clippy updates

src/albert/albert_model.rs

@@ -104,6 +104,13 @@ pub struct AlbertConfig {
 
 impl Config<AlbertConfig> for AlbertConfig {}
 
+pub struct AlbertOutput {
+    pub hidden_state: Tensor,
+    pub pooled_output: Tensor,
+    pub all_hidden_states: Option<Vec<Tensor>>,
+    pub all_attentions: Option<Vec<Vec<Tensor>>>,
+}
+
 /// # ALBERT Base model
 /// Base architecture for ALBERT models. Task-specific models will be built from this common base model
 /// It is made of the following blocks:
@@ -223,15 +230,7 @@ impl AlbertModel {
         position_ids: Option<Tensor>,
         input_embeds: Option<Tensor>,
         train: bool,
-    ) -> Result<
-        (
-            Tensor,
-            Tensor,
-            Option<Vec<Tensor>>,
-            Option<Vec<Vec<Tensor>>>,
-        ),
-        &'static str,
-    > {
+    ) -> Result<AlbertOutput, &'static str> {
         let (input_shape, device) = match &input_ids {
             Some(input_value) => match &input_embeds {
                 Some(_) => {
@@ -276,12 +275,12 @@ impl AlbertModel {
         let pooled_output = self.pooler.forward(&hidden_state.select(1, 0));
         let pooled_output = (self.pooler_activation)(&pooled_output);
 
-        Ok((
+        Ok(AlbertOutput {
             hidden_state,
             pooled_output,
             all_hidden_states,
             all_attentions,
-        ))
+        })
     }
 }
 
@@ -450,7 +449,7 @@ impl AlbertForMaskedLM {
         input_embeds: Option<Tensor>,
         train: bool,
     ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) {
-        let (hidden_state, _, all_hidden_states, all_attentions) = self
+        let base_model_output = self
             .albert
             .forward_t(
                 input_ids,
@@ -461,8 +460,12 @@ impl AlbertForMaskedLM {
                 train,
             )
             .unwrap();
-        let prediction_scores = self.predictions.forward(&hidden_state);
-        (prediction_scores, all_hidden_states, all_attentions)
+        let prediction_scores = self.predictions.forward(&base_model_output.hidden_state);
+        (
+            prediction_scores,
+            base_model_output.all_hidden_states,
+            base_model_output.all_attentions,
+        )
     }
 }
 
@@ -587,7 +590,7 @@ impl AlbertForSequenceClassification {
         input_embeds: Option<Tensor>,
         train: bool,
     ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) {
-        let (_, pooled_output, all_hidden_states, all_attentions) = self
+        let base_model_output = self
             .albert
             .forward_t(
                 input_ids,
@@ -598,10 +601,15 @@ impl AlbertForSequenceClassification {
                 train,
             )
             .unwrap();
-        let logits = pooled_output
+        let logits = base_model_output
+            .pooled_output
             .apply_t(&self.dropout, train)
             .apply(&self.classifier);
-        (logits, all_hidden_states, all_attentions)
+        (
+            logits,
+            base_model_output.all_hidden_states,
+            base_model_output.all_attentions,
+        )
     }
 }
 
@@ -723,7 +731,7 @@ impl AlbertForTokenClassification {
         input_embeds: Option<Tensor>,
         train: bool,
     ) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Vec<Tensor>>>) {
-        let (sequence_output, _, all_hidden_states, all_attentions) = self
+        let base_model_output = self
             .albert
             .forward_t(
                 input_ids,
@@ -734,10 +742,15 @@ impl AlbertForTokenClassification {
                 train,
             )
             .unwrap();
-        let logits = sequence_output
+        let logits = base_model_output
+            .hidden_state
             .apply_t(&self.dropout, train)
             .apply(&self.classifier);
-        (logits, all_hidden_states, all_attentions)
+        (
+            logits,
+            base_model_output.all_hidden_states,
+            base_model_output.all_attentions,
+        )
     }
 }
 
@@ -854,7 +867,7 @@ impl AlbertForQuestionAnswering {
         Option<Vec<Tensor>>,
         Option<Vec<Vec<Tensor>>>,
     ) {
-        let (sequence_output, _, all_hidden_states, all_attentions) = self
+        let base_model_output = self
             .albert
             .forward_t(
                 input_ids,
@@ -865,12 +878,20 @@ impl AlbertForQuestionAnswering {
                 train,
             )
             .unwrap();
-        let logits = sequence_output.apply(&self.qa_outputs).split(1, -1);
+        let logits = base_model_output
+            .hidden_state
+            .apply(&self.qa_outputs)
+            .split(1, -1);
         let (start_logits, end_logits) = (&logits[0], &logits[1]);
         let start_logits = start_logits.squeeze1(-1);
         let end_logits = end_logits.squeeze1(-1);
 
