Move more models to candle-transformers (#796)

* Move dinov2.

* Move efficientnet.

* Move the quantized llama model.

* Move segment-anything.

1 files changed, 0 insertions, 483 deletions

diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
deleted file mode 100644
index 76cd15d0..00000000
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ /dev/null
@@ -1,483 +0,0 @@
-use candle::{DType, IndexOp, Result, Tensor};
-use candle_nn::{layer_norm, LayerNorm, Module, VarBuilder};
-
-#[derive(Debug)]
-struct PatchEmbed {
-    proj: candle_nn::Conv2d,
-    span: tracing::Span,
-}
-
-impl PatchEmbed {
-    fn new(
-        in_chans: usize,
-        embed_dim: usize,
-        k_size: usize,
-        stride: usize,
-        padding: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let cfg = candle_nn::Conv2dConfig {
-            stride,
-            padding,
-            ..Default::default()
-        };
-        let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
-        let span = tracing::span!(tracing::Level::TRACE, "patch-embed");
-        Ok(Self { proj, span })
-    }
-}
-
-impl Module for PatchEmbed {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        xs.apply(&self.proj)?.permute((0, 2, 3, 1))
-    }
-}
-
-// A custom op to make add_decomposed_rel_pos faster. Most of the time is spent on the final
-// addition in the case where b = 12, q_h = q_w = 4096, k_h = k_w = 4096
-//   (attn.reshape((b, q_h, q_w, k_h, k_w))?
-//       + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
-//   .reshape((b, q_h * q_w, k_h * k_w))
-// Ideally we would perform this operation in place but this is not supported in candle at the
-// moment. We should also investigate using f16 rather than f32.
-struct Add3(usize, usize, usize, usize, usize);
-impl candle::CustomOp3 for Add3 {
-    fn name(&self) -> &'static str {
-        "add3"
-    }
-
-    fn cpu_fwd(
-        &self,
-        s1: &candle::CpuStorage,
-        l1: &candle::Layout,
-        s2: &candle::CpuStorage,
-        l2: &candle::Layout,
-        s3: &candle::CpuStorage,
-        l3: &candle::Layout,
-    ) -> Result<(candle::CpuStorage, candle::Shape)> {
-        use rayon::prelude::*;
-
-        let Add3(b, q_h, q_w, k_h, k_w) = *self;
-        let s1 = s1.as_slice::<f32>()?;
-        let s1 = match l1.contiguous_offsets() {
-            None => candle::bail!("input1 has to be contiguous"),
-            Some((o1, o2)) => &s1[o1..o2],
-        };
-        let s2 = s2.as_slice::<f32>()?;
-        let s2 = match l2.contiguous_offsets() {
-            None => candle::bail!("input2 has to be contiguous"),
-            Some((o1, o2)) => &s2[o1..o2],
-        };
-        let s3 = s3.as_slice::<f32>()?;
-        let s3 = match l3.contiguous_offsets() {
-            None => candle::bail!("input3 has to be contiguous"),
-            Some((o1, o2)) => &s3[o1..o2],
-        };
-        let mut dst = vec![0f32; b * q_h * q_w * k_h * k_w];
-        dst.par_chunks_exact_mut(k_h * k_w)
-            .enumerate()
-            .for_each(|(b_idx, dst)| {
-                let s1_idx = b_idx * k_h * k_w;
-                let s2_idx = b_idx * k_h;
-                let s3_idx = b_idx * k_w;
-                for h_idx in 0..k_h {
-                    let s1_idx = s1_idx + h_idx * k_w;
-                    let s2_idx = s2_idx + h_idx;
-                    let dst_idx = h_idx * k_w;
-                    for w_idx in 0..k_w {
-                        let s1_idx = s1_idx + w_idx;
-                        let s3_idx = s3_idx + w_idx;
-                        let dst_idx = dst_idx + w_idx;
-                        dst[dst_idx] = s1[s1_idx] + s2[s2_idx] + s3[s3_idx]
-                    }
-                }
-            });
-        let dst = candle::WithDType::to_cpu_storage_owned(dst);
-        Ok((dst, (b, q_h * q_w, k_h * k_w).into()))
