[ROCm] AITER fused RoPE+KVCache (#33443)

Signed-off-by: Rohan138 <rohanpotdar138@gmail.com>
Signed-off-by: charlifu <charlifu@amd.com>
Signed-off-by: Rohan Potdar <66227218+Rohan138@users.noreply.github.com>
Co-authored-by: charlifu <charlifu@amd.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
Co-authored-by: Douglas Lehr <91553416+dllehr-amd@users.noreply.github.com>

tests/compile/passes/test_functionalization.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import copy
+
 import pytest
 import torch
 
-import vllm.envs as envs
 from tests.compile.backend import TestBackend
 from tests.utils import TestFP8Layer
 from vllm.compilation.passes.fusion.act_quant_fusion import (
@@ -31,6 +32,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 )
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
+from vllm.utils.torch_utils import direct_register_custom_op
 
 TEST_FP8 = current_platform.supports_fp8()
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -198,23 +200,82 @@ class TestRotaryEmbeddingSliceScatter(torch.nn.Module):
         return [torch.ops.aten.slice_scatter.default]
 
 
-MODELS = [
-    TestSiluMul,
-    TestFusedAddRMSNorm,
-    TestRotaryEmbedding,
-    TestRotaryEmbeddingSliceScatter,
-]
+class TestFunctionWithMutatedArgsAndReturn(torch.nn.Module):
+    OP_REGISTERED = False
+
+    def __init__(self):
+        super().__init__()
+        self.register_test_custom_op()
+
+    @classmethod
+    def register_test_custom_op(cls):
+        if not cls.OP_REGISTERED:
+
+            def function_with_mutated_args_and_return_impl(
+                x: torch.Tensor,
+            ) -> torch.Tensor:
+                ret = x + 1
+                x.add_(2)
+                return ret
+
+            def function_with_mutated_args_and_return_fake(
+                x: torch.Tensor,
+            ) -> torch.Tensor:
+                return torch.empty_like(x)
+
+            direct_register_custom_op(
+                op_name="function_with_mutated_args_and_return",
+                op_func=function_with_mutated_args_and_return_impl,
+                mutates_args=["x"],
+                fake_impl=function_with_mutated_args_and_return_fake,
+            )
+
+            cls.OP_REGISTERED = True
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        # Clone x to avoid mutating the original tensor
+        ret = torch.ops.vllm.function_with_mutated_args_and_return(x)
+        return x, ret
+
+    def example_inputs(self, num_tokens=32):
+        hidden_states = torch.randn(num_tokens)
+        return (hidden_states,)
+
+    def ops_in_model(self, do_fusion):
+        return [torch.ops.vllm.function_with_mutated_args_and_return.default]
+
+    def ops_not_in_model(self):
+        return []
+
+
+MODELS_AND_DO_FUSION = {
+    TestSiluMul: [True, False],
+    TestFusedAddRMSNorm: [True, False],
+    TestRotaryEmbedding: [False],
+    TestRotaryEmbeddingSliceScatter: [False],
+    TestFunctionWithMutatedArgsAndReturn: [False],
+}
 
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("model_class", MODELS)
-@pytest.mark.parametrize("do_fusion", [True, False])
-@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE != "cuda", reason="Only test on CUDA")
+@pytest.mark.parametrize(
+    "model_class, do_fusion",
+    [
+        (model_class, do_fusion)
+        for model_class, fusions in MODELS_AND_DO_FUSION.items()
+        for do_fusion in fusions
+    ],
+)
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="Only test on cuda and rocm platform",
+)
 def test_fix_functionalization(
     model_class: torch.nn.Module, do_fusion: bool, dtype: torch.dtype
 ):
     torch.set_default_device("cuda")
     torch.set_default_dtype(dtype)
+    torch.manual_seed(0)
 
     vllm_config = VllmConfig(
         model_config=ModelConfig(dtype=dtype),
@@ -246,8 +307,14 @@ def test_fix_functionalization(
         backend_no_func = TestBackend(*passes)
 
         model = model_class()
-        torch.compile(model, backend=backend_func)(*model.example_inputs())
-        torch.compile(model, backend=backend_no_func)(*model.example_inputs())
+        inputs_func = model.example_inputs()
+        inputs_no_func = copy.deepcopy(inputs_func)
+        model_func = model_class()
+        model_no_func = copy.deepcopy(model_func)
+        model_func = torch.compile(model_func, backend=backend_func)
+        model_no_func = torch.compile(model_no_func, backend=backend_no_func)
+        model_func(*inputs_func)
+        model_no_func(*inputs_no_func)
 
         # check if the functionalization pass is applied
         for op in model.ops_in_model(do_fusion):
@@ -265,3 +332,8 @@ def test_fix_functionalization(
                     found[op] = True
         assert all(found[op] for op in model.ops_in_model(do_fusion))
         assert all(not found.get(op) for op in model.ops_not_in_model())
+
+        # TODO (Rohan138): compare the outputs from model_func and model_no_func
+        # currently runs into errors while comparing `TestFusedAddRMSNorm`
+        # Linked issue: https://github.com/vllm-project/vllm/issues/34996
+        # torch.testing.assert_close(outputs_func, outputs_no_func)

