[torch.compile] support all attention backends (vllm-project#10558)

Signed-off-by: youkaichao <youkaichao@gmail.com>

Author: youkaichao

tests/kernels/test_encoder_decoder_attn.py

@@ -18,8 +18,10 @@
 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)
+from vllm.config import VllmConfig
 from vllm.forward_context import set_forward_context
 from vllm.platforms import current_platform
+from vllm.plugins import set_current_vllm_config
 
 # List of support backends for encoder/decoder models
 LIST_ENC_DEC_SUPPORTED_BACKENDS = [_Backend.XFORMERS, _Backend.FLASH_ATTN]
@@ -594,6 +596,7 @@ def _run_encoder_attention_test(
     encoder_test_params: PhaseTestParameters,
     attn_metadata: AttentionMetadata,
     test_pt: TestPoint,
+    vllm_config: VllmConfig,
 ) -> torch.Tensor:
     '''
     Run encoder attention.
@@ -623,7 +626,7 @@ def _run_encoder_attention_test(
     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):
+    with set_forward_context(attn_metadata, vllm_config):
         # In the test setup the shape of the query is
         # [batch_size, seq_len, num_heads, head_size]. However
         # the attention backend expect the shape to be
@@ -648,6 +651,7 @@ def _run_decoder_self_attention_test(
     decoder_test_params: PhaseTestParameters,
     attn_metadata: AttentionMetadata,
     test_pt: TestPoint,
+    vllm_config: VllmConfig,
 ) -> torch.Tensor:
     '''
     Run decoder self-attention test.
@@ -677,7 +681,7 @@ def _run_decoder_self_attention_test(
     kv_cache = test_rsrcs.kv_cache
     packed_qkv = decoder_test_params.packed_qkvo.packed_qkv
     assert packed_qkv is not None
-    with set_forward_context(attn_metadata):
+    with set_forward_context(attn_metadata, vllm_config):
         # In the test setup the shape of the query is
         # [batch_size, seq_len, num_heads, head_size]. However
         # the attention backend expect the shape to be
@@ -701,6 +705,7 @@ def _run_encoder_decoder_cross_attention_test(
     cross_test_params: Optional[PhaseTestParameters],
     attn_metadata: AttentionMetadata,
     test_pt: TestPoint,
+    vllm_config: VllmConfig,
 ) -> torch.Tensor:
     '''
     Run encoder/decoder cross-attention test.
@@ -748,7 +753,7 @@ def _run_encoder_decoder_cross_attention_test(
         cross_pckd_qkv = cross_test_params.packed_qkvo.packed_qkv
         key = (None if cross_pckd_qkv is None else cross_pckd_qkv.key)
         value = (None if cross_pckd_qkv is None else cross_pckd_qkv.value)
-    with set_forward_context(attn_metadata):
+    with set_forward_context(attn_metadata, vllm_config):
         # In the test setup the shape of the query is
         # [batch_size, seq_len, num_heads, head_size]. However
         # the attention backend expect the shape to be
@@ -839,7 +844,9 @@ def test_encoder_only(
 
         # Attention scale factor, attention backend instance, attention wrapper
         # instance, KV cache init
-        test_rsrcs = _make_test_resources(test_pt)
+        vllm_config = VllmConfig()
+        with set_current_vllm_config(vllm_config):
+            test_rsrcs = _make_test_resources(test_pt)
 
         # Construct encoder attention test params (only used
         # during prefill)
@@ -863,7 +870,8 @@ def test_encoder_only(
             test_rsrcs.attn,
             enc_test_params,
             prephase_attn_metadata,
-            test_pt=test_pt))
+            test_pt=test_pt,
+            vllm_config=vllm_config))
 
         # - Is encoder attention result correct?
         assert_actual_matches_ideal(enc_test_params, enc_pckd_act_out,
@@ -960,7 +968,9 @@ def test_e2e_enc_dec_attn(
 
         # Attention scale factor, attention backend instance, attention wrapper
         # instance, KV cache init
-        test_rsrcs = _make_test_resources(test_pt)
+        vllm_config = VllmConfig()
+        with set_current_vllm_config(vllm_config):
+            test_rsrcs = _make_test_resources(test_pt)
 
