Reserve more eval memory and use ggml scratch buffers

ggml/src/lib.rs

@@ -303,6 +303,23 @@ impl Context {
     pub fn used_mem(&self) -> usize {
         unsafe { ggml_sys::ggml_used_mem(self.ptr.as_ptr()) }
     }
+
+    /// Set scratch buffer
+    pub fn use_scratch(&self, scratch_buffer: Option<&mut Buffer>) {
+        let (size, data) = if let Some(buffer) = scratch_buffer {
+            (buffer.data.len(), buffer.data.as_ptr() as *mut c_void)
+        } else {
+            (0, std::ptr::null_mut())
+        };
+        // SAFETY: this just passes (most likely uninitialized) memory buffer to the ggml C API
+        unsafe {
+            ggml_sys::ggml_set_scratch(self.ptr.as_ptr(), ggml_sys::ggml_scratch {
+                offs: 0,
+                size,
+                data,
+            });
+        }
+    }
 }
 
 impl Drop for Context {
@@ -315,6 +332,25 @@ impl Drop for Context {
     }
 }
 
+/// Pre-allocated buffer
+pub struct Buffer {
+    data: Vec<u8>,
+}
+
+impl Buffer {
+    /// Creates new buffer
+    pub fn new(size: usize) -> Self {
+        let mut data: Vec<u8> = Vec::with_capacity(size);
+        // SAFETY: contents are left uninitialized. Don't use them.
+        #[allow(clippy::uninit_vec)]
+        unsafe { data.set_len(size) };
+
+        Buffer {
+            data
+        }
+    }
+}
+
 /// Tensors are owned by the context. A tensor is alive as long as the
 /// underlying context it was created with is alive.
 pub struct Tensor {

llama-rs/src/lib.rs

@@ -27,6 +27,8 @@ mod util;
 /// The end of text token.
 pub const EOT_TOKEN_ID: TokenId = 2; // Hardcoded (for now?)
 
+const SCRATCH_SIZE: usize = 512 * 1024 * 1024; // 512MB
+
 /// The hyperparameters of the model.
 #[derive(Debug, Default, PartialEq, Eq, PartialOrd, Ord, Deserialize)]
 pub struct Hyperparameters {
@@ -103,6 +105,9 @@ pub struct InferenceSession {
 
     /// The logits that were last predicted by the network. Zeroed out otherwise.
     last_logits: Vec<f32>,
+
+    /// Scratch buffers
+    scratch: [ggml::Buffer; 2]
 }
 impl InferenceSession {
     fn repetition_penalty_tokens(&self) -> &[TokenId] {
@@ -128,10 +133,15 @@ impl Clone for InferenceSession {
             mem_per_token: self.mem_per_token,
             tokens: self.tokens.clone(),
             last_logits: self.last_logits.clone(),
+            scratch: inference_session_scratch_buffers(),
         }
     }
 }
 
+fn inference_session_scratch_buffers() -> [ggml::Buffer; 2] {
+    [ggml::Buffer::new(SCRATCH_SIZE), ggml::Buffer::new(SCRATCH_SIZE)]
+}
+
 #[derive(serde::Serialize, Clone, PartialEq)]
 /// A serializable snapshot of the inference process. Can be saved to disk.
 // Keep in sync with [InferenceSession] and [InferenceSnapshot]
@@ -1116,6 +1126,7 @@ impl Model {
             mem_per_token: 0,
             tokens: vec![],
             last_logits: vec![0.0; n_vocab],
+            scratch: inference_session_scratch_buffers(),
         }
     }
 
@@ -1150,7 +1161,13 @@ impl Model {
 
         // For the first run, we need to guess a maximum buffer size so we can measure
         // the actual memory consumption of the temporary ggml context.
-        let mut buf_size = 1024 * 1024 * 1024;
+        let mut buf_size = 1024 * 1024 * if n_layer >= 80 {
+            1536
+        } else if n_layer >= 60 {
+            1280
+        } else {
+            1024
+        };
         if session.mem_per_token > 0 && session.mem_per_token * n > buf_size {
             // add 10% to account for ggml object overhead
             buf_size = (1.1f64 * session.mem_per_token as f64 * n as f64) as usize;
@@ -1189,6 +1206,8 @@ impl Model {
             let input_self_attention = input_layer.share();
             let mut current: ggml::Tensor;
 
+            ctx0.use_scratch(Some(&mut session.scratch[0]));
+
             // norm
             {
                 current = ctx0.op_rms_norm(&input_layer);
@@ -1312,6 +1331,8 @@ impl Model {
                 current = ctx0.op_mul_mat(&self.layers[il].wo, &current);
             }
 
+            ctx0.use_scratch(Some(&mut session.scratch[1]));
+
             let input_feed_forward = ctx0.op_add(&current, &input_self_attention);
 
             // feed-forward network
@@ -1345,6 +1366,8 @@ impl Model {
             input_layer = current;
         }
 
+        ctx0.use_scratch(Some(&mut session.scratch[0]));
+
         // Used at the end to optionally extract the embeddings.
         let embeddings_tensor;
 
@@ -1362,6 +1385,8 @@ impl Model {
             input_layer = ctx0.op_mul_mat(&self.output, &input_layer);
         }
 
+        ctx0.use_scratch(None);
+
         // logits -> probs
         // inpL = ctx0.op_soft_max(&inpL);