tests/compile/passes/test_rope_kvcache_fusion.py

@@ -0,0 +1,325 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+import vllm.config
+from tests.compile.backend import TestBackend
+from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
+from vllm._aiter_ops import is_aiter_found_and_supported, rocm_aiter_ops
+from vllm.compilation.passes.fusion.matcher_utils import ROTARY_OP
+from vllm.compilation.passes.fusion.rope_kvcache_fusion import RopeKVCacheFusionPass
+from vllm.compilation.passes.utility.noop_elimination import NoOpEliminationPass
+from vllm.compilation.passes.utility.post_cleanup import PostCleanupPass
+from vllm.compilation.passes.utility.scatter_split_replace import (
+    ScatterSplitReplacementPass,
+)
+from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
+from vllm.config import (
+    CacheConfig,
+    CompilationConfig,
+    CompilationMode,
+    ModelConfig,
+    PassConfig,
+    VllmConfig,
+)
+from vllm.forward_context import get_forward_context, set_forward_context
+from vllm.model_executor.layers.attention import Attention
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+from vllm.platforms import current_platform
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    CommonAttentionMetadata,
+)
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+INDEX_SELECT_OP = torch.ops.aten.index.Tensor
+VLLM_UNIFIED_KV_CACHE_UPDATE_OP = torch.ops.vllm.unified_kv_cache_update
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+class QKRoPEKVCacheTestModel(torch.nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        attn_backend: AttentionBackendEnum,
+        num_heads: int,
+        num_kv_heads: int,
+        head_size: int,
+        is_neox: bool,
+        dtype: torch.dtype,
+        device: torch.device,
+        prefix: str = "model.layers.0.self_attn.attn",
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_size = head_size
+        self.block_size = vllm_config.cache_config.block_size
+        self.q_size = num_heads * head_size
+        self.kv_size = num_kv_heads * head_size
+        self.is_neox = is_neox
+        self.dtype = dtype
+        self.device = device
+        self.layer_name = prefix
+
+        self.rotary_emb = RotaryEmbedding(
+            head_size,
+            rotary_dim=head_size,
+            max_position_embeddings=4096,
+            base=10000,
+            is_neox_style=is_neox,
+            dtype=self.dtype,
+        )
+
+        # Whether to check for the RoPE custom op or component index_select
+        self.enable_rope_custom_op = self.rotary_emb.enabled()
+
+        # Register layer metadata for the fusion pass via Attention.
+        self.attn = Attention(
+            num_heads=num_heads,
+            head_size=head_size,
+            scale=1.0 / head_size**0.5,
+            num_kv_heads=num_kv_heads,
+            cache_config=vllm_config.cache_config,
+            quant_config=vllm_config.quant_config,
+            prefix=prefix,
+            attn_backend=attn_backend.get_class(),
+        )
+        self.attn_backend: type[AttentionBackend] = self.attn.get_attn_backend()
+        assert not self.attn_backend.forward_includes_kv_cache_update, (
+            f"Attention backend {self.attn_backend} does not support fuse_rope_kvcache."
+        )
+        self.attn._k_scale = self.attn._k_scale.to(device)
+        self.attn._v_scale = self.attn._v_scale.to(device)
+
+        kv_cache_dtype_str = vllm_config.cache_config.cache_dtype
+        self.kv_cache_dtype = (
+            FP8_DTYPE if kv_cache_dtype_str.startswith("fp8") else self.dtype
+        )
+
+        # Initialize attn MetadataBuilder
+        self.builder = self.attn.attn_backend.get_builder_cls()(
+            kv_cache_spec=AttentionSpec(
+                block_size=self.block_size,
+                num_kv_heads=self.num_kv_heads,
+                head_size=head_size,
+                dtype=self.kv_cache_dtype,
+            ),
+            layer_names=[self.attn.layer_name],
+            vllm_config=vllm_config,
+            device=device,
+        )
+
+    def build_attn_metadata(self, batch_size: int) -> CommonAttentionMetadata:
+        """Initialize attention metadata."""
+        # Create common attn metadata
+        batch_spec = BatchSpec(seq_lens=[1] * batch_size, query_lens=[1] * batch_size)
+        common_attn_metadata = create_common_attn_metadata(
+            batch_spec, self.block_size, self.device, arange_block_indices=True
+        )
+
+        max_blocks = (max(batch_spec.seq_lens) + self.block_size - 1) // self.block_size
+        num_blocks = batch_size * max_blocks
+
+        # Fetch the attention backend and kv cache shape and stride order
+        attn_backend = self.attn.attn_backend
+        kv_cache_shape = attn_backend.get_kv_cache_shape(
+            num_blocks, self.block_size, self.num_kv_heads, self.head_size
+        )
+        try:
+            kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
+        except (AttributeError, NotImplementedError):
+            kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
+
+        kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
+        inv_order = [
+            kv_cache_stride_order.index(i) for i in range(len(kv_cache_stride_order))
+        ]
+
+        # Create dummy KV cache
+        raw_tensor = torch.zeros(
+            2 * num_blocks * self.block_size * self.num_kv_heads * self.head_size,
+            dtype=self.kv_cache_dtype,
+            device=self.device,
+        )
+        raw_tensor = raw_tensor.view(kv_cache_shape)
+        kv_cache = raw_tensor.permute(*inv_order)
+
+        self.attn.kv_cache = [kv_cache]
+
+        # Build attn metadata
+        attn_metadata = self.builder.build(
+            common_prefix_len=0, common_attn_metadata=common_attn_metadata
+        )
+
+        return attn_metadata
+
+    def forward(
+        self, qkv: torch.Tensor, positions: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # Create copy so inplace ops do not modify the original tensors
+        qkv = qkv.clone()
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+
+        # Instead of a full forward pass, match only the KV cache update op here
+        q = q.view(-1, self.num_heads, self.head_size)
+        k = k.view(-1, self.num_kv_heads, self.head_size)
+        v = v.view(-1, self.num_kv_heads, self.head_size)
+        kv_cache_dummy_dep = torch.ops.vllm.unified_kv_cache_update(
+            k, v, self.layer_name
+        )
+        return q, k, v, kv_cache_dummy_dep
+
+    def ops_in_model_before(self) -> list[torch._ops.OpOverload]:
+        ops = []
+        if self.enable_rope_custom_op:
+            ops.append(ROTARY_OP)
+        else:
+            ops.append(INDEX_SELECT_OP)
+        ops.append(torch.ops.vllm.unified_kv_cache_update.default)
+        return ops
+
+    def ops_in_model_after(self) -> list[torch._ops.OpOverload]:
+        return [torch.ops.vllm.fused_rope_and_unified_kv_cache_update.default]
+
+
+@pytest.mark.parametrize(
+    "attn_backend",
+    [
+        AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN,
+        AttentionBackendEnum.TRITON_ATTN,
+        AttentionBackendEnum.ROCM_ATTN,
+    ],
+)
+@pytest.mark.parametrize("enable_rope_custom_op", [True])  # [True, False])
+@pytest.mark.parametrize("num_heads", [64])
+@pytest.mark.parametrize("num_kv_heads", [8])
+@pytest.mark.parametrize("head_size", [64])
+@pytest.mark.parametrize("block_size", [16])
+@pytest.mark.parametrize("is_neox", [True, False])
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])
+@pytest.mark.skipif(
+    not is_aiter_found_and_supported(),
+    reason="Only test on ROCm with AITER installed and supported",
+)
+def test_rope_kvcache_fusion(
+    attn_backend: AttentionBackendEnum,
+    enable_rope_custom_op: bool,
+    num_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    block_size: int,
+    is_neox: bool,
+    dtype: torch.dtype,
+    kv_cache_dtype: str,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    torch.set_default_device("cuda")
+    torch.set_default_dtype(dtype)
+    torch.manual_seed(0)
+
+    custom_ops: list[str] = []
+    if enable_rope_custom_op:
+        custom_ops.append("+rotary_embedding")
+
+    vllm_config = VllmConfig(
+        model_config=ModelConfig(dtype=dtype),
+        cache_config=CacheConfig(
+            block_size=block_size,
+            cache_dtype=kv_cache_dtype,
+        ),
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            custom_ops=custom_ops,
+            pass_config=PassConfig(
+                fuse_rope_kvcache=True,
+                eliminate_noops=True,
+            ),
+        ),
+    )
+
+    with vllm.config.set_current_vllm_config(vllm_config), monkeypatch.context() as m:
+        m.setenv("VLLM_ROCM_USE_AITER", "1")
+        rocm_aiter_ops.refresh_env_variables()
+
+        model = QKRoPEKVCacheTestModel(
+            vllm_config=vllm_config,
+            attn_backend=attn_backend,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            head_size=head_size,
+            is_neox=is_neox,
+            dtype=dtype,
+            device=torch.get_default_device(),
+        )
+
+        fusion_pass = RopeKVCacheFusionPass(vllm_config)
+        passes = [
+            NoOpEliminationPass(vllm_config),
+            SplitCoalescingPass(vllm_config),
+            ScatterSplitReplacementPass(vllm_config),
+            fusion_pass,
+            PostCleanupPass(vllm_config),
+        ]
+        backend = TestBackend(*passes)
+
+        T = 5
+
+        qkv = torch.randn(
+            T, num_heads * head_size + 2 * num_kv_heads * head_size, dtype=dtype
+        )
+        pos = torch.arange(T, dtype=torch.long)
+
+        qkv_unfused = qkv.clone()
+        pos_unfused = pos.clone()
+
+        with set_forward_context(None, vllm_config):
+            forward_context = get_forward_context()
+            attn_metadata = model.build_attn_metadata(T)
+            forward_context.slot_mapping = {
+                model.layer_name: attn_metadata.slot_mapping
+            }
+            q_unfused, k_unfused, v_unfused, dummy = model(qkv_unfused, pos_unfused)
+            attn_layer = forward_context.no_compile_layers[model.layer_name]
+            kv_cache_unfused = attn_layer.kv_cache[forward_context.virtual_engine]
+        del dummy
+
+        torch._dynamo.mark_dynamic(qkv, 0)
+        torch._dynamo.mark_dynamic(pos, 0)
+        with set_forward_context(None, vllm_config):
+            model_fused = torch.compile(model, backend=backend)
+            forward_context = get_forward_context()
+            attn_metadata = model_fused.build_attn_metadata(T)
+            forward_context.slot_mapping = {
+                model.layer_name: attn_metadata.slot_mapping
+            }
+            q_fused, k_fused, v_fused, dummy = model_fused(qkv, pos)
+            attn_layer = forward_context.no_compile_layers[model.layer_name]
+            kv_cache_fused = attn_layer.kv_cache[forward_context.virtual_engine]
+        del dummy
+
+        assert fusion_pass.matched_count == 1
+
+        backend.check_before_ops(model.ops_in_model_before())
+        backend.check_after_ops(model.ops_in_model_after())
+
+        if dtype == torch.float16:
+            ATOL, RTOL = (2e-3, 2e-3)
+        else:
+            ATOL, RTOL = (1e-2, 1e-2)
+
+        torch.testing.assert_close(q_unfused, q_fused, atol=ATOL, rtol=RTOL)
+        torch.testing.assert_close(k_unfused, k_fused, atol=ATOL, rtol=RTOL)
+        torch.testing.assert_close(v_unfused, v_fused, atol=ATOL, rtol=RTOL)
+        # Cannot compare fp8_* directly here, cast to model dtype instead
+        torch.testing.assert_close(
+            kv_cache_unfused.view(dtype),
+            kv_cache_fused.view(dtype),
+            atol=ATOL,
+            rtol=RTOL,
+        )

