[Misc] Add chunked-prefill support on FlashInfer. (#9781)

tests/basic_correctness/test_chunked_prefill.py

@@ -11,6 +11,8 @@ from contextlib import nullcontext
 
 import pytest
 
+from tests.kernels.utils import override_backend_env_variable
+
 from ..models.utils import check_logprobs_close, check_outputs_equal
 from ..utils import multi_gpu_test
 
@@ -28,6 +30,7 @@ MODELS = [
 # 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])
+@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
 def test_models(
     hf_runner,
     vllm_runner,
@@ -38,11 +41,15 @@ def test_models(
     chunked_prefill_token_size: int,
     enforce_eager: bool,
     tensor_parallel_size: int,
+    attention_backend: str,
+    monkeypatch,
 ) -> None:
     """
     Checks exact match decode between huggingface model and vllm runner with
     chunked prefill.
     """
+    override_backend_env_variable(monkeypatch, attention_backend)
+
     max_num_seqs = chunked_prefill_token_size
     max_num_batched_tokens = chunked_prefill_token_size
 
@@ -71,13 +78,18 @@ def test_models(
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize("distributed_executor_backend", ["ray", "mp"])
 @pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
 def test_models_distributed(
     hf_runner,
     vllm_runner,
     example_prompts,
     model: str,
     distributed_executor_backend: str,
+    attention_backend: str,
+    monkeypatch,
 ) -> None:
+    override_backend_env_variable(monkeypatch, attention_backend)
+
     if (model == "meta-llama/Llama-2-7b-hf"
             and distributed_executor_backend == "ray"):
         # test ray adag

vllm/attention/backends/flashinfer.py

@@ -268,6 +268,11 @@ class FlashInferMetadata(AttentionMetadata):
     # Maximum sequence length among prefill batch. 0 if there are decoding
     # requests only.
     max_prefill_seq_len: int
+    # Number of query tokens for each request in the batch.
+    # Currently, we require that all requests have the same number of query
+    # tokens during the decoding phase. When speculavie decoding is enabled,
+    # decode_query_len might be greater than 1. In all other cases, it is 1.
+    decode_query_len: Optional[int] = 1
 
     use_cuda_graph: bool = True
 
@@ -335,6 +340,7 @@ class FlashInferMetadata(AttentionMetadata):
             assert self.paged_kv_last_page_len is not None
             assert self.block_table_bound is not None
             assert self.seq_lens_tensor is not None
+            self.query_start_loc = self.query_start_loc[:self.num_prefills + 1]
             batch_size = self.query_start_loc.shape[0] - 1
             assert batch_size >= 0
             # We will use flash attention for profiling to
@@ -349,11 +355,13 @@ class FlashInferMetadata(AttentionMetadata):
                 self.paged_kv_indices = self.paged_kv_indices.to(self.device)
                 self.prefill_wrapper.end_forward()
                 self.prefill_wrapper.begin_forward(
-                    self.query_start_loc, self.paged_kv_indptr,
+                    self.query_start_loc,
-                    self.paged_kv_indices, self.paged_kv_last_page_len,
+                    self.paged_kv_indptr[:self.num_prefills + 1],
+                    self.paged_kv_indices,
+                    self.paged_kv_last_page_len[:self.num_prefills],
                     self.num_qo_heads, self.num_kv_heads, self.head_dim,
                     self.page_size)
-        else:
+        if self.num_decode_tokens > 0:
             assert self.paged_kv_indices is not None
             assert self.paged_kv_indptr is not None
             assert self.paged_kv_last_page_len is not None
@@ -370,9 +378,9 @@ class FlashInferMetadata(AttentionMetadata):
             assert self.decode_wrapper is not None
             self.decode_wrapper.end_forward()
             self.decode_wrapper.begin_forward(
-                self.paged_kv_indptr,
+                self.paged_kv_indptr[self.num_prefills:],
                 self.paged_kv_indices,
-                self.paged_kv_last_page_len,
+                self.paged_kv_last_page_len[self.num_prefills:],
                 self.num_qo_heads,
                 self.num_kv_heads,
                 self.head_dim,
@@ -397,21 +405,14 @@ class FlashInferMetadata(AttentionMetadata):
 
     @property
     def prefill_metadata(self) -> Optional["FlashInferMetadata"]:
-        # Currently chunked prefill is not supported
+        if self.num_prefills == 0:
-        if self.num_decode_tokens == 0:
+            return None
-            assert self.num_prefills > 0
+        return self
-            return self
-
-        return None
 
     @property
     def decode_metadata(self) -> Optional["FlashInferMetadata"]:
-        # Currently chunked prefill is not supported
+        if self.num_decode_tokens == 0:
-        if self.num_prefills > 0:
-            assert self.num_decode_tokens == 0, (
-                "Chunked prefill is not supported with flashinfer yet.")
             return None
-
         return self
 
     def advance_step(self,
@@ -599,11 +600,12 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
 
         max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
         num_decode_tokens = self.num_decode_tokens
+        decode_query_len = max(query_lens[self.num_prefills:], default=1)
 
         if use_captured_graph:
             self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
             self.block_tables.extend([] * cuda_graph_pad_size)
-            num_decode_tokens = batch_size
+            num_decode_tokens = batch_size - self.num_prefill_tokens
 
