[5/N][refactor]add torchair rotary ops (#2559)

### What this PR does / why we need it?
Move torchair related rotary ops into torchair dir to make the code
clear. Next step we'll remove all torchair related code outside of
torchair rotary ops.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
vLLM version: main
vLLM main:
ab9f2cfd19


- vLLM version: v0.10.1.1
- vLLM main:
81eea3d348

Signed-off-by: hust17yixuan <303660421@qq.com>

tests/ut/torchair/ops/test_torchair_rotary_embedding.py

@@ -0,0 +1,332 @@
+import math
+from unittest.mock import MagicMock, patch
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend.torchair.ops.torchair_rotary_embedding import (
+    custom_rotary_embedding_enabled, native_rope_deepseek_forward,
+    rope_forward_oot, rotate_half, yarn_find_correction_dim, yarn_get_mscale)
+
+
+class TestCustomRotaryEmbeddingEnabled(TestBase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.positions = torch.tensor([1, 2, 3])
+        self.query = torch.randn(3, 4, dtype=torch.float16)
+        self.key = torch.randn(3, 4, dtype=torch.float16)
+        self.head_size = 32
+        self.cos_sin_cache = torch.randn(3, 4)
+
+        # Mock self object for rope_forward_oot
+        self.mock_self = MagicMock()
+        self.mock_self.head_size = self.head_size
+        self.mock_self.cos_sin_cache = self.cos_sin_cache
+        self.mock_self.is_neox_style = True
+        self.mock_self.forward_native.return_value = (self.query, self.key)
+
+    def test_custom_rotary_embedding_enabled(self):
+        # Test when all conditions are True
+        with patch(
+                'vllm_ascend.torchair.ops.torchair_rotary_embedding.enable_custom_op',
+                return_value=True):
+            result = custom_rotary_embedding_enabled(self.query, True,
+                                                     self.head_size)
+            self.assertTrue(result)
+
+        # Test when dtype is not float16
+        with patch(
+                'vllm_ascend.torchair.ops.torchair_rotary_embedding.enable_custom_op',
+                return_value=True):
+            query = self.query.to(torch.float32)
+            result = custom_rotary_embedding_enabled(query, True,
+                                                     self.head_size)
+            self.assertFalse(result)
+
+        # Test when neox_style is False
+        with patch(
+                'vllm_ascend.torchair.ops.torchair_rotary_embedding.enable_custom_op',
+                return_value=True):
+            result = custom_rotary_embedding_enabled(self.query, False,
+                                                     self.head_size)
+            self.assertFalse(result)
+
+        # Test when head_size is not divisible by 32
+        with patch(
+                'vllm_ascend.torchair.ops.torchair_rotary_embedding.enable_custom_op',
+                return_value=True):
+            result = custom_rotary_embedding_enabled(self.query, True,
+                                                     self.head_size + 1)
+            self.assertFalse(result)
+
+        # Test when custom op is disabled
+        with patch(
+                'vllm_ascend.torchair.ops.torchair_rotary_embedding.enable_custom_op',
+                return_value=False):
+            result = custom_rotary_embedding_enabled(self.query, True,
+                                                     self.head_size)
+            self.assertFalse(result)
+
+
+class TestRopeForwardOot(TestBase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.positions = torch.tensor([1, 2, 3])
+        self.query = torch.randn(3, 4, dtype=torch.float16)
+        self.key = torch.randn(3, 4, dtype=torch.float16)
+        self.head_size = 32
+        self.cos_sin_cache = torch.randn(3, 4)
+
+        # Mock self object for rope_forward_oot
+        self.mock_self = MagicMock()
+        self.mock_self.head_size = self.head_size
+        self.mock_self.cos_sin_cache = self.cos_sin_cache
+        self.mock_self.is_neox_style = True
+        self.mock_self.forward_native.return_value = (self.query, self.key)
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
+    def test_rope_forward_oot_torchair_enabled_base(self,
+                                                    mock_get_ascend_config):
+        # Setup mock for torchair enabled
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = True
+        mock_get_ascend_config.return_value = mock_config
+
