Remove some dead-code annotations. (#629)

* Remove some dead-code annotations.

* More dead code removal.

* One more.

* CI fix.

candle-examples/examples/custom-ops/main.rs

@@ -2,19 +2,16 @@
 // own forward pass (CPU and GPU versions) as well as their backward pass.
 //
 // In this example we add the RMS normalization operation and implement it for f32.
-#![allow(dead_code)]
-#![allow(unused)]
 
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
+#[allow(unused)]
 mod cuda_kernels;
 
 use clap::Parser;
 
-use candle::backend::BackendStorage;
-use candle::cpu_backend;
-use candle::{CpuStorage, CustomOp1, DType, Device, Layout, Result, Shape, Tensor};
+use candle::{CpuStorage, CustomOp1, Layout, Result, Shape, Tensor};
 
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
@@ -57,8 +54,9 @@ impl CustomOp1 for LayerNorm {
         storage: &candle::CudaStorage,
         layout: &Layout,
     ) -> Result<(candle::CudaStorage, Shape)> {
-        use candle::cuda_backend::{cudarc, WrapErr};
-        use cudarc::driver::{LaunchAsync, LaunchConfig};
+        use candle::backend::BackendStorage;
+        use candle::cuda_backend::cudarc::driver::{LaunchAsync, LaunchConfig};
+        use candle::cuda_backend::WrapErr;
         let (d1, d2) = layout.shape().dims2()?;
         let d1 = d1 as u32;
         let d2 = d2 as u32;

candle-examples/examples/quantized/main.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
@@ -185,16 +184,6 @@ struct ModelWeights {
     span_output: tracing::Span,
 }
 
-struct WeightMap(HashMap<String, QTensor>);
-impl WeightMap {
-    fn get(&mut self, name: &str) -> Result<QTensor> {
-        match self.0.remove(name) {
-            None => candle::bail!("cannot find tensor with name '{name}'"),
-            Some(tensor) => Ok(tensor),
-        }
-    }
-}
-
 fn precomput_freqs_cis(head_dim: usize, freq_base: f32) -> Result<(Tensor, Tensor)> {
     let theta: Vec<_> = (0..head_dim)
         .step_by(2)

candle-examples/examples/stable-diffusion/clip.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 //! Contrastive Language-Image Pre-Training
 //!
 //! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on

candle-examples/examples/stable-diffusion/ddim.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 //! # Denoising Diffusion Implicit Models
 //!
 //! The Denoising Diffusion Implicit Models (DDIM) is a simple scheduler
@@ -164,17 +163,6 @@ impl DDIMScheduler {
         }
     }
 
-    pub fn add_noise(&self, original: &Tensor, noise: Tensor, timestep: usize) -> Result<Tensor> {
-        let timestep = if timestep >= self.alphas_cumprod.len() {
-            timestep - 1
-        } else {
-            timestep
-        };
-        let sqrt_alpha_prod = self.alphas_cumprod[timestep].sqrt();
-        let sqrt_one_minus_alpha_prod = (1.0 - self.alphas_cumprod[timestep]).sqrt();
-        (original * sqrt_alpha_prod)? + (noise * sqrt_one_minus_alpha_prod)?
-    }
-
     pub fn init_noise_sigma(&self) -> f64 {
         self.init_noise_sigma
     }

candle-examples/examples/stable-diffusion/embeddings.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 use candle::{Result, Tensor, D};
 use candle_nn as nn;
 use candle_nn::Module;

candle-examples/examples/stable-diffusion/resnet.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 //! ResNet Building Blocks
 //!
 //! Some Residual Network blocks used in UNet models.

candle-examples/examples/whisper/model.rs

@@ -19,34 +19,6 @@ pub struct Config {
     pub suppress_tokens: Vec<u32>,
 }
 
-impl Config {
-    #[allow(dead_code)]
-    pub fn tiny_en() -> Self {
-        let suppress_tokens = vec![
-            1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93,
-            357, 366, 438, 532, 685, 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, 1391,
-            1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, 4600, 4808, 5299, 5855, 6329,
-            7203, 9609, 9959, 10563, 10786, 11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306,
-            16410, 16791, 17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
-            34949, 40283, 40493, 40549, 47282, 49146, 50257, 50357, 50358, 50359, 50360, 50361,
-            50362,
-        ];
-        Self {
-            num_mel_bins: 80,
-            vocab_size: 51864,
-            max_source_positions: 1500,
-            d_model: 384,
-            encoder_attention_heads: 6,
-            encoder_layers: 4,
-            max_target_positions: 448,
-            // n_text_state: 384,
-            decoder_attention_heads: 6,
-            decoder_layers: 4,
-            suppress_tokens,
-        }
-    }
-}
-
 fn embedding(vocab_size: usize, hidden_size: usize, vb: VarBuilder) -> Result<Embedding> {
     let embeddings = vb.get((vocab_size, hidden_size), "weight")?;
     Ok(Embedding::new(embeddings, hidden_size))