             # The shape of graph_block_tables is
             # [max batch size, max context len // block size].
@@ -689,6 +691,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
                 self.runner.kv_cache_dtype, self.runner.model_config.dtype)
 
         return FlashInferMetadata(
+            decode_query_len=decode_query_len,
             num_prefills=self.num_prefills,
             slot_mapping=slot_mapping_tensor,
             num_prefill_tokens=self.num_prefill_tokens,
@@ -811,12 +814,6 @@ def unified_flash_infer(
     key = key.view(-1, num_kv_heads, head_size)
     value = value.view(-1, num_kv_heads, head_size)
 
-    if attn_metadata.num_prefill_tokens > 0:
-        assert attn_metadata.num_decode_tokens == 0, (
-            "Chunked prefill is not supported with flashinfer yet.")
-    if attn_metadata.num_decode_tokens > 0:
-        assert attn_metadata.num_prefill_tokens == 0, (
-            "Chunked prefill is not supported with flashinfer yet.")
     if kv_cache.numel() > 0:
         # Use the same reshape and cache kernel as flash attention.
         ops.reshape_and_cache_flash(
@@ -836,14 +833,33 @@ def unified_flash_infer(
                 kv_cache_dtype)
             kv_cache = kv_cache.view(torch_dtype)
 
+    num_prefill_tokens = attn_metadata.num_prefill_tokens
+    num_decode_tokens = attn_metadata.num_decode_tokens
+    assert key.shape[0] == num_prefill_tokens + num_decode_tokens, \
+                f"key : {key.shape} : #prefill tokens {num_prefill_tokens} : #decode tokens {num_decode_tokens}" # noqa
+    assert value.shape[0] == num_prefill_tokens + num_decode_tokens, \
+                f"value : {value.shape} : #prefill toks {num_prefill_tokens} : #decode toks {num_decode_tokens}" # noqa
     query = query.contiguous()  # Flashinfer requires query to be contiguous
+    # Query for decode. KV is not needed because it is already cached.
+    # QKV for prefill.
+    decode_query = query[num_prefill_tokens:]
+    query = query[:num_prefill_tokens]
+
+    key = key[:num_prefill_tokens]
+    value = value[:num_prefill_tokens]
+
+    assert query.shape[0] == num_prefill_tokens
+    assert decode_query.shape[0] == num_decode_tokens
+
+    prefill_output: Optional[torch.Tensor] = None
+    decode_output: Optional[torch.Tensor] = None
     if prefill_meta := attn_metadata.prefill_metadata:
         # We will use flash attention for prefill
         # when kv_cache is not provided.
         # This happens when vllm runs the profiling to
         # determine the number of blocks.
         if kv_cache.numel() == 0:
-            output = flash_attn_varlen_func(
+            prefill_output = flash_attn_varlen_func(
                 q=query,
                 k=key,
                 v=value,
@@ -859,18 +875,34 @@ def unified_flash_infer(
         else:
             assert prefill_meta is not None
             assert prefill_meta.prefill_wrapper is not None
-            output = prefill_meta.prefill_wrapper.forward(
+            prefill_output = prefill_meta.prefill_wrapper.forward(
                 query, kv_cache, logits_soft_cap=logits_soft_cap, causal=True)
-    else:
+    if decode_meta := attn_metadata.decode_metadata:
         assert attn_metadata.decode_metadata is not None
         assert attn_metadata.decode_metadata.decode_wrapper is not None
-        output = attn_metadata.decode_metadata.decode_wrapper.forward(
+        decode_output = attn_metadata.decode_metadata.decode_wrapper.forward(
-            query,
+            decode_query,
             kv_cache,
             sm_scale=softmax_scale,
             logits_soft_cap=logits_soft_cap,
             k_scale=k_scale,
             v_scale=v_scale)
+
+    if prefill_output is None and decode_output is not None:
+        # Decode only batch.
+        output, num_tokens = decode_output, num_decode_tokens
+    elif decode_output is None and prefill_output is not None:
+        # Prefill only batch.
+        output, num_tokens = prefill_output, num_prefill_tokens
+    else:
+        # Chunked prefill batch does not work with speculative decoding in
+        # FlashInfer backend, so the query length for decode should be 1.
+        assert prefill_output is not None
+        assert decode_output is not None
+        assert decode_meta is not None
+        assert decode_meta.decode_query_len == 1
+        decode_output = decode_output.squeeze(1)
+        output = torch.cat([prefill_output, decode_output], dim=0)
     return output.view(num_tokens, hidden_size)