引言
随着GPT、LLaMA、Falcon等大型语言模型（LLM）的广泛应用，如何实现高效、低延迟的大模型推理成为业界关注的核心问题。一个典型的数十亿参数规模的LLM，在通用硬件上进行完整推理可能需要数秒甚至更长时间，这对于实时对话、交互式应用等场景是不可接受的。
ascend-transformer-boost（简称ATB）是CANN生态系统中专门针对Transformer类大模型推理优化的加速库，通过一系列先进的优化技术，将大模型推理延迟降低到毫秒级别。本文将深入解析ATB的核心技术、实现原理和应用实践。
一、ascend-transformer-boost概述
1.1 设计目标
ATB加速库致力于解决大模型推理的核心痛点：
- 极致低延迟：首token延迟（TTFT）和后续token生成延迟（TPOT）双重优化
- 高吞吐量：支持批处理和并发推理
- 显存优化：通过KV缓存优化和算子融合降低显存占用
- 灵活部署：支持不同规模模型和硬件配置
1.2 核心优化技术
| 优化技术 | 目标问题 | 性能提升 |
|---|---|---|
| FlashAttention | 注意力计算 | 2-4x |
| KV Cache | 自回归推理 | 10x+ |
| PagedAttention | 动态批处理 | 5-8x |
| 算子融合 | 内存访问 | 1.5-2x |
| 量化压缩 | 模型大小 | 2-4x |
| 连续批处理 | 吞吐量 | 3-5x |
1.3 支持的模型架构
ATB支持主流的Transformer变体：
- Decoder-only: GPT系列、LLaMA系列、Falcon、Mistral
- Encoder-Decoder: T5、BART
- 多模态: CLIP、BLIP、Flamingo
- 专家混合: Mixtral 8x7B、Switch Transformer
二、核心优化技术详解
2.1 FlashAttention V2实现
FlashAttention通过分块计算和内存优化实现高效注意力计算：
cpp
#include "atb_attention.h"
#include "atb_tensor.h"
namespace atb {
template<typename T>
class FlashAttentionV2 {
public:
struct Config {
int batch_size;
int num_heads;
int head_dim;
int max_seq_len;
float scale;
bool is_causal;
};
FlashAttentionV2(const Config& config)
: config_(config) {
// 根据硬件特性选择最优分块大小
block_size_m_ = get_optimal_block_m();
block_size_n_ = get_optimal_block_n();
block_size_b_ = get_optimal_block_b();
}
// 前向计算
Tensor<T> forward(const Tensor<T>& q,
const Tensor<T>& k,
const Tensor<T>& v,
const Tensor<T>& mask = Tensor<T>()) {
// 输入形状: [batch, num_heads, seq_len, head_dim]
int batch = q.shape(0);
int num_heads = q.shape(1);
int seq_len = q.shape(2);
int head_dim = q.shape(3);
// 输出初始化
Tensor<T> output({batch, num_heads, seq_len, head_dim});
Tensor<float> l({batch, num_heads, seq_len, 1}); // 归一化因子
Tensor<float> m({batch, num_heads, seq_len, 1}); // 最大值
l.fill(0.0f);
m.fill(-std::numeric_limits<float>::infinity());
output.fill(0.0f);
// 分块遍历K和V
for (int start_n = 0; start_n < seq_len; start_n += block_size_n_) {
int end_n = std::min(start_n + block_size_n_, seq_len);
// 加载当前K、V块到片上内存
auto k_block = load_block(k, start_n, end_n);
auto v_block = load_block(v, start_n, end_n);
// 计算Q与当前K块的分值矩阵
auto q_block = load_full_q(q);
auto scores = compute_scores(q_block, k_block); // [B, H, M, N_block]
// 更新运行统计量
auto new_m = elementwise_max(m, reduce_max(scores, /*dim=*/-1));
auto scores_scaled = exp(scores - new_m);
auto new_l = l * exp(m - new_m) + reduce_sum(scores_scaled, /*dim=*/-1);
// 更新输出: o = o * (l / new_l) * exp(m - new_m) + softmax(scores) @ v
auto output_scaled = output * (l / new_l) * exp(m - new_m);
auto attn_contrib = matmul(scores_scaled, v_block) / new_l;
output = output_scaled + attn_contrib;
m = new_m;
l = new_l;
}
return output;
}
private:
Config config_;
int block_size_m_;
int block_size_n_;
int block_size_b_;
// 根据硬件缓存大小选择最优分块
int get_optimal_block_m() {
// 假设SRAM大小为192KB
int sram_size = 192 * 1024 / sizeof(T);
// 留出空间用于K、V块
int available = sram_size / 3;
// head_dim通常是64或128
return available / config_.head_dim;
}
int get_optimal_block_n() {
return get_optimal_block_m();
}
int get_optimal_block_b() {
return config_.batch_size;
}
Tensor<T> load_block(const Tensor<T>& tensor, int start, int end) {
// 高效加载指定块，考虑内存对齐
return tensor.slice(/*dim=*/2, start, end);
}
Tensor<T> load_full_q(const Tensor<T>& q) {
// Flash Attention中Q通常分块较小或全量加载
return q;
}
Tensor<T> compute_scores(const Tensor<T>& q, const Tensor<T>& k) {