+        result_q, result_k = rope_forward_oot(self.mock_self, self.positions,
+                                              self.query, self.key)
+
+        self.mock_self.forward_native.assert_called_once_with(
+            self.positions, self.query, self.key, None)
+        self.assertTrue(torch.equal(result_q, self.query))
+        self.assertTrue(torch.equal(result_k, self.key))
+
+    @patch('torch.ops._C')
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
+    @patch('vllm_ascend.torchair.ops.torchair_rotary_embedding.is_310p',
+           return_value=False)
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.custom_rotary_embedding_enabled',
+        return_value=True)
+    @patch('torch.ops._npu_rotary_embedding')
+    def test_rope_forward_oot_custom_kernel(self, mock_rotary_embedding,
+                                            mock_custom_enabled, mock_is_310p,
+                                            mock_get_ascend_config, mock__c):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+        mock_get_ascend_config.return_value = mock_config
+
+        # Setup mock for custom kernel path
+
+        mock__c.rotary_embedding.return_value = self.query, self.key
+
+        result_q, result_k = rope_forward_oot(self.mock_self, self.positions,
+                                              self.query, self.key)
+
+        self.assertEqual(result_q.shape, self.query.shape)
+        self.assertEqual(result_k.shape, self.key.shape)
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.custom_rotary_embedding_enabled',
+        return_value=False)
+    @patch('torch_npu._npu_rotary_embedding')
+    def test_rope_forward_oot_contiguous(self, mock_npu_rotary,
+                                         mock_custom_enabled,
+                                         mock_get_ascend_config):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+        mock_get_ascend_config.return_value = mock_config
+
+        # Test contiguous path when custom is disabled
+        non_contig_query = self.query.transpose(0, 1)
+        non_contig_key = self.key.transpose(0, 1)
+
+        result_q, result_k = rope_forward_oot(self.mock_self, self.positions,
+                                              non_contig_query, non_contig_key)
+
+        mock_npu_rotary.assert_called_once()
+        self.assertEqual(result_q.shape, non_contig_query.shape)
+        self.assertEqual(result_k.shape, non_contig_key.shape)
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
+    def test_rope_forward_oot_with_offsets(self, mock_get_ascend_config):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+        mock_get_ascend_config.return_value = mock_config
+
+        # Test that NotImplementedError is raised when offsets is provided
+        offsets = torch.tensor([1, 2, 3])
+        with self.assertRaises(NotImplementedError):
+            rope_forward_oot(self.mock_self, self.positions, self.query,
+                             self.key, offsets)
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.custom_rotary_embedding_enabled',
+        return_value=False)
+    @patch('torch_npu._npu_rotary_embedding')
+    def test_rope_forward_oot_neox_style_override(self, mock_npu_rotary,
+                                                  mock_custom_enabled,
+                                                  mock_get_ascend_config):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+        mock_get_ascend_config.return_value = mock_config
+
+        # Test neox_style override
+        result_q, result_k = rope_forward_oot(self.mock_self,
+                                              self.positions,
+                                              self.query,
+                                              self.key,
+                                              is_neox_style_override=False)
+
+        # Check that neox_style=False was passed to the NPU function
+        args, kwargs = mock_npu_rotary.call_args
+        self.assertFalse(args[-1])
+
+
+class MockRopeModule:
+
+    def __init__(self, max_seq_len=2048, is_neox_style=True):
+        self.max_seq_len = max_seq_len
+        self.is_neox_style = is_neox_style
+        self.cos_cached = None
+        self.sin_cached = None
+        self.rotary_dim = 1
+        self.base = 1
+
+
+class TestNativeRopeDeepseekForward(TestBase):
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.rope_forward_oot')
+    def test_native_rope_deepseek_forward_base(self, mock_rope_forward_oot):
