mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
synced 2026-02-20 19:50:15 +00:00
366 lines
14 KiB
Python
366 lines
14 KiB
Python
#
|
|
# 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")
|