Fixed Clippy warnings (#204)

benches/sst2_benchmark.rs

@@ -46,7 +46,6 @@ fn sst2_forward_pass(iters: u64, model: &SentimentModel, sst2_data: &[String]) -
 #[derive(Debug, Deserialize)]
 struct Record {
     sentence: String,
-    label: i8,
 }
 
 fn ss2_processor(file_path: PathBuf) -> Result<Vec<String>, Box<dyn Error>> {

examples/sentiment_analysis_sst2.rs

@@ -22,7 +22,6 @@ use std::{env, fs};
 #[derive(Debug, Deserialize)]
 struct Record {
     sentence: String,
-    label: i8,
 }
 
 fn ss2_processor(file_path: PathBuf) -> Result<Vec<String>, Box<dyn Error>> {
@@ -47,7 +46,7 @@ fn main() -> anyhow::Result<()> {
     let mut sst2_path = PathBuf::from(env::var("SST2_PATH")
         .expect("Please set the \"squad_dataset\" environment variable pointing to the SQuAD dataset folder"));
     sst2_path.push("train.tsv");
-    let inputs = ss2_processor(sst2_path).unwrap();
+    let inputs = &ss2_processor(sst2_path).unwrap()[..100];
 
     //    Run model
     let batch_size = 64;

src/pipelines/generation_utils.rs

@@ -1427,7 +1427,7 @@ pub struct GeneratedIndicesOutput {
     pub score: Option<f64>,
 }
 
-#[derive(Clone, Copy)]
+#[derive(Clone, Copy, Default)]
 /// # Generation options for text generation.
 /// When provided to a `generate` method, these options will take priority over the `GenerateConfig` used to create the
 /// `LanguageGenerator`. Some of these options may be left as `None`, options without a value will individually default
@@ -1476,32 +1476,6 @@ pub struct GenerateOptions<'a> {
     pub output_scores: bool,
 }
 
-impl Default for GenerateOptions<'_> {
-    fn default() -> Self {
-        GenerateOptions {
-            min_length: None,
-            max_length: None,
-            max_new_tokens: None,
-            early_stopping: None,
-            num_return_sequences: None,
-            num_beams: None,
-            num_beam_groups: None,
-            do_sample: None,
-            temperature: None,
-            top_k: None,
-            top_p: None,
-            repetition_penalty: None,
-            length_penalty: None,
-            no_repeat_ngram_size: None,
-            diversity_penalty: None,
-            decoder_start_token_id: None,
-            forced_bos_token_id: None,
-            prefix_allowed_tokens_fn: None,
-            bad_word_ids: None,
-            output_scores: false,
-        }
-    }
-}
 macro_rules! unpack_config {
     ($field_name:ident, $generate_options: ident, $generate_config: ident) => {
         $generate_options.map_or($generate_config.$field_name, |opts| {

src/reformer/attention.rs

@@ -936,10 +936,8 @@ pub struct LocalSelfAttention {
     num_chunks_after: i64,
     is_decoder: bool,
     dropout: Dropout,
-    pad_token_id: i64,
     num_attention_heads: i64,
     attention_head_size: i64,
-    hidden_size: i64,
     query: nn::Linear,
     key: nn::Linear,
     value: nn::Linear,
@@ -965,7 +963,6 @@ impl LocalSelfAttention {
         let num_chunks_before = config.local_num_chunks_before.unwrap_or(1);
         let num_chunks_after = config.local_num_chunks_after.unwrap_or(0);
         let is_decoder = config.is_decoder;
-        let pad_token_id = config.pad_token_id;
 
         let dropout = Dropout::new(config.hidden_dropout_prob);
 
@@ -994,10 +991,8 @@ impl LocalSelfAttention {
             num_chunks_after,
             is_decoder,
             dropout,
-            pad_token_id,
             num_attention_heads,
             attention_head_size,
-            hidden_size,
             query,
             key,
             value,

src/reformer/embeddings.rs

@@ -13,7 +13,6 @@
 
 use crate::common::dropout::Dropout;
 use crate::common::embeddings::process_ids_embeddings_pair;
-use crate::reformer::attention_utils::get_least_common_mult_chunk_len;
 use crate::reformer::ReformerConfig;
 use crate::RustBertError;
 use std::borrow::Borrow;
@@ -25,7 +24,6 @@ use tch::{nn, Kind, Tensor};
 pub struct AxialPositionEmbeddings {
     weights: Vec<Tensor>,
     axial_pos_shape: Vec<i64>,
-    least_common_mult_chunk_length: i64,
     dropout_prob: f64,
 }
 
@@ -46,12 +44,6 @@ impl AxialPositionEmbeddings {
             )));
         };
 
-        let least_common_mult_chunk_length = get_least_common_mult_chunk_len(
-            &config.attn_layers,
-            config.lsh_attn_chunk_length,
-            config.local_attn_chunk_length,
-        );
-
         let mut weights: Vec<Tensor> = vec![];
         let p_weights = p / "weights";
         for (axis_index, axial_pos_embd_dim) in config.axial_pos_embds_dim.iter().enumerate() {
@@ -64,7 +56,6 @@ impl AxialPositionEmbeddings {
         Ok(AxialPositionEmbeddings {
             weights,
             axial_pos_shape,
-            least_common_mult_chunk_length,
             dropout_prob: config.hidden_dropout_prob,
         })
     }

src/t5/attention.rs

@@ -49,7 +49,6 @@ pub struct T5Attention {
     is_bidirectional: bool,
     has_relative_attention_bias: bool,
     relative_attention_num_buckets: i64,
-    d_model: i64,
     d_kv: i64,
     n_heads: i64,
     dropout: Dropout,
@@ -106,7 +105,6 @@ impl T5Attention {
             is_bidirectional,
             has_relative_attention_bias,
             relative_attention_num_buckets: config.relative_attention_num_buckets,
-            d_model: config.d_model,
             d_kv: config.d_kv,
             n_heads: config.num_heads,
             dropout,

src/xlnet/attention.rs

@@ -42,9 +42,6 @@ impl LayerState {
 
 #[derive(Debug)]
 pub struct XLNetRelativeAttention {
-    num_attention_heads: i64,
-    attention_head_size: i64,
-    hidden_size: i64,
     dropout: Dropout,
     output_attentions: bool,
     query: Tensor,
@@ -135,9 +132,6 @@ impl XLNetRelativeAttention {
         let scale = 1f64 / ((config.d_head as f64).powf(0.5f64));
 
         XLNetRelativeAttention {
-            num_attention_heads: config.n_head,
-            attention_head_size: config.d_head,
-            hidden_size: config.d_model,
             dropout,
             output_attentions,
             query,