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network-poc/node/src/burn_smollm/attention.rs

@@ -0,0 +1,118 @@
+use burn::module::{Module, Param};
+use burn::tensor::{backend::Backend, Tensor};
+use super::rope::RoPE;
+use super::config::SmolLMConfig;
+
+#[derive(Clone, Debug)]
+pub struct KVCache<B: Backend> {
+    pub k: Tensor<B, 4>,
+    pub v: Tensor<B, 4>,
+}
+
+#[derive(Module, Debug)]
+pub struct Attention<B: Backend> {
+    pub q_proj: Param<Tensor<B, 2>>,  // [hidden, num_heads * head_dim]
+    pub k_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
+    pub v_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
+    pub o_proj: Param<Tensor<B, 2>>,  // [num_heads * head_dim, hidden]
+
+    num_heads: usize,
+    num_kv_heads: usize,
+    head_dim: usize,
+
+    rope: RoPE<B>,
+}
+
+impl<B: Backend> Attention<B> {
+    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
+        let head_dim = config.hidden_size / config.num_attention_heads;
+
+        Self {
+            q_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_attention_heads * head_dim], device)),
+            k_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
+            v_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
+            o_proj: Param::from_tensor(Tensor::zeros([config.num_attention_heads * head_dim, config.hidden_size], device)),
+
+            num_heads: config.num_attention_heads,
+            num_kv_heads: config.num_key_value_heads,
+            head_dim,
+
+            rope: RoPE::new(head_dim, config.max_position_embeddings, config.rope_theta, device),
+        }
+    }
+
+    pub fn forward(
+        &self,
+        x: Tensor<B, 3>,
+        offset: usize,
+        cache: Option<KVCache<B>>
+    ) -> (Tensor<B, 3>, KVCache<B>) {
+        let [batch, seq_len, hidden_dim] = x.dims();
+
+        // Project Q, K, V: x @ W -> [batch, seq, proj_dim]
+        let q = x.clone().matmul(self.q_proj.val().unsqueeze());
+        let k = x.clone().matmul(self.k_proj.val().unsqueeze());
+        let v = x.matmul(self.v_proj.val().unsqueeze());
+
+        // Reshape: [batch, seq, heads, head_dim] -> [batch, heads, seq, head_dim]
+        let q = q.reshape([batch, seq_len, self.num_heads, self.head_dim]).swap_dims(1, 2);
+        let k = k.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
+        let v = v.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
+
+        // Apply RoPE
+        let q = self.rope.forward(q, offset);
+        let k = self.rope.forward(k, offset);
+
+        // KV cache
+        let (k, v) = if let Some(c) = cache {
+            (Tensor::cat(vec![c.k, k], 2), Tensor::cat(vec![c.v, v], 2))
+        } else {
+            (k, v)
+        };
+
+        let new_cache = KVCache { k: k.clone(), v: v.clone() };
+        let kv_len = k.dims()[2];
+
+        // GQA: repeat K,V heads — [batch, kv_heads, kv_len, hd] -> [batch, num_heads, kv_len, hd]
+        let num_reps = self.num_heads / self.num_kv_heads;
+        let k = if num_reps > 1 {
+            let [b, kv_h, s, hd] = k.dims();
+            k.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
+        } else { k };
+        let v = if num_reps > 1 {
+            let [b, kv_h, s, hd] = v.dims();
+            v.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
+        } else { v };
+
+        // Attention: Q @ K^T / sqrt(d)
+        let scale = 1.0 / (self.head_dim as f64).sqrt();
+        let scores = q.matmul(k.swap_dims(2, 3)).mul_scalar(scale);
+        // scores: [batch, heads, seq_len, kv_len]
+
+        // Causal mask for prefill (seq_len > 1)
+        let scores = if seq_len > 1 {
+            let mask_data: Vec<f32> = (0..seq_len).flat_map(|i| {
+                (0..kv_len).map(move |j| {
+                    if j > offset + i { f32::NEG_INFINITY } else { 0.0 }
+                })
+            }).collect();
+            let mask = Tensor::<B, 2>::from_data(
+                burn::tensor::TensorData::new(mask_data, [seq_len, kv_len]),
+                &scores.device()
+            ).reshape([1, 1, seq_len, kv_len]);
+            scores + mask
+        } else {
+            scores
+        };
+
+        let attn_weights = burn::tensor::activation::softmax(scores, 3);
+
+        let context = attn_weights.matmul(v);
+        // [batch, heads, seq, hd] -> [batch, seq, heads*hd]
+        let context = context.swap_dims(1, 2).reshape([batch, seq_len, self.num_heads * self.head_dim]);
+
+        let output = context.matmul(self.o_proj.val().unsqueeze());
+
+        (output, new_cache)
+    }
+}