[Kernel] Flashinfer for prefill & decode, with Cudagraph support for decode

.buildkite/test-pipeline.yaml

@@ -211,3 +211,6 @@ steps:
   - pytest -v -s distributed/test_custom_all_reduce.py
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
+  - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.5/flashinfer-0.0.5+cu121torch2.3-cp310-cp310-linux_x86_64.whl
+  - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
+  - VLLM_ATTENTION_BACKEND=FLASHINFER TEST_DIST_MODEL=meta-llama/Meta-Llama-3-8B DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py

requirements-test.txt

@@ -19,4 +19,4 @@ sentence-transformers # required for embedding
 aiohttp
 
 # quantization
-bitsandbytes==0.42.0
+bitsandbytes==0.42.0

tests/basic_correctness/test_basic_correctness.py

@@ -2,7 +2,6 @@
 
 Run `pytest tests/basic_correctness/test_basic_correctness.py`.
 """
-import os
 import weakref
 
 import pytest
@@ -13,7 +12,6 @@
     "facebook/opt-125m",
     "meta-llama/Llama-2-7b-hf",
 ]
-VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
 
 
 def test_vllm_gc_ed():
@@ -39,10 +37,6 @@ def test_models(
     max_tokens: int,
     enforce_eager: bool,
 ) -> None:
-    backend_by_env_var = os.getenv(VLLM_ATTENTION_BACKEND)
-    if backend_by_env_var == "FLASHINFER" and enforce_eager is False:
-        pytest.skip("Skipping non-eager test for FlashInferBackend.")
-
     with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 

tests/distributed/test_basic_distributed_correctness.py

@@ -21,7 +21,6 @@
     os.environ["TEST_DIST_MODEL"],
 ]
 DISTRIBUTED_EXECUTOR_BACKEND = "DISTRIBUTED_EXECUTOR_BACKEND"
-VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2,
@@ -39,16 +38,12 @@ def test_models(
 ) -> None:
     distributed_executor_backend = os.getenv(DISTRIBUTED_EXECUTOR_BACKEND)
 
-    backend_by_env_var = os.getenv(VLLM_ATTENTION_BACKEND)
-    enforce_eager = backend_by_env_var == "FLASHINFER"
-
     with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
     with vllm_runner(model,
                      dtype=dtype,
                      tensor_parallel_size=2,
-                     enforce_eager=enforce_eager,
                      distributed_executor_backend=distributed_executor_backend
                      ) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)

vllm/attention/backends/flashinfer.py

@@ -1,10 +1,16 @@
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Set, Tuple, Type
 
-import flashinfer
+try:
+    from flashinfer import BatchDecodeWithPagedKVCacheWrapper
+    from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
+    from vllm_flash_attn import flash_attn_varlen_func
+except ImportError:
+    flash_attn_varlen_func = None
+    BatchDecodeWithPagedKVCacheWrapper = None
+    BatchPrefillWithPagedKVCacheWrapper = None
+
 import torch
-from flashinfer import BatchDecodeWithPagedKVCacheWrapper
-from vllm_flash_attn import flash_attn_varlen_func
 
 from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
@@ -60,19 +66,16 @@ class FlashInferMetadata(AttentionMetadata):
     # requests only.
     max_prefill_seq_len: int
 
-    use_cuda_graph: bool = False
+    use_cuda_graph: bool = True
 
+    prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
     decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
 
-    # Metadata for the prefill stage since we still
-    # use flash attention for prefill.
+    # Metadata for the prefill stage
     seq_start_loc: Optional[torch.Tensor] = None
+    query_start_loc: Optional[torch.Tensor] = None
     block_tables: Optional[torch.Tensor] = None
 
-    # Metadata for the decode stage
-    # Workspace buffer required by the kernel, the buffer should not
-    # be allocated/deacollated by the FalshInfermetadata object.
-    workspace_buffer: Optional[torch.Tensor] = None
     # An example for paged_kv_indices, paged_kv_indptr:
     # request 1, page indices [0, 5, 8]
     # request 2, page indices [1, 6, 7]
@@ -98,6 +101,7 @@ class FlashInferMetadata(AttentionMetadata):
     page_size: Optional[int] = None
     # The data type of the paged kv cache
     data_type: torch.dtype = None
+    device: torch.device = torch.device("cuda")
 
     def __post_init__(self):
         # Refer to
@@ -109,13 +113,35 @@ def __post_init__(self):
                 f"Only {supported_head_sizes} are supported for head_dim,",
                 f"received {self.head_dim}.")
 
