Chapter 3.5: attention

crates/llmfs/src/command/sample_data.rs

@@ -2,13 +2,15 @@ use anyhow::{Context, Result};
 use burn::{
 	Tensor,
 	backend::{Cuda, cuda::CudaDevice},
-	nn::{Embedding, EmbeddingConfig},
+	module::{Module, Param, ParamId},
-	tensor::Int,
+	nn::{Dropout, Embedding, EmbeddingConfig},
+	prelude::Backend,
+	tensor::{Bool, Distribution, Int, activation::softmax},
 };
 use clap::Args;
 use indicatif::MultiProgress;
 use ndarray::Array2;
-use std::{fs::File, path::PathBuf};
+use std::{f32, fs::File, path::PathBuf};
 use tokenizer::Tokenizer;
 
 use crate::data_reader::DataReader;
@@ -47,7 +49,11 @@ impl SampleDataArgs {
 		let stride = 4;
 
 		// Dimension of each token vector
-		let embedding_dim = 256;
+		let embedding_dim = 3;
+		// self-attention output dim
+		let sa_dim_out = 2;
+
+		let dropout = 0.5f64;
 
 		let batch_size = 10;
 		let mut input_batch = Vec::with_capacity(batch_size);
@@ -60,16 +66,18 @@ impl SampleDataArgs {
 		let tok_embedder = EmbeddingConfig::new(tokenizer.vocab_size() as usize, embedding_dim);
 		let tok_embedder: Embedding<Cuda> = tok_embedder.init(&device);
 
+		// TODO: do we want to train this?
 		let pos_embedder = EmbeddingConfig::new(context_size, embedding_dim);
 		let pos_embedder: Embedding<Cuda> = pos_embedder.init(&device);
 
 		let pos_tensor: Tensor<Cuda, 2, Int> =
 			Tensor::arange(0..context_size as i64, &device).unsqueeze_dim(0);
 
-		// [1, context_size, dim]
+		// [context_size, dim]
-		let pos_embedding = pos_embedder.forward(pos_tensor);
+		let pos_embedding = pos_embedder.forward(pos_tensor).squeeze_dim::<2>(0);
 
-		println!("{:?}", pos_embedding.shape());
+		let attention: CausalAttentionv1<Cuda> =
+			CausalAttentionv1::new(embedding_dim, sa_dim_out, context_size, dropout, &device);
 
 		for i in iter {
 			let tokens = tokenizer.encode(&i);
@@ -82,12 +90,13 @@ impl SampleDataArgs {
 				input_batch.push(a.to_owned());
 				output_batch.push(b.to_owned());
 
-				let context = a;
+				// TODO: non-uniform batches?
-				let desired = &b[b.len() - 1..];
-
-				println!("{context:?} -> {desired:?}");
 
 				/*
+				let context = a;
+				let desired = &b[b.len() - 1..];
+				println!("{context:?} -> {desired:?}");
+
 				let input = tokenizer.decode(context);
 				let target = tokenizer.decode(desired);
 				println!("{input:?} -> {target:?}");
@@ -110,9 +119,27 @@ impl SampleDataArgs {
 						Tensor::<_, 1, Int>::from_ints(output.as_slice().unwrap(), &device)
 							.reshape(shape);
 
