[ROCm][AITER] Support AITER Rope ops in RotaryEmbedding Module.

vllm/model_executor/layers/rotary_embedding/base.py

@@ -8,6 +8,7 @@
 from vllm.model_executor.custom_op import CustomOp
 
 from .common import apply_rotary_emb_dispatch, apply_rotary_emb_torch
+from .rocm_aiter_rope_ops import is_rocm_rotatry_embedding_enabled
 
 
 @CustomOp.register("rotary_embedding")
@@ -35,6 +36,7 @@ def __init__(
         cache = cache.to(dtype)
         self.cos_sin_cache: torch.Tensor
         self.register_buffer("cos_sin_cache", cache, persistent=False)
+        self.is_rocm_aiter_enabled = is_rocm_rotatry_embedding_enabled()
 
     def _compute_inv_freq(self, base: float) -> torch.Tensor:
         """Compute the inverse frequency."""
@@ -119,6 +121,75 @@ def forward_cuda(
                                  self.cos_sin_cache, self.is_neox_style)
         return query, key
 
+    def forward_hip(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        offsets: Optional[torch.Tensor] = None,
+        is_nope_first=False,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        # currently only rotary embedding ops from AITER package are
+        # supported for HiP forward.
+        if self.is_rocm_aiter_enabled:
+            return self.forward_hip_rocm_aiter(positions, query, key, offsets,
+                                               is_nope_first)
+        return self.forward_native(positions, query, key, offsets)
+
+    def forward_hip_rocm_aiter(
+        self,
+        positions: torch.Tensor,
+        # if     is_nope_first
+        # [[batch_size, seq_len, num_heads, nope_size+rope_size]
+        # if NOT is_nope_first
+        # [[batch_size, seq_len, num_heads, rope_size+nope_size],
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        offsets: Optional[torch.Tensor] = None,
+        is_nope_first: bool = False,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        if self.cos_sin_cache.device != query.device or \
+            self.cos_sin_cache.dtype != query.dtype:
+            self.cos_sin_cache = self.cos_sin_cache.to(query.device,
+                                                       dtype=query.dtype)
+        cos, sin = self.cos_sin_cache.chunk(2, dim=-1)
+
+        cos = cos.unsqueeze(-2).unsqueeze(-2)
+        sin = sin.unsqueeze(-2).unsqueeze(-2)
+
+        rotate_style = 0 if self.is_neox_style else 1
+
+        num_tokens = positions.numel()
+
+        query_shape = query.shape
+        query = query.view(1, num_tokens, -1, self.head_size)
+        if key is not None:
+            key_shape = key.shape
+            key = key.view(1, num_tokens, -1, self.head_size)
+
+        positions = positions.view(*query.shape[:2])
+        if offsets is not None:
+            offsets = offsets.view(*query.shape[:2])
+
+        if not is_nope_first:
+            query_ = query[..., :self.rotary_dim]
+            key_ = key[..., :self.rotary_dim] if key is not None else None
+        else:
+            query_ = query[..., -self.rotary_dim:]
+            key_ = key[..., -self.rotary_dim:] if key is not None else None
+
+        if key_ is None:
+            torch.ops.vllm.rocm_aiter_rotary_emb_without_key_forward_hip(
+                positions, sin, cos, query_, offsets, rotate_style,
+                is_nope_first)
+            return query.view(query_shape), None
+
+        torch.ops.vllm.rocm_aiter_rotary_emb_with_key_forward_hip(
+            positions, sin, cos, query_, key_, offsets, rotate_style,
+            is_nope_first)
+
+        return query.view(query_shape), key.view(key_shape)
+
     def forward_xpu(
         self,
         positions: torch.Tensor,

vllm/model_executor/layers/rotary_embedding/common.py

@@ -99,7 +99,7 @@ def yarn_linear_ramp_mask(low: float, high: float, dim: int,
     return ramp_func
 
