style: simplify string formatting for readability

candle-book/src/simplified.rs

@@ -123,7 +123,7 @@ async fn simplified() -> anyhow::Result<()> {
  break;
  },
  Err(e) => {
- println!("Error: {}", e);
+ println!("Error: {e}");
  continue;
  }
  }
@@ -142,8 +142,8 @@ async fn simplified() -> anyhow::Result<()> {
  .argmax(D::Minus1)?
  .to_dtype(DType::F32)?
  .get(0).map(|x| x.to_scalar::<f32>())??;
- println!("real_life_votes: {:?}", real_world_votes);
- println!("neural_network_prediction_result: {:?}", result);
+ println!("real_life_votes: {real_world_votes:?}");
+ println!("neural_network_prediction_result: {result:?}");
 
  Ok(())
 

candle-core/benches/benchmarks/mod.rs

@@ -22,13 +22,13 @@ impl BenchDevice for Device {
  #[cfg(feature = "cuda")]
  return Ok(device.synchronize()?);
  #[cfg(not(feature = "cuda"))]
- panic!("Cuda device without cuda feature enabled: {:?}", device)
+ panic!("Cuda device without cuda feature enabled: {device:?}")
  }
  Device::Metal(device) => {
  #[cfg(feature = "metal")]
  return Ok(device.wait_until_completed()?);
  #[cfg(not(feature = "metal"))]
- panic!("Metal device without metal feature enabled: {:?}", device)
+ panic!("Metal device without metal feature enabled: {device:?}")
  }
  }
  }

candle-core/benches/benchmarks/qmatmul.rs

@@ -31,7 +31,7 @@ fn run_bench(c: &mut Criterion, device: &Device, dtype: GgmlDType) {
 
  let flops = b * m * n * k;
 
- let mut group = c.benchmark_group(device.bench_name(format!("qmatmul_{:?}", dtype)));
+ let mut group = c.benchmark_group(device.bench_name(format!("qmatmul_{dtype:?}")));
  group.sample_size(200);
  group.throughput(Throughput::Bytes(flops as u64));
  group.bench_function("iter", move |b| {

candle-core/benches/benchmarks/unary.rs

@@ -40,7 +40,7 @@ fn criterion_benchmark(c: &mut Criterion) {
  let handler = BenchDeviceHandler::new().unwrap();
  for device in handler.devices {
  for dtype in [DType::F32, DType::BF16, DType::F16] {
- let name = format!("sqrt_{:?}", dtype);
+ let name = format!("sqrt_{dtype:?}");
  run_unary_benchmark(c, &device, dtype, &name);
  }
  }

candle-core/src/display.rs

@@ -12,10 +12,10 @@ impl Tensor {
  let device_str = match self.device().location() {
  crate::DeviceLocation::Cpu => "".to_owned(),
  crate::DeviceLocation::Cuda { gpu_id } => {
- format!(", cuda:{}", gpu_id)
+ format!(", cuda:{gpu_id}")
  }
  crate::DeviceLocation::Metal { gpu_id } => {
- format!(", metal:{}", gpu_id)
+ format!(", metal:{gpu_id}")
  }
  };
 
@@ -502,10 +502,10 @@ impl std::fmt::Display for Tensor {
  let device_str = match self.device().location() {
  crate::DeviceLocation::Cpu => "".to_owned(),
  crate::DeviceLocation::Cuda { gpu_id } => {
- format!(", cuda:{}", gpu_id)
+ format!(", cuda:{gpu_id}")
  }
  crate::DeviceLocation::Metal { gpu_id } => {
- format!(", metal:{}", gpu_id)
+ format!(", metal:{gpu_id}")
  }
  };
 

candle-core/tests/quantized_tests.rs

@@ -378,12 +378,7 @@ fn compare_with_error(values: &[f32], expected: &[f32], tolerance: f32) {
 
  assert!(
  difference < tolerance,
- "Error at index {}: value = {}, expected = {}. Difference = {} exceeds tolerance = {}.",
- i,
- value,
- expected_value,
- difference,
- tolerance
+ "Error at index {i}: value = {value}, expected = {expected_value}. Difference = {difference} exceeds tolerance = {tolerance}."
  );
  }
 }