tests/compile/passes/test_scatter_split_replace.py

@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+import torch.nn as nn
+
+import vllm
+from tests.compile.backend import TestBackend
+from vllm.compilation.passes.utility.scatter_split_replace import (
+    ScatterSplitReplacementPass,
+)
+from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
+from vllm.config import CompilationConfig, CompilationMode, VllmConfig
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+
+
+class ScatterSplitReplacementModel(nn.Module):
+    """Model with a rope+getitem+slice_scatter+split_with_sizes sequence."""
+
+    def __init__(
+        self,
+        num_heads: int,
+        num_kv_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+    ):
+        super().__init__()
+        self.q_size = num_heads * head_size
+        self.kv_size = num_kv_heads * head_size
+
+        self.rotary_emb = RotaryEmbedding(
+            head_size,
+            rotary_dim=head_size,
+            max_position_embeddings=4096,
+            base=10000,
+            is_neox_style=True,
+            dtype=dtype,
+        )
+
+    def forward(self, qkv: torch.Tensor, positions: torch.Tensor):
+        # Create copy so inplace ops do not modify the original tensors
+        qkv = qkv.clone()
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        q = q + 1
+        k = k + 2
+        v = v + 3
+        return q, k, v
+
+    def ops_in_model_before(self) -> list[torch._ops.OpOverload]:
+        return [
+            torch.ops.aten.slice_scatter.default,
+            torch.ops.aten.split_with_sizes.default,
+            torch.ops.aten.getitem.default,
+        ]
+
+    def ops_in_model_after(self) -> list[torch._ops.OpOverload]:
+        return [torch.ops.aten.getitem.default]
+
+
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+def test_scatter_split_replace(dtype):
+    torch.set_default_device("cuda")
+    torch.set_default_dtype(dtype)
+    torch.manual_seed(0)
+
+    num_heads = 8
+    num_kv_heads = 4
+    head_size = 64
+
+    vllm_config = VllmConfig(
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            custom_ops=["+rotary_embedding"],
+        ),
+    )
+    with vllm.config.set_current_vllm_config(vllm_config):
+        # ScatterSplitReplacementPass requires SplitCoalescingPass to be run before it
+        coalesce_pass = SplitCoalescingPass(vllm_config)
+        replace_pass = ScatterSplitReplacementPass(vllm_config)
+        passes = [coalesce_pass, replace_pass]
+        backend = TestBackend(*passes)
+
+        model = ScatterSplitReplacementModel(num_heads, num_kv_heads, head_size, dtype)
+
+        T = 5
+        qkv = torch.randn(
+            T, num_heads * head_size + 2 * num_kv_heads * head_size, dtype=dtype
+        )
+        pos = torch.arange(T, dtype=torch.long)
+
+        qkv_eager = qkv.clone()
+        pos_eager = pos.clone()
+        result_eager = model(qkv_eager, pos_eager)
+
+        torch._dynamo.mark_dynamic(qkv, 0)
+        torch._dynamo.mark_dynamic(pos, 0)
+
+        model_compiled = torch.compile(model, backend=backend)
+        result_compiled = model_compiled(qkv, pos)
+
+        for eager, compiled in zip(result_eager, result_compiled):
+            torch.testing.assert_close(eager, compiled)
+
+        assert backend.op_count(torch.ops.aten.slice_scatter.default) == 0
+        assert backend.op_count(torch.ops.aten.split_with_sizes.default) == 1

tests/v1/attention/test_attention_backends.py

@@ -179,7 +179,7 @@ def create_and_prepopulate_kv_cache(
         block_table[i, :num_blocks_for_seq] = inv_perm[start:end]
         start_block_idx += num_blocks_for_seq
 
-        # Create a realistic slot mapping that corresponds to the block table
+    # Create a realistic slot mapping that corresponds to the block table
     for i in range(batch_size):
         token_offsets = torch.arange(int(query_lens[i])) + int(context_lens[i])
         block_indices = token_offsets // block_size

vllm/_aiter_ops.py

@@ -1518,6 +1518,45 @@ class rocm_aiter_ops:
         query = query.view(query_shape)
         key = key.view(key_shape)
 
