[Kernel] Move attn_type to Attention.__init__() (#11690)

Signed-off-by: Chen Zhang <zhangch99@outlook.com>

tests/kernels/test_encoder_decoder_attn.py

@@ -13,8 +13,7 @@ import pytest
 import torch
 
 from tests.kernels.utils import *
-from vllm.attention import (Attention, AttentionBackend, AttentionMetadata,
-                            AttentionType)
+from vllm.attention import Attention, AttentionMetadata, AttentionType
 from vllm.attention.backends.utils import STR_NOT_IMPL_ENC_DEC_ROCM_HIP
 from vllm.attention.selector import (_Backend, _cached_get_attn_backend,
                                      global_force_attn_backend_context_manager)
@@ -64,6 +63,7 @@ class TestPoint(NamedTuple):
     max_dec_seq_len: int
     max_enc_seq_len: int
     num_blocks: int
+    attn_type: AttentionType
 
 
 class TestResources(NamedTuple):
@@ -96,7 +96,6 @@ class TestResources(NamedTuple):
     '''
 
     scale: float
-    attn_backend: AttentionBackend
     attn: Attention
     kv_cache: torch.Tensor
 
@@ -129,16 +128,17 @@ def _make_test_resources(test_pt: TestPoint, ) -> TestResources:
     '''
 
     scale = float(1.0 / (test_pt.head_size**0.5))
-    attn_backend = make_backend(test_pt.backend_name)
     attn = Attention(
         test_pt.num_heads,
         test_pt.head_size,
         scale=scale,
+        prefix=f"{test_pt.attn_type}",
+        attn_type=test_pt.attn_type,
     )
     if test_pt.num_blocks is None or test_pt.num_heads is None:
         # Caller does not require a KV cache
         return TestResources(
-            scale, attn_backend, attn,
+            scale, attn,
             torch.tensor([], dtype=torch.float32, device=CUDA_DEVICE))
 
     # Construct KV cache
@@ -148,7 +148,7 @@ def _make_test_resources(test_pt: TestPoint, ) -> TestResources:
                              test_pt.block_size,
                              device=CUDA_DEVICE,
                              backend=test_pt.backend_name)
-    return TestResources(scale, attn_backend, attn, kv_cache)
+    return TestResources(scale, attn, kv_cache)
 
 
 def _encoder_attn_setup(
@@ -193,6 +193,7 @@ def _encoder_attn_setup(
         _,
         max_q_seq_len,
         _,
+        _,
     ) = test_pt
 
     scale = test_rsrcs.scale
@@ -301,6 +302,7 @@ def _decoder_attn_setup(
         max_q_seq_len,
         _,
         _,
+        _,
     ) = test_pt
 
     scale = test_rsrcs.scale
@@ -488,6 +490,7 @@ def _enc_dec_cross_attn_setup_reuses_query(
         max_decoder_seq_len,
         max_encoder_seq_len,
         _,
+        _,
     ) = test_pt
 
     scale = test_rsrcs.scale
@@ -622,7 +625,6 @@ def _run_encoder_attention_test(
       & attn_metadata
     '''
     assert attn_metadata.num_decode_tokens == 0
-    attn_type = AttentionType.ENCODER
     packed_qkv = encoder_test_params.packed_qkvo.packed_qkv
     assert packed_qkv is not None
     with set_forward_context(attn_metadata, vllm_config):
@@ -635,14 +637,11 @@ def _run_encoder_attention_test(
         # is shaped as [num_tokens, hidden_size] and we can skip the reshape.
         reshaped_query = packed_qkv.query.view(
             -1, test_pt.num_heads * test_pt.head_size)
-        return attn.forward(reshaped_query,
-                            packed_qkv.key,
-                            packed_qkv.value,
-                            torch.tensor([],
-                                         dtype=torch.float32,
-                                         device=packed_qkv.query.device),
-                            attn_metadata,
-                            attn_type=attn_type)
+        return attn.forward(
+            reshaped_query, packed_qkv.key, packed_qkv.value,
+            torch.tensor([],
+                         dtype=torch.float32,
+                         device=packed_qkv.query.device), attn_metadata)
 