         # Construct encoder attention test params (only used
         # during prefill)
@@ -1011,7 +1021,8 @@ def test_e2e_enc_dec_attn(
         enc_pckd_act_out = _run_encoder_attention_test(test_rsrcs.attn,
                                                        enc_test_params,
                                                        prephase_attn_metadata,
-                                                       test_pt=test_pt)
+                                                       test_pt=test_pt,
+                                                       vllm_config=vllm_config)
 
         # - Is encoder attention result correct?
         assert_actual_matches_ideal(enc_test_params, enc_pckd_act_out,
@@ -1023,7 +1034,8 @@ def test_e2e_enc_dec_attn(
             test_rsrcs,
             prephase_dec_test_params,
             prephase_attn_metadata,
-            test_pt=test_pt)
+            test_pt=test_pt,
+            vllm_config=vllm_config)
 
         # - Is prefill decoder self-attention correct?
         assert_actual_matches_ideal(prephase_dec_test_params,
@@ -1037,7 +1049,8 @@ def test_e2e_enc_dec_attn(
             prephase_dec_test_params,
             prephase_cross_test_params,
             prephase_attn_metadata,
-            test_pt=test_pt)
+            test_pt=test_pt,
+            vllm_config=vllm_config)
 
         # - Is prefill encoder/decoder cross-attention correct?
         assert_actual_matches_ideal(prephase_cross_test_params,
@@ -1061,7 +1074,8 @@ def test_e2e_enc_dec_attn(
             test_rsrcs,
             decphase_dec_test_params,
             decphase_attn_metadata,
-            test_pt=test_pt)
+            test_pt=test_pt,
+            vllm_config=vllm_config)
 
         # - Is decode-phase decoder self-attention correct?
         assert_actual_matches_ideal(decphase_dec_test_params,
@@ -1075,7 +1089,8 @@ def test_e2e_enc_dec_attn(
             decphase_dec_test_params,
             None,
             decphase_attn_metadata,
-            test_pt=test_pt)
+            test_pt=test_pt,
+            vllm_config=vllm_config)
 
         # - Is decode-phase encoder/decoder cross-attention correct?
         assert_actual_matches_ideal(decphase_cross_test_params,

vllm/attention/backends/abstract.py

@@ -1,7 +1,6 @@
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from dataclasses import dataclass, fields
-from enum import Enum, auto
 from typing import (TYPE_CHECKING, Any, Dict, Generic, List, Optional, Set,
                     Tuple, Type, TypeVar)
 
@@ -15,13 +14,19 @@
                                                ModelRunnerInputBuilderBase)
 
 
-class AttentionType(Enum):
-    DECODER = auto()  # Decoder attention between previous layer Q/K/V
-    ENCODER = auto(
-    )  # Encoder attention between previous layer Q/K/V for encoder-decoder
-    ENCODER_ONLY = auto()  # Encoder attention between previous layer Q/K/V
-    ENCODER_DECODER = auto(
-    )  # Attention between dec. Q and enc. K/V for encoder-decoder
+class AttentionType:
+    """
+    Attention type.
+    Use string to be compatible with `torch.compile`.
+    """
+    # Decoder attention between previous layer Q/K/V
+    DECODER = "decoder"
+    # Encoder attention between previous layer Q/K/V for encoder-decoder
+    ENCODER = "encoder"
+    # Encoder attention between previous layer Q/K/V
+    ENCODER_ONLY = "encoder_only"
+    # Attention between dec. Q and enc. K/V for encoder-decoder
+    ENCODER_DECODER = "encoder_decoder"
 
 
 class AttentionBackend(ABC):
@@ -241,6 +246,6 @@ def forward(
         attn_metadata: T,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: AttentionType = AttentionType.DECODER,
+        attn_type: str = AttentionType.DECODER,
     ) -> torch.Tensor:
         raise NotImplementedError

vllm/attention/backends/blocksparse_attn.py

@@ -354,7 +354,7 @@ def forward(
         attn_metadata: BlocksparseFlashAttentionMetadata,
         k_scale: float = 1.0,
         v_scale: float = 1.0,
-        attn_type: AttentionType = AttentionType.DECODER,
+        attn_type: str = AttentionType.DECODER,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention and PagedAttention.