candle-examples/examples/clip/main.rs

@@ -95,7 +95,7 @@ pub fn main() -> anyhow::Result<()> {
  let (_logits_per_text, logits_per_image) = model.forward(&images, &input_ids)?;
  let softmax_image = softmax(&logits_per_image, 1)?;
  let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
- println!("softmax_image_vec: {:?}", softmax_image_vec);
+ println!("softmax_image_vec: {softmax_image_vec:?}");
  let probability_vec = softmax_image_vec
  .iter()
  .map(|v| v * 100.0)
@@ -105,7 +105,7 @@ pub fn main() -> anyhow::Result<()> {
  let start = i * probability_per_image;
  let end = start + probability_per_image;
  let prob = &probability_vec[start..end];
- println!("\n\nResults for image: {}\n", img);
+ println!("\n\nResults for image: {img}\n");
  for (i, p) in prob.iter().enumerate() {
  println!("Probability: {:.4}% Text: {} ", p, vec_seq[i]);
  }

candle-examples/examples/codegeex4-9b/main.rs

@@ -70,7 +70,7 @@ impl TextGeneration {
  let start_gen = std::time::Instant::now();
 
  println!("\n start_gen");
- println!("samplelen {}", sample_len);
+ println!("samplelen {sample_len}");
  let mut count = 0;
  let mut result = vec![];
  for index in 0..sample_len {
@@ -102,10 +102,7 @@ impl TextGeneration {
  .decode(&[next_token], true)
  .expect("Token error");
  if self.verbose_prompt {
- println!(
- "[Count: {}] [Raw Token: {}] [Decode Token: {}]",
- count, next_token, token
- );
+ println!("[Count: {count}] [Raw Token: {next_token}] [Decode Token: {token}]");
  }
  result.push(token);
  std::io::stdout().flush()?;

candle-examples/examples/efficientvit/main.rs

@@ -30,7 +30,7 @@ impl Which {
  Self::M4 => "m4",
  Self::M5 => "m5",
  };
- format!("timm/efficientvit_{}.r224_in1k", name)
+ format!("timm/efficientvit_{name}.r224_in1k")
  }
 
  fn config(&self) -> efficientvit::Config {

candle-examples/examples/fastvit/main.rs

@@ -32,7 +32,7 @@ impl Which {
  Self::SA36 => "sa36",
  Self::MA36 => "ma36",
  };
- format!("timm/fastvit_{}.apple_in1k", name)
+ format!("timm/fastvit_{name}.apple_in1k")
  }
 
  fn config(&self) -> fastvit::Config {

candle-examples/examples/glm4/main.rs

@@ -107,10 +107,7 @@ impl TextGeneration {
  .decode(&[next_token], true)
  .expect("Token error");
  if self.verbose_prompt {
- println!(
- "[Count: {}] [Raw Token: {}] [Decode Token: {}]",
- count, next_token, token
- );
+ println!("[Count: {count}] [Raw Token: {next_token}] [Decode Token: {token}]");
  }
  result.push(token);
  std::io::stdout().flush()?;

candle-examples/examples/hiera/main.rs

@@ -30,7 +30,7 @@ impl Which {
  Self::Large => "large",
  Self::Huge => "huge",
  };
- format!("timm/hiera_{}_224.mae_in1k_ft_in1k", name)
+ format!("timm/hiera_{name}_224.mae_in1k_ft_in1k")
  }
 
  fn config(&self) -> hiera::Config {

candle-examples/examples/llava/main.rs

@@ -206,10 +206,8 @@ fn main() -> Result<()> {
  let llava: LLaVA = LLaVA::load(vb, &llava_config, clip_vision_config)?;
 
  println!("generating conv template");
- let image_token_se = format!(
- "{}{}{}",
- DEFAULT_IM_START_TOKEN, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_END_TOKEN
- );
+ let image_token_se =
+ format!("{DEFAULT_IM_START_TOKEN}{DEFAULT_IMAGE_TOKEN}{DEFAULT_IM_END_TOKEN}");
  let qs = if args.prompt.contains(IMAGE_PLACEHOLDER) {
  if llava_config.mm_use_im_start_end {
  args.prompt.replace(IMAGE_PLACEHOLDER, &image_token_se)

candle-examples/examples/mamba-minimal/main.rs

@@ -123,7 +123,7 @@ enum Which {
 
 impl std::fmt::Display for Which {
  fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
- write!(f, "{:?}", self)
+ write!(f, "{self:?}")
  }
 }
 

candle-examples/examples/mamba/main.rs

@@ -135,7 +135,7 @@ enum Which {
 
 impl std::fmt::Display for Which {
  fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
- write!(f, "{:?}", self)
+ write!(f, "{self:?}")
  }
 }
 

