Adjust HPU warmup: use dummy inputs with shape more close to real scenario (#689)

Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>

Author: kaixuanliu

backends/src/lib.rs

@@ -168,7 +168,7 @@ impl Backend {
             }
         }
         for shape in shapes.iter() {
-            let batch = self.create_warmup_batch(*shape, max_token as u32);
+            let batch = self.create_warmup_batch(*shape, max_token as u32, seq_bucket_size as u32);
             match &self.model_type {
                 ModelType::Classifier => self.predict(batch).await.map(|_| ()),
                 ModelType::Embedding(_) => self.embed(batch).await.map(|_| ()),
@@ -179,19 +179,25 @@ impl Backend {
     }
 
     #[instrument(skip_all)]
-    pub fn create_warmup_batch(&self, shape: (u32, u32), max_token: u32) -> Batch {
+    pub fn create_warmup_batch(&self, shape: (u32, u32), max_token: u32, seq_bucket_size: u32) -> Batch {
         let (batch_size, length) = shape;
+        let min_length = length.saturating_sub(seq_bucket_size).saturating_add(1);
+        let tmp_length = if min_length < length {
+            rand::rng().random_range(min_length..length)
+        } else {
+            length
+        };
         let mut batched_input_ids = Vec::new();
         let mut batched_token_type_ids = Vec::new();
         let mut batched_position_ids = Vec::new();
         let mut cumulative_seq_lengths = Vec::with_capacity(batch_size as usize + 1);
         let mut pooled_indices = Vec::with_capacity(batch_size as usize);
         cumulative_seq_lengths.push(0);
-        let input_ids: Vec<u32> = (0..length)
+        let input_ids: Vec<u32> = (0..tmp_length)
             .map(|_| rand::rng().random_range(0..max_token))
             .collect();
-        let token_type_ids: Vec<u32> = vec![0; length as usize];
-        let position_ids: Vec<u32> = (0..length).collect();
+        let token_type_ids: Vec<u32> = vec![0; tmp_length as usize];
+        let position_ids: Vec<u32> = (0..tmp_length).collect();
         let mut current_length = 0;
         for batch_id in 0..batch_size {
             batched_input_ids.extend(input_ids.iter().cloned());
@@ -206,7 +212,7 @@ impl Backend {
             token_type_ids: batched_token_type_ids,
             position_ids: batched_position_ids,
             cumulative_seq_lengths,
-            max_length: length,
+            max_length: tmp_length,
             pooled_indices,
             raw_indices: vec![],
         }