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xc-llm-ascend/vllm_ascend/ops/rotary_embedding.py
wubin58 4230bc8646 [Bugfix]Modify NPU rotary encoding parameter fields，fix RopeOperation setup failed in condition of self.rotary_dim < self.head_size (#6310) 
### What this PR does / why we need it?
change self.head_size to self.rotary_dim.
only the rotary part is processed here, the dimension should be rotary_dim.

Fix bug #6060

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

### How was this patch tested?
Only a small section of code was modified to adjust the parameters, and
all standard tests were passed.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

Signed-off-by: fengshi666 <fengshi666@adsl-99-12-210-25.dsl.hstntx.sbcglobal.net>
Co-authored-by: fengshi666 <fengshi666@adsl-99-12-210-25.dsl.hstntx.sbcglobal.net>
2026-01-30 21:25:04 +08:00

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#
# 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_npu
from vllm.model_executor.layers.rotary_embedding import (
DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding,
YaRNScalingRotaryEmbedding)
from vllm.model_executor.layers.rotary_embedding.common import ApplyRotaryEmb
from vllm.triton_utils import HAS_TRITON
if HAS_TRITON:
from vllm.model_executor.layers.rotary_embedding.mrope import triton_mrope
from vllm_ascend.platform import NPUPlatform
from vllm_ascend.utils import (AscendDeviceType, enable_custom_op,
get_ascend_device_type, has_rope, is_vl_model)
# Currently, rope ops used on npu requires detached cos && sin as inputs.
# However, RotaryEmbedding in vllm use cos_sin_cache as a whole variable.
# So we have to preprocess cos_sin_cache int cos && sin. In the future,
# we shall implement a new rope ops which accept cos_sin_cache as inputs.
# NOTE(Angazenn): MLA && SFA models uses attn_metadata to pass cos && sin
# to rope in AscendMLA(SFA)Impl. However, since rope is isolated from
# AscendAttentionBackendImpl for GQA models, we cannot pass cos && sin by
# attn_metadata. This causes that rope in GQA models must pass cos && sin
# by different approaches.
_cos_mla: torch.Tensor = None
_sin_mla: torch.Tensor = None
_cos_cache: torch.Tensor = None
_sin_cache: torch.Tensor = None
_cos_sin_cache: torch.Tensor = None
_cos: torch.Tensor = None
_sin: torch.Tensor = None
_cos_slice: torch.Tensor = None
_sin_slice: torch.Tensor = None
def set_cos_and_sin(vllm_config, max_num_reqs, decode_token_per_req, dtype,
device):
global _cos_mla
global _sin_mla
global _cos
global _sin
if _cos_mla is not None or \
_sin_mla is not None or \
_cos is not None or \
_sin is not None:
return
model_config = vllm_config.model_config
max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
if model_config.use_mla:
rope_dim = model_config.hf_text_config.qk_rope_head_dim
_cos_mla = torch.ones(max_num_batched_tokens,
1,
1,
rope_dim,
dtype=dtype,
device=device)
_sin_mla = torch.zeros(max_num_batched_tokens,
1,
1,
rope_dim,
dtype=dtype,
device=device)
elif not is_vl_model(vllm_config) and has_rope(vllm_config):
rope_dim = model_config.get_head_size()
# For models using partial rope like Qwen3-Next.
if hasattr(model_config.hf_text_config, "partial_rotary_factor"):
rope_dim = int(rope_dim *
model_config.hf_text_config.partial_rotary_factor)
_cos = torch.ones(1,
max_num_batched_tokens,
1,
rope_dim,
dtype=dtype,
device=device)
_sin = torch.zeros(1,
max_num_batched_tokens,
1,
rope_dim,
dtype=dtype,
device=device)
def get_cos_and_sin_mla(positions, use_cache=False):
global _cos_cache
global _sin_cache
cos = _cos_cache[positions].unsqueeze(1).unsqueeze(2)
sin = _sin_cache[positions].unsqueeze(1).unsqueeze(2)
if not use_cache:
return cos, sin
global _cos_mla
global _sin_mla
num_tokens = positions.size(0)
_cos_mla[:num_tokens, ...] = cos
_sin_mla[:num_tokens, ...] = sin
return _cos_mla[:num_tokens, ...], _sin_mla[:num_tokens, ...]
def _record_cos_sin_cache(cos_sin_cache):
global _cos_sin_cache
if _cos_sin_cache is not None:
return
_cos_sin_cache = cos_sin_cache
def _record_cos_and_sin_cache(cos_cache, sin_cache):
global _cos_cache
global _sin_cache
_cos_cache = cos_cache
_sin_cache = sin_cache
def _record_cos_and_sin_cache_interleaved(cos_sin_cache):
global _cos_cache
global _sin_cache
if _cos_cache is not None or _sin_cache is not None:
return
hidden_dim = cos_sin_cache.shape[-1] // 2
cos_cache, sin_cache = cos_sin_cache.view(-1, 2, hidden_dim).repeat(
1, 1, 2).chunk(2, dim=1)
_cos_cache = cos_cache.squeeze(1)
_sin_cache = sin_cache.squeeze(1)
def update_cos_sin(positions):
global _cos
global _sin
global _cos_slice
global _sin_slice
if _cos_sin_cache is None or \
_cos is None or \
_sin is None:
return