candle-examples/examples/mobileclip/main.rs

@@ -25,7 +25,7 @@ impl Which {
  Self::S1 => "S1",
  Self::S2 => "S2",
  };
- format!("apple/MobileCLIP-{}-OpenCLIP", name)
+ format!("apple/MobileCLIP-{name}-OpenCLIP")
  }
 
  fn config(&self) -> mobileclip::MobileClipConfig {
@@ -107,7 +107,7 @@ pub fn main() -> anyhow::Result<()> {
  let (_logits_per_text, logits_per_image) = model.forward(&images, &input_ids)?;
  let softmax_image = softmax(&logits_per_image, 1)?;
  let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
- println!("softmax_image_vec: {:?}", softmax_image_vec);
+ println!("softmax_image_vec: {softmax_image_vec:?}");
  let probability_vec = softmax_image_vec
  .iter()
  .map(|v| v * 100.0)
@@ -118,7 +118,7 @@ pub fn main() -> anyhow::Result<()> {
  let start = i * probability_per_image;
  let end = start + probability_per_image;
  let prob = &probability_vec[start..end];
- println!("\n\nResults for image: {}\n", img);
+ println!("\n\nResults for image: {img}\n");
 
  for (i, p) in prob.iter().enumerate() {
  println!("Probability: {:.4}% Text: {}", p, vec_seq[i]);

candle-examples/examples/mobilenetv4/main.rs

@@ -28,7 +28,7 @@ impl Which {
  Self::Large => "conv_large.e600_r384",
  Self::HybridLarge => "hybrid_large.ix_e600_r384",
  };
- format!("timm/mobilenetv4_{}_in1k", name)
+ format!("timm/mobilenetv4_{name}_in1k")
  }
 
  fn resolution(&self) -> u32 {

candle-examples/examples/mobileone/main.rs

@@ -28,7 +28,7 @@ impl Which {
  Self::S3 => "s3",
  Self::S4 => "s4",
  };
- format!("timm/mobileone_{}.apple_in1k", name)
+ format!("timm/mobileone_{name}.apple_in1k")
  }
 
  fn config(&self) -> mobileone::Config {

candle-examples/examples/moondream/main.rs

@@ -106,7 +106,7 @@ impl TextGeneration {
  }
  };
  load_t = start_gen.elapsed();
- println!("load_t: {:?}", load_t);
+ println!("load_t: {load_t:?}");
  logits
  };
  let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;

candle-examples/examples/paligemma/main.rs

@@ -253,7 +253,7 @@ fn main() -> Result<()> {
  .to_device(&device)?
  .to_dtype(dtype)?
  .unsqueeze(0)?;
- println!("loaded image with shape {:?}", image);
+ println!("loaded image with shape {image:?}");
  let start = std::time::Instant::now();
  let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
  let model = Model::new(&config, vb)?;

candle-examples/examples/pixtral/main.rs

@@ -295,7 +295,7 @@ fn main() -> Result<()> {
  )?
  };
  let image = image.to_device(&device)?.unsqueeze(0)?;
- println!("loaded image with shape {:?}", image);
+ println!("loaded image with shape {image:?}");
  let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
 
  if args.vision_only {

candle-examples/examples/quantized-phi/main.rs

@@ -144,7 +144,7 @@ impl Args {
 
 fn format_size(size_in_bytes: usize) -> String {
  if size_in_bytes < 1_000 {
- format!("{}B", size_in_bytes)
+ format!("{size_in_bytes}B")
  } else if size_in_bytes < 1_000_000 {
  format!("{:.2}KB", size_in_bytes as f64 / 1e3)
  } else if size_in_bytes < 1_000_000_000 {

candle-examples/examples/quantized-qwen2-instruct/main.rs

@@ -151,7 +151,7 @@ impl Args {
 
 fn format_size(size_in_bytes: usize) -> String {
  if size_in_bytes < 1_000 {
- format!("{}B", size_in_bytes)
+ format!("{size_in_bytes}B")
  } else if size_in_bytes < 1_000_000 {
  format!("{:.2}KB", size_in_bytes as f64 / 1e3)
  } else if size_in_bytes < 1_000_000_000 {
@@ -212,10 +212,7 @@ fn main() -> anyhow::Result<()> {
  let tokenizer = args.tokenizer()?;
  let mut tos = TokenOutputStream::new(tokenizer);
  let prompt_str = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
- let prompt_str = format!(
- "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
- prompt_str
- );
+ let prompt_str = format!("<|im_start|>user\n{prompt_str}<|im_end|>\n<|im_start|>assistant\n");
  print!("formatted instruct prompt: {}", &prompt_str);
  let tokens = tos
  .tokenizer()

