Finish attention mechanisms (#77)

* wip

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* Update avg_pooling.rs

Signed-off-by: Rui Campos <mail@ruicampos.org>

* Update dev-env-aws.yml

Signed-off-by: Rui Campos <mail@ruicampos.org>

* wip

* wip

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* refactor

* refactor

---------

Signed-off-by: Rui Campos <mail@ruicampos.org>

.github/workflows/dev-env-aws.yml

@@ -77,6 +77,7 @@ jobs:
 
   deploy-vscode:
     name: Deploy development environment
+    timeout-minutes: 1500
     needs:
       - start-runner
     runs-on: ${{ needs.start-runner.outputs.label }}
@@ -125,8 +126,7 @@ jobs:
           curl -Lk 'https://code.visualstudio.com/sha/download?build=stable&os=cli-alpine-x64' --output vscode_cli.tar.gz
           tar -xf vscode_cli.tar.gz
       - name: Serve VSCode
-        continue-on-error: true
-        timeout-minutes: 999999999999 
+        continue-on-error: true 
         run: ./code tunnel
 
   stop-runner:

src/attention/avg_pooling.rs

@@ -0,0 +1,46 @@
+
+use tch::nn::Module;
+use tch::Tensor;
+
+
+
+/// implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0
+const DEFAULT_PADDING: i64 = 0;
+
+/// when True, will use ceil instead of floor in the formula to compute the output shape. Default: False
+const DEFAULT_CEIL_MODE: bool = false;
+
+/// when True, will include the zero-padding in the averaging calculation. Default: True
+const DEFAULT_COUNT_INCLUDE_PAD: bool = true;
+
+/// if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None
+const DEFAULT_DIVISOR_OVERRIDE: core::option::Option<i64> = None;
+
+
+/// Average pooling layer
+#[derive(Debug)]
+pub struct AvgPooling {
+    /// Size of the pooling region. Can be a single number or a tuple (kH, kW)
+    kernel_size: i64,
+}
+
+
+impl AvgPooling {
+    pub fn new(kernel_size: i64) -> Self {
+        AvgPooling {  kernel_size  }
+    }
+}
+
+impl Module for AvgPooling {
+    fn forward(&self, x_bcd: &Tensor) -> Tensor {
+        x_bcd.avg_pool2d(
+            self.kernel_size,
+            // stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default: kernel_size
+            self.kernel_size,
+            DEFAULT_PADDING,
+            DEFAULT_CEIL_MODE, 
+            DEFAULT_COUNT_INCLUDE_PAD, 
+            DEFAULT_DIVISOR_OVERRIDE, 
+        )
+    }
+}

src/attention/identity.rs

@@ -0,0 +1,21 @@
+
+use tch::nn;
+use tch::Tensor;
+
+
+
+#[derive(Debug)]
+pub struct Identity { }
+
+impl Identity {
+    pub fn new() -> Self {
+        Identity { }
+    }
+}
+
+impl nn::Module for Identity {
+    fn forward(&self, x_bcd: &Tensor) -> Tensor {   
+        x_bcd.g_mul_scalar(1)
+    }
+}
+

src/attention/metric_tensor.rs

@@ -0,0 +1 @@
+//! To be implemented with CUDA kernels

src/attention/mod.rs

@@ -1,5 +1,4 @@
 pub mod quadratic_form;
-
-
-
-
+pub mod scaled_dot_product;
+pub mod identity;
+pub mod avg_pooling;

src/attention/quadratic_form.rs

@@ -1,71 +1,93 @@
 
 use tch::nn;
+use tch::Tensor;
 
 
 pub fn generate_init() -> nn::Init {
     nn::Init::Randn { mean: 0., stdev: 1. }
 }
 