-        (start_logits, end_logits, all_hidden_states, all_attentions)
+        (
+            start_logits,
+            end_logits,
+            base_model_output.all_hidden_states,
+            base_model_output.all_attentions,
+        )
     }
 }
 
@@ -1024,7 +1045,7 @@ impl AlbertForMultipleChoice {
             None => None,
         };
 
-        let (_, pooled_output, all_hidden_states, all_attentions) = self
+        let base_model_output = self
             .albert
             .forward_t(
                 input_ids,
@@ -1035,11 +1056,16 @@ impl AlbertForMultipleChoice {
                 train,
             )
             .unwrap();
-        let logits = pooled_output
+        let logits = base_model_output
+            .pooled_output
             .apply_t(&self.dropout, train)
             .apply(&self.classifier)
             .view((-1, num_choices));
 
-        Ok((logits, all_hidden_states, all_attentions))
+        Ok((
+            logits,
+            base_model_output.all_hidden_states,
+            base_model_output.all_attentions,
+        ))
     }
 }

src/albert/encoder.rs

@@ -11,7 +11,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use crate::albert::albert::Activation;
+use crate::albert::albert_model::Activation;
 use crate::albert::attention::AlbertSelfAttention;
 use crate::albert::AlbertConfig;
 use crate::common::activations::{_gelu, _gelu_new, _mish, _relu};

src/albert/mod.rs

@@ -11,7 +11,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example is provided in `examples/albert.rs`, run with `cargo run --example albert`.
+//! A full working example is provided in `examples/albert`, run with `cargo run --example albert`.
 //! The example below illustrate a Masked language model example, the structure is similar for other models.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
@@ -53,12 +53,12 @@
 //! # }
 //! ```
 
-mod albert;
+mod albert_model;
 mod attention;
 mod embeddings;
 mod encoder;
 
-pub use albert::{
+pub use albert_model::{
     AlbertConfig, AlbertConfigResources, AlbertForMaskedLM, AlbertForMultipleChoice,
     AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification,
     AlbertModel, AlbertModelResources, AlbertVocabResources,

src/bart/decoder.rs

@@ -12,7 +12,7 @@
 // limitations under the License.
 
 use crate::bart::attention::{LayerState, SelfAttention};
-use crate::bart::bart::Activation;
+use crate::bart::bart_model::Activation;
 use crate::bart::embeddings::{
     EmbeddingOption, LearnedPositionalEmbedding, SinusoidalPositionalEmbedding,
 };

src/bart/encoder.rs

@@ -12,7 +12,7 @@
 // limitations under the License.
 
 use crate::bart::attention::SelfAttention;
-use crate::bart::bart::Activation;
+use crate::bart::bart_model::Activation;
 use crate::bart::embeddings::{
     EmbeddingOption, LearnedPositionalEmbedding, SinusoidalPositionalEmbedding,
 };

src/bart/mod.rs

@@ -6,7 +6,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example is provided in `examples/bart.rs`, run with `cargo run --example bart`.
+//! A full working example is provided in `examples/bart`, run with `cargo run --example bart`.
 //! Alternatively, the summarization capabilities are illustrated in `examples/summarization.rs`, run with `cargo run --example summarization`.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
@@ -58,13 +58,13 @@
 //! ```
 
 mod attention;
-mod bart;
+mod bart_model;
 mod decoder;
 mod embeddings;
 mod encoder;
 
 pub use attention::LayerState;
-pub use bart::{
+pub use bart_model::{
     Activation, BartConfig, BartConfigResources, BartForConditionalGeneration,
     BartForSequenceClassification, BartMergesResources, BartModel, BartModelResources,
     BartVocabResources,

src/bert/attention.rs

@@ -11,7 +11,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use crate::bert::bert::{Activation, BertConfig};
+use crate::bert::bert_model::{Activation, BertConfig};
 use crate::common::activations::{_gelu, _mish, _relu};
 use crate::common::dropout::Dropout;
 use std::borrow::Borrow;

src/bert/embeddings.rs

@@ -11,7 +11,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use crate::bert::bert::BertConfig;
+use crate::bert::bert_model::BertConfig;
 use crate::common::dropout::Dropout;
 use std::borrow::Borrow;
 use tch::nn::{embedding, EmbeddingConfig};

src/bert/encoder.rs

@@ -12,7 +12,7 @@
 // limitations under the License.
 