 
 def _run_decoder_self_attention_test(
@@ -675,7 +674,6 @@ def _run_decoder_self_attention_test(
     * Attention.forward() applied to packed_{query,key,value}, kv_cache
       & attn_metadata
     '''
-    attn_type = AttentionType.DECODER
     attn = test_rsrcs.attn
     kv_cache = test_rsrcs.kv_cache
     packed_qkv = decoder_test_params.packed_qkvo.packed_qkv
@@ -690,12 +688,8 @@ def _run_decoder_self_attention_test(
         # is shaped as [num_tokens, hidden_size] and we can skip the reshape.
         reshaped_query = packed_qkv.query.view(
             -1, test_pt.num_heads * test_pt.head_size)
-        return attn.forward(reshaped_query,
-                            packed_qkv.key,
-                            packed_qkv.value,
-                            kv_cache,
-                            attn_metadata,
-                            attn_type=attn_type)
+        return attn.forward(reshaped_query, packed_qkv.key, packed_qkv.value,
+                            kv_cache, attn_metadata)
 
 
 def _run_encoder_decoder_cross_attention_test(
@@ -742,7 +736,6 @@ def _run_encoder_decoder_cross_attention_test(
     '''
     assert decoder_test_params.packed_qkvo.packed_qkv is not None
 
-    attn_type = AttentionType.ENCODER_DECODER
     attn = test_rsrcs.attn
     kv_cache = test_rsrcs.kv_cache
     if cross_test_params is None:
@@ -762,12 +755,8 @@ def _run_encoder_decoder_cross_attention_test(
         # is shaped as [num_tokens, hidden_size] and we can skip the reshape.
         reshaped_query = decoder_test_params.packed_qkvo.packed_qkv.query.view(
             -1, test_pt.num_heads * test_pt.head_size)
-        return attn.forward(reshaped_query,
-                            key,
-                            value,
-                            kv_cache,
-                            attn_metadata,
-                            attn_type=attn_type)
+        return attn.forward(reshaped_query, key, value, kv_cache,
+                            attn_metadata)
 
 
 @pytest.fixture(autouse=True)
@@ -839,7 +828,7 @@ def test_encoder_only(
         # is not part of this test
         test_pt = TestPoint(num_heads, head_size, attn_backend.name,
                             batch_size, block_size, max_dec_seq_len,
-                            max_enc_seq_len, 4096)
+                            max_enc_seq_len, 4096, AttentionType.ENCODER)
 
         # Attention scale factor, attention backend instance, attention wrapper
         # instance, KV cache init
@@ -855,7 +844,7 @@ def test_encoder_only(
         # Shared prefill metadata structure
 
         prephase_attn_metadata: AttentionMetadata = make_test_metadata(
-            test_rsrcs.attn_backend,
+            attn_backend,
             True,
             None,
             decoder_test_params=None,
@@ -961,20 +950,29 @@ def test_e2e_enc_dec_attn(
         # Note: KV cache size of 4096 is arbitrary & chosen intentionally
         # to be more than necessary, since exceeding the kv cache size
         # is not part of this test
-        test_pt = TestPoint(num_heads, head_size, attn_backend.name,
-                            batch_size, block_size, max_dec_seq_len,
-                            max_enc_seq_len, 4096)
+        enc_test_pt = TestPoint(num_heads, head_size, attn_backend.name,
+                                batch_size, block_size, max_dec_seq_len,
+                                max_enc_seq_len, 4096, AttentionType.ENCODER)
+        enc_dec_test_pt = TestPoint(num_heads, head_size, attn_backend.name,
+                                    batch_size, block_size, max_dec_seq_len,
+                                    max_enc_seq_len, 4096,
+                                    AttentionType.ENCODER_DECODER)
+        dec_test_pt = TestPoint(num_heads, head_size, attn_backend.name,
+                                batch_size, block_size, max_dec_seq_len,
+                                max_enc_seq_len, 4096, AttentionType.DECODER)
 
         # Attention scale factor, attention backend instance, attention wrapper
         # instance, KV cache init
         vllm_config = VllmConfig()
         with set_current_vllm_config(vllm_config):
-            test_rsrcs = _make_test_resources(test_pt)
+            enc_test_rsrcs = _make_test_resources(enc_test_pt)
+            enc_dec_test_rsrcs = _make_test_resources(enc_dec_test_pt)
+            dec_test_rsrcs = _make_test_resources(dec_test_pt)
 
         # Construct encoder attention test params (only used
         # during prefill)
 
-        enc_test_params = _encoder_attn_setup(test_pt, test_rsrcs)
+        enc_test_params = _encoder_attn_setup(enc_test_pt, enc_test_rsrcs)
 
         # Construct Decoder self-attention prefill-phase & decode-phase
         # test params, including query/key/value tensors, decoder self-attention
@@ -987,7 +985,7 @@ def test_e2e_enc_dec_attn(
             prephase_dec_test_params,
             decphase_dec_test_params,
             cross_block_base_addr,
-        ) = _decoder_attn_setup(test_pt, test_rsrcs)
+        ) = _decoder_attn_setup(dec_test_pt, dec_test_rsrcs)
 
