[Core]: (2/N) Support prefill only models by Workflow Defined Engine - Prefill only attention

tests/attention/prefill_only/test_basic_correctness.py

@@ -0,0 +1,89 @@
+import itertools as it
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.attention.layer import Attention
+from vllm.attention.prefill_only.abstract import AttentionType
+from vllm.attention.prefill_only.selector import (AttentionImpls, AttnBackend,
+                                                  _Backend)
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
+
+
+def compare_embeddings(embeddings1, embeddings2):
+    similarities = [
+        F.cosine_similarity(torch.tensor(e1), torch.tensor(e2), dim=0)
+        for e1, e2 in zip(embeddings1, embeddings2)
+    ]
+    return similarities
+
+
+SEQ_LENS = [1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
+
+
+@pytest.mark.parametrize("head_dim", [64])
+@pytest.mark.parametrize("num_heads", [8, 16])
+@pytest.mark.parametrize("num_kv_heads", [1, 2, 4, 8])
+@pytest.mark.parametrize("dtype", ["float", "half", "bfloat16"])
+@pytest.mark.parametrize("attn_type", ["DECODER", "ENCODER"])
+@pytest.mark.parametrize("n_seqs", list(range(1, len(SEQ_LENS))))
+def test_basic_correctness(head_dim: int, num_heads: int, num_kv_heads: int,
+                           attn_type: str, dtype: str, n_seqs: int):
+    assert num_heads % num_kv_heads == 0
+
+    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
+
+    attention_impls = AttentionImpls[dtype]
+
+    seq_lens = SEQ_LENS[:n_seqs]
+    batchsize = sum(seq_lens)
+
+    query = torch.rand((batchsize, num_heads, head_dim),
+                       dtype=torch_dtype,
+                       device="cuda:0").view((batchsize, -1))
+    key = torch.rand((batchsize, num_kv_heads, head_dim),
+                     dtype=torch_dtype,
+                     device="cuda:0").view((batchsize, -1))
+    value = torch.rand((batchsize, num_kv_heads, head_dim),
+                       dtype=torch_dtype,
+                       device="cuda:0").view((batchsize, -1))
+
+    impl_outputs_list = []
+
+    for attention_impl in attention_impls:
+        selected_backend = _Backend.backend_name_to_enum(attention_impl)
+        backend_cls = AttnBackend.get_backend_cls(selected_backend)
+
+        attn_type_enum = AttentionType.attn_type_name_to_enum(attn_type)
+
+        attn_backend = backend_cls(attn_type_enum)
+        scaling = head_dim**-0.5
+
+        attn = Attention(num_heads,
+                         head_dim,
+                         scale=scaling,
+                         num_kv_heads=num_kv_heads,
+                         attn_backend=attn_backend)
+
+        metadata_builder = attn_backend.make_metadata_builder()
+        attn_metadata = metadata_builder(seq_lens=seq_lens)
+        attn_metadata = attn_metadata.to("cuda:0")
+
+        outputs = attn.forward(query,
+                               key,
+                               value,
+                               kv_cache=None,
+                               attn_metadata=attn_metadata)
+
+        impl_outputs_list.append((attention_impl, outputs))
+
+    tolerance = 1e-2
+    for a, b in it.combinations(impl_outputs_list, 2):
+        similarities = compare_embeddings(a[1], b[1])
+        all_similarities = torch.stack(similarities)
+
+        assert torch.all(
+            (all_similarities <= 1.0 + tolerance)
+            & (all_similarities >= 1.0 - tolerance)
+        ), f"{a[0]} vs {b[0]}, not all values are within {tolerance} of 1.0"

