tch 0.12.0 Update (guillaume-be#379)

* Fix 0.12 breaking changes

* Fix Clippy warnings

* Updated changelog

CHANGELOG.md

@@ -11,7 +11,7 @@ All notable changes to this project will be documented in this file. The format
 ## Changed
 - Bumped the tokenizers dependency from 7.x to 8.x, exposing additional options for special token mapping and adding the NLLBTokenizer.
 - (BREAKING) Simplified the generation traits (removal of LMHeadModel and elimination of unnecessary specification for LanguageGenerator)
-- Upgraded to `torch` 2.0 (via `tch` 0.11.0).
+- Upgraded to `torch` 2.0 (via `tch` 0.12.0).
 
 ## Fixed
 - MIN/MAX computation for float-like (was set to infinity instead of min/max)

Cargo.toml

@@ -70,7 +70,7 @@ features = ["doc-only"]
 
 [dependencies]
 rust_tokenizers = "8.1"
-tch = "0.11.0"
+tch = "0.12.0"
 serde_json = "1"
 serde = { version = "1", features = ["derive"] }
 ordered-float = "3"
@@ -88,6 +88,6 @@ anyhow = "1"
 csv = "1"
 criterion = "0.4"
 tokio = { version = "1.24", features = ["sync", "rt-multi-thread", "macros"] }
-torch-sys = "0.11.0"
+torch-sys = "0.12.0"
 tempfile = "3"
 itertools = "0.10"

benches/tensor_operations_benchmark.rs

@@ -21,8 +21,8 @@ fn bench_tensor_ops(c: &mut Criterion) {
     unsafe {
         torch_sys::dummy_cuda_dependency();
     }
-    let input = Tensor::rand(&[32, 128, 512], (Kind::Float, Device::cuda_if_available()));
-    let weights = Tensor::rand(&[512, 512], (Kind::Float, Device::cuda_if_available()));
+    let input = Tensor::rand([32, 128, 512], (Kind::Float, Device::cuda_if_available()));
+    let weights = Tensor::rand([512, 512], (Kind::Float, Device::cuda_if_available()));
 
     let _ = &input.matmul(&weights);
     c.bench_function("Matrix multiply ", |b| {

src/albert/albert_model.rs

@@ -257,7 +257,7 @@ impl AlbertModel {
             get_shape_and_device_from_ids_embeddings_pair(input_ids, input_embeds)?;
 
         let calc_mask = if mask.is_none() {
-            Some(Tensor::ones(&input_shape, (Kind::Int64, device)))
+            Some(Tensor::ones(input_shape, (Kind::Int64, device)))
         } else {
             None
         };

src/albert/attention.rs

@@ -130,8 +130,8 @@ impl AlbertSelfAttention {
             self.hidden_size,
         ));
 
-        let context: Tensor =
-            Tensor::einsum("bfnd,ndh->bfh", &[context, w], None) + self.dense.bs.as_ref().unwrap();
+        let context: Tensor = Tensor::einsum("bfnd,ndh->bfh", &[context, w], None::<i64>)
+            + self.dense.bs.as_ref().unwrap();
         let context = (input_ids + context.apply_t(&self.dropout, train)).apply(&self.layer_norm);
 
         if !self.output_attentions {

src/bart/attention.rs

@@ -176,7 +176,7 @@ impl BartAttention {
             .bmm(&value_states)
             .view([bs, self.num_heads, target_length, self.head_dim])
             .transpose(1, 2)
-            .reshape(&[bs, target_length, embed_dim])
+            .reshape([bs, target_length, embed_dim])
             .apply(&self.out_proj);
 
         (attention_output, saved_attention_weights, new_layer_state)

src/bart/bart_model.rs

@@ -270,7 +270,7 @@ pub(crate) fn _make_causal_mask(
     let target_length = input_ids_shape[1];
 