         # Construct encoder/decoder cross-attention prefill-phase
         # & decode-phase test params, including key/value tensors,
@@ -1000,14 +998,14 @@ def test_e2e_enc_dec_attn(
             dec_qkv,
             enc_test_params,
             prephase_dec_test_params,
-            test_pt,
-            test_rsrcs,
+            enc_dec_test_pt,
+            enc_dec_test_rsrcs,
             block_base_addr=cross_block_base_addr)
 
         # Shared prefill metadata structure
         assert prephase_dec_test_params.packed_qkvo.packed_qkv is not None
         prephase_attn_metadata: AttentionMetadata = make_test_metadata(
-            test_rsrcs.attn_backend,
+            attn_backend,
             True,
             prephase_dec_test_params.packed_qkvo.packed_qkv.q_seq_lens,
             decoder_test_params=prephase_dec_test_params,
@@ -1017,10 +1015,10 @@ def test_e2e_enc_dec_attn(
 
         # PREFILL: encoder attention
 
-        enc_pckd_act_out = _run_encoder_attention_test(test_rsrcs.attn,
+        enc_pckd_act_out = _run_encoder_attention_test(enc_test_rsrcs.attn,
                                                        enc_test_params,
                                                        prephase_attn_metadata,
-                                                       test_pt=test_pt,
+                                                       test_pt=enc_test_pt,
                                                        vllm_config=vllm_config)
 
         # - Is encoder attention result correct?
@@ -1030,10 +1028,10 @@ def test_e2e_enc_dec_attn(
         # PREFILL: decoder self-attention test
 
         prephase_dec_pckd_act_out = _run_decoder_self_attention_test(
-            test_rsrcs,
+            dec_test_rsrcs,
             prephase_dec_test_params,
             prephase_attn_metadata,
-            test_pt=test_pt,
+            test_pt=dec_test_pt,
             vllm_config=vllm_config)
 
         # - Is prefill decoder self-attention correct?
@@ -1044,11 +1042,11 @@ def test_e2e_enc_dec_attn(
         # PREFILL: encoder/decoder cross-attention test
 
         prephase_cross_pckd_act_out = _run_encoder_decoder_cross_attention_test(
-            test_rsrcs,
+            enc_dec_test_rsrcs,
             prephase_dec_test_params,
             prephase_cross_test_params,
             prephase_attn_metadata,
-            test_pt=test_pt,
+            test_pt=enc_dec_test_pt,
             vllm_config=vllm_config)
 
         # - Is prefill encoder/decoder cross-attention correct?
@@ -1059,7 +1057,7 @@ def test_e2e_enc_dec_attn(
         # DECODE: build decode-phase attention metadata
 
         decphase_attn_metadata: AttentionMetadata = make_test_metadata(
-            test_rsrcs.attn_backend,
+            attn_backend,
             False,
             dec_qkv.q_seq_lens,
             decoder_test_params=decphase_dec_test_params,
@@ -1070,10 +1068,10 @@ def test_e2e_enc_dec_attn(
         # DECODE: decoder self-attention test
 
         decphase_dec_pckd_act_out = _run_decoder_self_attention_test(
-            test_rsrcs,
+            dec_test_rsrcs,
             decphase_dec_test_params,
             decphase_attn_metadata,
-            test_pt=test_pt,
+            test_pt=dec_test_pt,
             vllm_config=vllm_config)
 
         # - Is decode-phase decoder self-attention correct?
@@ -1084,11 +1082,11 @@ def test_e2e_enc_dec_attn(
         # DECODE: encoder/decoder cross-attention test
 
         decphase_cross_pckd_act_out = _run_encoder_decoder_cross_attention_test(
-            test_rsrcs,
+            enc_dec_test_rsrcs,
             decphase_dec_test_params,
             None,
             decphase_attn_metadata,
-            test_pt=test_pt,
+            test_pt=enc_dec_test_pt,
             vllm_config=vllm_config)
 
         # - Is decode-phase encoder/decoder cross-attention correct?

tests/kernels/utils.py

@@ -13,6 +13,7 @@ from torch._prims_common import TensorLikeType
 
 from vllm.attention import AttentionBackend, AttentionMetadata, AttentionType
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.platforms.interface import _Backend
 from vllm.utils import (STR_BACKEND_ENV_VAR, STR_FLASH_ATTN_VAL,
                         STR_XFORMERS_ATTN_VAL, make_tensor_with_pad)
 