-    }
-}
-
-#[derive(Debug)]
-struct Attention {
-    qkv: crate::Linear,
-    proj: crate::Linear,
-    num_heads: usize,
-    scale: f64,
-    rel_pos_hw: Option<(Tensor, Tensor)>,
-    span: tracing::Span,
-    span_matmul: tracing::Span,
-    span_rel_pos: tracing::Span,
-    span_softmax: tracing::Span,
-}
-
-impl Attention {
-    fn new(
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        input_size: (usize, usize),
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let span = tracing::span!(tracing::Level::TRACE, "attention");
-        let span_matmul = tracing::span!(tracing::Level::TRACE, "attn-matmul");
-        let span_rel_pos = tracing::span!(tracing::Level::TRACE, "attn-rel-pos");
-        let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
-        let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
-        let proj = crate::linear(vb.pp("proj"), dim, dim, true)?;
-        let head_dim = dim / num_heads;
-        let scale = 1. / (head_dim as f64).sqrt();
-        let rel_pos_hw = if use_rel_pos {
-            let h = vb.get((2 * input_size.0 - 1, head_dim), "rel_pos_h")?;
-            let w = vb.get((2 * input_size.1 - 1, head_dim), "rel_pos_w")?;
-            Some((h, w))
-        } else {
-            None
-        };
-        Ok(Self {
-            qkv,
-            proj,
-            num_heads,
-            scale,
-            rel_pos_hw,
-            span,
-            span_matmul,
-            span_rel_pos,
-            span_softmax,
-        })
-    }
-
-    fn add_decomposed_rel_pos(
-        &self,
-        attn: Tensor,
-        q: &Tensor,
-        (q_h, q_w): (usize, usize),
-        (k_h, k_w): (usize, usize),
-    ) -> Result<Tensor> {
-        match &self.rel_pos_hw {
-            Some((rel_pos_h, rel_pos_w)) => {
-                let r_h = get_rel_pos(q_h, k_h, rel_pos_h)?;
-                let r_w = get_rel_pos(q_w, k_w, rel_pos_w)?;
-                let (b, _, dim) = q.dims3()?;
-                let r_q = q.reshape((b, q_h, q_w, dim))?;
-                // rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
-                let rel_h = r_q.matmul(&r_h.broadcast_left(b)?.t()?.contiguous()?)?;
-                // rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-                let rel_w = r_q
-                    .transpose(1, 2)? // -> bwhc
-                    .contiguous()?
-                    .matmul(&r_w.broadcast_left(b)?.t()?.contiguous()?)? // bwhc,bwck -> bwhk
-                    .transpose(1, 2)?
-                    .contiguous()?;
-                if attn.device().is_cpu() {
-                    let op = Add3(b, q_h, q_w, k_h, k_w);
-                    attn.apply_op3_no_bwd(&rel_h, &rel_w, &op)
-                } else {
-                    (attn.reshape((b, q_h, q_w, k_h, k_w))?
-                        + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
-                    .reshape((b, q_h * q_w, k_h * k_w))
-                }
-            }
-            None => Ok(attn),
-        }
-    }
-}
-
-fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor> {
-    let max_rel_dist = 2 * usize::max(q_size, k_size) - 1;
-    let dev = rel_pos.device();
-    let rel_pos_resized = if rel_pos.dim(0)? != max_rel_dist {
-        todo!("interpolation")
-    } else {
-        rel_pos
-    };
-    let q_coords = Tensor::arange(0u32, q_size as u32, dev)?
-        .reshape((q_size, 1))?
-        .to_dtype(DType::F32)?;
-    let k_coords = Tensor::arange(0u32, k_size as u32, dev)?
-        .reshape((1, k_size))?
-        .to_dtype(DType::F32)?;
-    let q_coords = (q_coords * f64::max(1f64, k_size as f64 / q_size as f64))?;
-    let k_coords = (k_coords * f64::max(1f64, q_size as f64 / k_size as f64))?;
-    let relative_coords = (q_coords.broadcast_sub(&k_coords)?