     let mut mask = Tensor::full(
-        &[target_length, target_length],
+        [target_length, target_length],
         get_min(dtype).unwrap(),
         (dtype, device),
     );
@@ -283,14 +283,14 @@ pub(crate) fn _make_causal_mask(
     if past_key_values_length > 0 {
         mask = Tensor::cat(
             &[
-                Tensor::zeros(&[target_length, past_key_values_length], (dtype, device)),
+                Tensor::zeros([target_length, past_key_values_length], (dtype, device)),
                 mask,
             ],
             -1,
         );
     }
     mask.unsqueeze(0).unsqueeze(0).expand(
-        &[
+        [
             batch_size,
             1,
             target_length,
@@ -306,7 +306,7 @@ pub(crate) fn _expand_mask(mask: &Tensor, target_length: Option<i64>, dtype: Kin
     let expanded_mask = mask
         .unsqueeze(1)
         .unsqueeze(1)
-        .expand(&[batch_size, 1, target_length, source_length], true)
+        .expand([batch_size, 1, target_length, source_length], true)
         .totype(dtype);
     let inverted_mask: Tensor = 1 - expanded_mask;
     inverted_mask.masked_fill(&inverted_mask.to_kind(Kind::Bool), get_min(dtype).unwrap())
@@ -863,7 +863,7 @@ impl BartForSequenceClassification {
         let reshape = eos_mask.sum_dim_intlist([1].as_slice(), true, input_ids.kind());
         let sentence_representation = base_model_output
             .decoder_output
-            .permute(&[2, 0, 1])
+            .permute([2, 0, 1])
             .masked_select(&eos_mask)
             .view((-1, reshape.size()[0] * reshape.int64_value(&[0, 0])))
             .transpose(0, 1)

src/bert/bert_model.rs

@@ -370,7 +370,7 @@ impl<T: BertEmbedding> BertModel<T> {
             2 => {
                 if self.is_decoder {
                     let seq_ids = Tensor::arange(input_shape[1], (Kind::Int8, device));
-                    let causal_mask = seq_ids.unsqueeze(0).unsqueeze(0).repeat(&[
+                    let causal_mask = seq_ids.unsqueeze(0).unsqueeze(0).repeat([
                         input_shape[0],
                         input_shape[1],
                         1,
@@ -407,7 +407,7 @@ impl<T: BertEmbedding> BertModel<T> {
                 let encoder_mask = match encoder_mask {
                     Some(value) => value.copy(),
                     None => Tensor::ones(
-                        &[
+                        [
                             encoder_hidden_states_shape[0],
                             encoder_hidden_states_shape[1],
                         ],

src/common/dropout.rs

@@ -43,7 +43,7 @@ impl XDropout {
 impl ModuleT for XDropout {
     fn forward_t(&self, input: &Tensor, train: bool) -> Tensor {
         if train {
-            let mask = (Tensor::ones(&[1], (input.kind(), input.device()))
+            let mask = (Tensor::ones([1], (input.kind(), input.device()))
                 - input
                     .empty_like()
                     .bernoulli_float_(1_f64 - self.dropout_prob))

src/deberta/attention.rs

@@ -37,7 +37,7 @@ pub trait DisentangledSelfAttention {
 pub fn build_relative_position(query_size: i64, key_size: i64, device: Device) -> Tensor {
     let q_ids = Tensor::arange(query_size, (Kind::Int64, device));
     let k_ids = Tensor::arange(key_size, (Kind::Int64, device));
-    let rel_pos_ids = q_ids.unsqueeze(-1) - k_ids.view([1, -1]).repeat(&[query_size, 1]);
+    let rel_pos_ids = q_ids.unsqueeze(-1) - k_ids.view([1, -1]).repeat([query_size, 1]);
     rel_pos_ids.slice(0, 0, query_size, 1).unsqueeze(0)
 }
 
@@ -62,7 +62,7 @@ impl DebertaDisentangledSelfAttention {
         let mut new_shape = x.size();
         let _ = new_shape.pop();
         new_shape.extend_from_slice(&[self.num_attention_heads, -1]);
-        x.view(new_shape.as_slice()).permute(&[0, 2, 1, 3])
+        x.view(new_shape.as_slice()).permute([0, 2, 1, 3])
     }
 
     fn linear(&self, weights: &Tensor, bias: Option<&Tensor>, x: &Tensor) -> Tensor {
@@ -81,7 +81,7 @@ impl DebertaDisentangledSelfAttention {
     ) -> Tensor {
         let query_layer_size = query_layer.size();
         c2p_pos.expand(
-            &[
+            [
                 query_layer_size[0],
                 query_layer_size[1],
                 query_layer_size[2],
@@ -101,7 +101,7 @@ impl DebertaDisentangledSelfAttention {
         let mut key_layer_size = key_layer.size();
         key_layer_size.reverse();
         c2p_pos.expand(
-            &[
+            [
                 query_layer_size[0],
                 query_layer_size[1],
                 key_layer_size[1],
@@ -182,7 +182,7 @@ impl DebertaDisentangledSelfAttention {
             )
             .unsqueeze(0);
 