+    @staticmethod
+    def triton_rope_and_cache(
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        positions: torch.Tensor,
+        cos_sin_cache: torch.Tensor,
+        is_neox: bool,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        layer_slot_mapping: torch.Tensor,
+        k_scale: torch.Tensor,
+        v_scale: torch.Tensor,
+        flash_layout: bool,
+        apply_scale: bool,
+    ):
+        from aiter.ops.triton.fused_kv_cache import fused_qk_rope_reshape_and_cache
+
+        cos, sin = cos_sin_cache.chunk(2, dim=-1)
+        fused_qk_rope_reshape_and_cache(
+            query,
+            key,
+            value,
+            key_cache,
+            value_cache,
+            layer_slot_mapping,
+            positions,
+            cos,
+            sin,
+            k_scale,
+            v_scale,
+            is_neox,
+            flash_layout=flash_layout,
+            apply_scale=apply_scale,
+            q_out=query,
+            k_out=key,
+            output_zeros=False,
+        )
+
     @staticmethod
     def batched_gemm_a16wfp4(
         X: torch.Tensor,

vllm/compilation/passes/fusion/rope_kvcache_fusion.py

@@ -0,0 +1,230 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+import torch._inductor.pattern_matcher as pm
+from torch import fx
+from torch._higher_order_ops import auto_functionalized
+from torch._inductor.fx_passes.post_grad import view_to_reshape
+from torch._inductor.pattern_matcher import PatternMatcherPass
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.config.utils import Range
+from vllm.logger import init_logger
+from vllm.model_executor.layers.attention.attention import (
+    Attention,
+    get_attention_context,
+)
+from vllm.utils.torch_utils import direct_register_custom_op
+
+from ..inductor_pass import enable_fake_mode
+from ..vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
+from .matcher_utils import (
+    MatcherRotaryEmbedding,
+)
+from .rms_quant_fusion import (
+    empty_bf16,
+    empty_i64,
+)
+
+logger = init_logger(__name__)
+
+
+def fused_rope_and_unified_kv_cache_update_impl(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    is_neox: bool,
+    layer_name: str = "",
+) -> torch.Tensor:
+    """
+    This impl fetches the KV cache and slot mapping from the forward context,
+    then calls the layer impl's `AttentionImpl.do_rope_and_kv_cache_update` method.
+    It also returns a dummy tensor, similar to `Attention.unified_kv_cache_update`,
+    that is passed to unified_attention to signal a side effect and
+    the data dependency between them to ensure torch.compile preserves ordering.
+    """
+    _, attn_layer, kv_cache, layer_slot_mapping = get_attention_context(layer_name)
+    if layer_slot_mapping is not None:
+        attn_layer.impl.do_rope_and_kv_cache_update(
+            attn_layer,
+            query,
+            key,
+            value,
+            positions,
+            cos_sin_cache,
+            is_neox,
+            kv_cache,
+            layer_slot_mapping,
+        )
+
+    return torch.empty(0, device=kv_cache.device, dtype=kv_cache.dtype)
+
+
+def fused_rope_and_unified_kv_cache_update_fake(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    positions: torch.Tensor,
+    cos_sin_cache: torch.Tensor,
+    is_neox: bool,
+    layer_name: str = "",
+) -> torch.Tensor:
+    return torch.empty(0, device=query.device, dtype=query.dtype)
+
+
+direct_register_custom_op(
+    op_name="fused_rope_and_unified_kv_cache_update",
+    op_func=fused_rope_and_unified_kv_cache_update_impl,
+    mutates_args=["query", "key"],
+    fake_impl=fused_rope_and_unified_kv_cache_update_fake,
+)
+
+
+class RopeReshapeKVCachePattern:
+    """
+    This pattern matches the following unfused inplace ops:
+      q, k = rotary_embedding(positions, q, k, head_size, cos_sin_cache, is_neox)
+      kv_cache_dummy = unified_kv_cache_update(k, v, layer_name)
+
+    and replaces it with the fused inplace op:
+      kv_cache_dummy = fused_rope_and_unified_kv_cache_update(
+        q, k, v, positions, cos_sin_cache, is_neox, layer_name
+      )
+    """
+
+    FUSED_OP = torch.ops.vllm.fused_rope_and_unified_kv_cache_update.default
+
+    def __init__(
+        self,
+        layer: Attention,
+        is_neox: bool,
+    ) -> None:
+        self.layer_name = layer.layer_name
+        self.num_heads = layer.num_heads
+        self.num_kv_heads = layer.num_kv_heads
+        self.head_size = layer.head_size
+        self.head_size_v = layer.head_size_v
+        self.is_neox = is_neox
+
+        self.q_size = self.num_heads * self.head_size
+        self.k_size = self.num_kv_heads * self.head_size
+        self.v_size = self.num_kv_heads * self.head_size_v
+
+        self.rope_matcher = MatcherRotaryEmbedding(
+            is_neox=self.is_neox,
+            head_size=self.head_size,
+            num_heads=self.num_heads,
+            num_kv_heads=self.num_kv_heads,
+        )
+
+    def get_inputs(self) -> list[torch.Tensor]:
+        # Sample inputs to help pattern tracing
+        T = 5
+        L = 4096
+        qkv = empty_bf16(T, self.q_size + self.k_size + self.v_size)
+        positions = empty_i64(T)
+        cos_sin_cache = empty_bf16(L, self.head_size)
+        return [
+            qkv,
+            positions,
+            cos_sin_cache,
+        ]
+
+    def register(self, pm_pass: PatternMatcherPass) -> None:
+        def pattern(
+            qkv: torch.Tensor,
+            positions: torch.Tensor,
+            cos_sin_cache: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+            q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
+            q, k = self.rope_matcher(positions, q, k, cos_sin_cache)
+            q = q.view(-1, self.num_heads, self.head_size)
+            k = k.view(-1, self.num_kv_heads, self.head_size)
+            v = v.view(-1, self.num_kv_heads, self.head_size_v)
+            dummy = torch.ops.vllm.unified_kv_cache_update(k, v, self.layer_name)
+            return dummy, q, k, v
+
+        def replacement(
+            qkv: torch.Tensor,
+            positions: torch.Tensor,
+            cos_sin_cache: torch.Tensor,
+        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+            q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
+            q = q.view(-1, self.num_heads, self.head_size)
+            k = k.view(-1, self.num_kv_heads, self.head_size)
+            v = v.view(-1, self.num_kv_heads, self.head_size_v)
+            results = auto_functionalized(
+                self.FUSED_OP,
+                query=q,
+                key=k,
+                value=v,
+                positions=positions,
+                cos_sin_cache=cos_sin_cache,
+                is_neox=self.is_neox,
+                layer_name=self.layer_name,
+            )
+            return results[0], results[1], results[2], v
+
+        # NOTE: use view_to_reshape to unify view/reshape to simplify
+        # pattern and increase matching opportunities
+        def fwd_and_view_to_reshape(*args, **kwargs) -> fx.GraphModule:
+            gm = pm.fwd_only(*args, **kwargs)
+            view_to_reshape(gm)
+            return gm
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), fwd_and_view_to_reshape, pm_pass
+        )
+
+
+class RopeKVCacheFusionPass(VllmPatternMatcherPass):
+    """
+    This pass fuses the rotary embedding and KV cache update operations
+    into a single fused kernel if available.
+
+    It uses the pattern matcher and matches each layer manually, as strings
+    cannot be wildcarded. This also lets us check support on attention layers
+    upon registration instead of during pattern matching.
+
+    This fusion eliminates the need for separate kernel launches and
+    intermediate memory operations between the RoPE and cache update steps.
+    """
+
+    @enable_fake_mode
+    def __init__(self, config: VllmConfig) -> None:
+        super().__init__(config)
+
+        self.patterns: PatternMatcherPass = PatternMatcherPass(
+            pass_name="rope_kv_cache_fusion_pass"
+        )
+
+        cc = config.compilation_config
+        self.max_token_num = cc.pass_config.rope_kvcache_fusion_max_token_num
+
+        attn_layers = get_layers_from_vllm_config(config, Attention)
+        for _, layer in attn_layers.items():
+            if layer.impl.fused_rope_kvcache_supported():
+                for is_neox in [True, False]:
+                    RopeReshapeKVCachePattern(
+                        layer=layer,
+                        is_neox=is_neox,
+                    ).register(self.patterns)
+
+        self.dump_patterns(config, self.patterns)
+
+    @VllmInductorPass.time_and_log
+    def __call__(self, graph: fx.Graph) -> None:
+        self.matched_count = self.patterns.apply(graph)
+        logger.debug("Replaced %s patterns", self.matched_count)
+
+    def is_applicable_for_range(self, compile_range: Range) -> bool:
+        # This pass works best for the small-batch decode setting.
+        # For large-batch e.g. prefill, it is better to use two separate kernels
+        # since they are compute bound and the fused kernels require further tuning.
+        return compile_range.end <= self.max_token_num
+
+    def uuid(self) -> str:
+        return VllmInductorPass.hash_source(self, RopeReshapeKVCachePattern)

