Clippy fixes for 1.81.0. (#2461)

* Clippy fixes for 1.81.0.

* Another fix.

candle-examples/examples/musicgen/musicgen_model.rs

@@ -284,11 +284,11 @@ impl MusicgenDecoder {
         };
         let embed_dim = cfg.vocab_size + 1;
         let embed_tokens = (0..cfg.num_codebooks)
-            .map(|i| embedding(embed_dim, h, vb.pp(&format!("embed_tokens.{i}"))))
+            .map(|i| embedding(embed_dim, h, vb.pp(format!("embed_tokens.{i}"))))
             .collect::<Result<Vec<_>>>()?;
         let embed_positions = MusicgenSinusoidalPositionalEmbedding::load(vb.clone(), cfg)?;
         let layers = (0..cfg.num_hidden_layers)
-            .map(|i| MusicgenDecoderLayer::load(vb.pp(&format!("layers.{i}")), cfg))
+            .map(|i| MusicgenDecoderLayer::load(vb.pp(format!("layers.{i}")), cfg))
             .collect::<Result<Vec<_>>>()?;
         let layer_norm = layer_norm(h, 1e-5, vb.pp("layer_norm"))?;
         Ok(Self {
@@ -341,7 +341,7 @@ impl MusicgenForCausalLM {
         let h = cfg.hidden_size;
         let decoder = MusicgenDecoder::load(vb.pp("model.decoder"), cfg)?;
         let lm_heads = (0..cfg.num_codebooks)
-            .map(|i| linear_no_bias(h, cfg.vocab_size, vb.pp(&format!("lm_heads.{i}"))))
+            .map(|i| linear_no_bias(h, cfg.vocab_size, vb.pp(format!("lm_heads.{i}"))))
             .collect::<Result<Vec<_>>>()?;
         Ok(Self {
             decoder,

candle-examples/examples/silero-vad/main.rs

@@ -8,7 +8,6 @@ use anyhow::Result;
 use clap::Parser;
 
 use candle::{DType, Tensor};
-use candle_onnx;
 
 #[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
 enum Which {

candle-examples/examples/yolo-v3/darknet.rs

@@ -123,7 +123,7 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
     let padding = if pad != 0 { (size - 1) / 2 } else { 0 };
     let (bn, bias) = match b.parameters.get("batch_normalize") {
         Some(p) if p.parse::<usize>()? != 0 => {
-            let bn = batch_norm(filters, 1e-5, vb.pp(&format!("batch_norm_{index}")))?;
+            let bn = batch_norm(filters, 1e-5, vb.pp(format!("batch_norm_{index}")))?;
             (Some(bn), false)
         }
         Some(_) | None => (None, true),
@@ -135,9 +135,9 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
         dilation: 1,
     };
     let conv = if bias {
-        conv2d(p, filters, size, conv_cfg, vb.pp(&format!("conv_{index}")))?
+        conv2d(p, filters, size, conv_cfg, vb.pp(format!("conv_{index}")))?
     } else {
-        conv2d_no_bias(p, filters, size, conv_cfg, vb.pp(&format!("conv_{index}")))?
+        conv2d_no_bias(p, filters, size, conv_cfg, vb.pp(format!("conv_{index}")))?
     };
     let leaky = match activation {
         "leaky" => true,

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

@@ -161,7 +161,7 @@ impl C2f {
         let cv2 = ConvBlock::load(vb.pp("cv2"), (2 + n) * c, c2, 1, 1, None)?;
         let mut bottleneck = Vec::with_capacity(n);
         for idx in 0..n {
-            let b = Bottleneck::load(vb.pp(&format!("bottleneck.{idx}")), c, c, shortcut)?;
+            let b = Bottleneck::load(vb.pp(format!("bottleneck.{idx}")), c, c, shortcut)?;
             bottleneck.push(b)
         }
         Ok(Self {

candle-transformers/src/models/bert.rs

@@ -419,7 +419,7 @@ struct BertEncoder {
 impl BertEncoder {
     fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
-            .map(|index| BertLayer::load(vb.pp(&format!("layer.{index}")), config))
+            .map(|index| BertLayer::load(vb.pp(format!("layer.{index}")), config))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
         Ok(BertEncoder { layers, span })
@@ -454,8 +454,8 @@ impl BertModel {
             (Err(err), _) | (_, Err(err)) => {
                 if let Some(model_type) = &config.model_type {
                     if let (Ok(embeddings), Ok(encoder)) = (
-                        BertEmbeddings::load(vb.pp(&format!("{model_type}.embeddings")), config),
+                        BertEmbeddings::load(vb.pp(format!("{model_type}.embeddings")), config),
-                        BertEncoder::load(vb.pp(&format!("{model_type}.encoder")), config),
+                        BertEncoder::load(vb.pp(format!("{model_type}.encoder")), config),
                     ) {
                         (embeddings, encoder)
                     } else {

