[Performance] Enable chunked prefill and prefix caching together

tests/basic_correctness/test_chunked_prefill.py

@@ -6,6 +6,7 @@
 
 Run `pytest tests/models/test_chunked_prefill.py`.
 """
+from contextlib import nullcontext
 
 import pytest
 
@@ -150,3 +151,68 @@ def test_models_with_fp8_kv_cache(
         name_0="no_chunked_prefill",
         name_1="chunked_prefill",
     )
+
+
+@pytest.mark.parametrize("max_tokens", [16])
+@pytest.mark.parametrize("enforce_eager", [False])
+@pytest.mark.parametrize("chunk_size", [30, 32])
+@pytest.mark.parametrize("use_v2_block_manager", [False, True])
+# NOTE: Increasing this in this suite will fail CI because we currently cannot
+# reset distributed env properly. Use a value > 1 just when you test.
+@pytest.mark.parametrize("tensor_parallel_size", [1])
+def test_with_prefix_caching(
+    vllm_runner,
+    max_tokens: int,
+    enforce_eager: bool,
+    chunk_size: int,
+    use_v2_block_manager: bool,
+    tensor_parallel_size: int,
+) -> None:
+    """
+    Checks exact match decode with and without prefix caching
+    with chunked prefill enabled.
+    """
+    model = "meta-llama/Llama-2-7b-chat-hf"
+    # The common prompt has 142 tokens with Llama-2 tokenizer.
+    common_prompt = "You are a helpful AI assistant " * 20
+    unique_prompts = [
+        "Question",  # Warmup
+        "Question",  # Fully cached
+        "Another question",  # Partial cached
+    ]
+    full_prompts = [f"{common_prompt}\n{p}" for p in unique_prompts]
+
+    max_num_batched_tokens = max_num_seqs = chunk_size
+    outputs = {}  # type: ignore
+    check_result = True
+    for enable in (True, False):
+        with vllm_runner(
+                model,
+                dtype="half",
+                max_num_batched_tokens=max_num_batched_tokens,
+                enable_chunked_prefill=True,
+                enable_prefix_caching=enable,
+                tensor_parallel_size=tensor_parallel_size,
+                use_v2_block_manager=use_v2_block_manager,
+                enforce_eager=enforce_eager,
+                max_num_seqs=max_num_seqs,
+        ) as vllm_model:
+            # It should fail when prefix caching is enable and chunk
+            # size is not a multiple of block size (16).
+            should_fail = chunk_size % 16 != 0 and enable
+            check_result &= not should_fail
+            outputs[enable] = []
+            # Send the request one-by-one to ensure the cache is populated.
+            with pytest.raises(ValueError) if should_fail else nullcontext():
+                for prompt in full_prompts:
+                    outputs[enable] += vllm_model.generate_greedy([prompt],
+                                                                  max_tokens)
+
+    # Check results only if we did not expect a failure.
+    if check_result:
+        check_outputs_equal(
+            outputs_0_lst=outputs[False],
+            outputs_1_lst=outputs[True],
+            name_0="w/o prefix caching",
+            name_1="with prefix caching",
+        )

tests/core/test_block_manager.py

@@ -595,3 +595,43 @@ def test_sliding_window_multi_seq():
 
     # assert all blocks are free now
     assert block_manager.get_num_free_gpu_blocks() == num_gpu_blocks
+
+
+def test_mark_blocks_as_computed_with_prefix_cache_and_chunked_prefill():
+    """When prefix cache and chunked prefill are enabled, the block manager
+    should only mark a chunk of blocks as computed instead of all blocks.
+    """
+
+    block_size = 4
+    num_cpu_blocks = 0
+    num_gpu_blocks = 16
+    block_manager = BlockSpaceManagerV1(block_size,
+                                        num_gpu_blocks,
+                                        num_cpu_blocks,
+                                        watermark=0,
+                                        enable_caching=True)
+
+    # Set prompt size to have num_gpu_blocks - 1 full blocks.
+    prompt_length = block_size * num_gpu_blocks - 1
+
+    # Allocate (reserve) all blocks.
+    _, seq_group = create_dummy_prompt("0",
+                                       prompt_length,
+                                       block_size=block_size)
+    block_manager.allocate(seq_group)
+    assert seq_group.seqs[0].n_blocks == num_gpu_blocks
+
+    # 1st chunk: Compute 2 and half blocks. Should mark 2 blocks as computed.
+    token_chunk_size = int(block_size * 2.5)
+    block_manager.mark_blocks_as_computed(seq_group, token_chunk_size)
+    computed_blocks = block_manager.get_all_computed_blocks(seq_group.seqs[0])
+    assert len(computed_blocks) == 2
+
+    # Actual computed tokens.
+    seq_group.seqs[0].data.update_num_computed_tokens(token_chunk_size)
+
+    # 2nd chunk: Complete 3rd block and additional 4 blocks.
+    token_chunk_size = int(block_size * 4.5)
+    block_manager.mark_blocks_as_computed(seq_group, token_chunk_size)
+    computed_blocks = block_manager.get_all_computed_blocks(seq_group.seqs[0])
+    assert len(computed_blocks) == 7

