threads(mut self, n_threads: i32) -> Self {
        self.context_params.n_threads = n_threads;
        self
    }

    /// Set the number of threads allocated for batches.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///    .with_n_threads_batch(8);
    /// assert_eq!(params.n_threads_batch(), 8);
    /// ```
    #[must_use]
    pub fn with_n_threads_batch(mut self, n_threads: i32) -> Self {
        self.context_params.n_threads_batch = n_threads;
        self
    }

    /// Check whether embeddings are enabled
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert!(!params.embeddings());
    /// ```
    #[must_use]
    pub fn embeddings(&self) -> bool {
        self.context_params.embeddings
    }

    /// Enable the use of embeddings
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default()
    ///    .with_embeddings(true);
    /// assert!(params.embeddings());
    /// ```
    #[must_use]
    pub fn with_embeddings(mut self, embedding: bool) -> Self {
        self.context_params.embeddings = embedding;
        self
    }

    /// Set the evaluation callback.
    ///
    /// # Examples
    ///
    /// ```no_run
    /// extern "C" fn cb_eval_fn(
    ///     t: *mut llama_cpp_sys_4::ggml_tensor,
    ///     ask: bool,
    ///     user_data: *mut std::ffi::c_void,
    /// ) -> bool {
    ///     false
    /// }
    ///
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default().with_cb_eval(Some(cb_eval_fn));
    /// ```
    #[must_use]
    pub fn with_cb_eval(
        mut self,
        cb_eval: llama_cpp_sys_4::ggml_backend_sched_eval_callback,
    ) -> Self {
        self.context_params.cb_eval = cb_eval;
        self
    }

    /// Set the evaluation callback user data.
    ///
    /// # Examples
    ///
    /// ```no_run
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// let params = LlamaContextParams::default();
    /// let user_data = std::ptr::null_mut();
    /// let params = params.with_cb_eval_user_data(user_data);
    /// ```
    #[must_use]
    pub fn with_cb_eval_user_data(mut self, cb_eval_user_data: *mut std::ffi::c_void) -> Self {
        self.context_params.cb_eval_user_data = cb_eval_user_data;
        self
    }

    /// Attach a [`TensorCapture`](super::tensor_capture::TensorCapture) to
    /// intercept intermediate tensor outputs during `decode()`.
    ///
    /// This sets up the `cb_eval` callback to capture tensors matching the
    /// capture's filter (e.g. specific layer outputs). After `decode()` the
    /// captured data can be read from the `TensorCapture`.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// use llama_cpp_4::context::tensor_capture::TensorCapture;
    ///
    /// let mut capture = TensorCapture::for_layers(&[13, 20, 27]);
    /// let ctx_params = LlamaContextParams::default()
    ///     .with_embeddings(true)
    ///     .with_tensor_capture(&mut capture);
    /// ```
    #[must_use]
    pub fn with_tensor_capture(
        self,
        capture: &mut super::tensor_capture::TensorCapture,
    ) -> Self {
        self.with_cb_eval(Some(super::tensor_capture::tensor_capture_callback))
            .with_cb_eval_user_data(
                capture as *mut super::tensor_capture::TensorCapture as *mut std::ffi::c_void,
            )
    }

    /// Set the storage type for the **K** (key) KV cache tensors.
    ///
    /// The default is `GgmlType::F16`.  Quantized types like `GgmlType::Q5_0`
    /// or `GgmlType::Q4_0` reduce VRAM usage significantly; combining them with
    /// TurboQuant attention rotation (the default) keeps quality high.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// use llama_cpp_4::quantize::GgmlType;
    /// let params = LlamaContextParams::default()
    ///     .with_cache_type_k(GgmlType::Q5_0);
    /// ```
    #[must_use]
    pub fn with_cache_type_k(mut self, ty: crate::quantize::GgmlType) -> Self {
        self.context_params.type_k = ty as llama_cpp_sys_4::ggml_type;
        self
    }