@@ -790,7 +791,7 @@ def make_block_tables_slot_mapping(
 
 
 def make_test_metadata(
-    attn_backend: AttentionBackend,
+    attn_backend: _Backend,
     is_prompt: bool,
     seq_lens: Optional[List[int]],
     decoder_test_params: Optional[PhaseTestParameters],
@@ -815,7 +816,7 @@ def make_test_metadata(
 
     Arguments:
 
-    * attn_backend: Backend for sourcing attention kernels
+    * attn_backend_name: Backend for sourcing attention kernels
     * is_prompt: prefill if True, o/w decode
     * seq_lens: list of token counts for each sequence
     * decoder_test_params: decoder self-attention test params; 
@@ -882,6 +883,8 @@ def make_test_metadata(
         #   (kv_mmap)
         cross_kv_mmap = cross_test_params.kv_mmap
 
+    attn_backend_obj = make_backend(attn_backend.name)
+
     if is_prompt:
         # Prefill-phase scenario
 
@@ -902,8 +905,7 @@ def make_test_metadata(
                                    context_lens,
                                    encoder_seq_lens,
                                    device=device)
-
-        return attn_backend.make_metadata(
+        return attn_backend_obj.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=(None if kv_mmap is None else kv_mmap.slot_mapping),
             multi_modal_placeholder_index_maps=None,
@@ -952,7 +954,7 @@ def make_test_metadata(
                                    encoder_seq_lens,
                                    device=device)
 
-        return attn_backend.make_metadata(
+        return attn_backend_obj.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=kv_mmap.slot_mapping,
             multi_modal_placeholder_index_maps=None,

vllm/attention/backends/abstract.py

@@ -233,6 +233,7 @@ class AttentionImpl(ABC, Generic[T]):
         kv_cache_dtype: str = "auto",
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         raise NotImplementedError
 
@@ -246,7 +247,6 @@ class AttentionImpl(ABC, Generic[T]):
         attn_metadata: T,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         raise NotImplementedError

vllm/attention/backends/blocksparse_attn.py

@@ -300,6 +300,7 @@ class BlocksparseFlashAttentionImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         assert blocksparse_params is not None
         assert alibi_slopes is None, ValueError(
@@ -350,6 +351,12 @@ class BlocksparseFlashAttentionImpl(AttentionImpl):
             active_head_range=self.blocksparse_params.active_head_range,
         )
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "BlocksparseFlashAttentionImpl")
+
     def forward(
         self,
         query: torch.Tensor,
@@ -359,7 +366,6 @@ class BlocksparseFlashAttentionImpl(AttentionImpl):
         attn_metadata: BlocksparseFlashAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention and PagedAttention.
@@ -375,12 +381,6 @@ class BlocksparseFlashAttentionImpl(AttentionImpl):
         Returns:
             shape = [num_tokens, num_heads * head_size]
         """
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "BlocksparseFlashAttentionImpl")
-
         num_tokens, hidden_size = query.shape
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)

vllm/attention/backends/flash_attn.py

@@ -600,6 +600,7 @@ class FlashAttentionImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -627,6 +628,7 @@ class FlashAttentionImpl(AttentionImpl):
             raise ValueError(
                 f"Head size {head_size} is not supported by FlashAttention. "
                 f"Supported head sizes are: {support_head_sizes}.")
+        self.attn_type = attn_type
 
     def forward(
         self,
@@ -637,7 +639,6 @@ class FlashAttentionImpl(AttentionImpl):
         attn_metadata: FlashAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention.
@@ -659,6 +660,7 @@ class FlashAttentionImpl(AttentionImpl):
 
         assert output is not None, "Output tensor must be provided."
 
+        attn_type = self.attn_type
         if (attn_type == AttentionType.ENCODER
                 and (not attn_metadata.is_all_encoder_attn_metadata_set)):
             raise AttributeError("Encoder attention requires setting "

vllm/attention/backends/flashinfer.py

@@ -748,6 +748,7 @@ class FlashInferImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         self.num_heads = num_heads
         self.head_size = head_size
@@ -764,6 +765,12 @@ class FlashInferImpl(AttentionImpl):
         assert self.num_heads % self.num_kv_heads == 0
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashInferImpl")
+
     def forward(
         self,
         query: torch.Tensor,
@@ -773,18 +780,10 @@ class FlashInferImpl(AttentionImpl):
         attn_metadata: FlashInferMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
 
