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[Performance]Optimize DeepSeekOCR2 RelPosAttention and CustomQwen2Decoder (#7737)

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### What this PR does / why we need it?
Optimize DeepSeekOCR2 RelPosAttention and CustomQwen2Decoder and add doc
for DeepSeekOCR2.md

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
- vllm 0.18.0
- vllm-ascend main

1. _create_custom_4d_mask during 141ms49us620ns -->
_create_npu_optimized_mask during 1ms227us780ns
2. convd2d : 27ms --> matmul <1ms
3. relposattention：sdpa->prompt_flash_attention

---------

Signed-off-by: Wangbei25 <wangbei41@huawie.com>
Signed-off-by: Wangbei25 <wangbei41@huawei.com>
Co-authored-by: Wangbei25 <wangbei41@huawie.com>
This commit is contained in:
Wangbei25
2026-03-31 14:49:29 +08:00
committed by GitHub
parent 2a0a588311
commit 4f259d4fd8
4 changed files with 432 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

68
vllm_ascend/ops/rel_pos_attention.py Normal file
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@@ -0,0 +1,68 @@
import torch
import torch_npu
from vllm.model_executor.models.deepencoder import RelPosAttention, add_decomposed_rel_pos
class AscendRelPosAttention(RelPosAttention):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
input_size: tuple[int, int] | None = None,
) -> None:
"""
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
qkv_bias (bool): If True, add a learnable bias to query, key, value.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
input_size (tuple(int, int) or None): Input resolution for calculating the relative
positional parameter size.
"""
super().__init__(dim, num_heads, qkv_bias, use_rel_pos, rel_pos_zero_init, input_size)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, H, W, _ = x.shape
# qkv with shape (3, B, nHead, H * W, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# q, k, v with shape (B * nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
rel_h, rel_w = None, None
if self.use_rel_pos:
rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
q = q.view(B, self.num_heads, H * W, -1)
k = k.view(B, self.num_heads, H * W, -1)
v = v.view(B, self.num_heads, H * W, -1)
if self.use_rel_pos:
assert rel_h is not None and rel_w is not None
rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
x = torch_npu.npu_prompt_flash_attention(
q,
k,
v,
pse_shift=attn_bias,
input_layout="BNSD",
scale_value=self.scale,
num_heads=self.num_heads,
)
else:
x = torch_npu.npu_prompt_flash_attention(
q,
k,
v,
input_layout="BNSD",
scale_value=self.scale,
num_heads=self.num_heads,
)
x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
x = self.proj(x)
return x

1
vllm_ascend/patch/worker/__init__.py
View File

@@ -49,3 +49,4 @@ import vllm_ascend.patch.worker.patch_deepseek_mtp # noqa
import vllm_ascend.patch.worker.patch_v2.patch_input_batch # noqa
import vllm_ascend.patch.worker.patch_v2.patch_model_state # noqa
import vllm_ascend.patch.worker.patch_v2.patch_block_table # noqa
import vllm_ascend.patch.worker.patch_deepencoder2 # noqa

360
vllm_ascend/patch/worker/patch_deepencoder2.py Normal file
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@@ -0,0 +1,360 @@
from collections.abc import Callable
import torch
import torch.nn as nn
import torch_npu
import vllm
from transformers import Qwen2Config
from transformers.cache_utils import Cache, DynamicCache
from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
from transformers.models.qwen2.modeling_qwen2 import (
Qwen2Attention,
Qwen2DecoderLayer,
Qwen2Model,
eager_attention_forward,
)
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs
from vllm.model_executor.models.deepencoder2 import CustomQwen2Decoder
class AscendCustomQwen2Decoder(CustomQwen2Decoder):
def __init__(
self,
decoder_layer: int = 24,
max_position_embeddings: int = 131072,
hidden_dimension: int = 896,
num_attention_heads: int = 14,
num_key_value_heads: int = 2,
intermediate_size: int = 4864,
vocab_size: int = 151936,
attn_implementation: str = "sdpa",
rms_norm_eps: float = 1e-06,
rope_theta: float = 1000000.0,
attention_dropout: float = 0.0,
hidden_act: str = "silu",
initializer_range: float = 0.02,
):
super().__init__(
decoder_layer,
max_position_embeddings,
hidden_dimension,
num_attention_heads,
num_key_value_heads,
intermediate_size,
vocab_size,
attn_implementation,
rms_norm_eps,
rope_theta,
attention_dropout,
hidden_act,
initializer_range,
)
# config
config = Qwen2Config(
hidden_size=hidden_dimension,
num_hidden_layers=decoder_layer,
num_attention_heads=num_attention_heads,
num_key_value_heads=num_key_value_heads,
intermediate_size=intermediate_size,
max_position_embeddings=max_position_embeddings,
vocab_size=vocab_size,
rms_norm_eps=rms_norm_eps,
rope_theta=rope_theta,
attention_dropout=attention_dropout,
hidden_act=hidden_act,
initializer_range=initializer_range,
_attn_implementation=attn_implementation,
)
self.model = self._create_optimized_custom_model(config)
del self.model.embed_tokens
def _create_optimized_custom_model(self, config):
class CustomQwen2ModelInner(AscendQwen2Model):
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
cache_position=None,
token_type_ids=None,
**kwargs: Unpack[TransformersKwargs],
):
# token_type_ids
self._current_token_type_ids = token_type_ids
causal_mask_mapping = {
"full_attention": self._create_npu_optimized_mask(
attention_mask=attention_mask,
input_tensor=inputs_embeds,
token_type_ids=token_type_ids,
)
}
outputs = super().forward(
input_ids=input_ids,
attention_mask=causal_mask_mapping,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
cache_position=cache_position,
)
return outputs
def _create_npu_optimized_mask(
self,
attention_mask,
input_tensor,
token_type_ids,
):
"""
4D Mask generation optimized for NPU
vector parallel implementation, replacing the original loop implementation
"""
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
batch_size, sequence_length = input_tensor.shape[0], input_tensor.shape[1]
if token_type_ids is None:
return self._create_standard_causal_mask(batch_size, sequence_length, dtype, device, attention_mask)
# ==========================================
# NPU optimization: vectorized parallel mask generation (replacing loops)
# ==========================================
# 1. create image token position mask [batch, seq_len]
is_image = (token_type_ids == 0).unsqueeze(-1).to(dtype=dtype, device=device) # [batch, seq_len, 1]
is_text = (token_type_ids == 1).unsqueeze(-1).to(dtype=dtype, device=device) # [batch, seq_len, 1]
# 2. Bidirectional attention (fully connected) between image tokens.
# image_attention: [batch, seq_len, seq_len]
image_attention = torch.bmm(is_image, is_image.transpose(1, 2))
# 3. Visibility of text tokens to image tokens (full connection)
text_to_image = torch.bmm(is_text, is_image.transpose(1, 2))
# 4. Causal attention from text to text.
# First, perform matrix multiplication to obtain the text-text relationship pairs of [B, L, L].
text_to_text_base = torch.bmm(is_text, is_text.transpose(1, 2))
# create casual triangular Lower
causal_mask = torch.tril(torch.ones((sequence_length, sequence_length), device=device, dtype=dtype))
text_to_text = text_to_text_base * causal_mask.unsqueeze(0)
# 5. Merge all attention patterns
combined_mask = image_attention + text_to_image + text_to_text
combined_mask = (1 - combined_mask) * min_dtype # reverse：0->min_dtype, 1->0
# 6. Process Padding Mask (attention_mask)
if attention_mask is not None:
# Ensure that padding_mask is on the same device
p_mask = attention_mask.to(device=device, dtype=dtype)
if p_mask.dim() == 2:
# Extended to [B, 1, 1, L] to adapt to 4D attention.
p_mask = (1.0 - p_mask[:, None, None, :]) * min_dtype
return combined_mask.unsqueeze(1) + p_mask
return combined_mask.unsqueeze(1)
def _create_standard_causal_mask(self, batch_size, seq_len, dtype, device, attention_mask):
"""Standard causal mask (when token_type_ids is None)"""
min_dtype = torch.finfo(dtype).min
mask = torch.triu(torch.full((seq_len, seq_len), min_dtype, dtype=dtype, device=device), diagonal=1)
mask = mask.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, seq_len, seq_len)
if attention_mask is not None and attention_mask.dim() == 2:
padding_mask = attention_mask[:, None, None, :].to(dtype=dtype)
padding_mask = (1.0 - padding_mask) * min_dtype
mask = mask + padding_mask
return mask
return CustomQwen2ModelInner(config)
class AscendQwen2Model(Qwen2Model):
def __init__(self, config: Qwen2Config):
super().__init__(config)
self.layers = nn.ModuleList(
[AscendQwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
inputs_embeds: torch.FloatTensor | None = None,
use_cache: bool | None = None,
cache_position: torch.LongTensor | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache(config=self.config)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
# It may already have been prepared by e.g. `generate`
if not isinstance(causal_mask_mapping := attention_mask, dict):
# Prepare mask arguments
mask_kwargs = {
"config": self.config,
"input_embeds": inputs_embeds,
"attention_mask": attention_mask,
"cache_position": cache_position,
"past_key_values": past_key_values,
"position_ids": position_ids,
}
# Create the masks
causal_mask_mapping = {
"full_attention": create_causal_mask(**mask_kwargs),
}
# The sliding window alternating layers are not always activated depending on the config
if self.has_sliding_layers:
causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = self.norm(hidden_states)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
)
class AscendQwen2DecoderLayer(Qwen2DecoderLayer):
def __init__(self, config: Qwen2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = AscendQwen2Attention(config=config, layer_idx=layer_idx)
self.input_layernorm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
use_cache: bool | None = False,
cache_position: torch.LongTensor | None = None,
position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
def optimized_apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = torch_npu.npu_rotary_mul(q, cos, sin)
k_embed = torch_npu.npu_rotary_mul(k, cos, sin)
return q_embed, k_embed
class AscendQwen2Attention(Qwen2Attention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Qwen2Config, layer_idx: int):
super().__init__(config, layer_idx)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: torch.Tensor | None,
past_key_values: Cache | None = None,
cache_position: torch.LongTensor | None = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, torch.Tensor | None]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = optimized_apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
sliding_window=self.sliding_window, # main diff with Llama
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class AscendQwen2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.variance_epsilon)[0]
vllm.model_executor.models.deepencoder2.CustomQwen2Decoder = AscendCustomQwen2Decoder

5
vllm_ascend/utils.py
View File

@@ -604,7 +604,7 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
from vllm.model_executor.custom_op import CustomOp
from vllm_ascend.ops.activation import AscendQuickGELU, AscendSiluAndMul
from vllm_ascend.ops.conv import AscendConv2dLayer, AscendConv3dLayer
from vllm_ascend.ops.conv import AscendConv3dLayer
from vllm_ascend.ops.fused_moe.fused_moe import AscendFusedMoE, AscendSharedFusedMoE
from vllm_ascend.ops.layernorm import AscendGemmaRMSNorm, AscendRMSNorm, AscendRMSNormGated
from vllm_ascend.ops.linear import (
@@ -616,6 +616,7 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
)
from vllm_ascend.ops.mla import AscendMultiHeadLatentAttention
from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention
from vllm_ascend.ops.rel_pos_attention import AscendRelPosAttention
from vllm_ascend.ops.rotary_embedding import (
AscendApplyRotaryEmb,
AscendDeepseekScalingRotaryEmbedding,
@@ -653,8 +654,8 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
"MMEncoderAttention": AscendMMEncoderAttention,
"ApplyRotaryEmb": AscendApplyRotaryEmb,
"RMSNormGated": AscendRMSNormGated,
"Conv2dLayer": AscendConv2dLayer,
"Conv3dLayer": AscendConv3dLayer,
"RelPosAttention": AscendRelPosAttention,
}
# 310P: override selected ops with 310P implementations (keep minimal changes outside _310p)