+        module = MockRopeModule()
+        positions = torch.tensor([1, 2, 3])
+        query = torch.randn(1, 8, 128)
+        key = torch.randn(1, 8, 128)
+
+        mock_rope_forward_oot.return_value = (query, key)
+
+        q_pe, k_pe = native_rope_deepseek_forward(module, positions, query,
+                                                  key)
+
+        assert q_pe.shape == query.shape
+        assert k_pe.shape == key.shape
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding._set_cos_sin_cache'
+    )
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.rope_forward_oot')
+    def test_native_rope_deepseek_forward_cache_handling(
+            self, mock_rope_forward_oot, mock_set_cache):
+        # Test cache situation is true
+        module = MockRopeModule(max_seq_len=1024)
+        positions = torch.tensor([1, 2, 3])
+        query = torch.randn(1, 8, 128)
+        key = torch.randn(1, 8, 128)
+
+        mock_rope_forward_oot.return_value = (query, key)
+
+        q_pe, k_pe = native_rope_deepseek_forward(module,
+                                                  positions,
+                                                  query,
+                                                  key,
+                                                  max_seq_len=2048)
+
+        assert q_pe.shape == query.shape
+        assert k_pe.shape == key.shape
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.rope_forward_oot')
+    def test_native_rope_deepseek_forward_key_reshaping(
+            self, mock_rope_forward_oot):
+        module = MockRopeModule()
+        positions = torch.tensor([1, 2, 3])
+        query = torch.randn(1, 8, 128)
+        key = torch.randn(1, 128)
+
+        mock_rope_forward_oot.return_value = (query, key)
+
+        q_pe, k_pe = native_rope_deepseek_forward(module, positions, query,
+                                                  key)
+
+        assert q_pe.shape == query.shape
+        assert k_pe.shape == (1, 128)
+
+    @patch(
+        'vllm_ascend.torchair.ops.torchair_rotary_embedding.rope_forward_oot')
+    def test_native_rope_deepseek_forward_non_neox_style(
+            self, mock_rope_forward_oot):
+        module = MockRopeModule(is_neox_style=False)
+        positions = torch.tensor([1, 2, 3])
+        query = torch.randn(1, 8, 128)
+        key = torch.randn(1, 8, 128)
+
+        mock_rope_forward_oot.return_value = (query, key)
+
+        q_pe, k_pe = native_rope_deepseek_forward(module, positions, query,
+                                                  key)
+
+        assert q_pe.shape == query.shape
+        assert k_pe.shape == key.shape
+
+
+class TestRotateHalf(TestBase):
+
+    def test_rotate_half_even_dim(self):
+        # Test with even dimension
+        x = torch.tensor([1.0, 2.0, 3.0, 4.0])
+        expected = torch.tensor([-3.0, -4.0, 1.0, 2.0])
+        result = rotate_half(x)
+        self.assertTrue(torch.allclose(result, expected))
+
+
+class TestYarnFindCorrectionDim(TestBase):
+
+    def test_basic_case(self):
+        # Test with standard values
+        num_rotations = 100
+        dim = 512
+        base = 10000
+        max_position_embeddings = 2048
+
+        result = yarn_find_correction_dim(num_rotations, dim, base,
+                                          max_position_embeddings)
+
+        # Calculate expected value manually
+        expected = (dim * torch.log(
+            torch.tensor(max_position_embeddings) /
+            (num_rotations * 2 * torch.pi))) / (2 *
+                                                torch.log(torch.tensor(base)))
+
+        self.assertTrue(torch.allclose(result, expected))
+
+
+class TestYarnGetMscale(TestBase):
+
+    def test_scale_less_than_or_equal_1(self):
+        self.assertEqual(yarn_get_mscale(scale=0.5), 1.0)
+        self.assertEqual(yarn_get_mscale(scale=1.0), 1.0)
+        self.assertEqual(yarn_get_mscale(scale=0.999), 1.0)
+
+    def test_scale_greater_than_1(self):
+        test_cases = [(2.0, 1.0, 1.0 + 0.1 * math.log(2.0)),
+                      (10.0, 1.0, 1.0 + 0.1 * math.log(10.0)),
+                      (5.0, 2.0, 1.0 + 0.2 * math.log(5.0)),
+                      (math.e, 1.0, 1.0 + 0.1)]
+
+        for scale, mscale, expected in test_cases:
+            result = yarn_get_mscale(scale, mscale)
+            self.assertAlmostEqual(
+                result,
+                expected,
+                places=6,
+                msg=f"Failed for scale={scale}, mscale={mscale}")

