Support cache_position inputs in Hugging Face models

Support the `cache_position` input that was added to Hugging Face Whisper models
as part of a revision of how it handles KV-caching.

This is like `position_ids`, but there is no batch dimension.

See huggingface/optimum#1971 and
huggingface/transformers#31166.

Author: robertknight

rten-generate/src/generator.rs

@@ -132,11 +132,18 @@ pub struct ModelInputsConfig<'a> {
     pub attention_mask: &'a str,
 
     /// Model input that contains position IDs for each position.
-    pub position_ids: &'a str,
+    ///
+    /// This input does not have a batch dimension.
+    pub cache_position: &'a str,
 
     /// Patterns for inputs and outputs used for key-value caches.
     pub kv_caches: Vec<KVCachePair<'a>>,
 
+    /// Model input that contains position IDs for each position.
+    ///
+    /// This input has a batch dimension.
+    pub position_ids: &'a str,
+
     /// Boolean input that is set to false on the first run and true on
     /// subsequent runs.
     pub use_cache_flag: &'a str,
@@ -159,6 +166,7 @@ impl<'a> Default for ModelInputsConfig<'a> {
             input_ids: "input_ids",
             logits: "logits",
             attention_mask: "attention_mask",
+            cache_position: "cache_position",
             position_ids: "position_ids",
             use_cache_flag: "use_cache_branch",
 
@@ -317,6 +325,7 @@ impl<'a> Generator<'a> {
     /// The model may have the optional inputs:
     ///
     ///  - `attention_mask` - (batch, sequence) tensor of booleans
+    ///  - `cache_position` - (sequence) tensor of position indices
     ///  - `position_ids` - (batch, sequence) tensor of position indices
     ///  - `past_key_values.N.key` - (batch, head, past_seq_len, size) key vector cache
     ///    where `N` is the layer index
@@ -480,6 +489,15 @@ impl<'a> Generator<'a> {
                 });
         }
 
+        let cache_position_input = model.find_node(model_inputs.cache_position);
+        if let Some(cache_position_input) = cache_position_input {
+            generator =
+                generator.with_varying_input(cache_position_input, &|_batch_size, positions| {
+                    NdTensor::from_fn([positions.len()], |[pos]| (positions.start + pos) as i32)
+                        .into()
+                });
+        }
+
         let use_cache_input = model.find_node(model_inputs.use_cache_flag);
         if let Some(use_cache_input) = use_cache_input {
             generator = generator.with_varying_input(use_cache_input, &|_batch_size, positions| {
@@ -981,6 +999,7 @@ mod tests {
         // Add inputs and outputs using the standard names.
         let mut inputs = vec![
             NodeInfo::from_name_shape("input_ids", &[]),
+            NodeInfo::from_name_shape("cache_position", &[]),
             NodeInfo::from_name_shape("position_ids", &[]),
             NodeInfo::from_name_shape("attention_mask", &[]),
         ];
@@ -1125,6 +1144,7 @@ mod tests {
         let position_ids = model.find_node("position_ids").unwrap();
         let attention_mask = model.find_node("attention_mask").unwrap();
         let cache_branch = model.find_node("use_cache_branch");
+        let cache_position = model.find_node("cache_position").unwrap();
 
         for step in 0..generation_len {
             let step_inputs = model.get_inputs(step, input_id).unwrap();
@@ -1133,6 +1153,9 @@ mod tests {
             let step_pos_ids = model.get_inputs(step, position_ids).unwrap();
             let step_pos_ids: NdTensor<i32, 2> = step_pos_ids.try_into().unwrap();
 
+            let step_cache_pos = model.get_inputs(step, cache_position).unwrap();
+            let step_cache_pos: NdTensor<i32, 1> = step_cache_pos.try_into().unwrap();
+
             let step_attn_mask = model.get_inputs(step, attention_mask).unwrap();
             let step_attn_mask: NdTensor<i32, 2> = step_attn_mask.try_into().unwrap();
 
@@ -1155,6 +1178,12 @@ mod tests {
                 assert_eq!(step_pos_ids.size(1), prompt.len());
                 assert!(step_pos_ids.iter().map(|x| *x as usize).eq(0..prompt.len()));
 
+                assert_eq!(step_cache_pos.size(0), prompt.len());
+                assert!(step_cache_pos
+                    .iter()
+                    .map(|x| *x as usize)
+                    .eq(0..prompt.len()));
+
                 if let Some(cache_branch) = cache_branch {
                     assert_eq!(cache_branch.item(), Some(&0));
                 }
@@ -1168,6 +1197,9 @@ mod tests {
                 assert_eq!(step_pos_ids.size(1), 1);
                 assert_eq!(step_pos_ids[[0, 0]], (prompt.len() + step - 1) as i32);
 
+                assert_eq!(step_cache_pos.size(0), 1);
+                assert_eq!(step_cache_pos[[0]], (prompt.len() + step - 1) as i32);
+
                 if let Some(cache_branch) = cache_branch {
                     assert_eq!(cache_branch.item(), Some(&1));
                 }
@@ -1194,7 +1226,11 @@ mod tests {
                     (0..prompt.len() + step).map(|x| x as i32).collect();
                 assert_eq!(
                     step_pos_ids,
-                    NdTensor::from_data([1, expected_pos_ids.len()], expected_pos_ids)
+                    NdTensor::from_data([1, expected_pos_ids.len()], expected_pos_ids.clone())
+                );
+                assert_eq!(
+                    step_cache_pos,
+                    NdTensor::from_data([expected_pos_ids.len()], expected_pos_ids)
                 );
             }
         }