 use crate::bert::attention::{BertAttention, BertIntermediate, BertOutput};
-use crate::bert::bert::BertConfig;
+use crate::bert::bert_model::BertConfig;
 use std::borrow::{Borrow, BorrowMut};
 use tch::{nn, Tensor};
 

src/bert/mod.rs

@@ -10,7 +10,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example is provided in `examples/bert.rs`, run with `cargo run --example bert`.
+//! A full working example is provided in `examples/bert`, run with `cargo run --example bert`.
 //! The example below illustrate a Masked language model example, the structure is similar for other models.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
@@ -53,11 +53,11 @@
 //! ```
 
 mod attention;
-mod bert;
+mod bert_model;
 mod embeddings;
 pub(crate) mod encoder;
 
-pub use bert::{
+pub use bert_model::{
     Activation, BertConfig, BertConfigResources, BertForMaskedLM, BertForMultipleChoice,
     BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertModel,
     BertModelResources, BertVocabResources,

src/distilbert/attention.rs

@@ -11,7 +11,7 @@
 // limitations under the License.
 
 use crate::common::dropout::Dropout;
-use crate::distilbert::distilbert::DistilBertConfig;
+use crate::distilbert::distilbert_model::DistilBertConfig;
 use std::borrow::Borrow;
 use tch::kind::Kind::Float;
 use tch::{nn, Tensor};

src/distilbert/embeddings.rs

@@ -11,7 +11,7 @@
 // limitations under the License.
 
 use crate::common::dropout::Dropout;
-use crate::distilbert::distilbert::DistilBertConfig;
+use crate::distilbert::distilbert_model::DistilBertConfig;
 use std::borrow::Borrow;
 use tch::kind::Kind::Float;
 use tch::nn::{embedding, EmbeddingConfig, ModuleT};

src/distilbert/mod.rs

@@ -55,11 +55,11 @@
 //! ```
 
 mod attention;
-mod distilbert;
+mod distilbert_model;
 mod embeddings;
 mod transformer;
 
-pub use distilbert::{
+pub use distilbert_model::{
     Activation, DistilBertConfig, DistilBertConfigResources, DistilBertForQuestionAnswering,
     DistilBertForTokenClassification, DistilBertModel, DistilBertModelClassifier,
     DistilBertModelMaskedLM, DistilBertModelResources, DistilBertVocabResources,

src/distilbert/transformer.rs

@@ -13,7 +13,7 @@
 use crate::common::activations::{_gelu, _relu};
 use crate::common::dropout::Dropout;
 use crate::distilbert::attention::MultiHeadSelfAttention;
-use crate::distilbert::distilbert::{Activation, DistilBertConfig};
+use crate::distilbert::distilbert_model::{Activation, DistilBertConfig};
 use std::borrow::{Borrow, BorrowMut};
 use tch::nn::LayerNorm;
 use tch::{nn, Tensor};

src/electra/embeddings.rs

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 use crate::common::dropout::Dropout;
-use crate::electra::electra::ElectraConfig;
+use crate::electra::electra_model::ElectraConfig;
 use std::borrow::Borrow;
 use tch::nn::{embedding, EmbeddingConfig};
 use tch::{nn, Kind, Tensor};

src/electra/mod.rs

@@ -56,10 +56,10 @@
 //! # }
 //! ```
 
-mod electra;
+mod electra_model;
 mod embeddings;
 
-pub use electra::{
+pub use electra_model::{
     ElectraConfig, ElectraConfigResources, ElectraDiscriminator, ElectraDiscriminatorHead,
     ElectraForMaskedLM, ElectraForTokenClassification, ElectraGeneratorHead, ElectraModel,
     ElectraModelResources, ElectraVocabResources,

src/gpt2/attention.rs

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 use crate::common::dropout::Dropout;
-use crate::gpt2::gpt2::Gpt2Config;
+use crate::gpt2::gpt2_model::Gpt2Config;
 use std::borrow::Borrow;
 use tch::kind::Kind::Float;
 use tch::nn::{Init, Module};
@@ -133,15 +133,15 @@ impl Attention {
 
     fn attention(
         &self,
-        q: &Tensor,
-        k: &Tensor,
-        v: &Tensor,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
         attention_mask: &Option<Tensor>,
         train: bool,
     ) -> (Tensor, Option<Tensor>) {
-        let mut w = q.matmul(&k);
+        let mut w = query.matmul(&key);
         if self.scale {
-            w = w / (*v.size().last().unwrap() as f64).sqrt();
+            w = w / (*value.size().last().unwrap() as f64).sqrt();
         }
 
