format

vllm/block.py

@@ -1,5 +1,5 @@
 """Token blocks."""
-from typing import List
+from typing import List, Optional
 
 from vllm.utils import Device
 
@@ -25,6 +25,7 @@ class LogicalTokenBlock:
 
         self.token_ids = [_BLANK_TOKEN_ID] * block_size
         self.num_tokens = 0
+        self.block_hash: Optional[int] = None
 
     def is_empty(self) -> bool:
         return self.num_tokens == 0

vllm/core/block_manager_v1.py

@@ -262,8 +262,7 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         self.cross_block_tables: Dict[str, BlockTable] = {}
 
     def _get_seq_num_required_blocks(self, seq: Sequence) -> int:
-        return 0 if seq is None \
+        return 0 if seq is None else len(seq.logical_token_blocks)
-            else len(seq.logical_token_blocks)
 
     def can_allocate(self, seq_group: SequenceGroup) -> AllocStatus:
         # FIXME(woosuk): Here we assume that all sequences in the group share
@@ -275,8 +274,8 @@ class BlockSpaceManagerV1(BlockSpaceManager):
             seq_group.get_seqs(status=SequenceStatus.WAITING)[0])
         cross_num_required_blocks = self._get_seq_num_required_blocks(
             seq_group.get_encoder_seq())
-        num_required_blocks = self_num_required_blocks + \
+        num_required_blocks = (self_num_required_blocks +
-                              cross_num_required_blocks
+                               cross_num_required_blocks)
 
         if self.block_sliding_window is not None:
 
@@ -293,9 +292,9 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         else:
             return AllocStatus.LATER
 
-    def _allocate_sequence(self, \
+    def _allocate_sequence(self,
-                           seq: Sequence, \
+                           seq: Sequence,
-                           ref_count: int, \
+                           ref_count: int,
                            is_encoder_decoder: bool = True) -> BlockTable:
         # Allocate new physical token blocks that will store the prompt tokens.
         num_prompt_blocks = len(seq.logical_token_blocks)
@@ -328,10 +327,8 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         # NOTE: Here we assume that all sequences in the group have the same
         # decoder prompt.
         seq = seq_group.get_seqs(status=SequenceStatus.WAITING)[0]
-        block_table: BlockTable = \
+        block_table: BlockTable = self._allocate_sequence(
-            self._allocate_sequence(seq,
+            seq, seq_group.num_seqs(), is_encoder_decoder)
-                                    seq_group.num_seqs(),
-                                    is_encoder_decoder)
 
         # Assign the self-attention block tables for each sequence.
         for seq in seq_group.get_seqs(status=SequenceStatus.WAITING):
@@ -368,6 +365,7 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         # Compute a new hash for the block so that it can be shared by other
         # Sequences
         new_hash = seq.hash_of_block(len(seq.logical_token_blocks) - 1)
+        assert new_hash is not None, "Last block is not full."
 
         # if new_hash is already in the cached table, then free last_block
         # and return the cached version
@@ -406,9 +404,7 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         # content hash.
         if not self.enable_caching:
             return self.gpu_allocator.allocate()
-        block_hash: Optional[int] = None
+        block_hash = seq.hash_of_block(len(seq.logical_token_blocks) - 1)
-        if (self._is_last_block_full(seq)):
-            block_hash = seq.hash_of_block(len(seq.logical_token_blocks) - 1)
         num_hashed_tokens = seq.num_hashed_tokens_of_block(
             len(seq.logical_token_blocks) - 1)
 