vllm/compilation/passes/pass_manager.py

@@ -28,7 +28,9 @@ if current_platform.is_cuda_alike():
     from .fusion.attn_quant_fusion import AttnFusionPass
     from .fusion.qk_norm_rope_fusion import QKNormRoPEFusionPass
     from .fusion.rms_quant_fusion import RMSNormQuantFusionPass
+    from .fusion.rope_kvcache_fusion import RopeKVCacheFusionPass
     from .fusion.sequence_parallelism import SequenceParallelismPass
+    from .utility.scatter_split_replace import ScatterSplitReplacementPass
     from .utility.split_coalescing import SplitCoalescingPass
 
 if current_platform.is_cuda():
@@ -136,6 +138,11 @@ class PostGradPassManager(CustomGraphPass):  # type: ignore[misc]
             if self.pass_config.fuse_act_padding and rocm_aiter_ops.is_enabled():
                 self.passes += [RocmAiterTritonAddRMSNormPadFusionPass(config)]
 
+            if self.pass_config.fuse_rope_kvcache:
+                self.passes += [SplitCoalescingPass(config)]
+                self.passes += [ScatterSplitReplacementPass(config)]
+                self.passes += [RopeKVCacheFusionPass(config)]
+
             if self.pass_config.fuse_attn_quant:
                 self.passes += [AttnFusionPass(config)]
 

vllm/compilation/passes/utility/fix_functionalization.py

@@ -162,6 +162,24 @@ class FixFunctionalizationPass(VllmInductorPass):
                     "position_ids",
                 )
                 self.defunctionalize(graph, node, mutated_args=mutated_args, args=args)
+            elif (
+                hasattr(torch.ops.vllm, "fused_rope_and_unified_kv_cache_update")
+                and at_target
+                == torch.ops.vllm.fused_rope_and_unified_kv_cache_update.default
+            ):
+                mutated_args = {
+                    1: "query",
+                    2: "key",
+                }
+                self.defunctionalize(graph, node, mutated_args=mutated_args)
+            # only used for test_functionalization::TestFunctionWithMutatedArgsAndReturn
+            elif (
+                hasattr(torch.ops.vllm, "function_with_mutated_args_and_return")
+                and at_target
+                == torch.ops.vllm.function_with_mutated_args_and_return.default
+            ):
+                mutated_args = {1: "x"}
+                self.defunctionalize(graph, node, mutated_args=mutated_args)
             else:
                 continue  # skip the count
 
@@ -208,13 +226,20 @@ class FixFunctionalizationPass(VllmInductorPass):
         self, node: torch.fx.Node, mutated_args: dict[int, torch.fx.Node | str]
     ) -> None:
         """
-        Replace all getitem users of the auto-functionalized node with the
+        Replace mutated getitem users of the auto-functionalized node with the
         mutated arguments.
         :param node: The auto-functionalized node
         :param mutated_args: The mutated arguments, indexed by getitem index.
         If the value of an arg is a string, `node.kwargs[arg]` is used.
         """
         for idx, user in self.getitem_users(node).items():
+            # Some functionalized nodes may return both a result at getitem[0]
+            # as well as mutated args at getitem[1:...]
+            if idx == 0:
+                assert idx not in mutated_args, (
+                    f"result at getitem[0] should not be in mutated_args for {node}"
+                )
+                continue
             arg = mutated_args[idx]
             arg = node.kwargs[arg] if isinstance(arg, str) else arg
             user.replace_all_uses_with(arg)
@@ -257,10 +282,20 @@ class FixFunctionalizationPass(VllmInductorPass):
         with graph.inserting_before(node):
             function = node.args[0]
             if args is None:
-                graph.call_function(function, kwargs=node.kwargs)
+                fn_node = graph.call_function(function, kwargs=node.kwargs)
             else:
                 # Args passed as strings refer to items in node.kwargs
                 args = tuple(
                     node.kwargs[arg] if isinstance(arg, str) else arg for arg in args
                 )
-                graph.call_function(function, args=args)
+                fn_node = graph.call_function(function, args=args)
+
+        # If the function returns a value as well as mutating args inplace,
+        # the functionalized node will have a getitem[0] user that holds this value
+        # Replace getitem[0] user of the auto-functionalized node
+        # with the new defunctionalized node directly if it exists
+        users = self.getitem_users(node)
+        if 0 in users:
+            user = users[0]
+            user.replace_all_uses_with(fn_node)
+            self._remove(user)

