CANN大模型加速核心ops-transformer全面解析：Transformer架构算子的高性能实现与优化

User_芊芊君子2026-02-07 11:23

引言

随着自然语言处理（NLP）、计算机视觉和多模态大模型的飞速发展，Transformer架构已经成为现代AI模型的核心支柱。从GPT、BERT到ViT、CLIP，这些大模型都依赖于自注意力机制、前馈神经网络等关键算子。ops-transformer作为CANN生态系统中专门针对Transformer类模型优化的算子库，为这些大模型在异构计算平台上的高效运行提供了坚实的基础支撑。

本文将深入解析ops-transformer算子库的设计理念、核心实现和应用实践，帮助开发者更好地理解和使用这一重要的加速工具。

一、ops-transformer算子库概述

1.1 设计目标

ops-transformer算子库专注于解决Transformer模型推理和训练中的性能瓶颈，主要设计目标包括：

  • 极致性能：充分利用硬件并行计算能力，优化内存访问模式
  • 灵活适配：支持多种Transformer变体和尺寸配置
  • 精度保障：提供FP32、FP16、BF16等多种精度支持
  • 内存高效：通过算子融合和内存复用降低显存占用

1.2 核心算子分类

ops-transformer包含以下主要算子类别：

算子类别 核心功能 典型应用
注意力算子 Self-Attention、Cross-Attention GPT、BERT、ViT
位置编码 Rotary Embedding、ALiBi LLaMA、BLOOM
层归一化 RMSNorm、LayerNorm 各类Transformer
激活函数 GELU、SwiGLU、GeGLU 现代大模型
前馈网络 FFN、MoE gating 混合专家模型
掩码操作 Causal Mask、Padding Mask 自回归生成

二、核心算子详解

2.1 多头自注意力算子

自注意力机制是Transformer的核心，其计算流程包括：QKV投影、注意力分数计算、Softmax归一化和值聚合。

2.1.1 标准Self-Attention实现
cpp 复制代码 
#include "ops_transformer.h"
#include "tensor.h"

namespace ops_transformer {

template<typename T>
class MultiHeadAttention {
public:
    struct Config {
        int hidden_size;
        int num_heads;
        int head_dim;
        float dropout_prob;
        bool use_causal_mask;
    };

    MultiHeadAttention(const Config& config)
        : config_(config) {
        // 初始化权重矩阵
        int weight_size = config.hidden_size * config.head_dim * config.num_heads;
        q_weight_ = std::make_shared<Tensor<T>>({config.hidden_size, weight_size});
        k_weight_ = std::make_shared<Tensor<T>>({config.hidden_size, weight_size});
        v_weight_ = std::make_shared<Tensor<T>>({config.hidden_size, weight_size});
        o_weight_ = std::make_shared<Tensor<T>>({weight_size, config.hidden_size});
    }

    // 前向计算
    Tensor<T> forward(const Tensor<T>& input,
                     const Tensor<T>& attention_mask = Tensor<T>()) {
        int batch_size = input.shape(0);
        int seq_len = input.shape(1);

        // 1. QKV投影 [batch, seq, hidden] -> [batch, num_heads, seq, head_dim]
        auto q = project_to_qkv(input, q_weight_);
        auto k = project_to_qkv(input, k_weight_);
        auto v = project_to_qkv(input, v_weight_);

        // 2. 计算注意力分数
        auto attn_scores = compute_attention_scores(q, k);

        // 3. 应用因果掩码（如果需要）
        if (config_.use_causal_mask) {
            apply_causal_mask(attn_scores, seq_len);
        }

        // 4. 应用外部掩码
        if (attention_mask.valid()) {
            attn_scores = attn_scores + attention_mask;
        }

        // 5. Softmax归一化
        auto attn_probs = softmax(attn_scores, /*dim=*/-1);

        // 6. 值聚合
        auto context = apply_attention(attn_probs, v);

        // 7. 输出投影
        auto output = project_output(context, o_weight_);

        return output;
    }

private:
    Config config_;
    std::shared_ptr<Tensor<T>> q_weight_, k_weight_, v_weight_, o_weight_;

    // QKV投影算子
    Tensor<T> project_to_qkv(const Tensor<T>& input,
                            const std::shared_ptr<Tensor<T>>& weight) {
        // [batch, seq, hidden] @ [hidden, num_heads * head_dim]
        // -> [batch, seq, num_heads * head_dim]
        auto projected = matmul(input, *weight);

