Add custom backend to enable flash attention

Cargo.toml

@@ -6,15 +6,8 @@ edition = "2021"
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [features]
-default = ["torch-backend"]
-torch-backend = ["burn-tch"]
 wgpu-backend = ["burn-wgpu"]
 
-[dependencies.burn-tch]
-package = "burn-tch"
-git = "https://github.com/burn-rs/burn.git"
-optional = true
-
 [dependencies.burn-wgpu]
 package = "burn-wgpu"
 git = "https://github.com/burn-rs/burn.git"
@@ -23,6 +16,9 @@ optional = true
 [dependencies]
 burn = { git = "https://github.com/burn-rs/burn.git" }
 burn-ndarray = { package = "burn-ndarray", git = "https://github.com/burn-rs/burn.git" }
+burn-tch = { package = "burn-tch", git = "https://github.com/burn-rs/burn.git" }
+burn-autodiff = { package = "burn-autodiff", git = "https://github.com/burn-rs/burn.git" }
+tch = "0.13.0"
 serde = {version = "1.0.171", features = ["std", "derive"]}
 npy = "0.4.0"
 num-traits = "0.2.15"

src/backend.rs

@@ -0,0 +1,136 @@
+use burn::tensor::{activation::softmax, Tensor};
+
+pub trait Backend: burn::tensor::backend::Backend {
+    fn qkv_attention(
+        q: Self::TensorPrimitive<3>,
+        k: Self::TensorPrimitive<3>,
+        v: Self::TensorPrimitive<3>,
+        mask: Option<Self::TensorPrimitive<2>>,
+        n_head: usize,
+    ) -> Self::TensorPrimitive<3> {
+        qkv_attention(
+            Tensor::<Self, 3>::from_primitive(q),
+            Tensor::from_primitive(k),
+            Tensor::from_primitive(v),
+            mask.map(|m| Tensor::from_primitive(m)),
+            n_head,
+        )
+        .into_primitive()
+    }
+
+    fn attn_decoder_mask(seq_length: usize, device: &Self::Device) -> Self::TensorPrimitive<2> {
+        attn_decoder_mask::<Self>(seq_length, device).into_primitive()
+    }
+}
+
+use burn::tensor::ops::TensorOps;
+use burn::tensor::Float;
+use burn_tch::{self, TchElement, TchTensor};
+use tch;
+
+impl<E: TchElement> Backend for burn_tch::TchBackend<E> {
+    fn qkv_attention(
+        q: Self::TensorPrimitive<3>,
+        k: Self::TensorPrimitive<3>,
+        v: Self::TensorPrimitive<3>,
+        mask: Option<Self::TensorPrimitive<2>>,
+        n_head: usize,
+    ) -> Self::TensorPrimitive<3> {
+        let q = Tensor::from_primitive(q);
+        let k = Tensor::from_primitive(k);
+        let v = Tensor::from_primitive(v);
+
+        let [n_batch, q_ctx, n_state] = q.dims();
+        let [_, k_ctx, _] = k.dims();
+        let n_hstate = n_state / n_head;
+
+        let rearrange = |t: Tensor<Self, 3>| {
+            let [_, n_ctx, _] = t.dims();
+            t.reshape([n_batch, n_ctx, n_head, n_hstate])
+                .swap_dims(1, 2)
+        };
+
+        let q = rearrange(q).into_primitive();
+        let k = rearrange(k).into_primitive();
+        let v = rearrange(v).into_primitive();
+
+        // for some reason torch crashes when mask is None
+        let mask = mask.unwrap_or_else(|| {
+            Tensor::<Self, 2, Float>::zeros_device([q_ctx, k_ctx], &Self::device(&v))
+                .into_primitive()
+        });
+
+        Tensor::<Self, 4>::from_primitive(TchTensor::new(
+            tch::Tensor::scaled_dot_product_attention(
+                &q.tensor,
+                &k.tensor,
+                &v.tensor,
+                Some(mask.tensor),
+                0.0,
+                false,
+            ),
+        ))
+        .swap_dims(1, 2)
+        .flatten(2, 3)
+        .into_primitive()
+    }
+}
+
+use burn_autodiff;
+
+impl<B: Backend> Backend for burn_autodiff::ADBackendDecorator<B> {}
+
+use std::f32::NEG_INFINITY;
+
+fn qkv_attention<B: Backend>(
+    q: Tensor<B, 3>,
+    k: Tensor<B, 3>,
+    v: Tensor<B, 3>,
+    mask: Option<Tensor<B, 2>>,
+    n_head: usize,
+) -> Tensor<B, 3> {
+    let [n_batch, n_qctx, n_state] = q.dims();
+    let [_, n_ctx, _] = k.dims();
+
+    let scale = (n_state as f64 / n_head as f64).powf(-0.25);
+    let n_hstate = n_state / n_head;
+
+    let q = q
+        .reshape([n_batch, n_qctx, n_head, n_hstate])
+        .swap_dims(1, 2)
+        * scale;
+    let k = k
+        .reshape([n_batch, n_ctx, n_head, n_hstate])
+        .swap_dims(1, 2)
+        .transpose()
+        * scale;
+    let v = v
+        .reshape([n_batch, n_ctx, n_head, n_hstate])
+        .swap_dims(1, 2);
+
+    let qk = q.matmul(k);
+
+    // apply mask
+    let qk = if let Some(mask) = mask {
+        qk + mask.slice([0..n_qctx, 0..n_ctx]).unsqueeze::<4>()
+    } else {
+        qk
+    };
+
+    // normalize value weightings
+    let w = softmax(qk, 3);
+    let o = w.matmul(v).swap_dims(1, 2).flatten(2, 3);
+
+    return o;
+}
+
+fn attn_decoder_mask<B: Backend>(seq_length: usize, device: &B::Device) -> Tensor<B, 2> {
+    let mut mask = Tensor::<B, 2>::zeros([seq_length, seq_length]);
+
+    for i in 0..(seq_length - 1) {
+        let values = Tensor::<B, 2>::zeros([1, seq_length - (i + 1)]).add_scalar(NEG_INFINITY);
+        mask = mask.slice_assign([i..i + 1, i + 1..seq_length], values);
+    }
+
+    return mask.to_device(device);
+}