tests/core/test_chunked_prefill_scheduler.py

@@ -562,3 +562,42 @@ def test_chunked_prefill_max_seqs():
     assert len(get_sequence_groups(out)) == max_seqs
     assert not running[0].is_prefill()
     assert not running[1].is_prefill()
+
+
+def test_perfix_caching():
+    """Verify allocating full blocks when prefix caching is enabled."""
+    block_size = 4
+    max_seqs = 10
+    max_model_len = 80
+    max_num_batched_tokens = 64
+    scheduler_config = SchedulerConfig(max_num_batched_tokens,
+                                       max_seqs,
+                                       max_model_len,
+                                       enable_chunked_prefill=True)
+    cache_config = CacheConfig(block_size,
+                               1.0,
+                               1,
+                               "auto",
+                               enable_prefix_caching=True)
+    cache_config.num_cpu_blocks = 0
+    cache_config.num_gpu_blocks = 32
+    scheduler = Scheduler(scheduler_config, cache_config, None)
+    running: List[SequenceGroup] = []
+
+    # Add seq groups to scheduler.
+    for i in range(2):
+        _, seq_group = create_dummy_prompt(str(i),
+                                           block_size=block_size,
+                                           prompt_length=50)
+        scheduler.add_seq_group(seq_group)
+        running.append(seq_group)
+
+    seq_group_meta, out = schedule_and_update_computed_tokens(scheduler)
+    assert set(get_sequence_groups(out)) == set(running)
+    assert seq_group_meta[0].token_chunk_size == 50
+    # Verify it is chunked. Note that although the budget is 64-50=14,
+    # we only allocate full blocks for prefix caching, so only 4*(14//4)=12
+    # tokens are allocated.
+    assert seq_group_meta[1].token_chunk_size == 12
+    assert out.num_prefill_groups == 2
+    assert out.num_batched_tokens == 62

vllm/core/block_manager_v1.py

@@ -680,14 +680,20 @@ def access_all_blocks_in_seq(
             for block in block_table:
                 block.last_accessed = access_time
 
-    def compute_full_blocks_in_seq(self, seq: Sequence):
+    def compute_full_blocks_in_seq(self, seq: Sequence, token_chunk_size: int):
         if seq.seq_id not in self.block_tables:
             return
-        max_full_block = seq.get_len() // self.block_size - 1
+
+        # When chunked prefill is enabled, the computed full blocks
+        # should be calculated based on the number of computed tokens.
+        max_computed_tokens = (seq.data.get_num_computed_tokens() +
+                               token_chunk_size)
+        computed_full_blocks = max_computed_tokens // self.block_size
+
         block_table = self.block_tables[seq.seq_id]
-        if max_full_block == -1:
+        if computed_full_blocks == 0:
             return
-        for i in reversed(range(max_full_block)):
+        for i in reversed(range(computed_full_blocks)):
             if block_table[i].computed:
                 break
             block_table[i].computed = True
@@ -717,10 +723,11 @@ def get_common_computed_block_ids(
         ids_list = [self.get_all_computed_blocks(seq) for seq in seqs]
         return commonprefix([ids for ids in ids_list if ids != []])
 
-    def mark_blocks_as_computed(self, seq_group: SequenceGroup):
+    def mark_blocks_as_computed(self, seq_group: SequenceGroup,
+                                token_chunk_size: int):
         if self.enable_caching:
             for seq in seq_group.get_seqs():
-                self.compute_full_blocks_in_seq(seq)
+                self.compute_full_blocks_in_seq(seq, token_chunk_size)
 
     def get_prefix_cache_hit_rate(self, device: Device) -> float:
         if device == Device.GPU:

vllm/core/block_manager_v2.py

@@ -286,7 +286,8 @@ def access_all_blocks_in_seq(self, seq: Sequence, now: float):
             self._last_access_blocks_tracker.update_last_access(
                 seq.seq_id, now)
 
-    def mark_blocks_as_computed(self, seq_group: SequenceGroup):
+    def mark_blocks_as_computed(self, seq_group: SequenceGroup,
+                                token_chunk_size: int):
         # If prefix caching is enabled, mark immutable blocks as computed
         # right after they have been scheduled (for prefill). This assumes
         # the scheduler is synchronous so blocks are actually computed when

vllm/core/embedding_model_block_manager.py

@@ -80,7 +80,8 @@ def get_common_computed_block_ids(self,
                                       seq_group: SequenceGroup) -> List[int]:
         return None  # type: ignore
 
-    def mark_blocks_as_computed(self, seq_group: SequenceGroup):
+    def mark_blocks_as_computed(self, seq_group: SequenceGroup,
+                                token_chunk_size: int):
         pass
 
     def get_prefix_cache_hit_rate(self, device: Device) -> float:

vllm/core/interfaces.py

@@ -115,7 +115,8 @@ def get_common_computed_block_ids(
         pass
 
     @abstractmethod
-    def mark_blocks_as_computed(self, seq_group: SequenceGroup):
+    def mark_blocks_as_computed(self, seq_group: SequenceGroup,
+                                token_chunk_size: int):
         pass
 
