Add the causal mask in mixformer. (#937)

candle-transformers/src/models/mixformer.rs

@@ -75,6 +75,20 @@ impl Module for Embedding {
     }
 }
 
+fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device)
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
 #[derive(Debug)]
 struct RotaryEmbedding {
     sin: Tensor,
@@ -198,6 +212,7 @@ struct MHA {
     rotary_emb: RotaryEmbedding,
     kv_cache: Option<(Tensor, Tensor)>,
     head_dim: usize,
+    n_head: usize,
     softmax_scale: f64,
     span: tracing::Span,
 }
@@ -214,6 +229,7 @@ impl MHA {
             wqkv,
             out_proj,
             head_dim,
+            n_head: cfg.n_head,
             kv_cache: None,
             rotary_emb,
             softmax_scale,
@@ -221,7 +237,7 @@ impl MHA {
         })
     }
 
-    fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
+    fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
         let _enter = self.span.enter();
         let (b_size, seq_len, _n_embd) = xs.dims3()?;
         let qkv = self
@@ -249,9 +265,16 @@ impl MHA {
         let v = v.transpose(1, 2)?.flatten_to(1)?; // b*h, s, d
         let attn_weights = (q.matmul(&k.t()?)? * self.softmax_scale)?; // b*h, t, s
 
-        // TODO: Add the causal mask.
         // causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0, device=scores.device), 1)
         // scores = scores + causal_mask.to(dtype=scores.dtype)
+        let attn_weights = match mask {
+            None => attn_weights,
+            Some(mask) => masked_fill(
+                &attn_weights,
+                &mask.broadcast_left(b_size * self.n_head)?,
+                f32::NEG_INFINITY,
+            )?,
+        };
         let attn_weights = candle_nn::ops::softmax(&attn_weights, D::Minus1)?;
 
         // output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
@@ -287,11 +310,11 @@ impl ParallelBlock {
         })
     }
 
-    fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
+    fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
         let _enter = self.span.enter();
         let residual = xs;
         let xs = xs.apply(&self.ln)?;
-        let attn_outputs = self.mixer.forward(&xs)?;
+        let attn_outputs = self.mixer.forward(&xs, mask)?;
         let feed_forward_hidden_states = self.mlp.forward(&xs)?;
         attn_outputs + feed_forward_hidden_states + residual
     }
@@ -327,8 +350,13 @@ impl MixFormerSequentialForCausalLM {
         let _enter = self.span.enter();
         let (_b_size, seq_len) = xs.dims2()?;
         let mut xs = xs.apply(&self.embedding)?;
+        let mask = if seq_len <= 1 {
+            None
+        } else {
+            Some(get_mask(seq_len, xs.device())?)
+        };
         for block in self.blocks.iter_mut() {
-            xs = block.forward(&xs)?
+            xs = block.forward(&xs, mask.as_ref())?
         }
         xs.narrow(1, seq_len - 1, 1)?.apply(&self.head)?.squeeze(1)
     }