+					// Input token embeddings
+					// dim: [batch, token, dim]
 					let tok_e = tok_embedder.forward(input);
-					let tok_e: Tensor<Cuda, 3> = Tensor::from_data(tok_e.to_data(), &device);
 					let tok_e = tok_e.add(pos_embedding.clone().unsqueeze_dim(0));
+
+					/*
+					// simple self-attention
+
+					// dim: [batch, query token, other token]
+					let attention_scores = tok_e.clone().matmul(tok_e.clone().transpose());
+					let attention_score = softmax(attention_scores, 1);
+
+					// context vectors for each input token
+					// dim: [batch, token, dim]
+					let context_vectors = attention_score.matmul(tok_e.clone());
+					*/
+
+					// Trainable self-attention
+
+					// shape: [batch, tokens, out_dim]
+					let a = attention.forward(tok_e);
 				}
 			}
 		}
@@ -120,3 +147,146 @@ impl SampleDataArgs {
 		Ok(())
 	}
 }
+
+#[derive(Module, Debug)]
+pub struct CausalAttentionv1<B: Backend> {
+	// Can also use Linear layers with disabled bias
+	// (they may also have a better initialization routine)
+	// TODO: see source code, make this equivalent
+	/// Query weight matrix.
+	/// Maps [tokens, dim] into [tokens, inner_dim].
+	///
+	/// Intuitively, this learns "what question to ask about the text"
+	/// for a given query token. (e.g, "it" -> what does "it" refer to?)
+	w_query: Param<Tensor<B, 2>>,
+
+	/// Key weight matrix.
+	/// Maps [tokens, dim] into [tokens, inner_dim].
+	///
+	/// Intuitively, this learns what properties a certain token
+	/// has when it appears as a context (non-query) token.
+	w_key: Param<Tensor<B, 2>>,
+
+	/// Value weight matrix.
+	/// Maps [tokens, dim] into [tokens, inner_dim].
+	///
+	/// Intuitively, ???
+	w_value: Param<Tensor<B, 2>>,
+
+	dropout: Dropout,
+
+	/// Upper-triangular matrix of ones, excluding diagonal.
+	/// Used to mask future tokens.
+	utri_mask: Tensor<B, 2, Bool>,
+}
+
+impl<B: Backend> CausalAttentionv1<B> {
+	pub fn new(
+		embedding_dim: usize,
+		out_dim: usize,
+		context_length: usize,
+		dropout: f64,
+		device: &B::Device,
+	) -> Self {
+		Self {
+			w_query: Param::uninitialized(
+				ParamId::new(),
+				move |device, is_require_grad| {
+					Tensor::random([embedding_dim, out_dim], Distribution::Default, device)
+						.set_require_grad(is_require_grad)
+				},
+				device.clone(),
+				true,
+				[embedding_dim, out_dim].into(),
+			),
+
+			w_key: Param::uninitialized(
+				ParamId::new(),
+				move |device, is_require_grad| {
+					Tensor::random([embedding_dim, out_dim], Distribution::Default, device)
+						.set_require_grad(is_require_grad)
+				},
+				device.clone(),
+				true,
+				[embedding_dim, out_dim].into(),
+			),
+
+			w_value: Param::uninitialized(
+				ParamId::new(),
+				move |device, is_require_grad| {
+					Tensor::random([embedding_dim, out_dim], Distribution::Default, device)
+						.set_require_grad(is_require_grad)
+				},
+				device.clone(),
+				true,
+				[embedding_dim, out_dim].into(),
+			),
+
+			dropout: Dropout { prob: dropout },
+
+			utri_mask: Tensor::<B, 2, _>::tril_mask([context_length, context_length], 0, &device),
+		}
+	}
+
+	/// Compute self-attention vector for the given batch
+	///
+	/// - input shape is [batch, token, token_dim]
+	/// - input shape is [batch, token, attn_dim]
+	pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
+		// Works similarly to self-attention, (where attn = softmax(tok @ tok^T); context = attn @ tok)
+		// But adds an "inner latent space" using Wq, Qk, and Wv.
+
+		let batch = input.dims()[0];
+
+		let w_query = self
+			.w_query
+			.val()
+			.unsqueeze_dim::<3>(0)
+			.expand([batch as i64, -1, -1]);
+
+		let w_key = self
+			.w_key
+			.val()
+			.unsqueeze_dim::<3>(0)
+			.expand([batch as i64, -1, -1]);
+
+		let w_value = self
+			.w_value
+			.val()
+			.unsqueeze_dim::<3>(0)
+			.expand([batch as i64, -1, -1]);
+
+		// Map batch to inner latent space.
+		// shape: [batch, token, inner_dim]
+		let queries = input.clone().matmul(w_query);
+		let keys = input.clone().matmul(w_key);
+		let values = input.clone().matmul(w_value);
+
+		// Compute attention scores
+		// (cosine similarity of each query token to each context token)
+		// shape: [batch, query_token, context_token]
+		let attn_scores = queries.matmul(keys.clone().transpose());
+
+		let mask = self
+			.utri_mask
+			.clone()
+			.unsqueeze_dim::<3>(0)
+			.expand(attn_scores.shape());
+
+		// Mask out future tokens by filling
+		// upper-triangular with -inf, which becomes 0.0 after softmax.
+		let attn_scores = attn_scores.mask_fill(mask, f32::NEG_INFINITY);
+
+		// Normalize attn weights.
+		//
+		// Divide by sqrt(inner_dim) because...
+		// - dot products get larger with larger dimensions
+		// - this causes softmax to "saturate", making all other values very small
+		// - which makes gradients vanish during training
+		let attn_weights = softmax(attn_scores / (keys.shape()[2] as f32).sqrt(), 2);
+		let attn_weights = self.dropout.forward(attn_weights);
+
+		let context_vec = attn_weights.matmul(values);
+		return context_vec;
+	}
+}