candle-transformers/src/models/bigcode.rs

@@ -298,7 +298,7 @@ impl GPTBigCode {
         let wte = embedding(cfg.vocab_size, hidden_size, vb_t.pp("wte"))?;
         let wpe = embedding(cfg.max_position_embeddings, hidden_size, vb_t.pp("wpe"))?;
         let blocks = (0..cfg.num_hidden_layers)
-            .map(|i| Block::load(vb_t.pp(&format!("h.{i}")), &cfg))
+            .map(|i| Block::load(vb_t.pp(format!("h.{i}")), &cfg))
             .collect::<Result<Vec<_>>>()?;
         let ln_f = layer_norm(hidden_size, cfg.layer_norm_epsilon, vb_t.pp("ln_f"))?;
         let lm_head = linear(hidden_size, cfg.vocab_size, false, vb_t.pp("wte"))?;

candle-transformers/src/models/distilbert.rs

@@ -275,7 +275,7 @@ struct Transformer {
 impl Transformer {
     fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
         let layers = (0..config.n_layers)
-            .map(|index| TransformerBlock::load(vb.pp(&format!("layer.{index}")), config))
+            .map(|index| TransformerBlock::load(vb.pp(format!("layer.{index}")), config))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
         Ok(Transformer { layers, span })
@@ -311,8 +311,8 @@ impl DistilBertModel {
             (Err(err), _) | (_, Err(err)) => {
                 if let Some(model_type) = &config.model_type {
                     if let (Ok(embeddings), Ok(encoder)) = (
-                        Embeddings::load(vb.pp(&format!("{model_type}.embeddings")), config),
+                        Embeddings::load(vb.pp(format!("{model_type}.embeddings")), config),
-                        Transformer::load(vb.pp(&format!("{model_type}.transformer")), config),
+                        Transformer::load(vb.pp(format!("{model_type}.transformer")), config),
                     ) {
                         (embeddings, encoder)
                     } else {

candle-transformers/src/models/falcon.rs

@@ -448,7 +448,7 @@ impl Falcon {
             vb.pp("transformer.word_embeddings"),
         )?;
         let blocks = (0..cfg.num_hidden_layers)
-            .map(|i| FalconDecoderLayer::load(vb.pp(&format!("transformer.h.{i}")), &cfg))
+            .map(|i| FalconDecoderLayer::load(vb.pp(format!("transformer.h.{i}")), &cfg))
             .collect::<Result<Vec<_>>>()?;
         let ln_f = layer_norm(
             cfg.hidden_size,

candle-transformers/src/models/jina_bert.rs

@@ -344,7 +344,7 @@ impl BertEncoder {
             candle::bail!("only alibi is supported as a position-embedding-type")
         }
         let layers = (0..cfg.num_hidden_layers)
-            .map(|index| BertLayer::new(vb.pp(&format!("layer.{index}")), cfg))
+            .map(|index| BertLayer::new(vb.pp(format!("layer.{index}")), cfg))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
         let alibi = build_alibi_bias(cfg)?.to_device(vb.device())?;

candle-transformers/src/models/llama.rs

@@ -507,7 +507,7 @@ impl Llama {
         let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
         let ln_f = RmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("model.norm"))?;
         let blocks: Vec<_> = (0..cfg.num_hidden_layers)
-            .map(|i| Block::load(vb.pp(&format!("model.layers.{i}")), cfg).unwrap())
+            .map(|i| Block::load(vb.pp(format!("model.layers.{i}")), cfg).unwrap())
             .collect();
 
         Ok(Self {

candle-transformers/src/models/llama2_c.rs

@@ -354,7 +354,7 @@ impl Llama {
         let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
         let ln_f = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
         let blocks: Vec<_> = (0..cfg.n_layers)
-            .map(|i| Block::load(vb.pp(&format!("model.layers.{i}")), &cfg).unwrap())
+            .map(|i| Block::load(vb.pp(format!("model.layers.{i}")), &cfg).unwrap())
             .collect();
         Ok(Self {
             wte,

candle-transformers/src/models/moondream.rs

@@ -167,7 +167,7 @@ impl VisionTransformer {
         let blocks = (0..cfg.num_blocks)
             .map(|i| {
                 VitBlock::new(
-                    vb.pp(&format!("blocks.{}", i)),
+                    vb.pp(format!("blocks.{}", i)),
                     cfg.embed_dim,
                     cfg.num_heads,
                     cfg,

