Improve the error message on shape mismatch for cat. (#897)

* Improve the error message on shape mismatch for cat.

* Cosmetic tweak.

candle-core/src/tensor.rs

@@ -1907,6 +1907,34 @@ impl Tensor {
         for arg in args {
             arg.as_ref().check_dim(dim, "cat")?;
         }
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg0.rank() != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: arg0.rank(),
+                    got: arg.rank(),
+                    shape: arg.shape().clone(),
+                }
+                .bt())?
+            }
+            for (dim_idx, (v1, v2)) in arg0
+                .shape()
+                .dims()
+                .iter()
+                .zip(arg.shape().dims().iter())
+                .enumerate()
+            {
+                if dim_idx != dim && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+        }
         if dim == 0 {
             Self::cat0(args)
         } else {

candle-examples/examples/wuerstchen/main.rs

@@ -295,12 +295,10 @@ fn run(args: Args) -> Result<()> {
         };
         let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
         let timesteps = prior_scheduler.timesteps();
+        let timesteps = &timesteps[..timesteps.len() - 1];
         println!("prior denoising");
         for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
-            if index == timesteps.len() - 1 {
-                continue;
-            }
             let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
             let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
             let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
@@ -358,11 +356,9 @@ fn run(args: Args) -> Result<()> {
         println!("diffusion process with prior {image_embeddings:?}");
         let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
         let timesteps = scheduler.timesteps();
+        let timesteps = &timesteps[..timesteps.len() - 1];
         for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
-            if index == timesteps.len() - 1 {
-                continue;
-            }
             let ratio = (Tensor::ones(1, DType::F32, &device)? * t)?;
             let noise_pred =
                 decoder.forward(&latents, &ratio, &image_embeddings, Some(&text_embeddings))?;

candle-transformers/src/models/wuerstchen/diffnext.rs

@@ -41,6 +41,7 @@ impl ResBlockStageB {
         };
         let xs = xs
             .permute((0, 2, 3, 1))?
+            .contiguous()?
             .apply(&self.channelwise_lin1)?
             .gelu()?
             .apply(&self.channelwise_grn)?
@@ -374,7 +375,7 @@ impl WDiffNeXt {
             .apply(&self.clf_ln)?
             .apply(&self.clf_conv)?
             .apply(&|xs: &_| candle_nn::ops::pixel_shuffle(xs, self.patch_size))?
-            .chunk(1, 2)?;
+            .chunk(2, 1)?;
         let b = ((candle_nn::ops::sigmoid(&ab[1])? * (1. - EPS * 2.))? + EPS)?;
         (x_in - &ab[0])? / b
     }

candle-transformers/src/models/wuerstchen/paella_vq.rs

@@ -52,6 +52,7 @@ impl Module for MixingResidualBlock {
             .affine(1. + mods[3] as f64, mods[4] as f64)?;
         let x_temp = x_temp
             .permute((0, 2, 3, 1))?
+            .contiguous()?
             .apply(&self.channelwise_lin1)?
             .gelu()?
             .apply(&self.channelwise_lin2)?