         let (nd, ns) = (w.size()[2], w.size()[3]);
@@ -152,7 +152,7 @@ impl Attention {
             w = w + mask;
         }
         w = w.softmax(-1, Float).apply_t(&self.attn_dropout, train);
-        let output = w.matmul(&v);
+        let output = w.matmul(&value);
 
         if self.output_attentions {
             (output, Some(w))

src/gpt2/mod.rs

@@ -56,10 +56,10 @@
 //! ```
 
 pub(crate) mod attention;
-mod gpt2;
+mod gpt2_model;
 pub(crate) mod transformer;
 
-pub use gpt2::{
+pub use gpt2_model::{
     GPT2LMHeadModel, Gpt2Config, Gpt2ConfigResources, Gpt2MergesResources, Gpt2Model,
     Gpt2ModelResources, Gpt2VocabResources, GptActivation,
 };

src/gpt2/transformer.rs

@@ -15,7 +15,7 @@
 use crate::common::activations::{_gelu_new, _relu, _swish};
 use crate::common::dropout::Dropout;
 use crate::gpt2::attention::{Attention, GPTConv1D};
-use crate::gpt2::gpt2::{Gpt2Config, GptActivation};
+use crate::gpt2::gpt2_model::{Gpt2Config, GptActivation};
 use std::borrow::Borrow;
 use tch::{nn, Tensor};
 

src/marian/mod.rs

@@ -57,9 +57,9 @@
 //! # }
 //! ```
 
-mod marian;
+mod marian_model;
 
-pub use marian::{
+pub use marian_model::{
     MarianConfigResources, MarianForConditionalGeneration, MarianModelResources, MarianPrefix,
     MarianSpmResources, MarianVocabResources,
 };

src/openai_gpt/mod.rs

@@ -6,7 +6,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example is provided in `examples/openai_gpt.rs`, run with `cargo run --example openai_gpt`.
+//! A full working example is provided in `examples/openai_gpt`, run with `cargo run --example openai_gpt`.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
 //! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.
@@ -55,10 +55,10 @@
 //! # }
 //! ```
 
-mod openai_gpt;
+mod openai_gpt_model;
 mod transformer;
 
-pub use openai_gpt::{
+pub use openai_gpt_model::{
     OpenAIGPTLMHeadModel, OpenAiGptConfigResources, OpenAiGptMergesResources, OpenAiGptModel,
     OpenAiGptModelResources, OpenAiGptVocabResources,
 };

src/pipelines/conversation.rs

@@ -306,12 +306,10 @@ impl Conversation {
     pub fn get_last_input(&self) -> Option<&str> {
         if self.new_user_input.is_some() {
             Some(self.new_user_input.as_ref().unwrap().as_str())
+        } else if self.past_user_inputs.len() > 0 {
+            Some(self.past_user_inputs.last().unwrap().as_str())
         } else {
-            if self.past_user_inputs.len() > 0 {
-                Some(self.past_user_inputs.last().unwrap().as_str())
-            } else {
-                None
-            }
+            None
         }
     }
 

src/pipelines/token_classification.rs

@@ -707,9 +707,9 @@ impl TokenClassificationModel {
             if sub_tokens.len() > 1 {
                 let (label_index, label) =
                     self.consolidate_labels(sub_tokens, label_aggregation_function);
-                let sentence = (&sub_tokens[0]).sentence;
-                let index = (&sub_tokens[0]).index;
-                let word_index = (&sub_tokens[0]).word_index;
+                let sentence = (sub_tokens[0]).sentence;
+                let index = (sub_tokens[0]).index;
+                let word_index = (sub_tokens[0]).word_index;
                 let offset_start = match &sub_tokens.first().unwrap().offset {
                     Some(offset) => Some(offset.begin),
                     None => None,

src/roberta/mod.rs

@@ -10,7 +10,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example is provided in `examples/robert.rs`, run with `cargo run --example roberta`.
+//! A full working example is provided in `examples/roberta.rs`, run with `cargo run --example roberta`.
 //! The example below illustrate a Masked language model example, the structure is similar for other models.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
@@ -63,10 +63,10 @@
 //! ```
 
 mod embeddings;
-mod roberta;
+mod roberta_model;
 
