Move query quantization to attention layer for Flashinfer & Triton. (#26534)

Signed-off-by: adabeyta <aabeyta@redhat.com>
Signed-off-by: Adrian Abeyta <aabeyta@redhat.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

tests/compile/test_fusion_attn.py

@@ -421,7 +421,9 @@ def test_attention_quant_pattern(
     ]
     if any(attn_fusion_supported):
         # Check quantization ops in the graph before and after fusion
-        test_backend.check_before_ops([QUANT_OPS[quant_key]], fully_replaced=True)
+        # Note: fully_replaced=False because query quant ops remain in graph.
+        # Only output quant ops are fused into attention.
+        test_backend.check_before_ops([QUANT_OPS[quant_key]], fully_replaced=False)
 
     # access the underlying `AttnFusionPass` on the `LazyInitPass`
     assert attn_pass.pass_.matched_count == sum(attn_fusion_supported)

vllm/attention/backends/abstract.py

@@ -41,14 +41,6 @@ class AttentionBackend(ABC):
     # makes sure the output tensor is allocated inside the cudagraph.
     accept_output_buffer: bool = False
 
-    # Whether this backend supports receiving pre-quantized query input.
-    # If True, the attention layer will handle query quantization instead
-    # of the backend, allowing torch.compile to fuse quantization with
-    # previous operations.
-    # Needs to be worked through for all backends
-    # https://github.com/vllm-project/vllm/issues/25584
-    supports_quant_query_input: bool = False
-
     @staticmethod
     @abstractmethod
     def get_name() -> str:
@@ -199,6 +191,22 @@ class AttentionImpl(ABC, Generic[T]):
         """
         return False
 
+    def supports_quant_query_input(self) -> bool:
+        """
+        Check if this attention implementation supports pre-quantized query input.
+
+        When True, the attention layer will quantize queries before passing them
+        to this backend, allowing torch.compile to fuse the quantization with
+        previous operations. This is typically supported when using FP8 KV cache
+        with compatible attention kernels (e.g., TRT-LLM).
+        TODO add support to more backends:
+        https://github.com/vllm-project/vllm/issues/25584
+
+        Returns:
+            bool: True if the implementation can accept pre-quantized queries.
+        """
+        return False
+
 
 class MLAAttentionImpl(AttentionImpl[T], Generic[T]):
     @abstractmethod

vllm/attention/layer.py

@@ -36,6 +36,7 @@ from vllm.model_executor.models.vision import get_vit_attn_backend
 from vllm.platforms import current_platform
 from vllm.utils import GiB_bytes, direct_register_custom_op
 
+FP8_DTYPE = current_platform.fp8_dtype()
 logger = init_logger(__name__)
 USE_XFORMERS_OPS = None
 
@@ -304,7 +305,7 @@ class Attention(nn.Module, AttentionLayerBase):
         self.query_quant = None
         if (
             self.kv_cache_dtype.startswith("fp8")
-            and self.attn_backend.supports_quant_query_input
+            and self.impl.supports_quant_query_input()
         ):
             self.query_quant = QuantFP8(static=True, group_shape=GroupShape.PER_TENSOR)
 
@@ -329,7 +330,6 @@ class Attention(nn.Module, AttentionLayerBase):
         """
         if self.calculate_kv_scales:
             torch.ops.vllm.maybe_calc_kv_scales(query, key, value, self.layer_name)
-
         output_dtype = query.dtype
         if self.query_quant is not None:
             # quantizing with a simple torch operation enables
@@ -338,7 +338,10 @@ class Attention(nn.Module, AttentionLayerBase):
             # Otherwise queries are quantized using custom ops
             # which causes decoding overheads
             assert self.kv_cache_dtype in {"fp8", "fp8_e4m3"}
-            query, _ = self.query_quant(query, self._q_scale)
+
+            # check if query quantization is supported
+            if self.impl.supports_quant_query_input():
+                query, _ = self.query_quant(query, self._q_scale)
 
         if self.use_output:
             output_shape = output_shape if output_shape is not None else query.shape

vllm/v1/attention/backends/flash_attn.py

@@ -49,7 +49,6 @@ logger = init_logger(__name__)
 
 class FlashAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
-    supports_quant_query_input: bool = True
 