tests/attention/prefill_only/test_enum_verify.py

@@ -0,0 +1,54 @@
+import pytest
+
+from vllm.attention.prefill_only.abstract import (AttentionType,
+                                                  PrefillOnlyAttentionBackend)
+from vllm.attention.prefill_only.selector import (AttentionImpls, AttnBackend,
+                                                  _Backend)
+
+
+def get_attn_backend(attention_impl: str, attn_type: str):
+    selected_backend = _Backend.backend_name_to_enum(attention_impl)
+    backend_cls = AttnBackend.get_backend_cls(selected_backend)
+
+    attn_type_enum = AttentionType.attn_type_name_to_enum(attn_type)
+
+    attn_backend = backend_cls(attn_type_enum)
+    return attn_backend
+
+
+@pytest.mark.parametrize("attn_type", ["DECODER", "ENCODER"])
+@pytest.mark.parametrize("dtype", ["float", "half", "bfloat16"])
+def test_backend(dtype: str, attn_type: str):
+    attention_impls = AttentionImpls[dtype]
+
+    for attention_impl in attention_impls:
+        attn_backend = get_attn_backend(attention_impl, attn_type)
+
+        assert isinstance(attn_backend, PrefillOnlyAttentionBackend)
+
+
+@pytest.mark.parametrize("attn_type", ["ENCODER_DECODER"])
+@pytest.mark.parametrize("dtype", ["float", "half", "bfloat16"])
+def test_ENCODER_DECODER_not_supported(dtype: str, attn_type: str):
+    attention_impls = AttentionImpls[dtype]
+
+    for attention_impl in attention_impls:
+        with pytest.raises(NotImplementedError):
+            get_attn_backend(attention_impl, attn_type)
+
+
+def test_not_supported_backend():
+    attention_impls = ["not_supported_backend", 0, 1.0]
+
+    for attention_impl in attention_impls:
+        with pytest.raises(ValueError):
+            selected_backend = _Backend.backend_name_to_enum(attention_impl)
+            AttnBackend.get_backend_cls(selected_backend)
+
+
+def test_not_supported_attn_type():
+    attn_types = ["not_supported_attn_type", 0, 1.0]
+
+    for attn_type in attn_types:
+        with pytest.raises(ValueError):
+            AttentionType.attn_type_name_to_enum(attn_type)

vllm/attention/backends/abstract.py

@@ -18,6 +18,19 @@ class AttentionType(Enum):
     ENCODER = auto()  # Encoder attention between previous layer Q/K/V
     ENCODER_DECODER = auto()  # Attention between dec. Q and enc. K/V
 
+    @staticmethod
+    def attn_type_name_to_enum(attn_type: str) -> "AttentionType":
+        assert attn_type is not None
+
+        attn_type_members = AttentionType.__members__
+        if attn_type not in attn_type_members:
+            raise ValueError(
+                f"Invalid attn_type '{attn_type}'. "
+                f"Available backends: {', '.join(attn_type_members)} "
+                "(case-sensitive).")
+
+        return AttentionType[attn_type]
+
 
 class AttentionBackend(ABC):
     """Abstract class for attention backends."""

vllm/attention/layer.py

@@ -4,7 +4,7 @@
 import torch
 import torch.nn as nn
 
-from vllm.attention import AttentionMetadata, AttentionType
+from vllm.attention import AttentionBackend, AttentionMetadata, AttentionType
 from vllm.attention.selector import get_attn_backend
 from vllm.config import CacheConfig
 from vllm.model_executor.layers.quantization.base_config import (
@@ -36,6 +36,7 @@ def __init__(
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
         prefix: str = "",
+        attn_backend: Optional[AttentionBackend] = None,
     ) -> None:
         super().__init__()
         if cache_config is not None:
@@ -73,14 +74,18 @@ def __init__(
             self.quant_method = quant_method
             self.quant_method.create_weights(self)
 
-        # During model initialization, the default dtype is set as the model
-        # weight and activation dtype.
-        dtype = torch.get_default_dtype()
-        attn_backend = get_attn_backend(num_heads, head_size, num_kv_heads,
-                                        sliding_window, dtype, kv_cache_dtype,
-                                        block_size, blocksparse_params
-                                        is not None)
-        impl_cls = attn_backend.get_impl_cls()
+        if attn_backend is None:
+            # During model initialization, the default dtype is set as the model
+            # weight and activation dtype.
+
+            dtype = torch.get_default_dtype()
+            self.attn_backend = get_attn_backend(
+                num_heads, head_size, num_kv_heads, sliding_window, dtype,
+                kv_cache_dtype, block_size, blocksparse_params is not None)()
+        else:
+            self.attn_backend = attn_backend
+
+        impl_cls = self.attn_backend.get_impl_cls()
         self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                              alibi_slopes, sliding_window, kv_cache_dtype,
                              blocksparse_params, logits_soft_cap)
@@ -94,6 +99,11 @@ def forward(
         attn_metadata: AttentionMetadata,
         attn_type: AttentionType = AttentionType.DECODER,
     ) -> torch.Tensor:
+        if hasattr(self.attn_backend, "attn_type"):
+            return self.impl.forward(query, key, value, kv_cache,
+                                     attn_metadata, self._k_scale,
+                                     self._v_scale,
+                                     self.attn_backend.attn_type)
 