        // 重排为 [batch, num_heads, seq, head_dim]
        int batch = input.shape(0);
        int seq = input.shape(1);
        int num_heads = config_.num_heads;
        int head_dim = config_.head_dim;

        return reshape(projected, {batch, num_heads, seq, head_dim});
    }

    // 计算注意力分数: Q @ K^T / sqrt(d_k)
    Tensor<T> compute_attention_scores(const Tensor<T>& q, const Tensor<T>& k) {
        // q: [batch, num_heads, seq_q, head_dim]
        // k: [batch, num_heads, seq_k, head_dim]
        // result: [batch, num_heads, seq_q, seq_k]

        auto k_transposed = transpose(k, {0, 1, 3, 2});  // 交换最后两维
        auto scores = matmul(q, k_transposed);

        // 缩放因子
        float scale = 1.0f / std::sqrt(static_cast<float>(config_.head_dim));
        return scalar_multiply(scores, scale);
    }

    // 应用因果掩码（下三角矩阵）
    void apply_causal_mask(Tensor<T>& scores, int seq_len) {
        for (int i = 0; i < seq_len; ++i) {
            for (int j = i + 1; j < seq_len; ++j) {
                scores.set_value(std::numeric_limits<T>::lowest(), i, j);
            }
        }
    }

    // Softmax归一化
    Tensor<T> softmax(const Tensor<T>& x, int dim) {
        auto exp_x = exp(x);
        auto sum_exp = sum(exp_x, dim, /*keepdim=*/true);
        return exp_x / sum_exp;
    }

    // 应用注意力权重到值
    Tensor<T> apply_attention(const Tensor<T>& attn_probs, const Tensor<T>& v) {
        // attn_probs: [batch, num_heads, seq_q, seq_k]
        // v: [batch, num_heads, seq_k, head_dim]
        return matmul(attn_probs, v);
    }

    // 输出投影
    Tensor<T> project_output(const Tensor<T>& context,
                            const std::shared_ptr<Tensor<T>>& weight) {
        // [batch, num_heads, seq, head_dim] -> [batch, seq, num_heads * head_dim]
        int batch = context.shape(0);
        int num_heads = context.shape(1);
        int seq = context.shape(2);
        int head_dim = context.shape(3);

        auto reshaped = reshape(context, {batch, seq, num_heads * head_dim});

        // [batch, seq, hidden] = [batch, seq, num_heads * head_dim] @ [num_heads * head_dim, hidden]
        return matmul(reshaped, *weight);
    }
};

} // namespace ops_transformer
2.1.2 Flash Attention优化实现

Flash Attention通过分块计算和内存优化大幅提升了注意力计算效率：

cpp 复制代码 
template<typename T>
class FlashAttention {
public:
    // 分块大小（需根据硬件缓存调整）
    static constexpr int BLOCK_SIZE_M = 64;   // 查询块大小
    static constexpr int BLOCK_SIZE_N = 64;   // 键块大小
    static constexpr int BLOCK_SIZE_B = 64;   // 批大小块

    Tensor<T> forward(const Tensor<T>& q, const Tensor<T>& k, const Tensor<T>& v) {
        // 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<T> running_sum({batch, num_heads, seq_len, 1});
        Tensor<T> running_max({batch, num_heads, seq_len, 1});

        // 初始化
        running_sum.fill(0.0f);
        running_max.fill(-std::numeric_limits<T>::infinity());
        output.fill(0.0f);

        // 分块遍历
        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
            auto k_block = get_block(k, start_n, end_n);
            auto v_block = get_block(v, start_n, end_n);

            // 计算Q与当前K块的分值
            auto q_block = get_full_q(q);
            auto scores = matmul(q_block, transpose(k_block, {0, 1, 3, 2}));
            scores = scores * (1.0f / std::sqrt(static_cast<float>(head_dim)));

            // 更新running max和sum
            auto new_max = maximum(running_max, reduce_max(scores, /*dim=*/-1, /*keepdim=*/true));
            auto scores_exp = exp(scores - new_max);
            auto new_sum = running_sum * exp(running_max - new_max) + sum(scores_exp, /*dim=*/-1, /*keepdim=*/true);

            // 更新输出
            output = output * (running_sum / new_sum * exp(running_max - new_max));
            output = output + matmul(scores_exp, v_block) / new_sum;