vllm/compilation/passes/utility/scatter_split_replace.py

@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Replace ``slice_scatter`` and ``split_with_sizes`` nodes with a single
+assignment if there are no users for the inplace tensor written to by
+the slice_scatter call.
+
+The inplace rotary_embedding custom op takes in mutable query and key inputs
+that are split+getitem outputs of a single qkv tensor.
+When functionalized, we fetch the rotated query and key from the functionalized op
+using `getitem` calls. However, we also write to the qkv tensor inplace using a
+`slice_scatter`, then split the inplace tensor to get the output tensors again.
+Instead, if the inplace tensor has no subsequent users, we can just replace the
+`slice_scatter` and `split_with_sizes` nodes with the `getitem` calls.
+
+This is already done in fix_functionalization::FixFunctionalizationPass, but
+writing a custom pass for it before defunctionalization allows matching against the
+qkv split+rotary_embedding subpattern as part of e.g. the RoPE+KVCache fusion pass.
+"""
+
+import operator
+
+import torch
+from torch import fx
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
+
+from vllm.logger import init_logger
+
+from ..fx_utils import is_func
+from ..vllm_inductor_pass import VllmInductorPass
+
+logger = init_logger(__name__)
+
+
+class ScatterSplitReplacementPass(VllmInductorPass):
+    """Replace getitem+slice_scatter+split nodes with a single getitem when
+    the inplace subtensor written to by the slice_scatter has no other users.
+
+    Here's an example graph with q_size = 512, kv_size = 64:
+    split_with_sizes_1 = torch.ops.aten.split_with_sizes.default(qkv, (512, 64, 64), -1)
+    at = auto_functionalized(torch.ops._C.rotary_embedding.default(positions, q, k))
+    q = operator.getitem(at, 1)
+    k = operator.getitem(at, 2)
+    torch.ops.aten.slice_scatter.default(qkv, q, [0, 512], -1)
+    torch.ops.aten.slice_scatter.default(qkv, k, [512, 512 + 64], -1)
+    split_with_sizes_2 = torch.ops.aten.split_with_sizes.default(qkv, (512, 64, 64), -1)
+    q = operator.getitem(split_with_sizes_2, 0)
+    k = operator.getitem(split_with_sizes_2, 1)
+    v = operator.getitem(split_with_sizes_2, 2)
+
+    After this pass, this sequence of nodes is replaced with:
+    split_with_sizes_1 = torch.ops.aten.split_with_sizes.default(qkv, (512, 64, 64), -1)
+    at = auto_functionalized(torch.ops._C.rotary_embedding.default(positions, q, k))
+    q = operator.getitem(at, 1)
+    k = operator.getitem(at, 2)
+    v = operator.getitem(split_with_sizes_1, 2)
+    """
+
+    @VllmInductorPass.time_and_log
+    def __call__(self, graph: fx.Graph) -> None:
+        count = 0
+
+        for node in graph.nodes:
+            if not is_func(node, auto_functionalized):
+                continue
+
+            kwargs = node.kwargs
+            at_target = node.args[0]
+
+            if at_target == torch.ops._C.rotary_embedding.default:
+                query = kwargs["query"]
+                key = kwargs["key"]
+                getitem_nodes = {}
+                for user in node.users:
+                    if is_func(user, operator.getitem):
+                        getitem_nodes[user.args[1]] = user
+
+                if (
+                    is_func(query, operator.getitem)
+                    and is_func(key, operator.getitem)
+                    and query.args[0] == key.args[0]
+                    and is_func(query.args[0], torch.ops.aten.split_with_sizes.default)
+                    and all(
+                        is_func(user, torch.ops.aten.slice_scatter.default)
+                        for getitem_node in getitem_nodes.values()
+                        for user in getitem_node.users
+                    )
+                ):
+                    # Pattern where query and key are slices of a qkv tensor.
+                    # While functionalized, results at [1] and [2] are scattered
+                    # back into qkv, then split again to get query and key.
+                    # If the inplace tensor has no other users, we can replace
+                    # the slice_scatter+split nodes with the original results.
+                    for user in getitem_nodes[1].users:
+                        slice_scatter_1_node = user
+                    if not is_func(
+                        slice_scatter_1_node, torch.ops.aten.slice_scatter.default
+                    ):
+                        continue
+
+                    for user in getitem_nodes[2].users:
+                        slice_scatter_2_node = user
+                    if not is_func(
+                        slice_scatter_2_node, torch.ops.aten.slice_scatter.default
+                    ):
+                        continue
+
+                    for user in slice_scatter_2_node.users:
+                        split_node = user
+                    if not is_func(split_node, torch.ops.aten.split_with_sizes.default):
+                        continue
+
+                    split_getitem_users = {}
+                    for user in split_node.users:
+                        if is_func(user, operator.getitem):
+                            split_getitem_users[user.args[1]] = user
+
+                    # Replace query node
+                    split_getitem_users[0].replace_all_uses_with(getitem_nodes[1])
+                    graph.erase_node(split_getitem_users[0])
+                    # Replace key node
+                    split_getitem_users[1].replace_all_uses_with(getitem_nodes[2])
+                    graph.erase_node(split_getitem_users[1])
+                    # Redirect value node to original qkv tensor
+                    split_getitem_users[2].replace_input_with(split_node, query.args[0])
+
+                    # Erase unused nodes
+                    graph.erase_node(split_node)
+                    graph.erase_node(slice_scatter_2_node)
+                    graph.erase_node(slice_scatter_1_node)
+
+                    count += 1
+
+        logger.debug("Eliminated %d slice_scatter+split nodes", count)

vllm/config/compilation.py

@@ -127,6 +127,13 @@ class PassConfig:
     # ROCm/AITER specific fusions
     fuse_act_padding: bool = Field(default=None)
     """Fuse the custom RMSNorm + padding ops."""
+    fuse_rope_kvcache: bool = Field(default=None)
+    """Fuse the QK rope + KV cache ops."""
+
+    rope_kvcache_fusion_max_token_num: int = 256
+    """The threshold for ROCm AITER RoPE+KVCache fusion e.g. for small batch decode.
+    Larger batch sizes e.g. during prefill will use the unfused kernels.
+    """
 
     fi_allreduce_fusion_max_size_mb: float | None = None
     """The threshold of the communicated tensor sizes under which
@@ -198,6 +205,7 @@ class PassConfig:
         "fuse_gemm_comms",
         "fuse_allreduce_rms",
         "fuse_act_padding",
+        "fuse_rope_kvcache",
         mode="wrap",
     )
     @classmethod
@@ -243,6 +251,12 @@ class PassConfig:
                 "The fusion will be disabled."
             )
             self.fuse_act_padding = False
+        if self.fuse_rope_kvcache and not current_platform.is_rocm():
+            logger.warning_once(
+                "KV cache fusion currently only enabled on ROCm. "
+                "The fusion will be disabled."
+            )
+            self.fuse_rope_kvcache = False
 
 
 class DynamicShapesType(str, enum.Enum):
@@ -824,6 +838,19 @@ class CompilationConfig:
             # TODO(zhuhaoran): support rope native forward match and remove this.
             # Linked issue: https://github.com/vllm-project/vllm/issues/28042
             self.custom_ops.append("+rotary_embedding")
+        if self.pass_config.fuse_rope_kvcache:
+            from vllm._aiter_ops import rocm_aiter_ops
+
+            if rocm_aiter_ops.is_triton_rotary_embed_enabled():
+                logger.warning(
+                    "Cannot use VLLM_ROCM_USE_AITER_TRITON_ROPE with "
+                    "fuse_rope_kvcache. Disabling fuse_rope_kvcache."
+                )
+                self.pass_config.fuse_rope_kvcache = False
+            else:
+                # TODO(Rohan138): support rope native forward match and remove this.
+                # Linked issue: https://github.com/vllm-project/vllm/issues/28042
+                self.custom_ops.append("+rotary_embedding")
 
         if (
             is_torch_equal_or_newer("2.9.0.dev")

vllm/config/vllm.py

@@ -1401,6 +1401,20 @@ class VllmConfig:
                         "allreduce-rms fusion will be enabled for all num_tokens."
                     )
 