         # TODO: directly write to output tensor
-
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "FlashInferImpl")
-
         num_heads: int = self.num_heads
         head_size: int = self.head_size
         num_kv_heads: int = self.num_kv_heads

vllm/attention/backends/hpu_attn.py

@@ -102,6 +102,7 @@ class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         max_seq_len: int = 4096,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         super(AttentionImpl, self).__init__()
         self.kv_cache_dtype = kv_cache_dtype
@@ -143,6 +144,12 @@ class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
                 f"Head size {head_size} is not supported by PagedAttention. "
                 f"Supported head sizes are: {suppored_head_sizes}.")
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "HPUAttentionImpl")
+
     def forward(
         self,
         query: torch.Tensor,
@@ -152,7 +159,6 @@ class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
         attn_metadata: HPUAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with xFormers and PagedAttention.
@@ -166,11 +172,6 @@ class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
         Returns:
             shape = [num_tokens, num_heads * head_size]
         """
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "HPUAttentionImpl")
         batch_size, seq_len, hidden_size = query.shape
         _, seq_len_kv, _ = key.shape
 

vllm/attention/backends/ipex_attn.py

@@ -115,6 +115,7 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -146,6 +147,11 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
             raise NotImplementedError(
                 "IPEX backend does not support FP8 KV cache. "
                 "Please use xFormers backend instead.")
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "IpexAttnBackendImpl")
 
     def split_kv_cache(
         self,
@@ -172,7 +178,6 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
         attn_metadata: IpexAttnMetadata,  # type: ignore
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with IPEX varlen_attention and PagedAttention.
@@ -189,11 +194,6 @@ class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
             shape = [num_tokens, num_heads * head_size]
         """
         assert k_scale == 1.0 and v_scale == 1.0
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "IpexAttnBackendImpl")
         num_tokens, hidden_size = query.shape
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)

vllm/attention/backends/pallas.py

@@ -100,6 +100,7 @@ class PallasAttentionBackendImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         self.num_heads = num_heads
         self.head_size = head_size
@@ -141,6 +142,12 @@ class PallasAttentionBackendImpl(AttentionImpl):
                 # megacore mode will be None.
                 self.megacore_mode = "batch"
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "PallasAttentionBackendImpl")
+
     def forward(
         self,
         query: torch.Tensor,
@@ -150,7 +157,6 @@ class PallasAttentionBackendImpl(AttentionImpl):
         attn_metadata: PallasMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with Pallas attention.
@@ -168,11 +174,6 @@ class PallasAttentionBackendImpl(AttentionImpl):
             shape = [batch_size, seq_len, num_heads * head_size]
         """
         assert k_scale == 1.0 and v_scale == 1.0
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "PallasAttentionBackendImpl")
         batch_size, seq_len, hidden_size = query.shape
         query = query.view(batch_size, seq_len, self.num_heads, self.head_size)
         key = key.view(batch_size, seq_len, self.num_kv_heads, self.head_size)

vllm/attention/backends/rocm_flash_attn.py

@@ -338,6 +338,7 @@ class ROCmFlashAttentionImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -397,6 +398,12 @@ class ROCmFlashAttentionImpl(AttentionImpl):
                 self.attn_func = _sdpa_attention
                 logger.debug("Using naive attention in ROCmBackend")
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "ROCmFlashAttentionImpl")
+
     def repeat_kv(self, x: torch.Tensor, n_rep: int) -> torch.Tensor:
         """torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
         tokens, n_kv_heads, head_dim = x.shape
@@ -414,7 +421,6 @@ class ROCmFlashAttentionImpl(AttentionImpl):
         attn_metadata: ROCmFlashAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention and PagedAttention.
@@ -432,12 +438,6 @@ class ROCmFlashAttentionImpl(AttentionImpl):
         """
         # Reminder: Please update docs/source/features/compatibility_matrix.md
         # If the feature combo become valid
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "ROCmFlashAttentionImpl")
-
         num_tokens, hidden_size = query.shape
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)

vllm/attention/backends/torch_sdpa.py

@@ -390,6 +390,7 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -421,6 +422,7 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
             raise NotImplementedError(
                 "Torch SDPA backend does not support FP8 KV cache. "
                 "Please use xFormers backend instead.")
+        self.attn_type = attn_type
 
     def forward(
         self,
@@ -431,7 +433,6 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
         attn_metadata: TorchSDPAMetadata,  # type: ignore
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with torch SDPA and PagedAttention.
@@ -448,6 +449,7 @@ class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
             shape = [num_tokens, num_heads * head_size]
         """
         assert k_scale == 1.0 and v_scale == 1.0
+        attn_type = self.attn_type
         if (attn_type == AttentionType.ENCODER
                 and (not attn_metadata.is_all_encoder_attn_metadata_set)):
             raise AttributeError("Encoder attention requires setting "

vllm/attention/backends/xformers.py

@@ -379,6 +379,7 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -405,6 +406,8 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                 f"Head size {head_size} is not supported by PagedAttention. "
                 f"Supported head sizes are: {suppored_head_sizes}.")
 