src/bin/sample/main.rs

@@ -11,10 +11,10 @@ use burn::{
 };
 
 cfg_if::cfg_if! {
-    if #[cfg(feature = "torch-backend")] {
+    if #[cfg(feature = "wgpu-backend")] {
-        use burn_tch::{TchBackend, TchDevice};
-    } else if #[cfg(feature = "wgpu-backend")] {
         use burn_wgpu::{WgpuBackend, WgpuDevice, AutoGraphicsApi};
+    } else {
+        use burn_tch::{TchBackend, TchDevice};
     }
 }
 
@@ -34,12 +34,12 @@ fn load_stable_diffusion_model_file<B: Backend>(
 
 fn main() {
     cfg_if::cfg_if! {
-        if #[cfg(feature = "torch-backend")] {
+        if #[cfg(feature = "wgpu-backend")] {
-            type Backend = TchBackend<f32>;
-            let device = TchDevice::Cuda(0);
-        } else if #[cfg(feature = "wgpu-backend")] {
             type Backend = WgpuBackend<AutoGraphicsApi, f32, i32>;
             let device = WgpuDevice::BestAvailable;
+        } else {
+            type Backend = TchBackend<f32>;
+            let device = TchDevice::Cuda(0);
         }
     }
 

src/lib.rs

@@ -1,2 +1,3 @@
+pub mod backend;
 pub mod model;
 pub mod tokenizer;

src/model/autoencoder/mod.rs

@@ -16,9 +16,9 @@ use burn::{
     },
 };
 
-use super::attention::qkv_attention;
 use super::groupnorm::*;
 use super::silu::*;
+use crate::backend::Backend as MyBackend;
 
 use std::iter;
 
@@ -51,7 +51,7 @@ pub struct Autoencoder<B: Backend> {
     post_quant_conv: Conv2d<B>,
 }
 
-impl<B: Backend> Autoencoder<B> {
+impl<B: MyBackend> Autoencoder<B> {
     pub fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
         self.decode_latent(self.encode_image(x))
     }
@@ -128,7 +128,7 @@ pub struct Encoder<B: Backend> {
     conv_out: Conv2d<B>,
 }
 
-impl<B: Backend> Encoder<B> {
+impl<B: MyBackend> Encoder<B> {
     fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
         let x = self.conv_in.forward(x);
 
@@ -200,7 +200,7 @@ pub struct Decoder<B: Backend> {
     conv_out: Conv2d<B>,
 }
 
-impl<B: Backend> Decoder<B> {
+impl<B: MyBackend> Decoder<B> {
     fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
         let x = self.conv_in.forward(x);
         let x = self.mid.forward(x);
@@ -383,10 +383,6 @@ pub struct PaddedConv2d<B: Backend> {
 
 impl<B: Backend> PaddedConv2d<B> {
     fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
-        println!(
-            "{} {} {:?} {:?}",
-            self.kernel_size, self.stride, self.padding, self.padding_actual
-        );
         let [n_batch, n_channel, height, width] = x.dims();
 
         let desired_height = (self.padding.pad_top + self.padding.pad_bottom + height
@@ -444,7 +440,7 @@ pub struct Mid<B: Backend> {
     block_2: ResnetBlock<B>,
 }
 