// scores = Q @ K^T * scale
auto k_t = transpose(k, {0, 1, 3, 2});
auto scores = matmul(q, k_t);
return scalar_multiply(scores, config_.scale);
}
Tensor<T> exp(const Tensor<T>& x) {
// 优化的指数函数
Tensor<T> result(x.shape());
for (size_t i = 0; i < x.size(); ++i) {
result.data()[i] = std::exp(x.data()[i]);
}
return result;
}
};
} // namespace atb2.2 PagedAttention实现
PagedAttention解决了KV缓存中的内存碎片问题：
cpp
#include "atb_kv_cache.h"
#include <vector>
#include <memory>
namespace atb {
// KV缓存块管理器
class KVCacheBlockManager {
public:
struct Block {
int block_id;
std::vector<std::shared_ptr<Tensor<float>>> keys;
std::vector<std::shared_ptr<Tensor<float>>> values;
std::vector<int> ref_counts; // 每层的引用计数
};
KVCacheBlockManager(int block_size, int num_blocks, int num_layers)
: block_size_(block_size),
num_blocks_(num_blocks),
num_layers_(num_layers) {
// 预分配所有块
for (int i = 0; i < num_blocks; ++i) {
Block block;
block.block_id = i;
block.ref_counts.resize(num_layers, 0);
for (int layer = 0; layer < num_layers; ++layer) {
block.keys.push_back(std::make_shared<Tensor<float>>(
Tensor<float>({block_size, 2, head_dim}))); // [seq, kv_heads, head_dim]
block.values.push_back(std::make_shared<Tensor<float>>(
Tensor<float>({block_size, 2, head_dim})));
}
blocks_.push_back(std::move(block));
}
}
// 分配块
std::vector<int> allocate(int num_blocks, int layer) {
std::vector<int> allocated;
for (auto& block : blocks_) {
if (block.ref_counts[layer] == 0) {
block.ref_counts[layer] = 1;
allocated.push_back(block.block_id);
if (allocated.size() == num_blocks) {
break;
}
}
}
return allocated;
}
// 释放块
void release(const std::vector<int>& block_ids, int layer) {
for (int block_id : block_ids) {
blocks_[block_id].ref_counts[layer]--;
}
}
// 获取块指针
Block* get_block(int block_id) {
return &blocks_[block_id];
}
private:
int block_size_;
int num_blocks_;
int num_layers_;
std::vector<Block> blocks_;
int head_dim = 128; // 默认头维度
};
// PagedAttention核心实现
template<typename T>
class PagedAttention {
public:
struct Config {
int num_heads;
int num_kv_heads;
int head_dim;
int block_size;
int num_blocks;
};
PagedAttention(const Config& config, KVCacheBlockManager* block_manager)
: config_(config), block_manager_(block_manager) {}
// 分配序列的KV缓存
int allocate_sequence(int num_tokens) {
int num_blocks = (num_tokens + config_.block_size - 1) / config_.block_size;
Sequence seq;
seq.seq_id = next_seq_id_++;
seq.block_ids = block_manager_->allocate(num_blocks, /*layer=*/0);
seq.num_tokens = 0;
sequences_[seq.seq_id] = seq;
return seq.seq_id;
}
// 追加token（解码阶段）
void append_token(int seq_id,
const Tensor<T>& new_key,
const Tensor<T>& new_value,
int layer) {
auto& seq = sequences_[seq_id];
// 计算应该写入的块和偏移
int block_idx = seq.num_tokens / config_.block_size;
int offset = seq.num_tokens % config_.block_size;
if (block_idx >= seq.block_ids.size()) {
// 需要分配新块
auto new_blocks = block_manager_->allocate(1, layer);
seq.block_ids.push_back(new_blocks[0]);
}
// 写入KV缓存
int block_id = seq.block_ids[block_idx];
auto* block = block_manager_->get_block(block_id);
write_to_block(block, new_key, new_value, offset, layer);
seq.num_tokens++;
}
// PagedAttention计算
Tensor<T> forward(int seq_id,
const Tensor<T>& query,
int layer) {
const auto& seq = sequences_[seq_id];
const auto& block_ids = seq.block_ids;
int num_tokens = seq.num_tokens;
int num_full_blocks = num_tokens / config_.block_size;
int rem_tokens = num_tokens % config_.block_size;
Tensor<T> output(query.shape());
// 处理完整块
for (int i = 0; i < num_full_blocks; ++i) {
int block_id = block_ids[i];
const auto* block = block_manager_->get_block(block_id);
auto k_block = block->keys[layer];
auto v_block = block->values[layer];
// 计算当前块的注意力
auto block_output = compute_attention_for_block(query, k_block, v_block);
output = output + block_output;
}
// 处理部分块
if (rem_tokens > 0 && num_full_blocks < block_ids.size()) {
int block_id = block_ids[num_full_blocks];
const auto* block = block_manager_->get_block(block_id);
auto k_partial = block->keys[layer]->slice(/*dim=*/0, 0, rem_tokens);
auto v_partial = block->values[layer]->slice(/*dim=*/0, 0, rem_tokens);
auto partial_output = compute_attention_for_block(query, k_partial, v_partial);
output = output + partial_output;
}
return output;
}
private:
Config config_;
KVCacheBlockManager* block_manager_;
int next_seq_id_ = 1;
struct Sequence {
int seq_id;
std::vector<int> block_ids;
int num_tokens;
};
std::unordered_map<int, Sequence> sequences_;
void write_to_block(typename KVCacheBlockManager::Block* block,
const Tensor<T>& new_key,
const Tensor<T>& new_value,
int offset,
int layer) {
// 写入指定偏移位置
// 实现略
}
Tensor<T> compute_attention_for_block(const Tensor<T>& query,
const std::shared_ptr<Tensor<float>>& k_block,
const std::shared_ptr<Tensor<float>>& v_block) {
// 计算单个块的注意力
auto scores = matmul(query, transpose(*k_block, {0, 2, 1}));
auto attn_weights = softmax(scores, /*dim=*/-1);
return matmul(attn_weights, *v_block);
}
};
} // namespace atb2.3 连续批处理（Continuous Batching）
cpp
#include "atb_scheduler.h"
#include <queue>
#include <vector>
namespace atb {
// 请求状态
enum class RequestState {
WAITING, // 等待处理
PROCESSING, // 正在处理
COMPLETED // 已完成
};
// 推理请求
struct InferenceRequest {
int request_id;
std::vector<int> input_tokens;
std::vector<int> output_tokens;
int max_tokens;
RequestState state;
int current_position;
// KV缓存块引用
std::vector<int> kv_block_ids;
};
// 连续批处理调度器
class ContinuousBatchingScheduler {
public:
struct Config {
int max_batch_size; // 最大批次大小
int max_num_seqs; // 最大并发序列数
int max_num_blocks; // KV缓存块总数
int block_size; // 每块token数
};
ContinuousBatchingScheduler(const Config& config)
: config_(config),
block_manager_(config.block_size, config.max_num_blocks, 32) {} // 假设32层
// 添加新请求
void add_request(std::shared_ptr<InferenceRequest> request) {
waiting_queue_.push(request);
}
// 调度一个批次
std::vector<std::shared_ptr<InferenceRequest>> schedule() {
std::vector<std::shared_ptr<InferenceRequest>> batch;
// 尝试添加等待中的请求
while (!waiting_queue_.empty() && active_requests_.size() < config_.max_num_seqs) {
auto request = waiting_queue_.front();
waiting_queue_.pop();
// 分配KV缓存
int num_blocks = (request->input_tokens.size() + config_.block_size - 1)
/ config_.block_size;
auto blocks = block_manager_.allocate(num_blocks, /*layer=*/0);
if (blocks.size() == num_blocks) {
// 成功分配
request->kv_block_ids = blocks;
request->state = RequestState::PROCESSING;
active_requests_[request->request_id] = request;
batch.push_back(request);
} else {
// 内存不足，放回队列
waiting_queue_.push(request);
break;
}
}
// 添加当前活跃的请求
for (auto& [id, request] : active_requests_) {
if (request->state == RequestState::PROCESSING &&
batch.size() < config_.max_batch_size) {
batch.push_back(request);
}
}
return batch;
}
// 完成一个请求
void complete_request(int request_id) {
auto it = active_requests_.find(request_id);
if (it != active_requests_.end()) {
// 释放KV缓存
block_manager_.release(it->second->kv_block_ids, /*layer=*/0);
active_requests_.erase(it);
}
}
// 步进：处理一个token
void step() {
auto batch = schedule();
if (batch.empty()) {
return;
}
// 准备批次数据
BatchInput batch_input = prepare_batch_input(batch);
// 执行模型推理
auto batch_output = model_forward(batch_input);
// 更新请求状态
for (size_t i = 0; i < batch.size(); ++i) {
auto& request = batch[i];
if (request->current_position == 0) {
// 处理阶段（prefill）
request->current_position = request->input_tokens.size();
} else {
// 解码阶段（decode）
int next_token = batch_output.tokens[i];
request->output_tokens.push_back(next_token);
if (next_token == EOS_TOKEN_ID ||
request->output_tokens.size() >= request->max_tokens) {
request->state = RequestState::COMPLETED;
complete_request(request->request_id);
}
}
}
}
private:
Config config_;
KVCacheBlockManager block_manager_;
std::queue<std::shared_ptr<InferenceRequest>> waiting_queue_;
std::unordered_map<int, std::shared_ptr<InferenceRequest>> active_requests_;
static constexpr int EOS_TOKEN_ID = 0; // 假设EOS token ID为0
struct BatchInput {
std::vector<int> token_ids;
std::vector<std::vector<int>> kv_block_ids;
std::vector<int> positions;
};
struct BatchOutput {
std::vector<int> tokens;
};
BatchInput prepare_batch_input(const std::vector<std::shared_ptr<InferenceRequest>>& batch) {
BatchInput batch_input;
for (const auto& request : batch) {
if (request->current_position == 0) {
// Prefill阶段：所有输入token
batch_input.token_ids.insert(batch_input.token_ids.end(),
request->input_tokens.begin(),
request->input_tokens.end());
batch_input.positions.push_back(0); // 标记为prefill
} else {
// Decode阶段：下一个token
batch_input.token_ids.push_back(
request->output_tokens.empty() ?
request->input_tokens.back() :
request->output_tokens.back()
);
batch_input.positions.push_back(request->output_tokens.size());
}
batch_input.kv_block_ids.push_back(request->kv_block_ids);
}
return batch_input;
}
BatchOutput model_forward(const BatchInput& batch_input) {
// 模型前向传播（简化）
BatchOutput output;
// ... 实际调用模型推理
return output;
}
};
} // namespace atb2.4 量化优化
cpp
#include "atb_quantization.h"
#include <cmath>
namespace atb {
// INT8量化
template<typename T>
class INT8Quantizer {
public:
struct QuantizedTensor {
Tensor<int8_t> data; // 量化后的数据
float scale; // 缩放因子
T zero_point; // 零点
};
// 对称量化（zero_point = 0）
static QuantizedTensor quantize_symmetric(const Tensor<T>& input) {
QuantizedTensor result;
// 计算缩放因子: scale = max(abs(x)) / 127
T max_abs = 0;
for (size_t i = 0; i < input.size(); ++i) {
max_abs = std::max(max_abs, std::abs(input.data()[i]));
}
result.scale = max_abs / 127.0f;
result.zero_point = 0;
// 量化
result.data = Tensor<int8_t>(input.shape());
for (size_t i = 0; i < input.size(); ++i) {
result.data.data()[i] = static_cast<int8_t>(
std::round(input.data()[i] / result.scale)
);
}
return result;
}
// 非对称量化
static QuantizedTensor quantize_asymmetric(const Tensor<T>& input) {
QuantizedTensor result;
// 计算最小最大值
T min_val = input.data()[0];
T max_val = input.data()[0];
for (size_t i = 0; i < input.size(); ++i) {
min_val = std::min(min_val, input.data()[i]);
max_val = std::max(max_val, input.data()[i]);
}
// 计算scale和zero_point
// q = (x - min) * 255 / (max - min) - 128
// scale = 255 / (max - min)
// zero_point = -128 - min / scale
result.scale = 255.0f / (max_val - min_val);
result.zero_point = static_cast<T>(-128 - min_val * result.scale);
// 量化
result.data = Tensor<int8_t>(input.shape());
for (size_t i = 0; i < input.size(); ++i) {
result.data.data()[i] = static_cast<int8_t>(
std::round(input.data()[i] * result.scale + result.zero_point)
);
}
return result;
}
// 反量化
static Tensor<T> dequantize(const QuantizedTensor& quantized) {
Tensor<T> output(quantized.data.shape());
for (size_t i = 0; i < quantized.data.size(); ++i) {
output.data()[i] = (quantized.data.data()[i] - quantized.zero_point)
/ quantized.scale;
}
return output;
}
};
// INT4量化（用于权重量化）
template<typename T>
class INT4WeightQuantizer {
public:
struct INT4Block {
std::vector<uint8_t> packed_data; // 每个字节存储2个INT4
std::vector<float> scales; // 每组一个scale
std::vector<float> zero_points; // 每组一个zero_point
};
// 分组量化（group_size个权重共享一个scale）
static INT4Block quantize_grouped(const Tensor<T>& weights, int group_size = 128) {
INT4Block result;
int num_groups = (weights.size() + group_size - 1) / group_size;
result.scales.resize(num_groups);
result.zero_points.resize(num_groups);
result.packed_data.resize((weights.size() + 1) / 2);
for (int g = 0; g < num_groups; ++g) {
int start = g * group_size;
int end = std::min(start + group_size, static_cast<int>(weights.size()));
// 计算当前组的scale
T max_val = weights.data()[start];
for (int i = start; i < end; ++i) {
max_val = std::max(max_val, weights.data()[i]);
}
result.scales[g] = max_val / 7.0f; // INT4范围: [-8, 7]
result.zero_points[g] = 0.0f; // 对称量化
// 量化并打包
for (int i = start; i < end; i += 2) {
int8_t q0 = static_cast<int8_t>(
std::round(weights.data()[i] / result.scales[g])
);
int8_t q1 = (i + 1 < end) ? static_cast<int8_t>(
std::round(weights.data()[i + 1] / result.scales[g])
) : 0;
// 打包: q1在高位，q0在低位
result.packed_data[i / 2] = ((q1 & 0x0F) << 4) | (q0 & 0x0F);
}
}
return result;
}
// INT4反量化
static Tensor<T> dequantize_grouped(const INT4Block& block,
const std::vector<int>& shape,
int group_size = 128) {
Tensor<T> weights(shape);
int num_groups = (weights.size() + group_size - 1) / group_size;
for (int g = 0; g < num_groups; ++g) {
float scale = block.scales[g];
int start = g * group_size;
int end = std::min(start + group_size, static_cast<int>(weights.size()));
for (int i = start; i < end; i += 2) {
uint8_t packed = block.packed_data[i / 2];
int8_t q0 = static_cast<int8_t>(packed & 0x0F);
int8_t q1 = static_cast<int8_t>((packed >> 4) & 0x0F);
// 符号扩展（INT4是有符号的）
if (q0 > 7) q0 -= 16;
if (q1 > 7) q1 -= 16;
weights.data()[i] = q0 * scale;
if (i + 1 < end) {
weights.data()[i + 1] = q1 * scale;
}
}
}
return weights;
}
};
// 动态量化（激活值量化）
class DynamicQuantizer {
public:
template<typename T>
struct DynamicQuantizedOutput {
Tensor<int8_t> activations;
float scale;
};
template<typename T>
static DynamicQuantizedOutput<T> quantize_activations(const Tensor<T>& input) {
DynamicQuantizedOutput<T> result;
// 每个样本独立量化
int batch_size = input.shape(0);
int seq_len = input.shape(1);
int hidden = input.shape(2);
result.activations = Tensor<int8_t>(input.shape());
for (int b = 0; b < batch_size; ++b) {
for (int s = 0; s < seq_len; ++s) {
// 计算当前token的scale
float max_abs = 0;
for (int h = 0; h < hidden; ++h) {
max_abs = std::max(max_abs,
std::abs(input.get_value(b, s, h)));
}
float scale = max_abs / 127.0f;
// 量化
for (int h = 0; h < hidden; ++h) {
int8_t q = static_cast<int8_t>(
std::round(input.get_value(b, s, h) / scale)
);
result.activations.set_value(q, b, s, h);
}
// 存储scale（简化起见，这里使用相同的scale）
result.scale = scale;
}
}
return result;
}
};
} // namespace atb2.5 算子融合实现
cpp
#include "atb_fusion.h"
namespace atb {
// 融合RMSNorm、Residual和多层投影
template<typename T>
class FusedTransformerBlock {
public:
struct Config {
int hidden_size;
int num_heads;
int head_dim;
int intermediate_size;
float epsilon;
};
FusedTransformerBlock(const Config& config)
: config_(config) {
// 初始化所有权重
init_weights();
}
// 融合前向传播
Tensor<T> forward(const Tensor<T>& input,
const Tensor<T>& residual,
KVCache& kv_cache,
int position) {
// 融合操作1: RMSNorm + QKV投影 + RoPE
auto [q, k, v] = fused_norm_qkv_rope(input, kv_cache, position);
// 融合操作2: Flash Attention
auto attn_output = flash_attention(q, k, v);
// 融合操作3: 残差连接 + RMSNorm + FFN门控
auto hidden = fused_residual_norm_ffn(residual, attn_output);
return hidden;
}
private:
Config config_;
Tensor<T> q_weight_, k_weight_, v_weight_, o_weight_;
Tensor<T> gate_weight_, up_weight_, down_weight_;
Tensor<T> norm_weight_;
void init_weights() {
// 初始化各层权重
q_weight_ = Tensor<T>({config_.hidden_size,
config_.num_heads * config_.head_dim});
k_weight_ = Tensor<T>({config_.hidden_size,
config_.num_heads * config_.head_dim});
v_weight_ = Tensor<T>({config_.hidden_size,
config_.num_heads * config_.head_dim});
o_weight_ = Tensor<T>({config_.num_heads * config_.head_dim,
config_.hidden_size});
gate_weight_ = Tensor<T>({config_.hidden_size,
config_.intermediate_size});
up_weight_ = Tensor<T>({config_.hidden_size,
config_.intermediate_size});
down_weight_ = Tensor<T>({config_.intermediate_size,
config_.hidden_size});
norm_weight_ = Tensor<T>({config_.hidden_size});
norm_weight_.fill(1.0f);
}
// 融合: RMSNorm -> QKV投影 -> RoPE
std::tuple<Tensor<T>, Tensor<T>, Tensor<T>>
fused_norm_qkv_rope(const Tensor<T>& input,
KVCache& kv_cache,
int position) {
int batch = input.shape(0);
int seq_len = input.shape(1);
int hidden = input.shape(2);
Tensor<T> q({batch, config_.num_heads, seq_len, config_.head_dim});
Tensor<T> k({batch, config_.num_heads, seq_len, config_.head_dim});
Tensor<T> v({batch, config_.num_heads, seq_len, config_.head_dim});
// 单次遍历完成所有操作
for (int b = 0; b < batch; ++b) {
for (int s = 0; s < seq_len; ++s) {
// 计算RMS（在片上累加）
float sum_sq = 0;
std::vector<float> normalized(hidden);
for (int h = 0; h < hidden; ++h) {
float val = input.get_value(b, s, h);
sum_sq += val * val;
}
float rms = std::sqrt(sum_sq / hidden + config_.epsilon);
// QKV投影 + RoPE（融合计算）
for (int head = 0; head < config_.num_heads; ++head) {
for (int d = 0; d < config_.head_dim; ++d) {
int h_idx = head * config_.head_dim + d;
// RMSNorm + 投影
float normed = input.get_value(b, s, h_idx) / rms;
float q_val = normed * q_weight_.get_value(h_idx, head * config_.head_dim + d);
float k_val = normed * k_weight_.get_value(h_idx, head * config_.head_dim + d);
float v_val = normed * v_weight_.get_value(h_idx, head * config_.head_dim + d);
// 应用RoPE（旋转位置编码）
int pos = position + s;
float freq = std::pow(10000.0f, -2.0f * d / config_.head_dim);
float angle = pos * freq;
float cos_val = std::cos(angle);
float sin_val = std::sin(angle);
// q_rotated = q * cos + k * sin（简化版本）
if (d % 2 == 0) {
q.set_value(q_val * cos_val - k_val * sin_val, b, head, s, d);
k.set_value(k_val * cos_val + q_val * sin_val, b, head, s, d);
} else {
q.set_value(q_val * cos_val + k_val * sin_val, b, head, s, d);
k.set_value(k_val * cos_val - q_val * sin_val, b, head, s, d);
}
v.set_value(v_val, b, head, s, d);
// 存储到KV缓存
kv_cache.store_key(b, head, position + s, d, k.get_value(b, head, s, d));
kv_cache.store_value(b, head, position + s, d, v.get_value(b, head, s, d));
}
}
}
}
return {q, k, v};
}
// Flash Attention
Tensor<T> flash_attention(const Tensor<T>& q,
const Tensor<T>& k,
const Tensor<T>& v) {
// Flash Attention实现
// ...（参见前面FlashAttentionV2的实现）
return Tensor<T>({1, 1, 1, 1}); // 简化
}
// 融合: 残差连接 + RMSNorm + SwiGLU FFN
Tensor<T> fused_residual_norm_ffn(const Tensor<T>& residual,
const Tensor<T>& attn_output) {
int batch = attn_output.shape(0);
int seq_len = attn_output.shape(1);
int hidden = attn_output.shape(2);
Tensor<T> output({batch, seq_len, hidden});
for (int b = 0; b < batch; ++b) {
for (int s = 0; s < seq_len; ++s) {
// 残差连接
std::vector<float> hidden_states(hidden);
for (int h = 0; h < hidden; ++h) {
hidden_states[h] = residual.get_value(b, s, h) +
attn_output.get_value(b, s, h);
}
// RMSNorm
float sum_sq = 0;
for (int h = 0; h < hidden; ++h) {
sum_sq += hidden_states[h] * hidden_states[h];
}
float rms = std::sqrt(sum_sq / hidden + config_.epsilon);
// SwiGLU FFN
for (int i = 0; i < config_.intermediate_size; ++i) {
float gate = 0;
float up = 0;
// 门控和上行投影
for (int h = 0; h < hidden; ++h) {
float normed = hidden_states[h] / rms;
gate += normed * gate_weight_.get_value(h, i);
up += normed * up_weight_.get_value(h, i);
}
// SwiGLU激活
float swish = gate / (1.0f + std::exp(-gate));
float activated = swish * up;
// 下行投影
for (int h = 0; h < hidden; ++h) {
output.set_value(
output.get_value(b, s, h) + activated * down_weight_.get_value(i, h),
b, s, h
);
}
}
}
}
return output;
}
};
} // namespace atb三、Python调用接口
ATB提供了简洁的Python接口：
python
import atb
import torch
# ===== 基础推理引擎 =====
class ATBInferenceEngine:
def __init__(self, model_path, config):
self.config = config
self.model = atb.load_model(model_path, config)
# 初始化KV缓存管理器
self.kv_cache = atb.KVCacheManager(
num_blocks=config.max_num_blocks,
block_size=config.block_size,
num_layers=config.num_hidden_layers
)
# 初始化调度器
self.scheduler = atb.ContinuousBatchingScheduler(
max_batch_size=config.max_batch_size,
max_num_seqs=config.max_num_seqs,
kv_cache_manager=self.kv_cache
)
def generate(self, prompt_ids, max_new_tokens=128):
"""文本生成"""
# 创建推理请求
request = atb.InferenceRequest(
prompt_ids=prompt_ids,
max_tokens=max_new_tokens
)
# 添加到调度器
self.scheduler.add_request(request)
# 迭代生成
output_tokens = []
while not request.is_complete():
# 获取批次
batch = self.scheduler.schedule()
if not batch:
break
# 批量推理
for req in batch:
if req.current_position == 0:
# Prefill阶段
logits = self.model.prefill(
input_ids=req.prompt_ids,
kv_cache=self.kv_cache.get_cache(req.request_id)
)
else:
# Decode阶段
logits = self.model.decode(
input_ids=[req.last_token],
position=req.current_position,
kv_cache=self.kv_cache.get_cache(req.request_id)
)
# 采样下一个token
next_token = self._sample(logits)
req.append_token(next_token)
# 更新请求状态
self.scheduler.update_batch(batch)
return request.output_tokens
def _sample(self, logits, temperature=1.0, top_k=50):
"""采样下一个token"""
logits = logits[0, -1, :] / temperature
if top_k > 0:
values, indices = torch.topk(logits, top_k)
logits = torch.full_like(logits, float('-inf'))
logits.scatter_(0, indices, values)
probs = torch.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
return next_token.item()
# ===== 量化示例 =====
def quantization_example():
# 加载模型
model = atb.load_model("llama-7b")
# INT4权重量化
quantizer = atb.INT4Quantizer(group_size=128)
quantized_model = quantizer.quantize_model(model)
# 保存量化模型
atb.save_model(quantized_model, "llama-7b-int4.pt")
# 测试精度
test_input = torch.randint(0, 32000, (1, 128))
with torch.no_grad():
original_output = model(test_input)
quantized_output = quantized_model(test_input)
error = torch.abs(original_output - quantized_output).mean()
print(f"量化误差: {error:.6f}")
# ===== 连续批处理示例 =====
def continuous_batching_example():
config = atb.InferenceConfig(
max_batch_size=32,
max_num_seqs=128,
max_num_blocks=1024,
block_size=16
)
engine = ATBInferenceEngine("llama-7b.pt", config)
# 模拟多个并发请求
prompts = [
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15]
]
# 添加所有请求
requests = []
for i, prompt in enumerate(prompts):
req = atb.InferenceRequest(
request_id=i,
prompt_ids=prompt,
max_tokens=100
)
engine.scheduler.add_request(req)
requests.append(req)
# 并发处理
completed = 0
step = 0
while completed < len(requests):
batch = engine.scheduler.schedule()
if batch:
# 处理批次
for req in batch:
if req.current_position == 0:
logits = engine.model.prefill(req.prompt_ids)
else:
logits = engine.model.decode([req.last_token])
next_token = engine._sample(logits)
req.append_token(next_token)
if req.is_complete():
completed += 1
print(f"请求 {req.request_id} 完成: {req.output_tokens}")
step += 1
# ===== 性能测试 =====
def benchmark():
import time
config = atb.InferenceConfig(
max_batch_size=1,
max_num_seqs=1,
max_num_blocks=512,
block_size=16
)
engine = ATBInferenceEngine("llama-7b.pt", config)
# 测试首token延迟（TTFT）
prompt = [1, 2, 3, 4, 5] * 20 # 100 tokens
start = time.time()
output = engine.generate(prompt, max_new_tokens=1)
ttft = (time.time() - start) * 1000 # 毫秒
print(f"首Token延迟 (TTFT): {ttft:.2f} ms")
# 测试吞吐量（TPOT）
prompt = [1, 2, 3, 4, 5]
start = time.time()
output = engine.generate(prompt, max_new_tokens=100)
total_time = time.time() - start
tpot = (total_time / len(output)) * 1000
throughput = len(output) / total_time
print(f"每Token延迟 (TPOT): {tpot:.2f} ms")
print(f"吞吐量: {throughput:.2f} tokens/sec")
if __name__ == "__main__":
# 运行示例
print("=== 量化示例 ===")
quantization_example()
print("\n=== 连续批处理示例 ===")
continuous_batching_example()
print("\n=== 性能测试 ===")
benchmark()四、性能对比与优化建议
4.1 优化技术性能对比
| 优化技术 | 延迟改善 | 吞吐量改善 | 显存节省 |
|---|---|---|---|
| FlashAttention | 40-60% | 2-3x | 30-50% |
| PagedAttention | 20-30% | 3-5x | 40-60% |
| 连续批处理 | 10-20% | 3-5x | - |
| INT4量化 | 10-20% | 1.5-2x | 75% |
| 算子融合 | 15-25% | 1.5-2x | 20-30% |
4.2 不同场景推荐配置
| 场景 | 推荐配置 | 预期性能 |
|---|---|---|
| 单用户实时对话 | batch_size=1, INT4, FlashAttention | TTFT<100ms |
| 多用户并发服务 | 连续批处理, PagedAttention | 吞吐量>1000 tok/s |
| 离线批量处理 | 大batch, 无KV缓存优化 | 最大吞吐 |
| 边缘设备部署 | INT4, 量化感知训练 | 显存<8GB |
4.3 最佳实践
-
根据场景选择优化策略：
- 实时对话：优先优化TTFT
- 批量处理：优先优化吞吐量
-
合理配置KV缓存：
- 根据并发量调整块数量
- 选择合适的块大小（通常16-32）
-
量化策略：
- 权重：INT4（精度损失小）
- 激活：INT8（需要校准）
-
监控关键指标：
- TTFT、TPOT、显存使用率
五、总结
ascend-transformer-boost为CANN生态提供了全面的大模型推理加速方案：
- 核心优化技术：FlashAttention、PagedAttention、连续批处理
- 显存优化：KV缓存管理、量化压缩
- 性能提升：延迟降低60%+，吞吐量提升5x+
- 灵活部署：支持不同规模和场景
通过ATB，开发者可以高效部署大模型应用，实现毫秒级响应的实时推理体验。
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