vllm_ascend/torchair/ops/torchair_rotary_embedding.py

@@ -0,0 +1,372 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+import torch_npu
+from vllm.model_executor.layers.rotary_embedding import (
+    DeepseekScalingRotaryEmbedding, RotaryEmbedding)
+
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.utils import enable_custom_op, is_310p
+
+
+def custom_rotary_embedding_enabled(query, neox_style, head_size):
+    return query.dtype == torch.float16 and neox_style and head_size % 32 == 0 and enable_custom_op(
+    )
+
+
+def rope_forward_oot(
+    self,
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    is_neox_style_override: Optional[bool] = None,
+    is_qwen_torchair: Optional[bool] = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    if get_ascend_config(
+    ).torchair_graph_config.enabled and not is_qwen_torchair:
+        return self.forward_native(
+            positions,
+            query,
+            key,
+            offsets,
+        )
+
+    query_shape, key_shape = query.shape, key.shape
+    if self.cos_sin_cache.device != query.device:
+        self.cos_sin_cache = self.cos_sin_cache.to(query.device)
+    if self.cos_sin_cache.dtype != query.dtype:
+        self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
+    neox_style = self.is_neox_style
+    if is_neox_style_override is not None:
+        neox_style = is_neox_style_override
+    # adopt custom kernel path for rotary_embedding
+    if custom_rotary_embedding_enabled(query, neox_style,
+                                       self.head_size) and not is_310p():
+        query, key = torch.ops._C.rotary_embedding(
+            positions,
+            query,
+            key,
+            self.head_size,
+            self.cos_sin_cache,
+            neox_style,
+        )
+        return query.view(query_shape), key.view(key_shape)
+    if offsets is not None:
+        raise NotImplementedError(
+            "Batched rotary embedding is currently not supported on NPU.")
+    else:
+        # TODO: Remove the contiguous in the future.
+        query = query.contiguous().view(query.shape[0], -1)
+        key = key.contiguous().view(key.shape[0], -1)
+        torch_npu._npu_rotary_embedding(
+            positions,
+            query,
+            key,
+            self.head_size,
+            self.cos_sin_cache,
+            neox_style,
+        )
+    return query.view(query_shape), key.view(key_shape)
+
+
+def native_rope_deepseek_forward(self,
+                                 positions: torch.Tensor,
+                                 query: torch.Tensor,
+                                 key: torch.Tensor,
+                                 offsets: Optional[torch.Tensor] = None,
+                                 max_seq_len: Optional[int] = None):
+    if max_seq_len is not None and max_seq_len > self.max_seq_len:
+        _set_cos_sin_cache(self, max_seq_len, query.device, query.dtype)
+    if len(key.shape) == 2:
+        key = key[:, None, :]
+    # Note: we implement the non neox_style method with shuffle the last dim and neox style
+    # calculation method which is also more compute friendly to the ascend machine
+    # https://huggingface.co/deepseek-ai/DeepSeek-V3-0324/blob/main/modeling_deepseek.py
+    neox_style = True
+    if self.is_neox_style is False:
+        b, h_q, d = query.shape
+        query = query.view(b, h_q, d // 2, 2).transpose(3,
+                                                        2).reshape(b, h_q, d)
+        b, h_k, d = key.shape
+        key = key.view(b, h_k, d // 2, 2).transpose(3, 2).reshape(b, h_k, d)
+    q_pe, k_pe = rope_forward_oot(self, positions, query, key, offsets,
+                                  neox_style)
+    return q_pe, k_pe
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., :x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(num_rotations,
+                             dim,
+                             base=10000,
+                             max_position_embeddings=2048):
+    # Note: use torch instead of math to solve MTP compilation error.
+    return (dim * torch.log(
+        torch.tensor(max_position_embeddings) /
+        (num_rotations * 2 * torch.pi))) / (2 * torch.log(torch.tensor(base)))
+
+
+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
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(low_rot,
+                               high_rot,
+                               dim,
+                               base=10000,
+                               max_position_embeddings=2048):
+    # Note: use torch instead of math to solve MTP compilation error.
+    low = torch.floor(
+        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
+    high = torch.ceil(
+        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings))
+    # Note: use torch instead of max/min to solve MTP compilation error.
+    return torch.clamp(low, min=0), torch.clamp(high, max=dim - 1)
+
+
+def yarn_linear_ramp_mask(min_value, max_value, dim):
+    # Note: The if conditional branch is not used here
+    # to solve MTP compilation error.
+    max_value += (min_value == max_value).float() * 0.001
+    linear_func = (torch.arange(dim, dtype=torch.float32) -
+                   min_value) / (max_value - min_value)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids]
+    sin = sin[position_ids]
+    cos = cos[:, None, None, :]
+    sin = sin[:, None, None, :]
+
+    if len(q.shape) == 3:
+        q = q[:, :, None, :]
+    if len(k.shape) == 2:
+        k = k[:, None, None, :]
+    elif len(k.shape) == 3:
+        k = k[:, :, None, :]
+
+    b, h_q, s, d = q.shape
+    q = q.view(b, h_q, s, d // 2, 2).transpose(4, 3).reshape(b, h_q, s, d)
+
+    b, h_k, s, d = k.shape
+    k = k.view(b, h_k, s, d // 2, 2).transpose(4, 3).reshape(b, h_k, s, d)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+
+    q_embed = q_embed.view(b, h_q, d)