-        + (k_size as f64 - 1.) * f64::max(1f64, q_size as f64 / k_size as f64))?;
-    let (d1, d2) = relative_coords.dims2()?;
-    let relative_coords = relative_coords.to_dtype(DType::U32)?;
-    rel_pos_resized
-        .index_select(&relative_coords.reshape(d1 * d2)?, 0)?
-        .reshape((d1, d2, ()))
-}
-
-impl Module for Attention {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        let (b, h, w, c) = xs.dims4()?;
-        let qkv = self
-            .qkv
-            .forward(&xs.flatten_to(1)?)?
-            .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
-            .permute((2, 0, 3, 1, 4))?
-            .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
-        let q = qkv.i(0)?;
-        let k = qkv.i(1)?;
-        let v = qkv.i(2)?;
-        let attn = {
-            let _enter = self.span_matmul.enter();
-            (&q * self.scale)?.matmul(&k.t()?)?
-        };
-        let attn = {
-            let _enter = self.span_rel_pos.enter();
-            self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?
-        };
-        let attn = {
-            let _enter = self.span_softmax.enter();
-            candle_nn::ops::softmax_last_dim(&attn)?
-        };
-        let attn = {
-            let _enter = self.span_matmul.enter();
-            attn.matmul(&v)?
-        };
-        let attn = attn
-            .reshape((b, self.num_heads, h, w, c / self.num_heads))?
-            .permute((0, 2, 3, 1, 4))?
-            .reshape((b, h * w, c))?;
-        self.proj.forward(&attn)?.reshape((b, h, w, c))
-    }
-}
-
-#[derive(Debug)]
-struct Block {
-    norm1: LayerNorm,
-    attn: Attention,
-    norm2: LayerNorm,
-    mlp: crate::MlpBlock,
-    window_size: usize,
-    span: tracing::Span,
-}
-
-impl Block {
-    fn new(
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        window_size: usize,
-        input_size: (usize, usize),
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let norm1 = layer_norm(dim, 1e-6, vb.pp("norm1"))?;
-        let norm2 = layer_norm(dim, 1e-6, vb.pp("norm2"))?;
-        let input_size_attn = if window_size == 0 {
-            input_size
-        } else {
-            (window_size, window_size)
-        };
-        let attn = Attention::new(
-            dim,
-            num_heads,
-            qkv_bias,
-            use_rel_pos,
-            input_size_attn,
-            vb.pp("attn"),
-        )?;
-        let mlp = crate::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
-        let span = tracing::span!(tracing::Level::TRACE, "ie-block");
-        Ok(Self {
-            norm1,
-            attn,
-            norm2,
-            mlp,
-            window_size,
-            span,
-        })
-    }
-}
-
-fn window_partition(xs: Tensor, window_size: usize) -> Result<(Tensor, (usize, usize))> {
-    let (b, h, w, c) = xs.dims4()?;
-    let pad_h = (window_size - h % window_size) % window_size;
-    let pad_w = (window_size - w % window_size) % window_size;
-    let xs = if pad_h > 0 {
-        xs.pad_with_zeros(1, 0, pad_h)?
-    } else {
-        xs
-    };
-    let xs = if pad_w > 0 {
-        xs.pad_with_zeros(2, 0, pad_w)?
-    } else {
-        xs
-    };
-    let (h_p, w_p) = (h + pad_h, w + pad_w);
-    let windows = xs
-        .reshape((
-            b,
-            h_p / window_size,
-            window_size,
-            w_p / window_size,
-            window_size,
-            c,
-        ))?
-        .transpose(2, 3)?
-        .contiguous()?
-        .flatten_to(2)?;
-    Ok((windows, (h_p, w_p)))
-}
-
-fn window_unpartition(
-    windows: Tensor,
-    window_size: usize,
-    (h_p, w_p): (usize, usize),
-    (h, w): (usize, usize),
-) -> Result<Tensor> {
-    let b = windows.dim(0)? / (h_p * w_p / window_size / window_size);
-    let xs = windows
-        .reshape((
-            b,
-            h_p / window_size,
-            w_p / window_size,
-            window_size,
-            window_size,
-            windows.elem_count() / b / h_p / w_p,
-        ))?
