add VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE support for VLLM_ROCM_USE_AITER_MHA

Author: k50112113

vllm/attention/layer.py

@@ -527,8 +527,9 @@ def unified_attention_with_output(
     kv_cache = self.kv_cache[forward_context.virtual_engine]
 
     from vllm.v1.attention.backends.triton_attn import TritonAttentionImpl
+    from vllm.v1.attention.backends.rocm_aiter_fa import AiterFlashAttentionImpl
     from vllm.v1.attention.backends.mla.rocm_aiter_mla import AiterMLAImpl
-    if VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE and (isinstance(self.impl, TritonAttentionImpl) or isinstance(self.impl, AiterMLAImpl)):
+    if VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE and (isinstance(self.impl, TritonAttentionImpl) or isinstance(self.impl, AiterFlashAttentionImpl) or isinstance(self.impl, AiterMLAImpl)):
         # fusing RoPE with flushing kv_cache operation
         assert hasattr(self.impl, "rotary_emb") and self.impl.rotary_emb is not None and positions is not None, f"rotary_emb not found in {self.impl=} and positions cannot be None"
         self.impl.forward(self,

vllm/model_executor/models/gpt_oss.py

@@ -39,7 +39,7 @@
 
 if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER:
     VLLM_ROCM_USE_AITER_TRITON_BF16_GEMM = envs.VLLM_ROCM_USE_AITER_TRITON_BF16_GEMM
-    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE and not envs.VLLM_ROCM_USE_AITER_MHA
+    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE
     VLLM_ROCM_USE_AITER_TRITON_FUSED_ADD_RMSNORM_PAD = envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ADD_RMSNORM_PAD
     if VLLM_ROCM_USE_AITER_TRITON_FUSED_ADD_RMSNORM_PAD:
         from aiter.ops.triton.fused_add_rmsnorm_pad import fused_add_rmsnorm_pad
@@ -51,7 +51,8 @@
     VLLM_ROCM_USE_AITER_TRITON_BF16_GEMM = False
 
 VLLM_ROCM_USE_AITER_MHA = envs.VLLM_ROCM_USE_AITER_MHA
-logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE=} {VLLM_ROCM_USE_AITER_MHA=}")
+logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE=}")
+logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_MHA=}")
 logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_FUSED_ADD_RMSNORM_PAD=}")
 logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_BF16_GEMM=}")
 

vllm/model_executor/models/llama.py

@@ -63,13 +63,14 @@
 
 if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER:
     from vllm.model_executor.layers.activation import VLLM_ROCM_USE_AITER_TRITON_SILU_MUL_FP8_QUANT
-    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE and not envs.VLLM_ROCM_USE_AITER_MHA
+    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE
 else:
     VLLM_ROCM_USE_AITER_TRITON_SILU_MUL_FP8_QUANT = False
     VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = False
 
 VLLM_ROCM_USE_AITER_MHA = envs.VLLM_ROCM_USE_AITER_MHA
-logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE=} {VLLM_ROCM_USE_AITER_MHA=}")
+logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE=}")
+logger.info(f"[Aiter] {VLLM_ROCM_USE_AITER_MHA=}")
 
 class LlamaMLP(nn.Module):
 

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -15,10 +15,24 @@
                                               AttentionMetadataBuilder,
                                               CommonAttentionMetadata)
 from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm import envs
+
+logger = init_logger(__name__)
 
 _PARTITION_SIZE_ROCM = 256
 
-if current_platform.is_rocm():
+if current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER:
+    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = (
+        envs.VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE
+    )
+    VLLM_USE_AITER_TRITON_ROPE = envs.VLLM_USE_AITER_TRITON_ROPE
+    if VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE:
+        from aiter.ops.triton.fused_kv_cache import fused_qk_rope_reshape_and_cache
+else:
+    VLLM_ROCM_USE_AITER_TRITON_FUSED_ROPE_ZEROS_KV_CACHE = False
+    VLLM_USE_AITER_TRITON_ROPE = False
+
+if current_platform.is_rocm():        
     import aiter
 
     from vllm.triton_utils import tl, triton
@@ -209,8 +223,6 @@ def flash_attn_varlen_func_fake(
                               flash_attn_varlen_func_fake,
                               dispatch_key=current_platform.dispatch_key)
 