-        let mut score = Tensor::zeros(&[1], (query_layer.kind(), key_layer.device()));
+        let mut score = Tensor::zeros([1], (query_layer.kind(), key_layer.device()));
 
         // content -> position
         if let Some(pos_proj) = &self.pos_proj {
@@ -410,24 +410,24 @@ impl DisentangledSelfAttention for DebertaDisentangledSelfAttention {
 
         if let Some(head_logits_proj) = &self.head_logits_proj {
             attention_scores = attention_scores
-                .permute(&[0, 2, 3, 1])
+                .permute([0, 2, 3, 1])
                 .apply(head_logits_proj)
-                .permute(&[0, 3, 1, 2]);
+                .permute([0, 3, 1, 2]);
         }
 
         let mut attention_probs =
             x_softmax(&attention_scores, attention_mask, -1).apply_t(&self.dropout, train);
 
         if let Some(head_weights_proj) = &self.head_weights_proj {
             attention_probs = attention_probs
-                .permute(&[0, 2, 3, 1])
+                .permute([0, 2, 3, 1])
                 .apply(head_weights_proj)
-                .permute(&[0, 3, 1, 2]);
+                .permute([0, 3, 1, 2]);
         }
 
         let context_layer = attention_probs
             .matmul(&value_layer)
-            .permute(&[0, 2, 1, 3])
+            .permute([0, 2, 1, 3])
             .contiguous();
 
         let mut new_context_layer_shape = context_layer.size();

src/deberta/embeddings.rs

@@ -127,15 +127,15 @@ where
         let calc_position_ids = if position_ids.is_none() {
             Some(
                 Tensor::arange(seq_length, (Kind::Int64, input_embeddings.device()))
-                    .expand(&[1, -1], true),
+                    .expand([1, -1], true),
             )
         } else {
             None
         };
 
         let calc_token_type_ids = if token_type_ids.is_none() {
             Some(Tensor::zeros(
-                &input_shape,
+                input_shape,
                 (Kind::Int64, input_embeddings.device()),
             ))
         } else {

src/deberta_v2/attention.rs

@@ -51,7 +51,7 @@ pub fn build_relative_position(
 ) -> Tensor {
     let q_ids = Tensor::arange(query_size, (Kind::Int64, device));
     let k_ids = Tensor::arange(key_size, (Kind::Int64, device));
-    let mut rel_pos_ids = q_ids.unsqueeze(-1) - k_ids.tile(&[q_ids.size()[0], 1]);
+    let mut rel_pos_ids = q_ids.unsqueeze(-1) - k_ids.tile([q_ids.size()[0], 1]);
     if (bucket_size > 0) & (max_position > 0) {
         rel_pos_ids = make_log_bucket_position(&rel_pos_ids, bucket_size, max_position);
     }
@@ -80,7 +80,7 @@ impl DebertaV2DisentangledSelfAttention {
         let _ = new_shape.pop();
         new_shape.extend_from_slice(&[self.num_attention_heads, -1]);
         let x = x.view(new_shape.as_slice());
-        x.permute(&[0, 2, 1, 3])
+        x.permute([0, 2, 1, 3])
             .contiguous()
             .view([-1, x.size()[1], *x.size().last().unwrap()])
     }
@@ -133,12 +133,12 @@ impl DebertaV2DisentangledSelfAttention {
 
         let pos_query_layer = self
             .transpose_for_scores(&relative_embeddings.apply(query_proj))
-            .repeat(&[query_layer.size()[0] / self.num_attention_heads, 1, 1]);
+            .repeat([query_layer.size()[0] / self.num_attention_heads, 1, 1]);
         let pos_key_layer = self
             .transpose_for_scores(&relative_embeddings.apply(key_proj))
-            .repeat(&[query_layer.size()[0] / self.num_attention_heads, 1, 1]);
+            .repeat([query_layer.size()[0] / self.num_attention_heads, 1, 1]);
 