-        .transpose(2, 3)?
-        .contiguous()?
-        .reshape((b, h_p, w_p, ()))?;
-    let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
-    let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
-    Ok(xs)
-}
-
-impl Module for Block {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        let shortcut = xs;
-        let xs = self.norm1.forward(xs)?;
-        let hw = (xs.dim(1)?, xs.dim(2)?);
-        let (xs, pad_hw) = if self.window_size > 0 {
-            window_partition(xs, self.window_size)?
-        } else {
-            (xs, (0, 0))
-        };
-        let xs = self.attn.forward(&xs)?;
-        let xs = if self.window_size > 0 {
-            window_unpartition(xs, self.window_size, pad_hw, hw)?
-        } else {
-            xs
-        };
-        let xs = (xs + shortcut)?;
-        &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
-    }
-}
-
-#[derive(Debug)]
-pub struct ImageEncoderViT {
-    patch_embed: PatchEmbed,
-    blocks: Vec<Block>,
-    neck_conv1: candle_nn::Conv2d,
-    neck_ln1: crate::LayerNorm2d,
-    neck_conv2: candle_nn::Conv2d,
-    neck_ln2: crate::LayerNorm2d,
-    pos_embed: Option<Tensor>,
-    span: tracing::Span,
-}
-
-impl ImageEncoderViT {
-    #[allow(clippy::too_many_arguments)]
-    pub fn new(
-        img_size: usize,
-        patch_size: usize,
-        in_chans: usize,
-        embed_dim: usize,
-        depth: usize,
-        num_heads: usize,
-        out_chans: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        use_abs_pos: bool,
-        window_size: usize,
-        global_attn_indexes: &[usize],
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let patch_embed = PatchEmbed::new(
-            in_chans,
-            embed_dim,
-            patch_size,
-            patch_size,
-            0,
-            vb.pp("patch_embed"),
-        )?;
-        let mut blocks = Vec::with_capacity(depth);
-        let vb_b = vb.pp("blocks");
-        for i in 0..depth {
-            let window_size = if global_attn_indexes.contains(&i) {
-                0
-            } else {
-                window_size
-            };
-            let block = Block::new(
-                embed_dim,
-                num_heads,
-                qkv_bias,
-                use_rel_pos,
-                window_size,
-                (img_size / patch_size, img_size / patch_size),
-                vb_b.pp(i),
-            )?;
-            blocks.push(block)
-        }
-        let neck_conv1 = candle_nn::conv2d_no_bias(
-            embed_dim,
-            out_chans,
-            1,
-            Default::default(),
-            vb.pp("neck.0"),
-        )?;
-        let neck_ln1 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.1"))?;
-        let cfg = candle_nn::Conv2dConfig {
-            padding: 1,
-            ..Default::default()
-        };
-        let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
-        let neck_ln2 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.3"))?;
-        let pos_embed = if use_abs_pos {
-            let p = vb.get(
-                (1, img_size / patch_size, img_size / patch_size, embed_dim),
-                "pos_embed",
-            )?;
-            Some(p)
-        } else {
-            None
-        };
-        let span = tracing::span!(tracing::Level::TRACE, "image-encoder-vit");
-        Ok(Self {
-            patch_embed,
-            blocks,
-            neck_conv1,
-            neck_ln1,
-            neck_conv2,
-            neck_ln2,
-            pos_embed,
-            span,
-        })
-    }
-}
-
-impl Module for ImageEncoderViT {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        let xs = self.patch_embed.forward(xs)?;
-        let mut xs = match &self.pos_embed {
-            Some(pos_embed) => (xs + pos_embed)?,
-            None => xs,
-        };
-        for block in self.blocks.iter() {
-            xs = block.forward(&xs)?
-        }
-        xs.permute((0, 3, 1, 2))?
-            .apply(&self.neck_conv1)?
-            .apply(&self.neck_ln1)?
-            .apply(&self.neck_conv2)?
-            .apply(&self.neck_ln2)
-    }
-}