candle-examples/examples/yolo-v8/model.rs

@@ -675,7 +675,6 @@ pub struct YoloV8Pose {
     head: PoseHead,
 }
 
-#[allow(unused)]
 impl YoloV8Pose {
     pub fn load(
         vb: VarBuilder,

candle-wasm-examples/whisper/src/lib.rs

@@ -1,5 +1,3 @@
-#![allow(dead_code)]
-
 pub const WITH_TIMER: bool = true;
 
 struct Timer {

candle-wasm-examples/whisper/src/model.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)]
 // We use anyhow rather than candle errors as it provides better support for getting the backtrace
 // back when using RUST_LIB_BACKTRACE=1.
 use anyhow::Result;
@@ -97,32 +96,6 @@ fn conv1d(
     Ok(Conv1d::new(weight, Some(bias), config))
 }
 
-fn conv1d_no_bias(
-    in_channels: usize,
-    out_channels: usize,
-    kernel_size: usize,
-    config: Conv1dConfig,
-    vb: VarBuilder,
-) -> Result<Conv1d> {
-    let weight = vb.get((out_channels, in_channels, kernel_size), "weight")?;
-    Ok(Conv1d::new(weight, None, config))
-}
-
-struct Dropout {
-    pr: f64,
-}
-
-impl Dropout {
-    fn new(pr: f64) -> Self {
-        Self { pr }
-    }
-
-    fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        // TODO
-        Ok(x.clone())
-    }
-}
-
 fn layer_norm(size: usize, vb: VarBuilder) -> Result<LayerNorm> {
     let weight = vb.get(size, "weight")?;
     let bias = vb.get(size, "bias")?;
@@ -414,10 +387,4 @@ impl Whisper {
             config,
         })
     }
-
-    pub fn forward(&self, mel: &Tensor, tokens: &Tensor) -> Result<Tensor> {
-        let enc = self.encoder.forward(mel)?;
-        let dec = self.decoder.forward(tokens, &enc)?;
-        Ok(dec)
-    }
 }

candle-wasm-examples/whisper/src/worker.rs

@@ -33,9 +33,6 @@ pub const HOP_LENGTH: usize = 160;
 pub const CHUNK_LENGTH: usize = 30;
 pub const N_SAMPLES: usize = CHUNK_LENGTH * SAMPLE_RATE; // 480000 samples in a 30-second chunk
 pub const N_FRAMES: usize = N_SAMPLES / HOP_LENGTH; // 3000 frames in a mel spectrogram input
-pub const N_SAMPLES_PER_TOKEN: usize = HOP_LENGTH * 2; // the initial convolutions has stride 2
-pub const FRAMES_PER_SECOND: usize = SAMPLE_RATE / HOP_LENGTH; // 10ms per audio frame
-pub const TOKENS_PER_SECOND: usize = SAMPLE_RATE / N_SAMPLES_PER_TOKEN; // 20ms per audio token
 
 pub const NO_SPEECH_THRESHOLD: f64 = 0.6;
 pub const LOGPROB_THRESHOLD: f64 = -1.0;
@@ -46,7 +43,6 @@ pub const COMPRESSION_RATIO_THRESHOLD: f64 = 2.4;
 pub const SOT_TOKEN: u32 = 50257;
 pub const EOT_TOKEN: u32 = 50256;
 pub const NO_SPEECH_TOKEN: u32 = 50361;
-pub const NO_TIMESTAMP_TOKEN: u32 = 50362;
 // From the _get_suppress_tokens function + 50362 (no timestamp)
 // https://github.com/openai/whisper/blob/f572f2161ba831bae131364c3bffdead7af6d210/whisper/decoding.py#L605
 pub const SUPPRESS_TOKENS: [u32; 91] = [

candle-wasm-examples/yolo/src/model.rs

@@ -1,13 +1,9 @@
-#![allow(dead_code)]
 use candle::{DType, IndexOp, Result, Tensor, D};
 use candle_nn::{
     batch_norm, conv2d, conv2d_no_bias, BatchNorm, Conv2d, Conv2dConfig, Module, VarBuilder,
 };
 use image::DynamicImage;
 
-const CONFIDENCE_THRESHOLD: f32 = 0.25;
-const NMS_THRESHOLD: f32 = 0.45;
-
 // Model architecture from https://github.com/ultralytics/ultralytics/issues/189
 // https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py