-impl<B: Backend> Mid<B> {
+impl<B: MyBackend> Mid<B> {
     fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
         let x = self.block_1.forward(x);
         let x = self.attn.forward(x);
@@ -550,7 +546,7 @@ pub struct ConvSelfAttentionBlock<B: Backend> {
     proj_out: Conv2d<B>,
 }
 
-impl<B: Backend> ConvSelfAttentionBlock<B> {
+impl<B: MyBackend> ConvSelfAttentionBlock<B> {
     fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> {
         let [n_batch, n_channel, height, width] = x.dims();
 
@@ -572,7 +568,13 @@ impl<B: Backend> ConvSelfAttentionBlock<B> {
             .reshape([n_batch, n_channel, height * width])
             .swap_dims(1, 2);
 
-        let wv = qkv_attention(q, k, v, None, 1)
+        let wv = Tensor::from_primitive(B::qkv_attention(
+            q.into_primitive(),
+            k.into_primitive(),
+            v.into_primitive(),
+            None,
+            1,
+        ))
         .swap_dims(1, 2)
         .reshape([n_batch, n_channel, height, width]);
 

src/model/clip/mod.rs

@@ -12,7 +12,7 @@ use burn::{
     },
 };
 
-use crate::model::attention::{attn_decoder_mask, qkv_attention};
+use crate::backend::Backend as MyBackend;
 
 #[derive(Config)]
 pub struct CLIPConfig {
@@ -51,11 +51,11 @@ pub struct CLIP<B: Backend> {
     layer_norm: nn::LayerNorm<B>,
 }
 
-impl<B: Backend> CLIP<B> {
+impl<B: MyBackend> CLIP<B> {
     pub fn forward(&self, x: Tensor<B, 2, Int>) -> Tensor<B, 3> {
         let [n_batch, seq_len] = x.dims();
 
-        let mask = attn_decoder_mask(seq_len, &x.device());
+        let mask = Tensor::from_primitive(B::attn_decoder_mask(seq_len, &x.device()));
 
         let embedded = self.token_embedding.forward(x)
             + self
@@ -104,7 +104,7 @@ pub struct ResidualDecoderAttentionBlock<B: Backend> {
     mlp_ln: nn::LayerNorm<B>,
 }
 
-impl<B: Backend> ResidualDecoderAttentionBlock<B> {
+impl<B: MyBackend> ResidualDecoderAttentionBlock<B> {
     fn forward(&self, x: Tensor<B, 3>, mask: Tensor<B, 2>) -> Tensor<B, 3> {
         let x = x.clone() + self.attn.forward(self.attn_ln.forward(x), Some(mask));
         let x = x.clone() + self.mlp.forward(self.mlp_ln.forward(x));
@@ -152,13 +152,19 @@ pub struct MultiHeadSelfAttention<B: Backend> {
     out: nn::Linear<B>,
 }
 
-impl<B: Backend> MultiHeadSelfAttention<B> {
+impl<B: MyBackend> MultiHeadSelfAttention<B> {
     pub fn forward(&self, x: Tensor<B, 3>, mask: Option<Tensor<B, 2>>) -> Tensor<B, 3> {
         let q = self.query.forward(x.clone());
         let k = self.key.forward(x.clone());
         let v = self.value.forward(x);
 
-        let wv = qkv_attention(q, k, v, mask, self.n_head);
+        let wv = Tensor::from_primitive(B::qkv_attention(
+            q.into_primitive(),
+            k.into_primitive(),
+            v.into_primitive(),
+            mask.map(|m| m.into_primitive()),
+            self.n_head,
+        ));
 
         return self.out.forward(wv);
     }

src/model/stablediffusion/mod.rs

@@ -8,6 +8,8 @@ use burn::{
 
 use num_traits::ToPrimitive;
 
+use crate::backend::Backend as MyBackend;
+
 use super::autoencoder::{Autoencoder, AutoencoderConfig};
 use super::clip::{CLIPConfig, CLIP};
 use super::unet::{UNet, UNetConfig};
@@ -44,7 +46,7 @@ pub struct StableDiffusion<B: Backend> {
     clip: CLIP<B>,
 }
 
-impl<B: Backend> StableDiffusion<B> {
+impl<B: MyBackend> StableDiffusion<B> {
     pub fn sample_image(
         &self,
         context: Tensor<B, 3>,