            // 更新running统计量
            running_max = new_max;
            running_sum = new_sum;
        }

        return output;
    }

private:
    Tensor<T> get_block(const Tensor<T>& tensor, int start, int end) {
        // 提取tensor的指定块
        return tensor.slice(/*dim=*/2, start, end);
    }

    Tensor<T> get_full_q(const Tensor<T>& q) {
        return q;  // Flash Attention中Q通常分块较小
    }
};

2.2 旋转位置编码（RoPE）

旋转位置编码（Rotary Position Embedding）通过旋转矩阵将位置信息注入到Query和Key中：

cpp 复制代码 
template<typename T>
class RotaryPositionEmbedding {
public:
    struct Config {
        int dim;
        int max_position_embeddings;
        float base;
    };

    RotaryPositionEmbedding(const Config& config)
        : config_(config) {
        precompute_freqs();
    }

    // 应用旋转位置编码
    void apply_rotary_pos_emb(Tensor<T>& q, Tensor<T>& k,
                             int start_pos = 0) const {
        int batch = q.shape(0);
        int num_heads = q.shape(1);
        int seq_len = q.shape(2);
        int head_dim = q.shape(3);

        // 确保head_dim是偶数
        assert(head_dim % 2 == 0);

        // 重排为复数形式: [batch, num_heads, seq_len, head_dim/2, 2]
        auto q_complex = reshape_to_complex(q);
        auto k_complex = reshape_to_complex(k);

        // 获取对应位置的频率
        auto freqs = get_freqs(start_pos, start_pos + seq_len);

        // 应用旋转: (a + bi) * (cos + i*sin) = (a*cos - b*sin) + i(a*sin + b*cos)
        apply_rotation(q_complex, freqs);
        apply_rotation(k_complex, freqs);

        // 重排回原形状
        q = reshape_from_complex(q_complex, q.shape());
        k = reshape_from_complex(k_complex, k.shape());
    }

private:
    Config config_;
    Tensor<float> freqs_;  // 预计算的频率 [max_pos, dim/2]

    void precompute_freqs() {
        // 计算频率: theta_i = base^(-2i/d)
        int half_dim = config_.dim / 2;
        freqs_ = Tensor<float>({config_.max_position_embeddings, half_dim});

        for (int pos = 0; pos < config_.max_position_embeddings; ++pos) {
            for (int i = 0; i < half_dim; ++i) {
                float theta = std::pow(config_.base, -2.0f * i / config_.dim);
                freqs_.set_value(pos * theta, pos, i);
            }
        }
    }

    Tensor<T> get_freqs(int start, int end) const {
        // 获取指定位置的频率，并转换为cos/sin对
        auto freqs_slice = freqs_.slice(/*dim=*/0, start, end);

        int seq_len = end - start;
        int half_dim = config_.dim / 2;

        Tensor<T> result({seq_len, half_dim, 2});

        for (int pos = 0; pos < seq_len; ++pos) {
            for (int i = 0; i < half_dim; ++i) {
                float freq = freqs_slice.get_value(pos, i);
                result.set_value(std::cos(freq), pos, i, 0);  // cos
                result.set_value(std::sin(freq), pos, i, 1);  // sin
            }
        }

        return result;
    }

    Tensor<T> reshape_to_complex(const Tensor<T>& x) const {
        // [batch, num_heads, seq_len, head_dim]
        // -> [batch, num_heads, seq_len, head_dim/2, 2]
        auto shape = x.shape();
        shape.back() = shape.back() / 2;
        shape.push_back(2);
        return reshape(x, shape);
    }

    Tensor<T> reshape_from_complex(const Tensor<T>& x_complex,
                                  const std::vector<int>& target_shape) const {
        return reshape(x_complex, target_shape);
    }

    void apply_rotation(Tensor<T>& x_complex, const Tensor<T>& freqs) const {
        // x_complex: [batch, num_heads, seq_len, dim/2, 2]
        // freqs: [seq_len, dim/2, 2]
        int batch = x_complex.shape(0);
        int num_heads = x_complex.shape(1);
        int seq_len = x_complex.shape(2);
        int half_dim = x_complex.shape(3);

        for (int b = 0; b < batch; ++b) {
            for (int h = 0; h < num_heads; ++h) {
                for (int pos = 0; pos < seq_len; ++pos) {
                    for (int i = 0; i < half_dim; ++i) {
                        T a = x_complex.get_value(b, h, pos, i, 0);
                        T b_val = x_complex.get_value(b, h, pos, i, 1);
                        T cos_val = freqs.get_value(pos, i, 0);
                        T sin_val = freqs.get_value(pos, i, 1);