         return self.impl.forward(query,
                                  key,

vllm/attention/prefill_only/abstract.py

@@ -0,0 +1,125 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, Generic, List, Optional, Type, TypeVar
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionType
+from vllm.utils import is_pin_memory_available
+
+pin_memory = is_pin_memory_available()
+
+
+class PrefillOnlyAttentionBackend(ABC):
+
+    def __init__(self, attn_type: AttentionType):
+        if attn_type == AttentionType.ENCODER_DECODER:
+            raise NotImplementedError("Encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "PrefillOnlyAttentionBackend")
+
+        self._attn_type = attn_type
+
+    @property
+    def attn_type(self) -> AttentionType:
+        return self._attn_type
+
+    @staticmethod
+    @abstractmethod
+    def get_name() -> str:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_impl_cls() -> Type["PrefillOnlyAttentionImpl"]:
+        raise NotImplementedError
+
+    @staticmethod
+    def get_metadata_cls() -> Type["PrefillOnlyAttentionMetadata"]:
+        return PrefillOnlyAttentionMetadata
+
+    @classmethod
+    def make_metadata(cls, *args, **kwargs) -> "PrefillOnlyAttentionMetadata":
+        return cls.get_metadata_cls()(*args, **kwargs)
+
+    @staticmethod
+    def get_builder_cls() -> Type["PrefillOnlyAttentionMetadataBuilder"]:
+        return PrefillOnlyAttentionMetadataBuilder
+
+    @classmethod
+    def make_metadata_builder(
+            cls, *args, **kwargs) -> "PrefillOnlyAttentionMetadataBuilder":
+        return cls.get_builder_cls()(*args, **kwargs)
+
+
+@dataclass
+class PrefillOnlyAttentionMetadata:
+    max_seq_len: int
+    seq_lens: List[int]
+
+    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
+    # the batch, used to index into sequence. E.g., if the sequence length is
+    # [4, 6], it is [0, 4, 10].
+    seq_start_loc: Optional[torch.Tensor]
+
+    def to(self, device, non_blocking=False):
+        for k, v in self.__dict__.items():
+            if isinstance(v, torch.Tensor):
+                self.__dict__[k] = v.to(device, non_blocking=non_blocking)
+
+        return self
+
+
+T = TypeVar("T", bound=PrefillOnlyAttentionMetadata)
+
+
+class PrefillOnlyAttentionMetadataBuilder(Generic[T]):
+
+    def __call__(self, seq_lens: List[int]):
+        seq_lens_tensor = torch.tensor(seq_lens,
+                                       dtype=torch.long,
+                                       pin_memory=pin_memory,
+                                       device="cpu")
+        seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
+                                    dtype=torch.int32,
+                                    device="cpu")
+        torch.cumsum(seq_lens_tensor,
+                     dim=0,
+                     dtype=seq_start_loc.dtype,
+                     out=seq_start_loc[1:])
+
+        return PrefillOnlyAttentionMetadata(seq_lens=seq_lens,
+                                            max_seq_len=max(seq_lens),
+                                            seq_start_loc=seq_start_loc)
+
+
+class PrefillOnlyAttentionImpl(ABC):
+
+    @abstractmethod
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: Optional[int] = None,
+        alibi_slopes: Optional[List[float]] = None,
+        sliding_window: Optional[int] = None,
+        kv_cache_dtype: str = "auto",
+        blocksparse_params: Optional[Dict[str, Any]] = None,
+        logits_soft_cap: Optional[float] = None,
+    ) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: Optional[torch.Tensor],
+        attn_metadata: T,
+        k_scale: float = 1.0,
+        v_scale: float = 1.0,
+        attn_type: AttentionType = AttentionType.DECODER,
+    ) -> torch.Tensor:
+        raise NotImplementedError