num_tokens = positions.size(0)
_cos[:, :num_tokens] = _cos_sin_cache.index_select(0, positions).view(
num_tokens, 2, -1).repeat(1, 1, 2).chunk(2, dim=-2)[0]
_sin[:, :num_tokens] = _cos_sin_cache.index_select(0, positions).view(
num_tokens, 2, -1).repeat(1, 1, 2).chunk(2, dim=-2)[1]
_cos_slice = _cos[:, :num_tokens]
_sin_slice = _sin[:, :num_tokens]
def get_cos_and_sin_slice():
return _cos_slice, _sin_slice
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,
is_neox_style: bool,
offsets: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
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)
cos, sin = get_cos_and_sin_slice()
# adopt custom kernel path for rotary_embedding
if _custom_rotary_embedding_enabled(
query, is_neox_style, self.head_size) and get_ascend_device_type(
) != AscendDeviceType._310P:
query, key = torch.ops._C_ascend.rotary_embedding(
positions,
query,
key,
self.head_size,
self.cos_sin_cache,
is_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:
if is_neox_style and self.head_size == 128 and self.cos_sin_cache.shape[
-1] == 128 and cos is not None and sin is not None:
# If cos and sin are generated outside, use npu_apply_rotary_pos_emb to avoid redundant calculation.
# This method requires head_size and rotary_dim equal 128 and neox_style is True
query = query.contiguous().view(1, query.shape[0], -1,
self.head_size)
key = key.contiguous().view(1, key.shape[0], -1, self.head_size)
# Although this function modifies in-place, please retain the function's return value.
# Otherwise, the graph fusion operation may fail.
query, key = torch_npu.npu_apply_rotary_pos_emb(
query, key, cos, sin)
elif self.rotary_dim < self.head_size:
if HAS_TRITON:
cos = cos.view(-1, self.rotary_dim)
sin = sin.view(-1, self.rotary_dim)
q = query.contiguous().view(query.shape[0], -1,
self.head_size)
k = key.contiguous().view(key.shape[0], -1, self.head_size)
query, key = torch.ops.vllm.rope_forward_triton(q,
k,
cos,
sin,
rope_dim=self.rotary_dim,
is_neox_style=True)
return query.view(query_shape), key.view(key_shape)
else:
num_tokens = query.shape[0]
query = query.view(num_tokens, -1, self.head_size)
key = key.view(num_tokens, -1, self.head_size)
q_rot = query[..., :self.rotary_dim]
q_pass = query[..., self.rotary_dim:]
k_rot = key[..., :self.rotary_dim]
k_pass = key[..., self.rotary_dim:]
q_rot = q_rot.contiguous().view(num_tokens, -1)
k_rot = k_rot.contiguous().view(num_tokens, -1)
# only the rotary part is processed here,
# the dimension should be rotary_dim
torch_npu._npu_rotary_embedding(
positions,
q_rot,
k_rot,
self.rotary_dim,
self.cos_sin_cache,
is_neox_style,
)
q_rot = q_rot.view(num_tokens, -1, self.rotary_dim)
k_rot = k_rot.view(num_tokens, -1, self.rotary_dim)
q = torch.cat((q_rot, q_pass), dim=-1).reshape(query_shape)
k = torch.cat((k_rot, k_pass), dim=-1).reshape(key_shape)
return q, k
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,
is_neox_style,
)
return query.view(query_shape), key.view(key_shape)
class AscendRotaryEmbedding(RotaryEmbedding):
def __init__(
self,
head_size: int,
rotary_dim: int,
max_position_embeddings: int,
base: float,
is_neox_style: bool,
dtype: torch.dtype,
) -> None:
super().__init__(head_size, rotary_dim, max_position_embeddings, base,
is_neox_style, dtype)
_record_cos_sin_cache(self.cos_sin_cache)
_record_cos_and_sin_cache_interleaved(self.cos_sin_cache)
def 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_neox_style = self.is_neox_style
if is_neox_style_override is not None:
is_neox_style = is_neox_style_override
return _rope_forward_oot(self, positions, query, key, is_neox_style,
offsets)
class AscendYaRNRotaryEmbedding(YaRNScalingRotaryEmbedding):
def __init__(
self,
head_size: int,
rotary_dim: int,
max_position_embeddings: int,
base: float,
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,
) -> None:
extra_kwargs = {
"extrapolation_factor": extrapolation_factor,
"attn_factor": attn_factor,
"beta_fast": beta_fast,
"beta_slow": beta_slow
}
super().__init__(head_size, rotary_dim, max_position_embeddings, base,
is_neox_style, scaling_factor, dtype, **extra_kwargs)
_record_cos_sin_cache(self.cos_sin_cache)
def forward_oot(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None,
is_neox_style_override: Optional[bool] = None,
):
return AscendRotaryEmbedding.forward_oot(self, positions, query, key,
offsets,
is_neox_style_override)
class AscendDeepseekScalingRotaryEmbedding(DeepseekScalingRotaryEmbedding):
def __init__(
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:
# Note: we adopt the native huggingface deepseek rope initialization code from
# https://huggingface.co/deepseek-ai/DeepSeek-V3-0324/blob/main/modeling_deepseek.py for
# its more ascend compute friendly
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(