    /// Get the K-cache storage type.
    #[must_use]
    pub fn cache_type_k(&self) -> llama_cpp_sys_4::ggml_type {
        self.context_params.type_k
    }

    /// Set the storage type for the **V** (value) KV cache tensors.
    ///
    /// See [`with_cache_type_k`](Self::with_cache_type_k) for details.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// use llama_cpp_4::quantize::GgmlType;
    /// let params = LlamaContextParams::default()
    ///     .with_cache_type_v(GgmlType::Q5_0);
    /// ```
    #[must_use]
    pub fn with_cache_type_v(mut self, ty: crate::quantize::GgmlType) -> Self {
        self.context_params.type_v = ty as llama_cpp_sys_4::ggml_type;
        self
    }

    /// Get the V-cache storage type.
    #[must_use]
    pub fn cache_type_v(&self) -> llama_cpp_sys_4::ggml_type {
        self.context_params.type_v
    }

    /// Control the TurboQuant attention-rotation feature (llama.cpp PR #21038).
    ///
    /// By default, llama.cpp applies a Hadamard rotation to Q/K/V tensors
    /// before writing them into the KV cache.  This significantly improves
    /// quantized KV-cache quality at near-zero overhead, and is enabled
    /// automatically for models whose head dimension is a power of two.
    ///
    /// Set `disabled = true` to opt out (equivalent to `LLAMA_ATTN_ROT_DISABLE=1`).
    /// The env-var is applied just before the context is created and restored
    /// afterwards, so this is safe to call from a single thread.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::LlamaContextParams;
    /// // Disable rotation for this context only:
    /// let params = LlamaContextParams::default().with_attn_rot_disabled(true);
    /// assert!(params.attn_rot_disabled());
    /// ```
    #[must_use]
    pub fn with_attn_rot_disabled(mut self, disabled: bool) -> Self {
        self.attn_rot_disabled = disabled;
        self
    }

    /// Returns `true` if TurboQuant attention rotation is disabled for this context.
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert!(!params.attn_rot_disabled());
    /// ```
    #[must_use]
    pub fn attn_rot_disabled(&self) -> bool {
        self.attn_rot_disabled
    }

    /// Set the type of pooling.
    ///
    /// # Examples
    ///
    /// ```rust
    /// use llama_cpp_4::context::params::{LlamaContextParams, LlamaPoolingType};
    /// let params = LlamaContextParams::default()
    ///     .with_pooling_type(LlamaPoolingType::Last);
    /// assert_eq!(params.pooling_type(), LlamaPoolingType::Last);
    /// ```
    #[must_use]
    pub fn with_pooling_type(mut self, pooling_type: LlamaPoolingType) -> Self {
        self.context_params.pooling_type = i32::from(pooling_type);
        self
    }

    /// Get the type of pooling.
    ///
    /// # Examples
    ///
    /// ```rust
    /// let params = llama_cpp_4::context::params::LlamaContextParams::default();
    /// assert_eq!(params.pooling_type(), llama_cpp_4::context::params::LlamaPoolingType::Unspecified);
    /// ```
    #[must_use]
    pub fn pooling_type(&self) -> LlamaPoolingType {
        LlamaPoolingType::from(self.context_params.pooling_type)
    }
}

/// Default parameters for `LlamaContext`. (as defined in llama.cpp by `llama_context_default_params`)
/// ```
/// # use std::num::NonZeroU32;
/// use llama_cpp_4::context::params::{LlamaContextParams, RopeScalingType};
/// let params = LlamaContextParams::default();
/// assert_eq!(params.n_ctx(), NonZeroU32::new(512), "n_ctx should be 512");
/// assert_eq!(params.rope_scaling_type(), RopeScalingType::Unspecified);
/// ```
impl Default for LlamaContextParams {
    fn default() -> Self {
        let context_params = unsafe { llama_cpp_sys_4::llama_context_default_params() };
        Self {
            context_params,
            attn_rot_disabled: false,
        }
    }
}