+        if compilation_config.pass_config.fuse_rope_kvcache:
+            max_token_num = (
+                compilation_config.pass_config.rope_kvcache_fusion_max_token_num
+            )
+            if max_token_num is not None:
+                if compile_range_end is not None and max_token_num < compile_range_end:
+                    computed_compile_ranges_split_points.append(max_token_num)
+                else:
+                    logger.debug(
+                        "Max num batched tokens below rope+kvcache fusion threshold, "
+                        "rope+kvcache fusion enabled for num_tokens <= %d.",
+                        compile_range_end,
+                    )
+
         if compilation_config.compile_ranges_split_points is not None:
             for x in compilation_config.compile_ranges_split_points:
                 assert isinstance(x, int)

vllm/model_executor/layers/attention/attention.py

@@ -570,11 +570,11 @@ direct_register_custom_op(
 
 def get_attention_context(
     layer_name: str,
-) -> tuple[Any, "Attention | MLAAttention", torch.Tensor]:
+) -> tuple[Any, "Attention | MLAAttention", torch.Tensor, torch.Tensor]:
     """Extract attention context for a given layer.
 
     This helper function extracts the attention metadata, attention layer
-    instance, and KV cache tensor for a specific layer.
+    instance, KV cache tensor, and slot mapping for a specific layer.
 
     Args:
         layer_name: The name/identifier of the attention layer.
@@ -585,6 +585,7 @@ def get_attention_context(
             no metadata available
         - attn_layer: The attention layer instance (Attention or MLAAttention)
         - kv_cache: The KV cache tensor for current virtual engine
+        - slot_mapping: The slot mapping for this specific layer
 
         Note: attn_metadata may be None, but attn_layer and kv_cache are always
         extracted from the forward context.
@@ -593,9 +594,14 @@ def get_attention_context(
     attn_metadata = forward_context.attn_metadata
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[layer_name]
-    attn_layer = forward_context.no_compile_layers[layer_name]
+    attn_layer: Attention | MLAAttention = forward_context.no_compile_layers[layer_name]
     kv_cache = attn_layer.kv_cache[forward_context.virtual_engine]
-    return attn_metadata, attn_layer, kv_cache
+    slot_mapping = forward_context.slot_mapping
+    assert isinstance(slot_mapping, dict), (
+        f"Expected slot_mapping to be a dict, got {type(slot_mapping)}. "
+    )
+    layer_slot_mapping = slot_mapping.get(layer_name)
+    return attn_metadata, attn_layer, kv_cache, layer_slot_mapping
 
 
 @maybe_transfer_kv_layer
@@ -605,7 +611,7 @@ def unified_attention(
     value: torch.Tensor,
     layer_name: str,
 ) -> torch.Tensor:
-    attn_metadata, self, kv_cache = get_attention_context(layer_name)
+    attn_metadata, self, kv_cache, _ = get_attention_context(layer_name)
     output = self.impl.forward(self, query, key, value, kv_cache, attn_metadata)
 
     return output
@@ -636,15 +642,7 @@ def unified_kv_cache_update(
     Returns a dummy that is passed to unified_attention to signal a side effect and
     the data dependency between them to ensure torch.compile preserves ordering.
     """
-    forward_context = get_forward_context()
-    attn_layer = forward_context.no_compile_layers[layer_name]
-    kv_cache = attn_layer.kv_cache[forward_context.virtual_engine]
-
-    slot_mapping = forward_context.slot_mapping
-    assert isinstance(slot_mapping, dict), (
-        f"Expected slot_mapping to be a dict, got {type(slot_mapping)}. "
-    )
-    layer_slot_mapping = slot_mapping.get(layer_name)
+    _, attn_layer, kv_cache, layer_slot_mapping = get_attention_context(layer_name)
     if layer_slot_mapping is not None:
         assert hasattr(attn_layer.impl, "do_kv_cache_update"), (
             f"{attn_layer.impl.__class__.__name__} does not support kv cache update"
@@ -691,7 +689,7 @@ def unified_attention_with_output(
     # that ensures torch.compile preserves ordering between KV cache update and
     # attention forward.
     del kv_cache_dummy_dep
-    attn_metadata, self, kv_cache = get_attention_context(layer_name)
+    attn_metadata, self, kv_cache, _ = get_attention_context(layer_name)
 
     self.impl.forward(
         self,

vllm/model_executor/layers/attention/kv_transfer_utils.py

@@ -40,8 +40,8 @@ def maybe_transfer_kv_layer(func: Callable) -> Callable:
 
         layer_name: str = args[layer_name_index]
 
-        # Extract attention context (layer-specific metadata, layer, and kv_cache)
-        attn_metadata, attn_layer, kv_cache = get_attention_context(layer_name)
+        # Extract attention context (metadata, layer, kv_cache, layer_slot_mapping)
+        attn_metadata, _, kv_cache, _ = get_attention_context(layer_name)
         connector = get_kv_transfer_group()
         if attn_metadata is None or not connector.has_connector_metadata():
             return func(*args, **kwargs)

vllm/model_executor/layers/attention/mla_attention.py

@@ -828,7 +828,7 @@ def unified_mla_attention(
     k_pe: torch.Tensor,
     layer_name: str,
 ) -> torch.Tensor:
-    attn_metadata, layer, kv_cache = get_attention_context(layer_name)
+    attn_metadata, layer, kv_cache, _ = get_attention_context(layer_name)
     output = layer.forward_impl(q, kv_c_normed, k_pe, kv_cache, attn_metadata)
 
     return output
@@ -862,7 +862,7 @@ def unified_mla_attention_with_output(
     output_scale: torch.Tensor | None = None,
     output_block_scale: torch.Tensor | None = None,
 ) -> None:
-    attn_metadata, layer, kv_cache = get_attention_context(layer_name)
+    attn_metadata, layer, kv_cache, _ = get_attention_context(layer_name)
     layer.forward_impl(
         q,
         kv_c_normed,

vllm/v1/attention/backend.py

@@ -723,6 +723,33 @@ class AttentionImpl(AttentionImplBase[T], Generic[T]):
         """
         return False
 
+    def fused_rope_kvcache_supported(self):
+        """
+        Does this attention implementation support RoPE+KVCache fusion.
+        This is used by the RopeKVCacheFusionPass to only fuse the RoPE ops
+        with the KV cache update for implementations that support it.
+        """
+        return False
+
+    def do_rope_and_kv_cache_update(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        positions: torch.Tensor,
+        cos_sin_cache: torch.Tensor,
+        is_neox: bool,
+        kv_cache: torch.Tensor,
+        layer_slot_mapping: torch.Tensor,
+    ):
+        """
+        If `fused_rope_kvcache_supported` returns True, this method will be called
+        by torch.ops.vllm.fused_rope_and_unified_kv_cache_update
+        to perform the inplace RoPE and KV cache update.
+        """
+        raise NotImplementedError
+
 
 class MLAAttentionImpl(AttentionImplBase[T], Generic[T]):
     """MLA attention implementation with forward_mqa and forward_mha methods."""