                        // 复数乘法
                        x_complex.set_value(a * cos_val - b_val * sin_val, b, h, pos, i, 0);
                        x_complex.set_value(a * sin_val + b_val * cos_val, b, h, pos, i, 1);
                    }
                }
            }
        }
    }
};

2.3 RMS归一化

RMSNorm是现代大模型（如LLaMA）常用的归一化方法：

cpp 复制代码 
template<typename T>
class RMSNorm {
public:
    struct Config {
        int hidden_size;
        float epsilon;
        bool use_bias;
    };

    RMSNorm(const Config& config)
        : config_(config),
          weight_(Tensor<T>({config.hidden_size})),
          bias_(Tensor<T>({config.hidden_size})) {
        weight_.fill(1.0f);
        if (config_.use_bias) {
            bias_.fill(0.0f);
        }
    }

    Tensor<T> forward(const Tensor<T>& input) const {
        // input: [batch, seq_len, hidden_size]
        int batch = input.shape(0);
        int seq_len = input.shape(1);
        int hidden = input.shape(2);

        Tensor<T> output(input.shape());

        // 对每个位置独立归一化
        for (int b = 0; b < batch; ++b) {
            for (int s = 0; s < seq_len; ++s) {
                // 计算均方根: sqrt(sum(x^2) / dim + epsilon)
                float sum_sq = 0.0f;
                for (int h = 0; h < hidden; ++h) {
                    T val = input.get_value(b, s, h);
                    sum_sq += val * val;
                }

                float rms = std::sqrt(sum_sq / hidden + config_.epsilon);

                // 归一化并应用权重
                for (int h = 0; h < hidden; ++h) {
                    T val = input.get_value(b, s, h);
                    T w = weight_.get_value(h);
                    T output_val = (val / rms) * w;

                    if (config_.use_bias) {
                        output_val += bias_.get_value(h);
                    }

                    output.set_value(output_val, b, s, h);
                }
            }
        }

        return output;
    }

private:
    Config config_;
    Tensor<T> weight_;
    Tensor<T> bias_;
};

2.4 SwiGLU激活函数

SwiGLU是PaLM、LLaMA等大模型使用的激活函数：

cpp 复制代码 
template<typename T>
class SwiGLU {
public:
    // SwiGLU(x) = Swish(xW) ⊙ (xV)
    // 其中 Swish(x) = x * sigmoid(x)
    // ⊙ 表示逐元素乘法

    static Tensor<T> forward(const Tensor<T>& input,
                            const Tensor<T>& gate_weight,
                            const Tensor<T>& up_weight) {
        // input: [batch, seq_len, hidden_size]
        // gate_weight: [hidden_size, intermediate_size]
        // up_weight: [hidden_size, intermediate_size]

        // 门控分支: xW
        auto gate = matmul(input, gate_weight);

        // 上行分支: xV
        auto up = matmul(input, up_weight);

        // Swish激活: gate * sigmoid(gate)
        auto gate_swish = swish(gate);

        // 逐元素乘法
        return elementwise_multiply(gate_swish, up);
    }

    static Tensor<T> swish(const Tensor<T>& x) {
        Tensor<T> result(x.shape());
        for (size_t i = 0; i < x.size(); ++i) {
            T val = x.data()[i];
            T sigmoid_val = 1.0f / (1.0f + std::exp(-val));
            result.data()[i] = val * sigmoid_val;
        }
        return result;
    }

private:
    static Tensor<T> elementwise_multiply(const Tensor<T>& a, const Tensor<T>& b) {
        assert(a.shape() == b.shape());
        Tensor<T> result(a.shape());
        for (size_t i = 0; i < a.size(); ++i) {
            result.data()[i] = a.data()[i] * b.data()[i];
        }
        return result;
    }
};

2.5 分组查询注意力（GQA）

分组查询注意力是Multi-Query Attention和Multi-Head Attention的折中方案：

cpp 复制代码 
template<typename T>
class GroupedQueryAttention {
public:
    struct Config {
        int hidden_size;
        int num_heads;          // 查询头数
        int num_kv_heads;       // 键值头数（分组数）
        int head_dim;
        bool use_causal_mask;
    };

    GroupedQueryAttention(const Config& config)
        : config_(config) {
        assert(config.num_heads % config.num_kv_heads == 0);

        qkv_ratio_ = config.num_heads / config.num_kv_heads;