self._yarn_get_mscale(self.scaling_factor, float(mscale)) /
self._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)
# NOTE: For ascend friendly computing, reorder sin and cos cache
self.max_seq_len = math.ceil(max_position_embeddings * scaling_factor)
self._set_cos_sin_cache(self.max_seq_len,
device=NPUPlatform.device_type,
dtype=dtype)
def _yarn_get_mscale(self, scale: float = 1, mscale: float = 1) -> float:
if scale <= 1:
return 1.0
return 0.1 * mscale * math.log(scale) + 1.0
def _rotate_half(self, 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)
def _yarn_linear_ramp_mask(self, 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
# Inverse dim formula to find dim based on number of rotations
def _yarn_find_correction_dim(self,
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)))
# Find dim range bounds based on rotations
def _yarn_find_correction_range(self,
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(
self._yarn_find_correction_dim(low_rot, dim, base,
max_position_embeddings))
high = torch.ceil(
self._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)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def _apply_rotary_pos_emb(self,
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) + (self._rotate_half(q) * sin)
k_embed = (k * cos) + (self._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, max_seq_len, device, dtype):
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 = self._yarn_find_correction_range(
self.beta_fast,
self.beta_slow,
dim,
self.base,
self.max_position_embeddings,
)
inv_freq_mask = 1.0 - self._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(max_seq_len, 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)
_record_cos_sin_cache(cache)
_record_cos_and_sin_cache(cos_cached, sin_cached)
def forward(self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None):
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
is_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,
is_neox_style, offsets)
return q_pe, k_pe
class AscendMRotaryEmbedding(MRotaryEmbedding):
def forward_triton(self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor | None = None,
offsets: torch.Tensor | None = None):
assert positions.ndim == 2
assert key is not None
self._match_cos_sin_cache_dtype(query)
self.cos = None
self.sin = None
if self.cos is None and self.sin is None:
cos_sin = self.cos_sin_cache[positions] # type: ignore
cos, sin = cos_sin.chunk(2, dim=-1)
self.cos = cos.contiguous()
self.sin = sin.contiguous()
query_shape = query.shape
key_shape = key.shape
assert self.mrope_section
q, k = triton_mrope(
query,
key,
self.cos,
self.sin,
self.mrope_section,
self.head_size,
self.rotary_dim,
self.mrope_interleaved,
)
return q.reshape(query_shape), k.reshape(key_shape)
def forward_oot(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
):
if HAS_TRITON and positions.ndim == 2 and self.mrope_interleaved:
# todo: need cann update in 8.5.0
return self.forward_triton(positions, query, key)
if self.mrope_section != [16, 24, 24] or \
get_ascend_device_type() == AscendDeviceType.A5:
return super().forward_oot(positions, query, key)
import torch_npu
mrope_section = [0, 0, 0
] if positions.ndim == 1 else self.mrope_section
if self.cos_sin_cache.device != query.device: # type: ignore
self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore
query.device) # type: ignore
if self.cos_sin_cache.dtype != query.dtype: # type: ignore
self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore
query.dtype) # type: ignore
query, key = torch_npu.npu_mrope(positions.contiguous(),
query.contiguous(),
key.contiguous(),
self.cos_sin_cache.contiguous(),
self.head_size,
mrope_section=mrope_section,
rotary_mode='half')
return query, key
class AscendApplyRotaryEmb(ApplyRotaryEmb):
def __init__(
self,
enforce_enable: bool = False,
is_neox_style: bool = True,
enable_fp32_compute: bool = False,
) -> None:
super().__init__(
enforce_enable=enforce_enable,
is_neox_style=is_neox_style,
enable_fp32_compute=enable_fp32_compute,
)
def forward_oot(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> torch.Tensor:
x, cos, sin, origin_shape, origin_dtype = self._pre_process(
x, cos, sin)
head_dim = x.shape[-1]
# cos, sin: [seq_len, head_dim // 2]
cos = torch.cat((cos, cos), dim=-1)
sin = torch.cat((sin, sin), dim=-1)
# cos, sin: [1, seq_len, 1, head_dim]
cos = cos.reshape(1, -1, 1, head_dim)
sin = sin.reshape(1, -1, 1, head_dim)
output = torch_npu.npu_rotary_mul(x, cos, sin)
output = self._post_process(output, origin_shape, origin_dtype)
return output
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