-        let mut score = Tensor::zeros(&[1], (query_layer.kind(), query_layer.device()));
+        let mut score = Tensor::zeros([1], (query_layer.kind(), query_layer.device()));
 
         let c2p_pos = if self.pos_att_type.has_type(PositionAttentionType::c2p)
             | self.pos_att_type.has_type(PositionAttentionType::p2p)
@@ -149,7 +149,7 @@ impl DebertaV2DisentangledSelfAttention {
             let c2p_att = c2p_att.gather(
                 -1,
                 &c2p_pos.squeeze_dim(0).expand(
-                    &[
+                    [
                         query_layer.size()[0],
                         query_layer.size()[1],
                         *relative_pos.size().last().unwrap(),
@@ -186,7 +186,7 @@ impl DebertaV2DisentangledSelfAttention {
                 .gather(
                     -1,
                     &p2c_pos.squeeze_dim(0).expand(
-                        &[query_layer.size()[0], key_layer_size[1], key_layer_size[1]],
+                        [query_layer.size()[0], key_layer_size[1], key_layer_size[1]],
                         true,
                     ),
                     true,
@@ -203,7 +203,7 @@ impl DebertaV2DisentangledSelfAttention {
             let p2p_att = p2p_att.gather(
                 -1,
                 &c2p_pos.unwrap().expand(
-                    &[
+                    [
                         query_layer.size()[0],
                         query_layer.size()[1],
                         query_layer.size()[2],
@@ -402,7 +402,7 @@ impl DisentangledSelfAttention for DebertaV2DisentangledSelfAttention {
                 reverse_context_layer_size[1],
                 reverse_context_layer_size[0],
             ])
-            .permute(&[0, 2, 1, 3])
+            .permute([0, 2, 1, 3])
             .contiguous();
 
         let mut new_context_layer_shape = context_layer.size();

src/deberta_v2/encoder.rs

@@ -78,10 +78,10 @@ impl ConvLayer {
         train: bool,
     ) -> Tensor {
         let out = hidden_states
-            .permute(&[0, 2, 1])
+            .permute([0, 2, 1])
             .contiguous()
             .apply(&self.conv)
-            .permute(&[0, 2, 1])
+            .permute([0, 2, 1])
             .contiguous();
         let reverse_mask: Tensor = 1 - input_mask;
         let out = out.masked_fill(

src/electra/electra_model.rs

@@ -266,7 +266,7 @@ impl ElectraModel {
             get_shape_and_device_from_ids_embeddings_pair(input_ids, input_embeds)?;
 
         let calc_mask = if mask.is_none() {
-            Some(Tensor::ones(&input_shape, (Kind::Int64, device)))
+            Some(Tensor::ones(input_shape, (Kind::Int64, device)))
         } else {
             None
         };

src/fnet/attention.rs

@@ -42,7 +42,9 @@ impl FNetFourierTransform {
     }
 
     pub fn forward(&self, hidden_states: &Tensor) -> Tensor {
-        let self_outputs = hidden_states.fft_fft2(None, &[1, 2], "backward").real();
+        let self_outputs = hidden_states
+            .fft_fft2(None::<i64>, [1, 2], "backward")
+            .real();
         (self_outputs + hidden_states).apply(&self.layer_norm)
     }
 }

src/gpt2/attention.rs

@@ -71,7 +71,7 @@ impl Attention {
     {
         let p = p.borrow();
 
-        let bias = Tensor::ones(&[config.n_ctx, config.n_ctx], (Float, p.device()))
+        let bias = Tensor::ones([config.n_ctx, config.n_ctx], (Float, p.device()))
             .tril(0)
             .view((1, 1, config.n_ctx, config.n_ctx));
 
@@ -111,9 +111,9 @@ impl Attention {
     fn split_heads(&self, x: &Tensor, k: bool) -> Tensor {
         let x = x.view((x.size()[0], -1, self.n_head, self.dim_per_head));
         if k {
-            x.permute(&[0, 2, 3, 1])
+            x.permute([0, 2, 3, 1])
         } else {
-            x.permute(&[0, 2, 1, 3])
+            x.permute([0, 2, 1, 3])
         }
     }