@@ -553,18 +549,14 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         # dict is efficient in lookup `if cpu_block in mapping`
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
         for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED):
-            self.block_tables[seq.seq_id] = \
+            self.block_tables[seq.seq_id] = self._swap_block_table(
-                self._swap_block_table(self.block_tables[seq.seq_id],
+                self.block_tables[seq.seq_id], self.cpu_allocator,
-                                       self.cpu_allocator,
+                self.gpu_allocator, mapping)
-                                       self.gpu_allocator,
-                                       mapping)
 
         if seq_group.is_encoder_decoder():
-            self.cross_block_tables[request_id] = \
+            self.cross_block_tables[request_id] = self._swap_block_table(
-                self._swap_block_table(self.cross_block_tables[request_id],
+                self.cross_block_tables[request_id], self.cpu_allocator,
-                                       self.cpu_allocator,
+                self.gpu_allocator, mapping)
-                                       self.gpu_allocator,
-                                       mapping)
 
         return [(cpu_block.block_number, gpu_block.block_number)
                 for cpu_block, gpu_block in mapping.items()]
@@ -580,18 +572,14 @@ class BlockSpaceManagerV1(BlockSpaceManager):
         # dict is efficient in lookup `if gpu_block in mapping`
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
         for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
-            self.block_tables[seq.seq_id] = \
+            self.block_tables[seq.seq_id] = self._swap_block_table(
-                self._swap_block_table(self.block_tables[seq.seq_id],
+                self.block_tables[seq.seq_id], self.gpu_allocator,
-                                       self.gpu_allocator,
+                self.cpu_allocator, mapping)
-                                       self.cpu_allocator,
-                                       mapping)
 
         if seq_group.is_encoder_decoder():
-            self.cross_block_tables[request_id] = \
+            self.cross_block_tables[request_id] = self._swap_block_table(
-                self._swap_block_table(self.cross_block_tables[request_id],
+                self.cross_block_tables[request_id], self.gpu_allocator,
-                                       self.gpu_allocator,
+                self.cpu_allocator, mapping)
-                                       self.cpu_allocator,
-                                       mapping)
 
         return [(cpu_block.block_number, gpu_block.block_number)
                 for cpu_block, gpu_block in mapping.items()]

vllm/sequence.py

@@ -269,15 +269,24 @@ class Sequence:
         return self.output_text[:-buffer_length] if truncate else (
             self.output_text)
 
-    def hash_of_block(self, logical_idx: int) -> int:
+    def hash_of_block(self, logical_idx: int) -> Optional[int]:
-        # TODO This can produce incorrect hash when block size > prompt size
+        """Return the hash of the block if it is full."""
-
-        # Compute the number of tokens in the sequence
         # TODO: The current hashing function is O(L^2). We should optimize
         # this in the future.
-        num_tokens = self.num_hashed_tokens_of_block(logical_idx)
+        assert logical_idx < len(self.logical_token_blocks), (
-        hashed_tokens = self.data.get_prefix_token_ids(num_tokens)
+            f"logical_idx={logical_idx} is out of range for "
-        return hash((hashed_tokens, self.lora_int_id))
+            f"logical_token_blocks={len(self.logical_token_blocks)}")
+        block = self.logical_token_blocks[logical_idx]
+        if block.block_hash is not None:
+            return block.block_hash
+        if not block.is_full():
+            return None
+        num_hashed_tokens = self.num_hashed_tokens_of_block(logical_idx)
+        hashed_tokens = self.data.get_prefix_token_ids(num_hashed_tokens)
+        block_hash = hash((hashed_tokens, self.lora_int_id))
+        # Cache the block hash for future use.
+        block.block_hash = block_hash
+        return block_hash
 
     def num_hashed_tokens_of_block(self, logical_idx: int):
         return logical_idx * self.block_size + self.block_size
@@ -632,7 +641,7 @@ class SequenceGroupMetadata:
         state: Internal state tied to this sequence group.
         multi_modal_data: Multi modal data.
         encoder_seq_data: Optional sequence data for encoder prompt
-                          (SequenceGroup.encoder_seq). Should be None 
+                          (SequenceGroup.encoder_seq). Should be None
                           unless you are working with an encoder/decoder
                           model.
         cross_block_table: Optional cross-attention block table associated