candle-transformers/src/models/segformer.rs

@@ -404,7 +404,7 @@ impl SegformerEncoder {
                 stride,
                 num_channels,
                 hidden_size,
-                vb.pp(&format!("patch_embeddings.{}", i)),
+                vb.pp(format!("patch_embeddings.{}", i)),
             )?);
             let mut layers = Vec::with_capacity(config.depths[i]);
             for j in 0..config.depths[i] {
@@ -417,14 +417,14 @@ impl SegformerEncoder {
                     num_attention_heads,
                     sequence_reduction_ratio,
                     mlp_ratio,
-                    vb.pp(&format!("block.{}.{}", i, j)),
+                    vb.pp(format!("block.{}.{}", i, j)),
                 )?);
             }
             blocks.push(layers);
             layer_norms.push(layer_norm(
                 hidden_size,
                 config.layer_norm_eps,
-                vb.pp(&format!("layer_norm.{}", i)),
+                vb.pp(format!("layer_norm.{}", i)),
             )?);
         }
         Ok(Self {
@@ -507,7 +507,7 @@ impl SegformerDecodeHead {
             linear_c.push(SegformerMLP::new(
                 config,
                 hidden_size,
-                vb.pp(&format!("linear_c.{}", i)),
+                vb.pp(format!("linear_c.{}", i)),
             )?);
         }
         let linear_fuse = conv2d_no_bias(

candle-transformers/src/models/t5.rs

@@ -659,7 +659,7 @@ struct T5Stack {
 impl T5Stack {
     fn load(decoder: bool, vb: VarBuilder, shared: &Arc<Embedding>, cfg: &Config) -> Result<Self> {
         let block = (0..cfg.num_layers)
-            .map(|i| T5Block::load(i == 0, decoder, vb.pp(&format!("block.{i}")), cfg))
+            .map(|i| T5Block::load(i == 0, decoder, vb.pp(format!("block.{i}")), cfg))
             .collect::<Result<Vec<_>>>()?;
         let final_layer_norm = T5LayerNorm::load(
             cfg.d_model,

candle-transformers/src/models/whisper/model.rs

@@ -260,7 +260,7 @@ impl AudioEncoder {
         let positional_embedding = sinusoids(n_ctx, n_state, vb.device())?;
         let blocks = (0..cfg.encoder_layers)
             .map(|i| {
-                ResidualAttentionBlock::load(n_state, n_head, false, vb.pp(&format!("layers.{i}")))
+                ResidualAttentionBlock::load(n_state, n_head, false, vb.pp(format!("layers.{i}")))
             })
             .collect::<Result<Vec<_>>>()?;
         let ln_post = layer_norm(n_state, vb.pp("layer_norm"))?;
@@ -321,7 +321,7 @@ impl TextDecoder {
         let positional_embedding = vb.get((n_ctx, n_state), "embed_positions.weight")?;
         let blocks = (0..cfg.decoder_layers)
             .map(|i| {
-                ResidualAttentionBlock::load(n_state, n_head, true, vb.pp(&format!("layers.{i}")))
+                ResidualAttentionBlock::load(n_state, n_head, true, vb.pp(format!("layers.{i}")))
             })
             .collect::<Result<Vec<_>>>()?;
         let ln = layer_norm(n_state, vb.pp("layer_norm"))?;

candle-wasm-examples/llama2-c/src/model.rs

@@ -275,7 +275,7 @@ impl Llama {
         let lm_head = linear(cfg.dim, cfg.vocab_size, vb.pp("lm_head"))?;
         let norm = rms_norm(cfg.dim, cfg.norm_eps, vb.pp("model.norm"))?;
         let blocks: Vec<_> = (0..cfg.n_layers)
-            .map(|i| Block::load(vb.pp(&format!("model.layers.{i}")), cache, cfg).unwrap())
+            .map(|i| Block::load(vb.pp(format!("model.layers.{i}")), cache, cfg).unwrap())
             .collect();
         Ok(Self::new(wte, blocks, norm, lm_head))
     }

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

@@ -155,7 +155,7 @@ impl C2f {
         let cv2 = ConvBlock::load(vb.pp("cv2"), (2 + n) * c, c2, 1, 1, None)?;
         let mut bottleneck = Vec::with_capacity(n);
         for idx in 0..n {
-            let b = Bottleneck::load(vb.pp(&format!("bottleneck.{idx}")), c, c, shortcut)?;
+            let b = Bottleneck::load(vb.pp(format!("bottleneck.{idx}")), c, c, shortcut)?;
             bottleneck.push(b)
         }
         Ok(Self {