+        self.attn_type = attn_type
+
     def forward(
         self,
         query: torch.Tensor,
@@ -414,7 +417,6 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
         attn_metadata: "XFormersMetadata",
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: str = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with xFormers and PagedAttention.
@@ -468,7 +470,7 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
         Returns:
             shape = [num_tokens, num_heads * head_size]
         """
-
+        attn_type = self.attn_type
         # Check that appropriate attention metadata attributes are
         # selected for the desired attention type
         if (attn_type == AttentionType.ENCODER

vllm/attention/layer.py

@@ -41,6 +41,7 @@ class Attention(nn.Module):
         logits_soft_cap: Optional[float] = None,
         per_layer_sliding_window: Optional[int] = None,
         prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
     ) -> None:
         super().__init__()
         if per_layer_sliding_window is not None:
@@ -96,7 +97,7 @@ class Attention(nn.Module):
         impl_cls = 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)
+                             blocksparse_params, logits_soft_cap, attn_type)
         self.num_heads = num_heads
         self.head_size = head_size
         self.num_kv_heads = num_kv_heads
@@ -119,6 +120,7 @@ class Attention(nn.Module):
             raise ValueError(f"Duplicate layer name: {prefix}")
         compilation_config.static_forward_context[prefix] = self
         self.layer_name = prefix
+        self.attn_type = attn_type
 
     def forward(
         self,
@@ -127,18 +129,12 @@ class Attention(nn.Module):
         value: torch.Tensor,
         kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
-        attn_type: str = AttentionType.DECODER,
     ) -> torch.Tensor:
 
         if self.use_direct_call:
-            return self.impl.forward(query,
-                                     key,
-                                     value,
-                                     kv_cache,
-                                     attn_metadata,
-                                     self._k_scale,
-                                     self._v_scale,
-                                     attn_type=attn_type)
+            return self.impl.forward(query, key, value, kv_cache,
+                                     attn_metadata, self._k_scale,
+                                     self._v_scale)
         elif self.use_output:
             output = torch.empty_like(query)
             hidden_size = query.size(-1)
@@ -152,13 +148,11 @@ class Attention(nn.Module):
             if value is not None:
                 value = value.view(-1, self.num_kv_heads, self.head_size)
             torch.ops.vllm.unified_attention_with_output(
-                query, key, value, output, kv_cache, attn_type,
-                self.layer_name)
+                query, key, value, output, kv_cache, self.layer_name)
             return output.view(-1, hidden_size)
         else:
             return torch.ops.vllm.unified_attention(query, key, value,
-                                                    kv_cache, attn_type,
-                                                    self.layer_name)
+                                                    kv_cache, self.layer_name)
 
     def extra_repr(self) -> str:
         s = f"head_size={self.impl.head_size}"  # type: ignore
@@ -237,20 +231,13 @@ def unified_attention(
     key: torch.Tensor,
     value: torch.Tensor,
     kv_cache: torch.Tensor,
-    attn_type: str,
     layer_name: str,
 ) -> torch.Tensor:
     forward_context: ForwardContext = get_forward_context()
     attn_metadata = forward_context.dynamic_forward_context
     self = forward_context.static_forward_context[layer_name]
-    return self.impl.forward(query,
-                             key,
-                             value,
-                             kv_cache,
-                             attn_metadata,
-                             self._k_scale,
-                             self._v_scale,
-                             attn_type=attn_type)
+    return self.impl.forward(query, key, value, kv_cache, attn_metadata,
+                             self._k_scale, self._v_scale)
 
 
 def unified_attention_fake(
@@ -258,7 +245,6 @@ def unified_attention_fake(
     key: torch.Tensor,
     value: torch.Tensor,
     kv_cache: torch.Tensor,
-    attn_type: str,
     layer_name: str,
 ) -> torch.Tensor:
     return torch.empty_like(query).contiguous()
@@ -279,7 +265,6 @@ def unified_attention_with_output(
     value: torch.Tensor,
     output: torch.Tensor,
     kv_cache: torch.Tensor,
-    attn_type: str,
     layer_name: str,
 ) -> None:
     forward_context: ForwardContext = get_forward_context()
@@ -292,7 +277,6 @@ def unified_attention_with_output(
                       attn_metadata,
                       self._k_scale,
                       self._v_scale,
-                      attn_type=attn_type,
                       output=output)
 