 pub use embeddings::RobertaEmbeddings;
-pub use roberta::{
+pub use roberta_model::{
     RobertaConfigResources, RobertaForMaskedLM, RobertaForMultipleChoice,
     RobertaForQuestionAnswering, RobertaForSequenceClassification, RobertaForTokenClassification,
     RobertaMergesResources, RobertaModelResources, RobertaVocabResources,

src/t5/attention.rs

@@ -55,10 +55,10 @@ pub struct T5Attention {
     inner_dim: i64,
     output_attentions: bool,
     store_cache: bool,
-    q: nn::Linear,
-    k: nn::Linear,
-    v: nn::Linear,
-    o: nn::Linear,
+    query: nn::Linear,
+    key: nn::Linear,
+    value: nn::Linear,
+    output: nn::Linear,
     relative_attention_bias: Option<nn::Embedding>,
 }
 
@@ -82,10 +82,10 @@ impl T5Attention {
         };
 
         let inner_dim = config.num_heads * config.d_kv;
-        let k = nn::linear(p / "k", config.d_model, inner_dim, linear_config);
-        let v = nn::linear(p / "v", config.d_model, inner_dim, linear_config);
-        let q = nn::linear(p / "q", config.d_model, inner_dim, linear_config);
-        let o = nn::linear(p / "o", inner_dim, config.d_model, linear_config);
+        let key = nn::linear(p / "k", config.d_model, inner_dim, linear_config);
+        let value = nn::linear(p / "v", config.d_model, inner_dim, linear_config);
+        let query = nn::linear(p / "q", config.d_model, inner_dim, linear_config);
+        let output = nn::linear(p / "o", inner_dim, config.d_model, linear_config);
 
         let dropout = Dropout::new(config.dropout_rate);
         let relative_attention_bias = if has_relative_attention_bias {
@@ -110,10 +110,10 @@ impl T5Attention {
             inner_dim,
             output_attentions,
             store_cache,
-            q,
-            k,
-            v,
-            o,
+            query,
+            key,
+            value,
+            output,
             relative_attention_bias,
         }
     }
@@ -155,17 +155,17 @@ impl T5Attention {
             None => real_query_length,
         };
 
-        let q: Tensor = self.shape(hidden_states.as_ref().apply(&self.q), bs);
+        let q: Tensor = self.shape(hidden_states.as_ref().apply(&self.query), bs);
 
         let (mut k, mut v) = if kv.is_none() {
             (
-                self.shape(hidden_states.apply(&self.k), bs),
-                self.shape(hidden_states.apply(&self.v), bs),
+                self.shape(hidden_states.apply(&self.key), bs),
+                self.shape(hidden_states.apply(&self.value), bs),
             )
         } else {
             (
-                self.shape(kv.as_ref().unwrap().apply(&self.k), bs),
-                self.shape(kv.as_ref().unwrap().apply(&self.v), bs),
+                self.shape(kv.as_ref().unwrap().apply(&self.key), bs),
+                self.shape(kv.as_ref().unwrap().apply(&self.value), bs),
             )
         };
 
@@ -219,7 +219,7 @@ impl T5Attention {
             .apply_t(&self.dropout, train);
         let context = self
             .unshape(attention_weights.matmul(&v), bs)
-            .apply(&self.o);
+            .apply(&self.output);
 
         let attention_weights = if self.output_attentions {
             Some(attention_weights)

src/t5/mod.rs

@@ -6,7 +6,7 @@
 //!
 //! # Model set-up and pre-trained weights loading
 //!
-//! A full working example (translation) is provided in `examples/t5.rs`, run with `cargo run --example t5`.
+//! A full working example (translation) is provided in `examples/t5`, run with `cargo run --example t5`.
 //! All models expect the following resources:
 //! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)
 //! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.
@@ -51,10 +51,10 @@
 mod attention;
 mod encoder;
 mod layer_norm;
-mod t5;
+mod t5_model;
 
 pub use attention::LayerState;
-pub use t5::{
+pub use t5_model::{
     T5Config, T5ConfigResources, T5ForConditionalGeneration, T5Model, T5ModelResources, T5Prefix,
     T5VocabResources,
 };

src/t5/t5_model.rs

@@ -717,7 +717,7 @@ impl LMHeadModel for T5ForConditionalGeneration {
                 None,
                 train,
             ),
-            _ => Err("Cache not compatible with T5 Model")?,
+            _ => return Err("Cache not compatible with T5 Model"),
         };
 
         let lm_logits = decoder_output.linear::<Tensor>(&self.base_model.embeddings.ws, None)