 
-def yarn_get_mscale(scale: float = 1) -> float:
+def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
     if scale <= 1:
         return 1.0
-    return 0.1 * math.log(scale) + 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0

vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import math
 from typing import Optional
 
 import torch
@@ -10,13 +9,7 @@
 
 from .base import RotaryEmbedding
 from .common import (rotate_gptj, rotate_neox, yarn_find_correction_range,
-                     yarn_linear_ramp_mask)
-
-
-def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
-    if scale <= 1:
-        return 1.0
-    return 0.1 * mscale * math.log(scale) + 1.0
+                     yarn_get_mscale, yarn_linear_ramp_mask)
 
 
 class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
@@ -96,6 +89,12 @@ def forward(
         offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """PyTorch-native implementation equivalent to forward()."""
+        if self.is_rocm_aiter_enabled:
+            query, key = super().forward(positions, query, key, offsets)
+            if positions.numel() == 1:
+                key = key.clone()
+            return query, key
+
         assert key is not None
         query_rot = query[..., :self.rotary_dim]
         key_rot = key[..., :self.rotary_dim]

vllm/model_executor/layers/rotary_embedding/rocm_aiter_rope_ops.py

@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+import vllm.envs as envs
+from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
+
+
+def is_rocm_rotatry_embedding_enabled() -> bool:
+    return (current_platform.is_rocm() and envs.VLLM_ROCM_USE_AITER)
+
+
+def rocm_aiter_rotary_emb_without_key_forward_hip_impl(
+    positions: torch.Tensor,
+    sin: torch.Tensor,
+    cos: torch.Tensor,
+    query: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    rotate_style: int = 0,
+    is_nope_first: bool = False,
+) -> None:
+    import aiter as ops
+    if offsets is None:
+        ops.rope_cached_positions_fwd_inplace(
+            query,
+            cos,
+            sin,
+            positions,
+            rotate_style,
+            reuse_freqs_front_part=True,
+            nope_first=is_nope_first,
+        )
+    else:
+        ops.rope_cached_positions_offsets_fwd_inplace(
+            query,
+            cos,
+            sin,
+            positions,
+            offsets,
+            rotate_style,
+            reuse_freqs_front_part=True,
+            nope_first=is_nope_first,
+        )
+
+
+def rocm_aiter_rotary_emb_with_key_forward_hip_impl(
+    positions: torch.Tensor,
+    sin: torch.Tensor,
+    cos: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    rotate_style: int = 0,
+    is_nope_first: bool = False,
+) -> None:
+    import aiter as ops
+    if offsets is None:
+        ops.rope_cached_positions_2c_fwd_inplace(
+            query,
+            key,
+            cos,
+            sin,
+            positions,
+            rotate_style,
+            reuse_freqs_front_part=True,
+            nope_first=is_nope_first,
+        )
+    else:
+        ops.rope_cached_positions_offsets_2c_fwd_inplace(
+            query,
+            key,
+            cos,
+            sin,
+            positions,
+            offsets,
+            rotate_style,
+            reuse_freqs_front_part=True,
+            nope_first=is_nope_first,
+        )
+
+
+def rocm_aiter_rotary_emb_with_key_forward_hip_fake(
+    positions: torch.Tensor,
+    sin: torch.Tensor,
+    cos: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    rotate_style: int = 0,
+    is_nope_first: bool = False,
+) -> None:
+    pass
+
+
+def rocm_aiter_rotary_emb_without_key_forward_hip_fake(
+    positions: torch.Tensor,
+    sin: torch.Tensor,
+    cos: torch.Tensor,
+    query: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    rotate_style: int = 0,
+    is_nope_first: bool = False,
+) -> None:
+    pass
+
+
+if is_rocm_rotatry_embedding_enabled():
+
+    direct_register_custom_op(
+        op_name="rocm_aiter_rotary_emb_with_key_forward_hip",
+        op_func=rocm_aiter_rotary_emb_with_key_forward_hip_impl,
+        mutates_args=["key", "query"],
+        fake_impl=rocm_aiter_rotary_emb_with_key_forward_hip_fake,
+        dispatch_key=current_platform.dispatch_key,
+    )
+
+    direct_register_custom_op(
+        op_name="rocm_aiter_rotary_emb_without_key_forward_hip",
+        op_func=rocm_aiter_rotary_emb_without_key_forward_hip_impl,
+        mutates_args=["query"],
+        fake_impl=rocm_aiter_rotary_emb_without_key_forward_hip_fake,
+        dispatch_key=current_platform.dispatch_key,
+    )