 
@@ -302,7 +286,6 @@ def unified_attention_with_output_fake(
     value: torch.Tensor,
     output: torch.Tensor,
     kv_cache: torch.Tensor,
-    attn_type: str,
     layer_name: str,
 ) -> None:
     return

vllm/model_executor/models/bart.py

@@ -71,12 +71,8 @@ class BartLearnedPositionalEmbedding(VocabParallelEmbedding):
     def forward(
         self,
         positions: torch.Tensor,
-        attn_type: AttentionType,
     ) -> torch.Tensor:
         """`input_ids' shape is expected to be [bsz x seqlen]."""
-
-        assert attn_type != AttentionType.ENCODER_DECODER
-
         return super().forward(positions + self.offset)
 
 
@@ -180,7 +176,8 @@ class BartEncoderAttention(nn.Module):
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
-                              prefix=f"{prefix}.attn")
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.ENCODER)
 
     def forward(self, hidden_states: torch.Tensor, kv_cache: torch.Tensor,
                 attn_metadata: AttentionMetadata) -> torch.Tensor:
@@ -189,12 +186,7 @@ class BartEncoderAttention(nn.Module):
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
 
-        attn_output = self.attn(q,
-                                k,
-                                v,
-                                kv_cache,
-                                attn_metadata,
-                                attn_type=AttentionType.ENCODER)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
 
         output, _ = self.out_proj(attn_output)
         return output
@@ -264,7 +256,8 @@ class BartDecoderSelfAttention(nn.Module):
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
-                              prefix=f"{prefix}.attn")
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.DECODER)
 
     def forward(self, hidden_states: torch.Tensor, kv_cache: torch.Tensor,
                 attn_metadata: AttentionMetadata) -> torch.Tensor:
@@ -273,12 +266,7 @@ class BartDecoderSelfAttention(nn.Module):
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
 
-        attn_output = self.attn(q,
-                                k,
-                                v,
-                                kv_cache,
-                                attn_metadata,
-                                attn_type=AttentionType.DECODER)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
 
         output, _ = self.out_proj(attn_output)
         return output
@@ -348,7 +336,8 @@ class BartCrossAttention(nn.Module):
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
-                              prefix=f"{prefix}.attn")
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.ENCODER_DECODER)
 
     def forward(
         self,
@@ -372,12 +361,7 @@ class BartCrossAttention(nn.Module):
             _, k, v = qkv_enc.split([self.q_size, self.kv_size, self.kv_size],
                                     dim=-1)
 
-        attn_output = self.attn(q,
-                                k,
-                                v,
-                                kv_cache,
-                                attn_metadata,
-                                attn_type=AttentionType.ENCODER_DECODER)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
 
         output, _ = self.out_proj(attn_output)
         return output
@@ -644,10 +628,7 @@ class BartEncoder(nn.Module):
         # retrieve input_ids and inputs_embeds
         inputs_embeds = self.embed_tokens(input_ids)
 
-        embed_pos = self.embed_positions(
-            positions,
-            AttentionType.ENCODER,
-        )
+        embed_pos = self.embed_positions(positions)
         embed_pos = embed_pos.to(inputs_embeds.device)
 
         hidden_states = inputs_embeds + embed_pos
@@ -734,10 +715,7 @@ class BartDecoder(nn.Module):
         inputs_embeds = self.embed_tokens(decoder_input_ids)
 
         # embed positions
-        embed_pos = self.embed_positions(
-            decoder_positions,
-            AttentionType.DECODER,
-        )
+        embed_pos = self.embed_positions(decoder_positions)
         embed_pos = embed_pos.to(inputs_embeds.device)
 
         hidden_states = inputs_embeds + embed_pos

vllm/model_executor/models/bert.py

@@ -238,7 +238,8 @@ class BertSelfAttention(nn.Module):
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
-                              prefix=f"{prefix}.attn")
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.ENCODER_ONLY)
 
     def forward(
         self,
@@ -248,12 +249,7 @@ class BertSelfAttention(nn.Module):
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        output = self.attn(q,
-                           k,
-                           v,
-                           kv_cache,
-                           attn_metadata,
-                           attn_type=AttentionType.ENCODER_ONLY)
+        output = self.attn(q, k, v, kv_cache, attn_metadata)
         return output
 