candle-examples/examples/quantized/main.rs

@@ -364,7 +364,7 @@ impl Args {
 
 fn format_size(size_in_bytes: usize) -> String {
  if size_in_bytes < 1_000 {
- format!("{}B", size_in_bytes)
+ format!("{size_in_bytes}B")
  } else if size_in_bytes < 1_000_000 {
  format!("{:.2}KB", size_in_bytes as f64 / 1e3)
  } else if size_in_bytes < 1_000_000_000 {

candle-examples/examples/repvgg/main.rs

@@ -38,7 +38,7 @@ impl Which {
  Self::B2G4 => "b2g4",
  Self::B3G4 => "b3g4",
  };
- format!("timm/repvgg_{}.rvgg_in1k", name)
+ format!("timm/repvgg_{name}.rvgg_in1k")
  }
 
  fn config(&self) -> repvgg::Config {

candle-examples/examples/rwkv/main.rs

@@ -134,7 +134,7 @@ enum Which {
 
 impl std::fmt::Display for Which {
  fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
- write!(f, "{:?}", self)
+ write!(f, "{self:?}")
  }
 }
 

candle-examples/examples/segformer/main.rs

@@ -57,16 +57,16 @@ enum Commands {
 }
 
 fn get_vb_and_config(model_name: String, device: &Device) -> anyhow::Result<(VarBuilder, Config)> {
- println!("loading model {} via huggingface hub", model_name);
+ println!("loading model {model_name} via huggingface hub");
  let api = hf_hub::api::sync::Api::new()?;
  let api = api.model(model_name.clone());
  let model_file = api.get("model.safetensors")?;
- println!("model {} downloaded and loaded", model_name);
+ println!("model {model_name} downloaded and loaded");
  let vb =
  unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], candle::DType::F32, device)? };
  let config = std::fs::read_to_string(api.get("config.json")?)?;
  let config: Config = serde_json::from_str(&config)?;
- println!("{:?}", config);
+ println!("{config:?}");
  Ok((vb, config))
 }
 
@@ -138,7 +138,7 @@ fn classification_task(args: ClassificationArgs, device: &Device) -> anyhow::Res
  classification.to_vec1::<f32>()?
  );
  let label_id = classification.argmax(0)?.to_scalar::<u32>()?;
- let label_id = format!("{}", label_id);
+ let label_id = format!("{label_id}");
  println!("label: {}", config.id2label[&label_id]);
  Ok(())
 }

candle-examples/examples/siglip/main.rs

@@ -89,7 +89,7 @@ pub fn main() -> anyhow::Result<()> {
  let (_logits_per_text, logits_per_image) = model.forward(&images, &input_ids)?;
  let softmax_image = softmax(&logits_per_image, 1)?;
  let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
- println!("softmax_image_vec: {:?}", softmax_image_vec);
+ println!("softmax_image_vec: {softmax_image_vec:?}");
  let probability_vec = softmax_image_vec
  .iter()
  .map(|v| v * 100.0)
@@ -99,7 +99,7 @@ pub fn main() -> anyhow::Result<()> {
  let start = i * probability_per_image;
  let end = start + probability_per_image;
  let prob = &probability_vec[start..end];
- println!("\n\nResults for image: {}\n", img);
+ println!("\n\nResults for image: {img}\n");
  for (i, p) in prob.iter().enumerate() {
  println!("Probability: {:.4}% Text: {} ", p, vec_seq[i]);
  }

candle-examples/examples/splade/main.rs

@@ -73,7 +73,7 @@ fn main() -> Result<()> {
  Err(_) => match repo.get("pytorch_model.bin") {
  Ok(pytorch_model) => pytorch_model,
  Err(e) => {
- return Err(anyhow::Error::msg(format!("Model weights not found. The weights should either be a `model.safetensors` or `pytorch_model.bin` file. Error: {}", e)));
+ return Err(anyhow::Error::msg(format!("Model weights not found. The weights should either be a `model.safetensors` or `pytorch_model.bin` file. Error: {e}")));
  }
  },
  },

candle-examples/examples/trocr/main.rs

@@ -93,7 +93,7 @@ pub fn main() -> anyhow::Result<()> {
  .get("model.safetensors")?
  }
  };
- println!("model: {:?}", model);
+ println!("model: {model:?}");
  unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
  };