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vllm_npu/torchair/ops/torchair_rotary_embedding.py Normal file
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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.nn.functional as F
import torch_npu
from vllm.model_executor.layers.rotary_embedding import (
DeepseekScalingRotaryEmbedding, RotaryEmbedding)
from vllm_npu.ascend_config import get_ascend_config
from vllm_npu.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_ascend.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):
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, 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 = 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(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)
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)
# NOTE: For ascend friendly computing, reorder sin and cos cache
self.max_seq_len = math.ceil(max_position_embeddings * scaling_factor)
_set_cos_sin_cache(self, self.max_seq_len, dtype=dtype, device="npu")