 

vllm/model_executor/models/mllama.py

@@ -770,6 +770,7 @@ class MllamaTextCrossAttention(nn.Module):
             self.scaling,
             self.num_local_key_value_heads,
             prefix=f"{prefix}.attn",
+            attn_type=AttentionType.ENCODER_DECODER,
         )
 
     def forward(
@@ -805,13 +806,9 @@ class MllamaTextCrossAttention(nn.Module):
                                                kv_range_for_decode,
                                                attn_metadata)
         else:
-            output = self.attn(q.view(-1,
-                                      self.num_local_heads * self.head_dim),
-                               k,
-                               v,
-                               kv_cache,
-                               attn_metadata,
-                               attn_type=AttentionType.ENCODER_DECODER)
+            output = self.attn(
+                q.view(-1, self.num_local_heads * self.head_dim), k, v,
+                kv_cache, attn_metadata)
         out, _ = self.o_proj(output)
         return out
 

vllm/model_executor/models/qwen2.py

@@ -107,7 +107,8 @@ class Qwen2Attention(nn.Module):
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  rope_scaling: Optional[Tuple] = None,
-                 prefix: str = "") -> None:
+                 prefix: str = "",
+                 attn_type: str = AttentionType.DECODER) -> None:
         super().__init__()
         self.hidden_size = hidden_size
         tp_size = get_tensor_model_parallel_world_size()
@@ -160,7 +161,8 @@ class Qwen2Attention(nn.Module):
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
-                              prefix=f"{prefix}.attn")
+                              prefix=f"{prefix}.attn",
+                              attn_type=attn_type)
 
     def forward(
         self,
@@ -168,17 +170,11 @@ class Qwen2Attention(nn.Module):
         hidden_states: torch.Tensor,
         kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
-        attn_type: str = AttentionType.DECODER,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q,
-                                k,
-                                v,
-                                kv_cache,
-                                attn_metadata,
-                                attn_type=attn_type)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -197,6 +193,16 @@ class Qwen2DecoderLayer(nn.Module):
         # Requires transformers > 4.32.0
         rope_theta = getattr(config, "rope_theta", 1000000)
         rope_scaling = getattr(config, "rope_scaling", None)
+
+        # By default, Qwen2 uses causal attention as it is a decoder-only model.
+        # You can override the HF config with `is_causal=False` to enable
+        # bidirectional attention, which is used in some embedding models
+        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
+        if getattr(config, "is_causal", True):
+            attn_type = AttentionType.DECODER
+        else:
+            attn_type = AttentionType.ENCODER_ONLY
+
         self.self_attn = Qwen2Attention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -207,6 +213,7 @@ class Qwen2DecoderLayer(nn.Module):
             quant_config=quant_config,
             rope_scaling=rope_scaling,
             prefix=f"{prefix}.self_attn",
+            attn_type=attn_type,
         )
         self.mlp = Qwen2MLP(
             hidden_size=self.hidden_size,
@@ -220,15 +227,6 @@ class Qwen2DecoderLayer(nn.Module):
         self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                 eps=config.rms_norm_eps)
 
-        # By default, Qwen2 uses causal attention as it is a decoder-only model.
-        # You can override the HF config with `is_causal=False` to enable
-        # bidirectional attention, which is used in some embedding models
-        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
-        if getattr(config, "is_causal", True):
-            self._attn_type = AttentionType.DECODER
-        else:
-            self._attn_type = AttentionType.ENCODER_ONLY
-
     def forward(
         self,
         positions: torch.Tensor,
@@ -249,7 +247,6 @@ class Qwen2DecoderLayer(nn.Module):
             hidden_states=hidden_states,
             kv_cache=kv_cache,
             attn_metadata=attn_metadata,
-            attn_type=self._attn_type,
         )
 
         # Fully Connected

vllm/v1/attention/backends/flash_attn.py

@@ -89,6 +89,7 @@ class FlashAttentionImpl(AttentionImpl):
         kv_cache_dtype: str,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        attn_type: AttentionType = AttentionType.DECODER,
     ) -> None:
         if blocksparse_params is not None:
             raise ValueError(
@@ -119,6 +120,12 @@ class FlashAttentionImpl(AttentionImpl):
                 f"Head size {head_size} is not supported by FlashAttention. "
                 f"Supported head sizes are: {support_head_sizes}.")
 
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "FlashAttentionImpl")
+
     def forward(
         self,
         query: torch.Tensor,
@@ -128,7 +135,6 @@ class FlashAttentionImpl(AttentionImpl):
         attn_metadata: FlashAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: AttentionType = AttentionType.DECODER,
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention.
@@ -142,12 +148,6 @@ class FlashAttentionImpl(AttentionImpl):
         Returns:
             shape = [num_tokens, num_heads * head_size]
         """
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "FlashAttentionImpl")
-
         # NOTE(woosuk): FlashAttention does not support FP8 KV cache.
         assert k_scale == 1.0 and v_scale == 1.0, (
             "key/v_scale is not supported in FlashAttention.")