+    k_embed = k_embed.view(b, h_k, d)
+
+    return q_embed, k_embed
+
+
+def _set_cos_sin_cache(self, seq_len, device, dtype):
+    self.max_seq_len_cached = seq_len
+    dim = self.rotary_dim
+
+    freq_extra = 1.0 / (self.base**(
+        torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+    freq_inter = 1.0 / (self.scaling_factor * self.base**(
+        torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+
+    low, high = yarn_find_correction_range(
+        self.beta_fast,
+        self.beta_slow,
+        dim,
+        self.base,
+        self.max_position_embeddings,
+    )
+    inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
+        device=device, dtype=torch.float32)
+    inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+    self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    t = torch.arange(seq_len * self.scaling_factor,
+                     device=device,
+                     dtype=torch.float32)
+
+    freqs = torch.outer(t, inv_freq)
+    cos_cached = torch.cat([freqs, freqs], dim=-1).cos() * self.mscale
+    sin_cached = torch.cat([freqs, freqs], dim=-1).sin() * self.mscale
+    cos_cached = cos_cached.to(dtype)
+    sin_cached = sin_cached.to(dtype)
+    cache = torch.cat([freqs.cos() * self.mscale,
+                       freqs.sin() * self.mscale],
+                      dim=-1).to(dtype)
+    self.register_buffer("cos_sin_cache", cache, persistent=False)
+    self.register_buffer("cos_cached", cos_cached, persistent=False)
+    self.register_buffer("sin_cached", sin_cached, persistent=False)
+
+
+def __set_cos_sin_cache(self, seq_len, device, dtype):
+    inv_freq = 1.0 / (self.base**(torch.arange(
+        0, self.rotary_dim, 2, device=device, dtype=torch.float32) *
+                                  (1 / self.rotary_dim)))
+    self.register_buffer("inv_freq", inv_freq)
+
+    t = torch.arange(self.max_position_embeddings,
+                     device=self.inv_freq.device,
+                     dtype=torch.float32)
+    freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+
+    emb = torch.cat((freqs, freqs), dim=-1)
+    self.register_buffer("cos", emb.cos().to(dtype=dtype), persistent=False)
+    self.register_buffer("sin", emb.sin().to(dtype=dtype), persistent=False)
+    self.embed = F.embedding
+
+
+_original_re_init = RotaryEmbedding.__init__
+
+
+def qwen_rope_init_func(
+    self,
+    head_size: int,
+    rotary_dim: int,
+    max_position_embeddings: int,
+    base: float,
+    is_neox_style: bool,
+    dtype: torch.dtype,
+) -> None:
+    _original_re_init(self, head_size, rotary_dim, max_position_embeddings,
+                      base, is_neox_style, dtype)
+    if get_ascend_config().torchair_graph_config.enabled:
+        __set_cos_sin_cache(self,
+                            seq_len=max_position_embeddings,
+                            device="npu",
+                            dtype=dtype)
+
+
+def rope_forward(
+    self,
+    positions: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    offsets: Optional[torch.Tensor] = None,
+    is_neox_style_override: Optional[bool] = None,
+    max_seq_len: Optional[int] = None,
+    is_prefill: Optional[bool] = True,
+    is_qwen_torchair: Optional[bool] = False,
+):
+    if get_ascend_config().torchair_graph_config.enabled \
+            and is_qwen_torchair and not is_prefill:
+        if max_seq_len is not None and torch.gt(max_seq_len,
+                                                self.max_position_embeddings):
+            __set_cos_sin_cache(self,
+                                seq_len=max_seq_len,
+                                device=query.device,
+                                dtype=torch.float32)
+
+        # bsnd/bnsd
+        if positions is not None:
+            cos = self.embed(positions, self.cos)
+            sin = self.embed(positions, self.sin)
+            self.cos_embed = cos
+            self.sin_embed = sin
+        else:
+            cos = self.cos_embed
+            sin = self.sin_embed
+
+        query = query.view(*query.shape[:-1], -1, self.head_size).contiguous()
+        key = key.view(*key.shape[:-1], -1, self.head_size).contiguous()
+
+        cos = cos.unsqueeze(-2).unsqueeze(-2)
+        sin = sin.unsqueeze(-2).unsqueeze(-2)
+
+        query = query.unsqueeze(1)
+        key = key.unsqueeze(1)
+
+        q_embed, k_embed = torch_npu.npu_apply_rotary_pos_emb(
+            query, key, cos, sin)
+        return q_embed.flatten(-2), k_embed.flatten(-2)
+    else:
+        return rope_forward_oot(self, positions, query, key, offsets,
+                                is_neox_style_override,
+                                is_qwen_torchair)  # type: ignore
+
+
+def deepseek_rope_init_func(
+    self,
+    head_size: int,
+    rotary_dim: int,
+    max_position_embeddings: int,
+    base: int,
+    is_neox_style: bool,
+    scaling_factor: float,
+    dtype: torch.dtype,
+    *,
+    extrapolation_factor: float = 1,
+    attn_factor: float = 1,
+    beta_fast: int = 32,
+    beta_slow: int = 1,
+    mscale: float = 1,
+    mscale_all_dim: float = 0,
+) -> None:
+    self.scaling_factor = scaling_factor
+    self.extrapolation_factor = extrapolation_factor
+    self.attn_factor = attn_factor
+    self.beta_fast = beta_fast
+    self.beta_slow = beta_slow
+    # Get n-d magnitude scaling corrected for interpolation.
+    self.mscale = float(
+        yarn_get_mscale(self.scaling_factor, float(mscale)) /
+        yarn_get_mscale(self.scaling_factor, float(mscale_all_dim)) *
+        attn_factor)
+    super(DeepseekScalingRotaryEmbedding,
+          self).__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+    self.max_seq_len = max_position_embeddings
+    _set_cos_sin_cache(self,
+                       max_position_embeddings,
+                       dtype=dtype,
+                       device="npu")

