[Inference] Fix request handler and add recycle logic

colossalai/inference/core/request_handler.py

@@ -57,6 +57,9 @@ def ready_for_prefill(self):
     def is_empty(self):
         return not self.decoding and not self.prefill
 
+    def total_seq_num(self):
+        return len(self.decoding) + len(self.prefill)
+
 
 class RequestHandler:
     """
@@ -105,6 +108,11 @@ def schedule(self):
                             )
                             self.abort_sequence(seq.request_id)
                             break
+
+                        # stop feeding new sequence into running list to assure
+                        if self.cache_manager.num_available_blocks <= self.running_list.total_seq_num:
+                            break
+
                         # Try to allocate cache blocks for the sequence.
                         if self.cache_manager.check_allocation(seq):
                             # If succeed, add the sequence to running list.
@@ -113,6 +121,7 @@ def schedule(self):
                             self.cache_manager.allocate_context_from_block_table(seq.block_table, seq.input_len)
                     for seq in remove_list:
                         lst.remove(seq)
+
         if self.running_list.ready_for_prefill():
             for seq in self.running_list.prefill:
                 seq.mark_running()
@@ -121,7 +130,12 @@ def schedule(self):
 
         if not self.running_batch.is_empty:
             for seq in self.running_batch.sequences_set:
-                self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len)
+                recycle = self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len)
+                if recycle:
+                    seq.recycle()
+                    self.running_batch.remove(seq)
+                    self.waiting_list[-1].append(seq)
+                    # the recycled sequences are handled with highest priority.
 
         return self.running_batch
 
@@ -227,4 +241,4 @@ def update(self):
 
         self.done_list.extend(finish_seqs)
 
-        return finish_seqs
+        return finish_seqs

colossalai/inference/kv_cache/kvcache_manager.py

@@ -208,9 +208,9 @@ def allocate_token_from_block_table(self, block_table: torch.Tensor, context_len
         # The last allocated block may be either partially or fully occupied.
         # `alloc_local_block_idx` is the index of block to be allocated on provided block table.
         alloc_local_block_idx = context_len // self.block_size
-        self.allocate_single_block(block_table, alloc_local_block_idx, 1)
+        return self.allocate_single_block(block_table, alloc_local_block_idx)
 
-    def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int, space_asked: int) -> int:
+    def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int) -> int:
         """Allocate space asked on a single block in the block table, specified by the provided position id,
         and updates the provided block table with the allocated block.
 
@@ -221,11 +221,14 @@ def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int,
         Returns:
             The remaining space required to be allocated (in other blocks).
         """
-        assert block_table.dim() == 1
+        space_asked = 1
         block_global_id = block_table[block_local_idx].item()
         if block_global_id < 0:
             # Allocate a new block if the current position is not assigned a block yet
-            assert self._available_blocks > 0, "No available blocks to allocate."
+            if self._available_blocks <= 0:
+                # No available blocks to allocate, we free current sequence and return it to
+                self.free_block_table(block_table)
+                return True
             free_block_id = torch.nonzero(self._block_states == 1).view(-1)[0]
             block: CacheBlock = self._cache_blocks[free_block_id]
             block.add_ref()
@@ -235,6 +238,7 @@ def allocate_single_block(self, block_table: torch.Tensor, block_local_idx: int,
             block_table[block_local_idx] = block_global_id
         block: CacheBlock = self._cache_blocks[block_global_id]
         return self._allocate_on_block(block, space_asked)
+        # only when space asked if fully satisfied, the return value will be zero.
 
     def free_block_table(self, block_table: torch.Tensor) -> None:
         """Free the logical cache blocks for **a single sequence**."""
@@ -269,7 +273,7 @@ def _allocate_on_block(self, block: CacheBlock, space_asked: int) -> int:
         Returns:
             The remaining space required to be allocated (in other blocks).
         """
-        assert block.available_space > 0, "No available space on block to allocate."
+        assert block.available_space > 0, "No available space on block to allocate but requries allocation."
         space_to_allocate = min(block.available_space, space_asked)
         block.allocate(space_to_allocate)
         return space_asked - space_to_allocate

colossalai/inference/struct.py

@@ -134,6 +134,16 @@ def mark_aborted(self) -> None:
         """
         self.status = RequestStatus.ABORTED
 
+    def recycle(self) -> None:
+        """
+        Recycle a running sequnce to waiitting list
+        """
+        assert (
+            not self.status.is_finished and not self.status == RequestStatus.ABORTED
+        ), "The running sequence \
+        is already done but it still in running list"
+        self.status = RequestStatus.WAITING
+
     def __repr__(self) -> str:
         return (
             f"(request_id={self.request_id}, "