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -11,7 +11,6 @@ from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
-from vllm.model_executor.layers.attention.attention import get_attention_context
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
 from vllm.utils.platform_utils import get_cu_count
@@ -1290,11 +1289,6 @@ class AiterFlashAttentionImpl(AttentionImpl):
         kv_cache: torch.Tensor,
         slot_mapping: torch.Tensor,
     ):
-        attn_metadata, _, _ = get_attention_context(layer.layer_name)
-        if attn_metadata is None:
-            # Profiling run.
-            return
-
         key_cache, value_cache = kv_cache.unbind(0)
 
         # key and value may be None in the case of cross attention. They are
@@ -1303,45 +1297,40 @@ class AiterFlashAttentionImpl(AttentionImpl):
         if self.kv_cache_dtype.startswith("fp8"):
             key_cache = key_cache.view(current_platform.fp8_dtype())
             value_cache = value_cache.view(current_platform.fp8_dtype())
-        if (
-            self.kv_sharing_target_layer_name is None
-            and key is not None
-            and value is not 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.
-            if rocm_aiter_ops.is_shuffle_kv_cache_enabled():
-                # We may calculate per token quant scale in
-                # reshape_and_cache_shuffle_triton which might differ from
-                # vllm's style when shuffle layout is used.
-                k_scale = attn_metadata.k_scale
-                v_scale = attn_metadata.v_scale
-                assert k_scale is not None and v_scale is not None, (
-                    "k_scale and v_scale are required for shuffled update"
-                )
-                reshape_and_cache_shuffle_triton(
-                    key,
-                    value,
-                    key_cache,
-                    value_cache,
-                    slot_mapping,
-                    self.kv_cache_dtype,
-                    k_scale,
-                    v_scale,
-                )
-            else:
-                torch.ops._C_cache_ops.reshape_and_cache_flash(
-                    key,
-                    value,
-                    key_cache,
-                    value_cache,
-                    slot_mapping,
-                    self.kv_cache_dtype,
-                    layer._k_scale,
-                    layer._v_scale,
-                )
+        # 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.
+        if rocm_aiter_ops.is_shuffle_kv_cache_enabled():
+            # We may calculate per token quant scale in
+            # reshape_and_cache_shuffle_triton which might differ from
+            # vllm's style when shuffle layout is used.
+            k_scale = layer._k_scale
+            v_scale = layer._v_scale
+            assert k_scale is not None and v_scale is not None, (
+                "k_scale and v_scale are required for shuffled update"
+            )
+            reshape_and_cache_shuffle_triton(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                slot_mapping,
+                self.kv_cache_dtype,
+                k_scale,
+                v_scale,
+            )
+        else:
+            torch.ops._C_cache_ops.reshape_and_cache_flash(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                slot_mapping,
+                self.kv_cache_dtype,
+                layer._k_scale,
+                layer._v_scale,
+            )

vllm/v1/attention/backends/rocm_aiter_unified_attn.py

@@ -5,6 +5,7 @@
 import torch
 
 from vllm import _custom_ops as ops
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
@@ -207,3 +208,42 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
             layer._k_scale,
             layer._v_scale,
         )
+
+    def fused_rope_kvcache_supported(self):
+        return rocm_aiter_ops.is_enabled()
+
+    def do_rope_and_kv_cache_update(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        positions: torch.Tensor,
+        cos_sin_cache: torch.Tensor,
+        is_neox: bool,
+        kv_cache: torch.Tensor,
+        layer_slot_mapping: torch.Tensor,
+    ):
+        key_cache, value_cache = kv_cache.unbind(0)
+        flash_layout = True
+
+        is_fp8_kv_cache = self.kv_cache_dtype.startswith("fp8")
+        if is_fp8_kv_cache:
+            key_cache = key_cache.view(self.fp8_dtype)
+            value_cache = value_cache.view(self.fp8_dtype)
+
+        rocm_aiter_ops.triton_rope_and_cache(
+            query,
+            key,
+            value,
+            positions,
+            cos_sin_cache,
+            is_neox,
+            key_cache,
+            value_cache,
+            layer_slot_mapping,
+            layer._k_scale,
+            layer._v_scale,
+            flash_layout,
+            is_fp8_kv_cache,
+        )

vllm/v1/attention/backends/rocm_attn.py

@@ -7,6 +7,7 @@ from typing import ClassVar
 
 import torch
 
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
@@ -415,3 +416,46 @@ class RocmAttentionImpl(AttentionImpl):
                 layer._k_scale,
                 layer._v_scale,
             )
+
+    def fused_rope_kvcache_supported(self):
+        return rocm_aiter_ops.is_enabled()
+
+    def do_rope_and_kv_cache_update(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        positions: torch.Tensor,
+        cos_sin_cache: torch.Tensor,
+        is_neox: bool,
+        kv_cache: torch.Tensor,
+        layer_slot_mapping: torch.Tensor,
+    ):
+        key_cache, value_cache = PagedAttention.split_kv_cache(
+            kv_cache,
+            layer.num_kv_heads,  # type: ignore[attr-defined]
+            layer.head_size,  # type: ignore[attr-defined]
+        )
+        flash_layout = False
+
+        is_fp8_kv_cache = self.kv_cache_dtype.startswith("fp8")
+        if is_fp8_kv_cache:
+            key_cache = key_cache.view(self.fp8_dtype)
+            value_cache = value_cache.view(self.fp8_dtype)
+
+        rocm_aiter_ops.triton_rope_and_cache(
+            query,
+            key,
+            value,
+            positions,
+            cos_sin_cache,
+            is_neox,
+            key_cache,
+            value_cache,
+            layer_slot_mapping,
+            layer._k_scale,
+            layer._v_scale,
+            flash_layout,
+            is_fp8_kv_cache,
+        )

vllm/v1/attention/backends/triton_attn.py

@@ -7,6 +7,7 @@ from typing import ClassVar
 
 import torch
 
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import CUDAGraphMode, VllmConfig
 from vllm.config.cache import CacheDType
 from vllm.logger import init_logger
@@ -596,3 +597,42 @@ class TritonAttentionImpl(AttentionImpl):
             layer._k_scale,
             layer._v_scale,
         )
+
+    def fused_rope_kvcache_supported(self):
+        return rocm_aiter_ops.is_enabled()
+
+    def do_rope_and_kv_cache_update(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        positions: torch.Tensor,
+        cos_sin_cache: torch.Tensor,
+        is_neox: bool,
+        kv_cache: torch.Tensor,
+        layer_slot_mapping: torch.Tensor,
+    ):
+        key_cache, value_cache = kv_cache.unbind(1)
+        flash_layout = True
+
+        is_fp8_kv_cache = self.kv_cache_dtype.startswith("fp8")
+        if is_fp8_kv_cache:
+            key_cache = key_cache.view(self.fp8_dtype)
+            value_cache = value_cache.view(self.fp8_dtype)
+
+        rocm_aiter_ops.triton_rope_and_cache(
+            query,
+            key,
+            value,
+            positions,
+            cos_sin_cache,
+            is_neox,
+            key_cache,
+            value_cache,
+            layer_slot_mapping,
+            layer._k_scale,
+            layer._v_scale,
+            flash_layout,
+            is_fp8_kv_cache,
+        )