     @abstractmethod

vllm/core/scheduler.py

@@ -1145,7 +1145,8 @@ def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
         # will crash the vLLM instance / will not retry.
         for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups:
             self.block_manager.mark_blocks_as_computed(
-                scheduled_seq_group.seq_group)
+                scheduled_seq_group.seq_group,
+                scheduled_seq_group.token_chunk_size)
 
         scheduler_time = time.perf_counter() - scheduler_start_time
         # Add this to scheduler time to all the sequences that are currently
@@ -1347,10 +1348,27 @@ def _get_num_new_tokens(self, seq_group: SequenceGroup,
         for seq in seqs:
             num_new_tokens += seq.get_num_new_tokens()
         assert num_new_tokens > 0
-        # Chunk if a running request cannot fit in.
-        # If number of seq > 1, it means it is doing beam search in a
-        # decode phase. Do not chunk in that case.
+        # Chunk if a running request cannot fit in the given budget.
+        # If number of seq > 1, it means it is doing beam search
+        # in a decode phase. Do not chunk.
         if enable_chunking and len(seqs) == 1:
-            num_new_tokens = min(num_new_tokens,
-                                 budget.remaining_token_budget())
+            remaining_token_budget = budget.remaining_token_budget()
+            if self.cache_config.enable_prefix_caching:
+                # When prefix caching is enabled, we always allocate
+                # the number of new tokens that is dividable by the block size
+                # to avoid partial block matching.
+                block_size = self.cache_config.block_size
+                reminder = budget.token_budget % block_size
+                if reminder != 0:
+                    raise ValueError("When enabling chunked prefill and "
+                                     "prefix caching, max_num_batched_tokens "
+                                     "(chunk size) must be dividable by "
+                                     "block size, but got chunk_size "
+                                     f"({budget.token_budget}) % block_size "
+                                     f"({block_size}) = {reminder}")
+                if remaining_token_budget < num_new_tokens:
+                    num_new_tokens = (remaining_token_budget //
+                                      block_size) * block_size
+            else:
+                num_new_tokens = min(num_new_tokens, remaining_token_budget)
         return num_new_tokens

vllm/worker/model_runner.py

@@ -499,23 +499,48 @@ def _compute_for_prefix_cache_hit(
                             and self.sliding_window is None
                             and inter_data.is_prompt)
         inter_data.prefix_cache_hit = prefix_cache_hit
-        if self.chunked_prefill_enabled and prefix_cache_hit:
-            raise RuntimeError(
-                "chunked prefill cannot be used with prefix caching now.")
-
-        # If prefix cache is hit, advance context length to bypass
-        # hit blocks. Accordingly, input tokens, position and query length
-        # have to be updated.
-        if prefix_cache_hit:
-            assert computed_block_nums is not None
-            context_len = len(computed_block_nums) * self.block_size
+
+        if not prefix_cache_hit:
+            return
+
+        assert computed_block_nums is not None
+        # The cache hit prompt tokens in this sequence. Note that
+        # this may be larger than the sequence length if chunked
+        # prefill is enabled.
+        prefix_cache_len = len(computed_block_nums) * self.block_size
+        # The number of so far computed prompt tokens in this sequence.
+        context_len = inter_data.context_lens[seq_idx]
+        # The total number of prompt tokens in this sequence.
+        # When chunked prefill is enabled, this is the token number of
+        # computed chunks + current chunk.
+        seq_len = inter_data.seq_lens[seq_idx]
+        if prefix_cache_len <= context_len:
+            # We already passed the cache hit region,
+            # so do normal computation.
+            pass
+        elif context_len < prefix_cache_len < seq_len:
+            # Partial hit. Compute the missing part.
+            uncomputed_start = prefix_cache_len - context_len
             inter_data.input_tokens[seq_idx] = inter_data.input_tokens[
-                seq_idx][context_len:]
+                seq_idx][uncomputed_start:]
             inter_data.input_positions[seq_idx] = inter_data.input_positions[
-                seq_idx][context_len:]
+                seq_idx][uncomputed_start:]
+            context_len = prefix_cache_len
+
             inter_data.context_lens[seq_idx] = context_len
             inter_data.query_lens[
                 seq_idx] = inter_data.seq_lens[seq_idx] - context_len
+        elif seq_len <= prefix_cache_len:
+            # Full hit. Only compute the last token to avoid
+            # erroneous behavior. FIXME: Ideally we should directly
+            # mark all tokens as computed in the scheduler and do not
+            # schedule this sequence, so this case should not happen.
+            inter_data.input_tokens[seq_idx] = inter_data.input_tokens[
+                seq_idx][-1:]
+            inter_data.input_positions[seq_idx] = inter_data.input_positions[
+                seq_idx][-1:]
+            inter_data.query_lens[seq_idx] = 1
+            inter_data.context_lens[seq_idx] = inter_data.seq_lens[seq_idx] - 1
 
     def _compute_for_sliding_window(self, inter_data: InterDataForSeqGroup,
                                     seq_idx: int,