     @classmethod
     def get_supported_dtypes(cls) -> list[torch.dtype]:
@@ -494,6 +493,9 @@ class FlashAttentionImpl(AttentionImpl):
                 "heads in the layer"
             )
 
+    def supports_quant_query_input(self) -> bool:
+        return True
+
     def forward(
         self,
         layer: torch.nn.Module,

vllm/v1/attention/backends/flashinfer.py

@@ -16,7 +16,6 @@ from flashinfer.decode import _get_range_buf, trtllm_batch_decode_with_kv_cache
 from flashinfer.prefill import trtllm_batch_context_with_kv_cache
 from flashinfer.utils import FP4Tensor
 
-from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
@@ -828,6 +827,12 @@ class FlashInferImpl(AttentionImpl):
             and quant_key in (kFp8StaticTensorSym, kNvfp4Quant)
         )
 
+    def supports_quant_query_input(self) -> bool:
+        if flashinfer_disable_q_quantization():
+            return False
+
+        return self.support_trtllm_attn
+
     def forward(
         self,
         layer: torch.nn.Module,
@@ -859,6 +864,12 @@ class FlashInferImpl(AttentionImpl):
             # Profiling run.
             return output.fill_(0)
 
+        # Ensure query dtype matches the expected dtype from attention metadata
+        assert attn_metadata.q_data_type == query.dtype, (
+            f"Query dtype mismatch: expected {attn_metadata.q_data_type}, "
+            f"got {query.dtype}"
+        )
+
         if self.bmm1_scale is None:
             self.bmm1_scale = layer._q_scale_float * layer._k_scale_float * self.scale
 
@@ -899,15 +910,6 @@ class FlashInferImpl(AttentionImpl):
                 elif output.dtype == FP4_DTYPE:
                     self.o_sf_scale = layer._o_scale_float
 
-        # Insert FP8 quant for query
-        if attn_metadata.q_data_type == FP8_DTYPE:
-            num_tokens, num_heads, head_size = query.shape
-            query, _ = ops.scaled_fp8_quant(
-                query.reshape((num_tokens, num_heads * head_size)).contiguous(),
-                layer._q_scale,
-            )
-            query = query.reshape((num_tokens, num_heads, head_size))
-
         # IMPORTANT!
         # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
         # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead

vllm/v1/attention/backends/triton_attn.py

@@ -32,11 +32,6 @@ from vllm.v1.attention.backends.utils import (
 )
 from vllm.v1.kv_cache_interface import AttentionSpec
 
-if current_platform.is_cuda_alike():
-    from vllm import _custom_ops as ops
-elif current_platform.is_xpu():
-    from vllm._ipex_ops import ipex_ops as ops
-
 logger = init_logger(__name__)
 
 
@@ -210,6 +205,9 @@ class TritonAttentionImpl(AttentionImpl):
     def fused_output_quant_supported(self, quant_key: QuantKey):
         return quant_key == kFp8StaticTensorSym
 
+    def supports_quant_query_input(self) -> bool:
+        return current_platform.is_cuda()
+
     def __init__(
         self,
         num_heads: int,
@@ -338,19 +336,9 @@ class TritonAttentionImpl(AttentionImpl):
             if key_cache.dtype != self.fp8_dtype:
                 key_cache = key_cache.view(self.fp8_dtype)
                 value_cache = value_cache.view(self.fp8_dtype)
-            num_tokens, num_heads, head_size = query.shape
             assert layer._q_scale_float == 1.0, (
                 "A non 1.0 q_scale is not currently supported."
             )
-            if current_platform.is_cuda():
-                # Skip Q quantization on ROCm and XPU, enable this on cuda
-                # only, since dequantizing back to f32 in the attention kernel
-                # is not supported.
-                query, _ = ops.scaled_fp8_quant(
-                    query.reshape((num_tokens, num_heads * head_size)).contiguous(),
-                    layer._q_scale,
-                )
-                query = query.reshape((num_tokens, num_heads, head_size))
 
         cu_seqlens_q = attn_metadata.query_start_loc
         seqused_k = attn_metadata.seq_lens