feat(clip): 分片图片批量编码

Signed-off-by: YdrMaster <ydrml@hotmail.com>

Author: YdrMaster

Cargo.toml

@@ -28,7 +28,7 @@ itertools = "0.13"
 build-script-cfg = "0.0"
 
 ndarray-layout = { git = "https://github.com/YdrMaster/ndarray-layout", rev = "48d36c5" }
-operators = { git = "https://github.com/YdrMaster/operators-rs", rev = "1b08473", default-features = false }
+operators = { git = "https://github.com/YdrMaster/operators-rs", rev = "d73a53e", default-features = false }
 
 search-cl-tools = { git = "https://github.com/InfiniTensor/clrt", rev = "6846d52" }
 search-cuda-tools = { git = "https://github.com/YdrMaster/cuda-driver", rev = "e2ec203" }

models/clip/common-cpu/src/test_infer.rs

@@ -56,19 +56,15 @@ fn test_infer() {
         )
         .unwrap();
 
-    let [x, y] = slices.grid();
-    for i in 0..y {
-        for j in 0..x {
-            let patch = slices.patch(j, i);
-            worker
-                .launch(
-                    ClipArgs {
-                        raw: patch.to_nchw(),
-                    },
-                    &mut [],
-                    &ThisThread,
-                )
-                .unwrap();
-        }
+    if let Some(patches) = slices.patches_nchw() {
+        worker
+            .launch(
+                ClipArgs {
+                    raw: patches.map_slice(),
+                },
+                &mut [],
+                &ThisThread,
+            )
+            .unwrap();
     }
 }

models/clip/common/src/compute.rs

@@ -3,7 +3,10 @@ use operators::{
     conv::{self, Conv},
     ByteOf, Hardware, LaunchError, Operator, QueueAlloc, QueueOf, TopoNode,
 };
-use std::ops::{Deref, DerefMut};
+use std::{
+    ops::{Deref, DerefMut},
+    time::Instant,
+};
 use tensor::Tensor;
 
 pub trait Operators {
@@ -60,6 +63,7 @@ where
     where
         QA: QueueAlloc<Hardware = Ops::Hardware>,
     {
+        let time = Instant::now();
         let Args { raw } = args;
         let queue = queue_alloc.queue();
 
@@ -74,7 +78,13 @@ where
         };
 
         let mut embd = Tensor::new(dt_embd, &[n, m, h / hk, w / wk]).map(|s| queue_alloc.alloc(s));
-        self.conv(&mut embd, &raw, &k, &b, workspace, queue_alloc)
+        self.conv(&mut embd, &raw, &k, &b, workspace, queue_alloc)?;
+
+        if self.debug {
+            println!("encode {n} x {h} x {w} image in {:?}", time.elapsed());
+        }
+
+        Ok(())
     }
 }
 

models/clip/common/src/image.rs

@@ -1,4 +1,5 @@
-use def::*;
+use common::{borrow, own, Contiguous};
+use def::*;
 use gguf::ggml_quants::{
     digit_layout::{types as ty, DigitLayout},
     f16,
@@ -7,7 +8,7 @@ use image::ImageReader;
 use itertools::izip;
 use rayon::iter::{IntoParallelIterator, ParallelIterator};
 use std::{iter::zip, ops::Deref, path::Path, slice::from_raw_parts_mut};
-use tensor::{Blob, Tensor};
+use tensor::{rearrange, Blob, Tensor};
 
 #[repr(transparent)]
 pub struct Image<T>(Tensor<T>);
@@ -161,11 +162,7 @@ where
 
     /// NHWC rgb Tensor -> NCHW value Tensor
     pub fn to_nchw(&self) -> Tensor<&[u8]> {
-        self.0
-            .destruct_array()
-            .map(|t| &**t)
-            .transpose(&[2, 0, 1])
-            .tile(0, &[1, 3])
+        rgb_to_chw(&self.0).tile(0, &[1, 3])
     }
 }
 
@@ -198,6 +195,19 @@ impl ImageGrid {
         )
     }
 
+    pub fn patches_nchw(&self) -> Option<Tensor<Contiguous<Blob>>> {
+        self.grid.as_ref().map(|data| {
+            let xychw = rgb_to_chw(data);
+            if let Some(nchw) = xychw.as_ref().merge(0..2) {
+                nchw.map(|s| borrow(s))
+            } else {
+                let mut blob = Tensor::new(xychw.dt(), xychw.shape()).map(Blob::new);
+                rearrange(&mut blob, &xychw);
+                blob.merge(0..2).unwrap().map(own)
+            }
+        })
+    }
+
     /// [urgb] 转 [frgb]
     pub fn normalize(&self, dt: DigitLayout, mean: frgb96, std: frgb96) -> Self {
         let dt = match dt {
@@ -317,6 +327,16 @@ where
     ans
 }
 
+fn rgb_to_chw<T>(data: &Tensor<T>) -> Tensor<&[u8]>
+where
+    T: Deref<Target = [u8]>,
+{
+    let ndim = data.shape().len();
+    data.map_slice()
+        .destruct_array()
+        .transpose(&[ndim, ndim - 2, ndim - 1])
+}
+
 #[test]
 fn test() {
     use std::time::Instant;

tensor/src/lib.rs

@@ -34,19 +34,30 @@ impl Tensor<usize> {
 /// access
 impl<T> Tensor<T> {
     /// 打开数组数据类型
-    pub fn destruct_array(&self) -> Tensor<&T> {
+    pub fn destruct_array(self) -> Self {
         use ggus::ggml_quants::digit_layout::LayoutContent::{Real, Unsigned};
         use std::iter::once;
 
-        let len = self.dt.group_size();
-        let dt = match self.dt.decode() {
+        let Self {
+            dt,
+            layout,
+            physical,
+        } = self;
+
+        let len = dt.group_size();
+        let dt = match dt.decode() {
             Unsigned { width } if len > 1 => DigitLayout::unsigned(width as _, 1),
             Real { exponent, mantissa } if len > 1 => {
                 DigitLayout::real(exponent as _, mantissa as _, 1)
             }
-            _ => return self.as_ref(),
+            _ => {
+                return Self {
+                    dt,
+                    layout,
+                    physical,
+                }
+            }
         };
-        let layout = &self.layout;
         let shape = layout
             .shape()
             .iter()
@@ -63,7 +74,7 @@ impl<T> Tensor<T> {
         Tensor {
             dt,
             layout: ArrayLayout::new(&shape, &strides, offset),
-            physical: &self.physical,
+            physical,
         }
     }