-        # When using flashinfer, we are also creating the FlashInferMetadata,
-        # which will also call post_init by default, here we want to skip the
-        # post_init if it's the prefill phase.
-        if self.num_prefills == 0:
-            assert self.num_decode_tokens > 0
-            self.decode_wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
-                self.workspace_buffer, "NHD")
+    def begin_forward(self):
+        if self.num_prefill_tokens > 0:
+            if self.paged_kv_indices is None:
+                return
+
+            assert self.prefill_wrapper is not None
+            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
+            self.paged_kv_indices = self.paged_kv_indices.to(self.device)
+            self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
+            self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
+                self.device)
+            self.prefill_wrapper.begin_forward(
+                self.query_start_loc, self.paged_kv_indptr,
+                self.paged_kv_indices, self.paged_kv_last_page_len,
+                self.num_qo_heads, self.num_kv_heads, self.head_dim,
+                self.page_size)
+        else:
+            if not self.use_cuda_graph:
+                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
+                self.paged_kv_indices = self.paged_kv_indices.to(self.device)
+                self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
+                self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
+                    self.device)
+
+            assert self.decode_wrapper is not None
             self.decode_wrapper.begin_forward(
                 self.paged_kv_indptr,
                 self.paged_kv_indices,
@@ -133,8 +159,9 @@ def asdict_zerocopy(self,
                         ) -> Dict[str, Any]:
         if skip_fields is None:
             skip_fields = set()
-        # We need to skip the decode_wrapper field since it cannot be
+        # We need to skip the prefill/decode_wrapper field since it cannot be
         # broadcasted with nccl when TP is enabled.
+        skip_fields.add('prefill_wrapper')
         skip_fields.add('decode_wrapper')
         return super().asdict_zerocopy(skip_fields)
 
@@ -168,6 +195,7 @@ def __init__(
         alibi_slopes: Optional[List[float]],
         sliding_window: Optional[int],
         kv_cache_dtype: str,
+        blocksparse_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         self.num_heads = num_heads
         self.head_size = head_size
@@ -217,10 +245,14 @@ def forward(
                 self.kv_cache_dtype,
             )
 
+        query = query.contiguous(
+        )  # Flashinfer requires query to be contiguous
         if prefill_meta := attn_metadata.prefill_metadata:
-            # Prompt run.
-            assert prefill_meta.block_tables is not None
-            if kv_cache is None or prefill_meta.block_tables.numel() == 0:
+            # 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 is None:
                 output = flash_attn_varlen_func(
                     q=query,
                     k=key,
@@ -235,13 +267,14 @@ def forward(
                     alibi_slopes=self.alibi_slopes,
                 )
             else:
-                raise NotImplementedError(
-                    "Prefix caching is not supported with flashinfer yet.")
+                assert prefill_meta is not None
+                assert prefill_meta.prefill_wrapper is not None
+                output = prefill_meta.prefill_wrapper.forward(query,
+                                                              kv_cache,
+                                                              causal=True)
         else:
             assert attn_metadata.decode_metadata is not None
             assert attn_metadata.decode_metadata.decode_wrapper is not None
-            query = query.contiguous(
-            )  # Flashinfer requires query to be contiguous
             output = attn_metadata.decode_metadata.decode_wrapper.forward(
                 query,
                 kv_cache,

vllm/attention/selector.py

@@ -77,8 +77,9 @@ def get_attn_backend(
         return IpexAttnBackend
     elif backend == _Backend.FLASHINFER:
         logger.info("Using Flashinfer backend.")
-        logger.warning("Eager mode is required for the Flashinfer backend. "
-                       "Please make sure --enforce-eager is set.")
+        logger.warning(("Flashinfer will be stuck on llma-2-7b,"
+                        " please avoid using Flashinfer as the"
+                        "backend when running on llma-2-7b."))
         from vllm.attention.backends.flashinfer import FlashInferBackend
         return FlashInferBackend
     elif backend == _Backend.PALLAS: