Prevented fully cached sequences from being processed in prefill phase

nanovllm/engine/block_manager.py

@@ -102,11 +102,11 @@ def may_append(self, seq: Sequence):
             self._allocate_block(block_id)
             block_table.append(block_id)
         elif len(seq) % self.block_size == 0:
-            assert last_block.hash == -1
-            token_ids = seq.block(seq.num_blocks-1)
-            prefix = self.blocks[block_table[-2]].hash if len(block_table) > 1 else -1
-            h = self.compute_hash(token_ids, prefix)
-            last_block.update(h, token_ids)
-            self.hash_to_block_id[h] = last_block.block_id
+            if last_block.hash == -1:
+                token_ids = seq.block(seq.num_blocks-1)
+                prefix = self.blocks[block_table[-2]].hash if len(block_table) > 1 else -1
+                h = self.compute_hash(token_ids, prefix)
+                last_block.update(h, token_ids)
+                self.hash_to_block_id[h] = last_block.block_id
         else:
             assert last_block.hash == -1

nanovllm/engine/scheduler.py

@@ -30,13 +30,15 @@ def schedule(self) -> tuple[list[Sequence], bool]:
             seq = self.waiting[0]
             if num_batched_tokens + len(seq) > self.max_num_batched_tokens or not self.block_manager.can_allocate(seq):
                 break
-            num_seqs += 1
             self.block_manager.allocate(seq)
-            num_batched_tokens += len(seq) - seq.num_cached_tokens
             seq.status = SequenceStatus.RUNNING
             self.waiting.popleft()
             self.running.append(seq)
-            scheduled_seqs.append(seq)
+            tokens_to_compute=len(seq) - seq.num_cached_tokens
+            if tokens_to_compute > 0:
+                num_seqs += 1
+                scheduled_seqs.append(seq)
+                num_batched_tokens += tokens_to_compute
         if scheduled_seqs:
             return scheduled_seqs, True