Clippy fixes for onnx + fix a broken test.

candle-onnx/src/eval.rs

@@ -2,7 +2,7 @@ use crate::onnx::attribute_proto::AttributeType;
 use crate::onnx::tensor_proto::DataType;
 use crate::onnx::{self, GraphProto};
 use candle::{bail, DType, Device, Result, Tensor};
-use std::{collections::HashMap, usize};
+use std::collections::HashMap;
 
 pub type Value = Tensor;
 
@@ -321,7 +321,7 @@ fn simple_eval_(
  for node in graph.node.iter() {
  let get = |input_name: &str| match values.get(input_name) {
  Some(value) => Ok(value),
- None => bail!("cannot find {input_name} for op {}", node.name),
+ None => bail!("cannot find {input_name} for op '{}'", node.name),
  };
  let get_opt = |i: usize| {
  node.input
@@ -362,7 +362,7 @@ fn simple_eval_(
  // HACK: current implementation of broadcast_pow cannot handle negative base,
  // so we use powf where we can, which *does* correctly handle negative base.
  if let Ok(exp) = (|| input1.to_dtype(DType::F64)?.to_scalar::<f64>())() {
- let output = input0.powf(exp as f64)?;
+ let output = input0.powf(exp)?;
  values.insert(node.output[0].clone(), output);
  } else {
  let output = input0.broadcast_pow(input1)?;
@@ -643,7 +643,7 @@ fn simple_eval_(
  let mask = indices.lt(&zeros)?;
  mask.to_dtype(indices.dtype())?
  .broadcast_mul(&max)?
- .add(&indices)?
+ .add(indices)?
  };
 
  // In Pytorch or Numpy this can be done by indexing the xs tensor using the indices
@@ -767,7 +767,7 @@ fn simple_eval_(
 
  // where_cond requires that all inputs are the same shape.
  // In contrast, the Where op in ONNX only requires that they are broadcastable.
- let shape = broadcast_shape_from_many(&[&cond.dims(), &a.dims(), &b.dims()])?;
+ let shape = broadcast_shape_from_many(&[cond.dims(), a.dims(), b.dims()])?;
  let cond = cond.broadcast_as(shape.clone())?;
  let a = a.broadcast_as(shape.clone())?;
  let b = b.broadcast_as(shape)?;
@@ -1283,8 +1283,7 @@ fn simple_eval_(
  .map(|x| x as usize)
  .collect::<Vec<_>>();
 
- let target_shape =
- broadcast_shape(&input_tensor_dims, input_shape_dims.as_slice())?;
+ let target_shape = broadcast_shape(input_tensor_dims, input_shape_dims.as_slice())?;
 
  let expanded_tensor = input_tensor.broadcast_as(target_shape)?;
 
@@ -1301,12 +1300,12 @@ fn simple_eval_(
  .unwrap_or(0);
 
  let axes = match axes {
- Some(axes) => axes?
+ Some(Ok(axes)) => axes
  .to_vec1::<i64>()?
  .into_iter()
  .map(|x| x as usize)
  .collect::<Vec<_>>(),
- None => {
+ Some(Err(_)) | None => {
  if noop_with_empty_axes == 1 {
  vec![]
  } else {
@@ -1640,7 +1639,7 @@ fn simple_eval_(
  let w = w.get(0)?; // w[iofc] has shape [4*hidden_size, input_size]
  let r = r.get(0)?; // r[iofc] has shape [4*hidden_size, hidden_size]
  let b = b.get(0)?; // concat of [wb[iofc],rb[iofc]] has shape [8*hidden_size]
- let idx_wb = Tensor::arange(0 * hidden_size, 4 * hidden_size, x.device())?;
+ let idx_wb = Tensor::arange(0, 4 * hidden_size, x.device())?;
  let idx_rb = Tensor::arange(4 * hidden_size, 8 * hidden_size, x.device())?;
  let wb = b.index_select(&idx_wb, 0)?;
  let rb = b.index_select(&idx_rb, 0)?;
@@ -1649,8 +1648,8 @@ fn simple_eval_(
 
  // w, r, wb, rb are all iofc but lstm expects ifco
  // so we need to move some stuff around
- let idx_i = Tensor::arange(0 * hidden_size, 1 * hidden_size, x.device())?;
- let idx_o = Tensor::arange(1 * hidden_size, 2 * hidden_size, x.device())?;
+ let idx_i = Tensor::arange(0, hidden_size, x.device())?;
+ let idx_o = Tensor::arange(hidden_size, 2 * hidden_size, x.device())?;
  let idx_f = Tensor::arange(2 * hidden_size, 3 * hidden_size, x.device())?;
  let idx_c = Tensor::arange(3 * hidden_size, 4 * hidden_size, x.device())?;
  let idx_ifco = Tensor::cat(&[&idx_i, &idx_f, &idx_c, &idx_o], 0)?;
@@ -1674,7 +1673,7 @@ fn simple_eval_(
  )?;
 
  let mut lstm_state = candle_nn::rnn::LSTMState::new(h, c);
- let mut h_acc = if node.output.get(0).map(String::as_str).unwrap_or("") != "" {
+ let mut h_acc = if node.output.first().map(String::as_str).unwrap_or("") != "" {
  Some(vec![])
  } else {
  None
@@ -1688,7 +1687,7 @@ fn simple_eval_(
  }
 
  assert_eq!(num_directions, 1, "if support for bidirectional is ever added, outputs will have to be concatenated, not simply reshaped");
- if let Some(name) = node.output.get(0) {
+ if let Some(name) = node.output.first() {
  let h_acc = h_acc.as_ref().unwrap();
  let h_acc = lstm.states_to_tensor(h_acc)?;
  let h_acc = h_acc.reshape((