+#[derive(Debug)]
+pub struct QuadraticAttention {
+    projections_1ndq: Tensor,
+    metric_tensors_1nqq: Tensor,
+    adapter_1pd: Tensor,
+    sqrt_q: f64,
+    cp: (i64, i64),
+}
+
+
 /// Performs self attention N times using the quadratic form $xW_nx.T$ where $W_n$ is a learnable matrix.
 /// This is an early version of the metric self attention, where $W$ is forced to have the properties a metric tensor.
 /// https://arxiv.org/abs/2111.11418 - evidence that any of the attention mechanisms might have similar performance 
-pub fn quadratic_self_attention_module(
-    vs_path: &nn::Path,
-    n: i64,
-    d: i64,
-    q: i64,
-    c: i64,
-) ->  impl nn::Module {
-
-    assert!(d % n == 0, "Embeddings dimension must be divisible by the requested number of heads.");
-    debug_assert_eq!(n*q, d);
-
-    let projections_1ndq = vs_path.var("projections_1ndq", &[1, n, d, q], generate_init());
-    let metric_tensors_1nqq = vs_path.var("metric_tensors_1nqq", &[1, n, q, q], generate_init());
-    let mixer_1dd = vs_path.var("mixer_1dd", &[1, d, d], generate_init());
-
-    debug_assert_eq!(projections_1ndq.size(), vec![1, n, d, q]);
-    debug_assert_eq!(metric_tensors_1nqq.size(), vec![1, n, q, q]);
-    debug_assert_eq!(mixer_1dd.size(), vec![1, d, d]);
-
-    let sqrt_q = f64::sqrt(q as f64);
+impl QuadraticAttention {
+    pub fn new(
+        vs_path: &nn::Path,
+        number_of_heads: i64,
+        embedding_dimension: i64,
+        latent_dimension: i64,
+        sequence_length: i64,
+    ) -> Self {
+
+        let n = number_of_heads;
+        let d = embedding_dimension;
+        let c = sequence_length;
+        let q = latent_dimension;
+        let p = latent_dimension*number_of_heads;
+
+        let projections_1ndq = vs_path.var("projections_1ndq", &[1, n, d, q], generate_init());
+        let metric_tensors_1nqq = vs_path.var("metric_tensors_1nqq", &[1, n, q, q], generate_init());
+        let adapter_1pd = vs_path.var("adapter_1pd", &[1, p, d], generate_init());
+
+        let sqrt_q = f64::sqrt(q as f64);
+        QuadraticAttention { 
+            projections_1ndq, 
+            metric_tensors_1nqq,
+            adapter_1pd,
+            sqrt_q,
+            cp: (c, p)
+        }
+    }
+}
 
-
+// Implement the nn::Module trait for QuadraticAttention.
+impl nn::Module for QuadraticAttention {
+    fn forward(&self, x_bcd: &Tensor) -> Tensor {
 
-    nn::func(move |x_bcd: &tch::Tensor| {
-
         let b = x_bcd.size()[0];
-        assert_eq!(x_bcd.size(), vec![b, c, d]);
-
+        // assert_eq!(x_bcd.size(), vec![b, c, d]);
 
         // Apply n projections to the input 
         let x_b1cd = &x_bcd.unsqueeze(1);
-        let x_bncq = &x_b1cd.matmul(&projections_1ndq);
-        debug_assert_eq!(x_bncq.size(), vec![b, n, c, q]);
-
+        let x_bncq = &x_b1cd.matmul(&self.projections_1ndq);
+        // debug_assert_eq!(x_bncq.size(), vec![b, n, c, q]);
 
         // Use n custom dot products to generate n score tables
         let x_bnqc = &x_bncq.transpose(-1, -2);
-        let dotproducts_bncc = &x_bncq.matmul(&metric_tensors_1nqq.matmul(x_bnqc));
-        debug_assert!(dotproducts_bncc.size() == vec![b, n, c, c]);
+        let dotproducts_bncc = &x_bncq.matmul(&self.metric_tensors_1nqq.matmul(x_bnqc));
+        // debug_assert!(dotproducts_bncc.size() == vec![b, n, c, c]);
 
         // From scaled dot product attention introduced in https://arxiv.org/abs/1706.03762
-        let scaled_dotproducts_bncc = &dotproducts_bncc.divide_scalar(sqrt_q);
+        let scaled_dotproducts_bncc = &dotproducts_bncc.divide_scalar(self.sqrt_q);
 
         let softmaxed_scaled_dotproducts_bncc = &scaled_dotproducts_bncc.softmax(-1, tch::kind::Kind::Float);
         let y_bnqc = &x_bncq.transpose(-1, -2).matmul(softmaxed_scaled_dotproducts_bncc);
-        debug_assert!(y_bnqc.size() == vec![b, n, q, c]);
+        // debug_assert!(y_bnqc.size() == vec![b, n, q, c]);
 
-        let y_bcd = &y_bnqc.reshape(x_bcd.size());
-        debug_assert!(y_bcd.size() == vec![b, c, d]);
+        let y_bcp = &y_bnqc.reshape(&[b, self.cp.0, self.cp.1]);
+        // debug_assert!(y_bcp.size() == vec![b, c, p]);
 
-        y_bcd.matmul(&mixer_1dd)
-    })
+        y_bcp.matmul(&self.adapter_1pd)
+    }
 }
 
 
 
 
+
+
+/* 
+
 #[cfg(test)]
 mod tests {
     use super::*; 
@@ -94,4 +116,8 @@ mod tests {
 
     }
 
-}
+}
+
+*/
+
+