vllm_ascend/torchair/torchair_model_runner.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 # This file is a part of the vllm-ascend project.
 # Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
-#
+# isort: skip_file
 
 import types
 from typing import Optional
@@ -34,12 +34,10 @@ from vllm.logger import logger
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.platform import NPUPlatform
-from vllm_ascend.torchair.utils import (TorchairCommonAttentionMetadata,
-                                        check_torchair_cache_exist,
-                                        converting_weight_acl_format,
-                                        register_torchair_model,
-                                        torchair_quant_method_register,
-                                        write_kv_cache_bytes_to_file)
+from vllm_ascend.torchair.utils import (
+    TorchairCommonAttentionMetadata, check_torchair_cache_exist,
+    converting_weight_acl_format, register_torchair_model, torchair_ops_patch,
+    torchair_quant_method_register, write_kv_cache_bytes_to_file)
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
                                is_310p)
 from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
@@ -68,6 +66,7 @@ class NPUTorchairModelRunner(NPUModelRunner):
 
         self._check_batch_sizes_consistency()
         register_torchair_model()
+        torchair_ops_patch()
         torchair_quant_method_register()
 
     def _sync_metadata_across_dp(

vllm_ascend/torchair/utils.py

@@ -182,3 +182,18 @@ def torchair_quant_method_register():
         "W8A8_DYNAMIC"] = TorchairW8A8DYNAMICQuantizer
     SUPPORT_ASCEND_QUANTIZER_TYPE[
         "W4A8_DYNAMIC"] = TorchairW4A8DYNAMICQuantizer
+
+
+def torchair_ops_patch():
+    from vllm.model_executor.layers.rotary_embedding import (
+        DeepseekScalingRotaryEmbedding, RotaryEmbedding)
+
+    from vllm_ascend.torchair.ops.torchair_rotary_embedding import (
+        deepseek_rope_init_func, native_rope_deepseek_forward,
+        qwen_rope_init_func, rope_forward)
+
+    RotaryEmbedding.__init__ = qwen_rope_init_func
+    RotaryEmbedding.forward_oot = rope_forward
+
+    DeepseekScalingRotaryEmbedding.__init__ = deepseek_rope_init_func
+    DeepseekScalingRotaryEmbedding.forward = native_rope_deepseek_forward