-logger = init_logger(__name__)
-
 
 @dataclass
 class AiterFlashAttentionMetadata:
@@ -430,6 +442,7 @@ def forward(
         attn_metadata: AiterFlashAttentionMetadata,
         output: Optional[torch.Tensor] = None,
         output_scale: Optional[torch.Tensor] = None,
+        positions: torch.Tensor = None,
         output_block_scale: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with AiterFlashAttention.
@@ -469,24 +482,70 @@ def forward(
 
         num_actual_tokens = attn_metadata.num_actual_tokens
         key_cache, value_cache = kv_cache.unbind(0)
-        if self.kv_sharing_target_layer_name is None:
-            # Reshape the input keys and values and store them in the cache.
-            # Skip this if sharing KV cache with an earlier attention layer.
-            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
-            # not padded. However, we don't need to do key[:num_actual_tokens]
-            # and value[:num_actual_tokens] because the reshape_and_cache_flash
-            # op uses the slot_mapping's shape to determine the number of
-            # actual tokens.
-            torch.ops._C_cache_ops.reshape_and_cache_flash(
-                key,
-                value,
-                key_cache,
-                value_cache,
-                attn_metadata.slot_mapping,
-                self.kv_cache_dtype,
-                layer._k_scale,
-                layer._v_scale,
+        if positions is not None and query.shape[0] <= 256:
+            assert (
+                self.kv_sharing_target_layer_name is None
+            ), "self.kv_sharing_target_layer_name cannot be None"
+            assert hasattr(self, "rotary_emb"), f"rotary_emb not found in {self}"
+            cos_sin_cache = self.rotary_emb.cos_sin_cache
+            is_neox = self.rotary_emb.is_neox_style
+            cos, sin = cos_sin_cache.chunk(2, dim=-1)
+            is_fp8_kv_cache = self.kv_cache_dtype.startswith("fp8")
+            if is_fp8_kv_cache:
+                key_cache_og_dtype = key_cache.dtype
+                value_cache_og_dtype = value_cache.dtype
+                key_cache = key_cache.view(self.fp8_dtype)
+                value_cache = value_cache.view(self.fp8_dtype)
+            query, key, key_cache, value_cache, output = (
+                fused_qk_rope_reshape_and_cache(
+                    query,
+                    key,
+                    value,
+                    key_cache,
+                    value_cache,
+                    attn_metadata.slot_mapping,
+                    positions,
+                    cos,
+                    sin,
+                    layer._k_scale,
+                    layer._v_scale,
+                    is_neox,
+                    flash_layout=True,
+                    apply_scale=is_fp8_kv_cache,
+                    offs=None,
+                    q_out=query,
+                    k_out=key,
+                    output_zeros=True,
+                    zeros_out=output,
+                )
             )
+            if is_fp8_kv_cache:
+                key_cache = key_cache.view(key_cache_og_dtype)
+                value_cache = value_cache.view(value_cache_og_dtype)
+        else:
+            if positions is not None:
+                if VLLM_USE_AITER_TRITON_ROPE:
+                    query, key = self.rotary_emb.forward_cuda(positions, query, key)
+                else:
+                    query, key = self.rotary_emb(positions, query, key)
+            if self.kv_sharing_target_layer_name is None:
+                # Reshape the input keys and values and store them in the cache.
+                # Skip this if sharing KV cache with an earlier attention layer.
+                # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+                # not padded. However, we don't need to do key[:num_actual_tokens]
+                # and value[:num_actual_tokens] because the reshape_and_cache_flash
+                # op uses the slot_mapping's shape to determine the number of
+                # actual tokens.
+                torch.ops._C_cache_ops.reshape_and_cache_flash(
+                    key,
+                    value,
+                    key_cache,
+                    value_cache,
+                    attn_metadata.slot_mapping,
+                    self.kv_cache_dtype,
+                    layer._k_scale,
+                    layer._v_scale,
+                )
 
         if self.kv_cache_dtype.startswith("fp8"):
             if current_platform.is_fp8_fnuz():

vllm/v1/attention/backends/triton_attn.py

@@ -295,8 +295,6 @@ def forward(
         output: Optional[torch.Tensor] = None,
         output_scale: Optional[torch.Tensor] = None,
         positions: torch.Tensor = None,
-        cos_sin_cache: torch.Tensor = None,
-        is_neox: bool = False,
         output_block_scale: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention.