CANN_Transformer加速库ascend-transformer-boost的大模型推理性能优化实践

前言

Transformer架构已成为自然语言处理和计算机视觉领域的主流模型架构。随着GPT、LLaMA等大语言模型的兴起，如何高效地在NPU上部署和推理Transformer模型成为关键挑战。ascend-transformer-boost是CANN生态中专门针对Transformer模型优化的加速库，提供了从算子级别到模型级别的全方位优化方案。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• ascend-transformer-boost仓库链接：https://atomgit.com/cann/ascend-transformer-boost

---

一、ascend-transformer-boost概述

1.1 设计目标

ascend-transformer-boost（以下简称ATB）的核心设计目标包括：

• 极致性能：充分发挥NPU硬件的并行计算能力
• 内存高效：优化KV Cache管理，降低显存占用
• 灵活配置：支持多种注意力机制的变体
• 易用性：提供简洁的API，快速集成到现有框架
• 可扩展：支持自定义算子和优化策略

1.2 核心组件

ascend-transformer-boost/
├── 注意力机制优化
│   ├── Flash Attention - 内存高效的注意力计算
│   ├── Paged Attention - 分页KV Cache管理
│   ├── MQA/GQA支持 - 多查询/分组查询注意力
│   └── Sliding Window - 滑动窗口注意力
├── 位置编码优化
│   ├── RoPE - 旋转位置编码
│   ├── ALiBi - Attention with Linear Biases
│   └── xPos - 绝对位置编码扩展
├── 优化算子库
│   ├── LayerNorm - RMSNorm、LayerNorm优化
│   ├── Activation - GeLU、SwiGLU激活函数
│   ├── FFN - 前馈网络优化
│   └── Embedding - 词嵌入查找优化
├── 推理加速
│   ├── Continuous Batching - 连续批处理
│   ├── Speculative Decoding - 投机解码
│   ├── KV Cache压缩 - 量化、剪枝
│   └── Static/Dynamic Shape - 静态/动态形状支持
└── Python API
    ├── atb.ops - 算子API
    ├── atb.models - 模型API
    └── atb.utils - 工具函数

---

二、核心API详解

2.1 Flash Attention API

Flash Attention是ATB的核心优化技术，通过分块计算和内存重排显著减少内存访问：

import atb
import torch

def flash_attention(
    q: torch.Tensor,           # Query: [batch, seq_len, num_heads, head_dim]
    k: torch.Tensor,           # Key: [batch, seq_len, num_heads, head_dim]
    v: torch.Tensor,           # Value: [batch, seq_len, num_heads, head_dim]
    causal: bool = True,       # 是否使用因果掩码
    dropout_p: float = 0.0,    # Dropout概率
    scale: float = None        # 缩放因子
) -> torch.Tensor:
    """
    Flash Attention实现

    相比标准注意力，Flash Attention具有以下优势：
    1. 内存复杂度从O(N^2)降至O(N)
    2. 通过融合算子减少HBM访问
    3. 分块计算提高缓存命中率
    """
    return atb.ops.flash_attention(
        q, k, v,
        causal=causal,
        dropout_p=dropout_p,
        scale=scale,
        algo=atb.ops.FlashAlgo.FWD          # 前向计算
    )

# 使用示例
batch_size, seq_len, num_heads, head_dim = 1, 2048, 32, 128

q = torch.randn(batch_size, seq_len, num_heads, head_dim, dtype=torch.float16).cuda()
k = torch.randn(batch_size, seq_len, num_heads, head_dim, dtype=torch.float16).cuda()
v = torch.randn(batch_size, seq_len, num_heads, head_dim, dtype=torch.float16).cuda()

# 执行Flash Attention
output = flash_attention(q, k, v, causal=True)

# 带梯度的Flash Attention（训练时使用）
class FlashAttentionFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, q, k, v, causal, dropout_p):
        out = atb.ops.flash_attention(q, k, v, causal=causal,
                                      dropout_p=dropout_p,
                                      algo=atb.ops.FlashAlgo.FWD)
        ctx.save_for_backward(q, k, v)
        ctx.causal = causal
        ctx.dropout_p = dropout_p
        return out

    @staticmethod
    def backward(ctx, grad_output):
        q, k, v = ctx.saved_tensors
        grad_q, grad_k, grad_v = atb.ops.flash_attention(
            q, k, v,
            grad_output=grad_output,
            causal=ctx.causal,
            dropout_p=ctx.dropout_p,
            algo=atb.ops.FlashAlgo.BWD           # 反向传播
        )
        return grad_q, grad_k, grad_v, None, None

2.2 PagedAttention API

PagedAttention实现了高效的KV Cache管理，支持动态批处理：

from atb.ops import PagedAttention

class PagedAttentionCache:
    """
    分页KV Cache管理器

    优势：
    1. 避免内存碎片化
    2. 支持动态批处理
    3. 内存复用率高
    """

    def __init__(
        self,
        num_blocks: int,              # 总块数
        block_size: int,              # 每块的token数量
        num_heads: int,               # 注意力头数
        head_dim: int,                # 头维度
        dtype: torch.dtype = torch.float16
    ):
        self.num_blocks = num_blocks
        self.block_size = block_size
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.dtype = dtype

        # 分配KV Cache块池
        self.k_cache = torch.empty(
            num_blocks, block_size, num_heads, head_dim,
            dtype=dtype, device='cuda'
        )
        self.v_cache = torch.empty(
            num_blocks, block_size, num_heads, head_dim,
            dtype=dtype, device='cuda'
        )

        # 块分配表（记录每个序列使用的块）
        self.block_allocator = BlockAllocator(num_blocks)

    def allocate(self, seq_len: int) -> list[int]:
        """为序列分配KV Cache块"""
        num_blocks_needed = (seq_len + self.block_size - 1) // self.block_size
        return self.block_allocator.allocate(num_blocks_needed)

    def append(self, block_ids: list[int], tokens: torch.Tensor):
        """追加新token到KV Cache"""
        # 将新token的KV写入对应块
        pass

    def read(self, block_ids: list[int]) -> tuple[torch.Tensor, torch.Tensor]:
        """读取KV Cache数据"""
        # 根据块ID收集KV数据
        pass

    def free(self, block_ids: list[int]):
        """释放KV Cache块"""
        self.block_allocator.free(block_ids)


class BlockAllocator:
    """块分配器 - 管理空闲块列表"""

    def __init__(self, num_blocks: int):
        self.num_blocks = num_blocks
        self.free_blocks = list(range(num_blocks))
        self.allocated = {}  # seq_id -> block_ids

    def allocate(self, num_blocks: int) -> list[int]:
        if len(self.free_blocks) < num_blocks:
            raise RuntimeError("Insufficient free blocks")

        blocks = self.free_blocks[:num_blocks]
        self.free_blocks = self.free_blocks[num_blocks:]
        return blocks

    def free(self, block_ids: list[int]):
        self.free_blocks.extend(block_ids)
        self.free_blocks.sort()


# PagedAttention计算
def paged_attention(
    q: torch.Tensor,                  # [batch, num_heads, head_dim]
    k_cache: torch.Tensor,            # [num_blocks, block_size, num_heads, head_dim]
    v_cache: torch.Tensor,            # [num_blocks, block_size, num_heads, head_dim]
    block_ids: list[list[int]],       # 每个序列的块ID列表
    context_lens: list[int],          # 每个序列的上下文长度
    scale: float = None
) -> torch.Tensor:
    """
    使用Paged KV Cache计算注意力

    处理流程：
    1. 根据block_ids从分页Cache中收集KV
    2. 对每个序列执行注意力计算
    3. 处理不同长度的序列（动态批处理）
    """
    return PagedAttention.forward(
        q, k_cache, v_cache,
        block_ids=block_ids,
        context_lens=context_lens,
        scale=scale
    )

2.3 MQA/GQA API

多查询注意力和分组查询注意力减少了KV Cache的内存占用：

from atb.ops import MultiQueryAttention

class MultiQueryAttentionLayer(nn.Module):
    """
    多查询注意力 (MQA) / 分组查询注意力 (GQA)

    MQA: 所有头共享一对KV
    GQA: 头分组共享KV

    内存节省：
    - MQA: KV Cache减少到1/num_heads
    - GQA: KV Cache减少到1/(num_heads/num_kv_heads)
    """

    def __init__(
        self,
        num_heads: int,
        num_kv_heads: int,         # GQA的KV头数，MQA时为1
        head_dim: int,
        dropout: float = 0.0
    ):
        super().__init__()
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads
        self.head_dim = head_dim
        self.dropout = dropout

        # Q投影: (hidden_size, num_heads * head_dim)
        # K/V投影: (hidden_size, num_kv_heads * head_dim)
        hidden_size = num_heads * head_dim
        self.q_proj = nn.Linear(hidden_size, num_heads * head_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, num_kv_heads * head_dim, bias=False)
        self.v_proj = nn.Linear(hidden_size, num_kv_heads * head_dim, bias=False)
        self.o_proj = nn.Linear(num_heads * head_dim, hidden_size, bias=False)

        # 使用ATB优化的MQA/GQA算子
        self.attn_fn = atb.ops.multi_query_attention

    def forward(
        self,
        x: torch.Tensor,
        past_kv: tuple[torch.Tensor, torch.Tensor] = None,
        use_cache: bool = False
    ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
        batch_size, seq_len, hidden_size = x.shape

        # 投影到QKV
        q = self.q_proj(x)  # [batch, seq_len, num_heads, head_dim]
        k = self.k_proj(x)  # [batch, seq_len, num_kv_heads, head_dim]
        v = self.v_proj(x)  # [batch, seq_len, num_kv_heads, head_dim]

        # 重排为 [batch, num_heads, seq_len, head_dim]
        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)

        # 拼接历史KV（用于生成）
        if past_kv is not None:
            past_k, past_v = past_kv
            k = torch.cat([past_k, k], dim=2)
            v = torch.cat([past_v, v], dim=2)

        # 使用ATB优化的MQA/GQA算子
        attn_output = self.attn_fn(
            q, k, v,
            num_heads=self.num_heads,
            num_kv_heads=self.num_kv_heads,
            causal=True,
            dropout_p=self.dropout if self.training else 0.0
        )

        # 输出投影
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(batch_size, seq_len, hidden_size)
        output = self.o_proj(attn_output)

        if use_cache:
            return output, (k, v)
        return output, None


# GQA配置示例
class GQALLaMAConfig:
    """LLaMA 2的GQA配置"""

    def __init__(self):
        self.hidden_size = 4096
        self.num_attention_heads = 32
        # LLaMA 2使用GQA减少KV Cache
        self.num_key_value_heads = 8   # 每4个头共享1对KV
        self.head_dim = 128

    @property
    def kv_cache_ratio(self):
        return self.num_key_value_heads / self.num_attention_heads  # 0.25

2.4 位置编码API

from atb.ops import RotaryEmbedding

class RotaryPositionEmbedding(nn.Module):
    """
    旋转位置编码 (RoPE)

    优势：
    1. 相对位置编码，可外推到更长序列
    2. 无额外参数
    3. 计算高效
    """

    def __init__(
        self,
        head_dim: int,
        max_position: int = 8192,
        base: int = 10000,
        theta: float = 10000.0
    ):
        super().__init__()
        self.head_dim = head_dim
        self.max_position = max_position
        self.base = base
        self.theta = theta

        # 预计算频率
        self._init_freqs()

        # 使用ATB优化的RoPE算子
        self.rope_fn = atb.ops.apply_rotary_pos_emb

    def _init_freqs(self):
        """初始化旋转频率"""
        half_dim = self.head_dim // 2
        freqs = torch.log(torch.arange(self.base, dtype=torch.float32))
        freqs = freqs / torch.arange(half_dim, dtype=torch.float32)
        freqs = torch.exp(freqs * -torch.log(torch.tensor(self.theta)))
        self.register_buffer('freqs', freqs)

    def forward(
        self,
        x: torch.Tensor,        # [batch, num_heads, seq_len, head_dim]
        positions: torch.Tensor  # [seq_len] 或 [batch, seq_len]
    ) -> torch.Tensor:
        """
        应用旋转位置编码

        对Query和Key的每对维度应用旋转：
        (x, y) -> (x*cosθ - y*sinθ, x*sinθ + y*cosθ)
        """
        return self.rope_fn(x, positions, self.freqs)


class ALiBiPositionEmbedding(nn.Module):
    """
    Attention with Linear Biases (ALiBi)

    优势：
    1. 无需训练位置编码
    2. 可外推到更长序列
    3. 计算简单高效
    """

    def __init__(
        self,
        num_heads: int,
        max_seq_len: int = 4096
    ):
        super().__init__()
        self.num_heads = num_heads
        self.max_seq_len = max_seq_len
        self.alibi_fn = atb.ops.apply_alibi_bias

    def forward(
        self,
        attn_scores: torch.Tensor,  # [batch, num_heads, seq_len, seq_len]
        seq_len: int
    ) -> torch.Tensor:
        """应用ALiBi偏置"""
        # ALiBi偏置: -|i - j| * head_scale
        return self.alibi_fn(attn_scores, seq_len, self.num_heads)

2.5 连续批处理API

from atb.serving import ContinuousBatchScheduler

class ContinuousBatchManager:
    """
    连续批处理管理器

    核心思想：
    1. 动态地将请求添加/移出批次
    2. 不同序列可以处于不同生成阶段
    3. 最大化GPU利用率
    """

    def __init__(
        self,
        max_batch_size: int,
        max_seq_len: int,
        block_size: int = 16
    ):
        self.max_batch_size = max_batch_size
        self.max_seq_len = max_seq_len
        self.block_size = block_size

        # KV Cache管理
        self.cache_manager = PagedAttentionCache(
            num_blocks=max_batch_size * (max_seq_len // block_size),
            block_size=block_size,
            num_heads=32,
            head_dim=128
        )

        # 请求队列
        self.pending_queue = []      # 等待处理的请求
        self.running_requests = {}   # 正在处理的请求
        self.completed_requests = [] # 已完成的请求

        # 调度器
        self.scheduler = ContinuousBatchScheduler(
            max_batch_size=max_batch_size,
            max_seq_len=max_seq_len
        )

    def add_request(self, request_id: str, prompt_tokens: torch.Tensor):
        """添加新请求"""
        self.pending_queue.append({
            'request_id': request_id,
            'prompt_tokens': prompt_tokens,
            'state': 'pending'
        })

    def step(self) -> dict[str, torch.Tensor]:
        """
        执行一步推理

        返回: {request_id: generated_token}
        """
        # 1. 调度：选择可以处理的请求
        batched_requests = self.scheduler.schedule(
            self.pending_queue,
            self.running_requests
        )

        # 2. 准备批次数据
        batch_data = self._prepare_batch(batched_requests)

        # 3. 执行前向计算
        outputs = self._forward_batch(batch_data)

        # 4. 更新状态和KV Cache
        completed, generated = self._update_state(batched_requests, outputs)

        # 5. 返回生成的token
        return generated

    def _prepare_batch(self, requests: list) -> dict:
        """准备批次数据"""
        batch_tokens = []
        block_ids_list = []
        context_lens = []

        for req in requests:
            req_id = req['request_id']
            req_state = self.running_requests[req_id]

            # 获取当前要生成的token
            batch_tokens.append(req_state['next_token'])

            # 获取KV Cache块
            block_ids_list.append(req_state['block_ids'])

            # 记录上下文长度
            context_lens.append(req_state['context_len'])

        return {
            'tokens': torch.stack(batch_tokens),
            'block_ids': block_ids_list,
            'context_lens': context_lens
        }

    def _forward_batch(self, batch_data: dict) -> torch.Tensor:
        """执行批次前向计算"""
        # 1. 嵌入查找
        input_ids = batch_data['tokens']
        hidden_states = self.model.embedding(input_ids)

        # 2. Transformer层（使用PagedAttention）
        for layer in self.model.layers:
            hidden_states = layer(
                hidden_states,
                block_ids=batch_data['block_ids'],
                context_lens=batch_data['context_lens']
            )

        # 3. 输出投影
        logits = self.model.lm_head(hidden_states)
        next_tokens = torch.argmax(logits, dim=-1)

        return next_tokens

    def _update_state(
        self,
        requests: list,
        outputs: torch.Tensor
    ) -> tuple[list[str], dict[str, torch.Tensor]]:
        """更新请求状态"""
        completed = []
        generated = {}

        for i, req in enumerate(requests):
            req_id = req['request_id']
            token = outputs[i]

            # 检查是否完成
            if token.item() == self.model.eos_token_id:
                completed.append(req_id)
                del self.running_requests[req_id]
            else:
                # 更新状态
                self.running_requests[req_id]['next_token'] = token
                self.running_requests[req_id]['context_len'] += 1
                generated[req_id] = token

        # 将完成的请求移到完成队列
        for req_id in completed:
            req = next(r for r in requests if r['request_id'] == req_id)
            self.completed_requests.append(req)

        return completed, generated

---

三、应用实践

3.1 完整的LLaMA模型实现

import atb
import torch.nn as nn

class LLaMAAttention(nn.Module):
    """LLaMA风格的注意力层"""

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.num_kv_heads = config.num_key_value_heads

        # 投影层
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        # RoPE
        self.rotary_emb = RotaryEmbedding(
            head_dim=self.head_dim,
            max_position=config.max_position_embeddings
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        past_kv: tuple = None,
        use_cache: bool = False,
        position_ids: torch.Tensor = None
    ):
        batch_size, seq_len, _ = hidden_states.shape

        # 投影
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        # 重排形状
        query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(batch_size, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)

        # 应用RoPE
        query_states = self.rotary_emb(query_states, position_ids)
        key_states = self.rotary_emb(key_states, position_ids)

        # 拼接历史KV
        if past_kv is not None:
            past_key, past_value = past_kv
            key_states = torch.cat([past_key, key_states], dim=2)
            value_states = torch.cat([past_value, value_states], dim=2)

        # 使用ATB优化的注意力算子
        attn_output = atb.ops.multi_query_attention(
            query_states,
            key_states,
            value_states,
            num_heads=self.num_heads,
            num_kv_heads=self.num_kv_heads,
            causal=True,
            use_flash_attn=True  # 使用Flash Attention
        )

        # 输出投影
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(batch_size, seq_len, self.hidden_size)
        output = self.o_proj(attn_output)

        if use_cache:
            return output, (key_states, value_states)
        return output, None


class LLaMAMLP(nn.Module):
    """LLaMA的MLP层 (使用SwiGLU激活)"""

    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

    def forward(self, x):
        # SwiGLU: Swish(xW) ⊙ (xV)
        gate = self.gate_proj(x)
        gate = atb.ops.swish(gate)  # 使用ATB优化的SwiGLU
        up = self.up_proj(x)
        return self.down_proj(gate * up)


class LLaMADecoderLayer(nn.Module):
    """LLaMA解码器层"""

    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size

        # RMSNorm
        self.input_layernorm = atb.ops.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = atb.ops.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # 自注意力和MLP
        self.self_attn = LLaMAAttention(config)
        self.mlp = LLaMAMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        past_kv: tuple = None,
        use_cache: bool = False,
        position_ids: torch.Tensor = None
    ):
        # 自注意力
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, present_kv = self.self_attn(
            hidden_states,
            past_kv=past_kv,
            use_cache=use_cache,
            position_ids=position_ids
        )
        hidden_states = residual + hidden_states

        # MLP
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        if use_cache:
            return hidden_states, present_kv
        return hidden_states, None


class LLaMAModel(nn.Module):
    """完整的LLaMA模型"""

    def __init__(self, config):
        super().__init__()
        self.config = config

        self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([
            LLaMADecoderLayer(config) for _ in range(config.num_hidden_layers)
        ])
        self.norm = atb.ops.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def forward(
        self,
        input_ids: torch.Tensor,
        past_kvs: list[tuple] = None,
        use_cache: bool = False,
        position_ids: torch.Tensor = None
    ):
        batch_size, seq_len = input_ids.shape

        # 嵌入
        hidden_states = self.embedding(input_ids)

        # 生成位置ID
        if position_ids is None:
            position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)
            position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)

        # 通过所有层
        present_kvs = []
        for i, layer in enumerate(self.layers):
            past_kv = past_kvs[i] if past_kvs is not None else None
            hidden_states, present_kv = layer(
                hidden_states,
                past_kv=past_kv,
                use_cache=use_cache,
                position_ids=position_ids
            )
            if use_cache:
                present_kvs.append(present_kv)

        # 归一化和输出
        hidden_states = self.norm(hidden_states)
        logits = self.lm_head(hidden_states)

        if use_cache:
            return logits, present_kvs
        return logits, None


class LLaMAGenerator:
    """LLaMA文本生成器 - 使用ATB优化"""

    def __init__(self, model: LLaMAModel, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    @torch.no_grad()
    def generate(
        self,
        prompt: str,
        max_new_tokens: int = 100,
        temperature: float = 1.0,
        top_p: float = 0.9,
        use_kv_cache: bool = True
    ) -> str:
        # 编码prompt
        input_ids = self.tokenizer.encode(prompt, return_tensors='pt').cuda()

        # Prefill阶段
        past_kvs = None
        position_ids = None

        outputs = self.model(input_ids, past_kvs=past_kvs, use_cache=use_kv_cache)
        logits, past_kvs = outputs
        next_token = self._sample(logits[:, -1], temperature, top_p)

        generated = [next_token]

        # Decode阶段 - 逐token生成
        for _ in range(max_new_tokens - 1):
            # 使用KV Cache加速
            outputs = self.model(
                next_token.unsqueeze(-1),
                past_kvs=past_kvs,
                use_cache=use_kv_cache,
                position_ids=torch.tensor([[past_kvs[0][0].shape[2]]], device=input_ids.device)
            )
            logits, past_kvs = outputs
            next_token = self._sample(logits[:, -1], temperature, top_p)

            generated.append(next_token)

            # 检查EOS
            if next_token.item() == self.tokenizer.eos_token_id:
                break

        # 解码
        output_ids = torch.cat([input_ids, torch.stack(generated).squeeze(-1)], dim=1)
        return self.tokenizer.decode(output_ids[0], skip_special_tokens=True)

    def _sample(self, logits: torch.Tensor, temperature: float, top_p: float) -> torch.Tensor:
        """采样下一个token"""
        # 温度缩放
        logits = logits / temperature

        # Top-p采样
        if top_p < 1.0:
            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
            cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)

            sorted_indices_to_remove = cumulative_probs > top_p
            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
            sorted_indices_to_remove[..., 0] = 0

            indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
            logits[indices_to_remove] = float('-inf')

        # 采样
        probs = torch.softmax(logits, dim=-1)
        next_token = torch.multinomial(probs, num_samples=1)
        return next_token

3.2 投机解码实现

from atb.ops import SpeculativeDecoder

class SpeculativeDecodingHelper:
    """
    投机解码助手

    核心思想：
    1. 使用小模型快速预测多个token
    2. 使用大模型并行验证预测结果
    3. 接受正确的预测，拒绝错误的预测
    4. 平均加速比可达2-3x
    """

    def __init__(
        self,
        draft_model: nn.Module,      # 小模型（用于快速预测）
        target_model: nn.Module,      # 大模型（用于验证）
        tokenizer,
        speculate_k: int = 5          # 每次预测的token数
    ):
        self.draft_model = draft_model
        self.target_model = target_model
        self.tokenizer = tokenizer
        self.speculate_k = speculate_k

    @torch.no_grad()
    def generate(
        self,
        prompt: str,
        max_new_tokens: int = 100
    ) -> str:
        input_ids = self.tokenizer.encode(prompt, return_tensors='pt').cuda()

        for _ in range(max_new_tokens // self.speculate_k):
            # 1. Draft模型预测K个token
            draft_tokens = self._draft_predict(input_ids, self.speculate_k)

            # 2. 将draft tokens连接到输入
            candidate_ids = torch.cat([input_ids, draft_tokens], dim=1)

            # 3. Target模型并行验证
            verified_tokens = self._target_verify(input_ids, draft_tokens, candidate_ids)

            # 4. 更新输入
            input_ids = torch.cat([input_ids, verified_tokens], dim=1)

            # 5. 检查是否停止
            if verified_tokens.shape[1] < self.speculate_k:
                break

        return self.tokenizer.decode(input_ids[0], skip_special_tokens=True)

    def _draft_predict(self, input_ids: torch.Tensor, k: int) -> torch.Tensor:
        """使用draft模型预测k个token"""
        tokens = []
        current_ids = input_ids

        for _ in range(k):
            logits = self.draft_model(current_ids)
            next_token = torch.argmax(logits[:, -1], dim=-1, keepdim=True)
            tokens.append(next_token)
            current_ids = torch.cat([current_ids, next_token], dim=1)

        return torch.cat(tokens, dim=1)

    def _target_verify(
        self,
        input_ids: torch.Tensor,
        draft_tokens: torch.Tensor,
        candidate_ids: torch.Tensor
    ) -> torch.Tensor:
        """使用target模型验证draft tokens"""
        # 获取target模型在候选序列上的logits
        logits = self.target_model(candidate_ids)

        # 提取对应draft tokens位置的logits
        # input_ids长度为L，draft_tokens长度为K
        # 我们需要验证位置L, L+1, ..., L+K-1的token
        start_idx = input_ids.shape[1] - 1

        verified_tokens = []
        for i in range(draft_tokens.shape[1]):
            pos = start_idx + i
            target_logits = logits[:, pos, :]  # [batch, vocab_size]
            draft_token = draft_tokens[:, i]   # [batch]

            # 检查target模型是否也预测draft_token
            target_prediction = torch.argmax(target_logits, dim=-1)
            if torch.equal(target_prediction, draft_token):
                verified_tokens.append(draft_token[:, i:i+1])
            else:
                # 验证失败，使用target模型的预测
                verified_tokens.append(target_prediction[:, i:i+1].unsqueeze(0))
                break  # 停止验证

        if verified_tokens:
            return torch.cat(verified_tokens, dim=1)
        return torch.empty(1, 0, dtype=torch.long, device=input_ids.device)

---

四、性能优化技巧

4.1 量化优化

from atb.quantization import quantize_weights, quantize_kv_cache

# 权重量化
def quantize_model_for_inference(model: nn.Module):
    """
    量化模型以减少内存占用和提升性能

    INT8量化:
    - 权重: FP32 -> INT8
    - 激活: FP32 -> INT8
    - 内存节省: 4x
    - 加速: 2-3x
    """
    # 量化Linear层权重
    for name, module in model.named_modules():
        if isinstance(module, nn.Linear):
            q_weight, scale, zero_point = quantize_weights(module.weight.data)
            module.register_buffer('q_weight', q_weight)
            module.register_buffer('scale', scale)
            module.register_buffer('zero_point', zero_point)
            module.weight.data = q_weight

    return model

# KV Cache量化
class QuantizedKVCache:
    """量化KV Cache - 减少显存占用"""

    def __init__(self, dtype: torch.dtype = torch.float16):
        self.dtype = dtype
        self.quant_dtype = torch.int8  # 量化为INT8

    def quantize(self, kv: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        """
        量化KV Cache

        FP16 -> INT8
        内存节省: 2x
        """
        scale = kv.abs().max() / 127.0
        q_kv = (kv / scale).round().to(torch.int8)
        return q_kv, scale

    def dequantize(
        self,
        q_kv: torch.Tensor,
        scale: torch.Tensor
    ) -> torch.Tensor:
        """反量化"""
        return q_kv.to(self.dtype) * scale

4.2 算子融合优化

from atb.fusion import fused_ops

class FusedTransformerBlock(nn.Module):
    """融合的Transformer块"""

    def __init__(self, config):
        super().__init__()
        # 使用融合算子
        self.fused_mha = fused_ops.FusedMultiHeadAttention(
            num_heads=config.num_heads,
            head_dim=config.head_dim
        )

        self.fused_mlp = fused_ops.FusedMLP(
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            activation='swiglu'
        )

        self.fused_norm = fused_ops.FusedRMSNorm(config.hidden_size)

    def forward(self, x, past_kv=None):
        # 单个融合算子完成: QKV投影 + 注意力 + 输出投影 + 残差连接 + LayerNorm
        x, past_kv = self.fused_mha(x, past_kv=past_kv)

        # 单个融合算子完成: 门控投影 + 激活 + 上投影 + 下投影 + 残差连接 + LayerNorm
        x = self.fused_mlp(x)

        return x, past_kv

---

五、总结

ascend-transformer-boost提供了全方位的Transformer模型加速方案，从底层算子优化到高层推理策略。通过Flash Attention、PagedAttention、连续批处理等技术的组合使用，可以在保持精度的同时显著提升大语言模型的推理性能。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• ascend-transformer-boost仓库链接：https://atomgit.com/cann/ascend-transformer-boost