        // 初始化权重
        q_weight_ = Tensor<T>({config.hidden_size,
                              config.num_heads * config.head_dim});
        k_weight_ = Tensor<T>({config.hidden_size,
                              config.num_kv_heads * config.head_dim});
        v_weight_ = Tensor<T>({config.hidden_size,
                              config.num_kv_heads * config.head_dim});
        o_weight_ = Tensor<T>({config.num_heads * config.head_dim,
                              config.hidden_size});
    }

    Tensor<T> forward(const Tensor<T>& input,
                     const Tensor<T>& attention_mask = Tensor<T>()) {
        int batch_size = input.shape(0);
        int seq_len = input.shape(1);

        // 1. 投影QKV
        auto q = project_q(input);   // [batch, num_heads, seq, head_dim]
        auto k = project_kv(input);  // [batch, num_kv_heads, seq, head_dim]
        auto v = project_kv(input);  // [batch, num_kv_heads, seq, head_dim]

        // 2. 扩展KV以匹配Q头数
        k = expand_kv_heads(k);
        v = expand_kv_heads(v);

        // 3. 标准注意力计算
        auto attn_scores = compute_attention_scores(q, k);

        if (config_.use_causal_mask) {
            apply_causal_mask(attn_scores, seq_len);
        }

        if (attention_mask.valid()) {
            attn_scores = attn_scores + attention_mask;
        }

        auto attn_probs = softmax(attn_scores, /*dim=*/-1);
        auto context = matmul(attn_probs, v);

        // 4. 输出投影
        return project_output(context);
    }

private:
    Config config_;
    int qkv_ratio_;
    Tensor<T> q_weight_, k_weight_, v_weight_, o_weight_;

    Tensor<T> project_q(const Tensor<T>& input) {
        auto projected = matmul(input, q_weight_);
        return reshape(projected, {input.shape(0), config_.num_heads,
                                   input.shape(1), config_.head_dim});
    }

    Tensor<T> project_kv(const Tensor<T>& input) {
        auto projected = matmul(input, k_weight_);
        return reshape(projected, {input.shape(0), config_.num_kv_heads,
                                   input.shape(1), config_.head_dim});
    }

    Tensor<T> expand_kv_heads(const Tensor<T>& kv) {
        // kv: [batch, num_kv_heads, seq, head_dim]
        // 扩展为: [batch, num_heads, seq, head_dim]

        int batch = kv.shape(0);
        int seq = kv.shape(2);
        int head_dim = kv.shape(3);

        Tensor<T> expanded({batch, config_.num_heads, seq, head_dim});

        for (int b = 0; b < batch; ++b) {
            for (int h = 0; h < config_.num_heads; ++h) {
                int kv_head = h / qkv_ratio_;  // 计算对应的KV头
                for (int s = 0; s < seq; ++s) {
                    for (int d = 0; d < head_dim; ++d) {
                        expanded.set_value(kv.get_value(b, kv_head, s, d),
                                          b, h, s, d);
                    }
                }
            }
        }

        return expanded;
    }

    Tensor<T> compute_attention_scores(const Tensor<T>& q, const Tensor<T>& k) {
        auto k_t = transpose(k, {0, 1, 3, 2});
        auto scores = matmul(q, k_t);
        float scale = 1.0f / std::sqrt(static_cast<float>(config_.head_dim));
        return scalar_multiply(scores, scale);
    }

    void apply_causal_mask(Tensor<T>& scores, int seq_len) {
        for (int i = 0; i < seq_len; ++i) {
            for (int j = i + 1; j < seq_len; ++j) {
                scores.set_value(std::numeric_limits<T>::lowest(), i, j);
            }
        }
    }

    Tensor<T> project_output(const Tensor<T>& context) {
        int batch = context.shape(0);
        int seq = context.shape(2);
        auto reshaped = reshape(context, {batch, seq,
                                          config_.num_heads * config_.head_dim});
        return matmul(reshaped, o_weight_);
    }
};

三、完整Transformer层实现

以下是一个完整的Transformer解码器层的实现示例：

cpp 复制代码 
template<typename T>
class TransformerDecoderLayer {
public:
    struct Config {
        int hidden_size;
        int num_heads;
        int num_kv_heads;       // 0表示使用标准MHA
        int intermediate_size;
        float epsilon;
        float dropout_prob;
        bool use_rms_norm;
        bool use_swiglu;
        bool use_rope;
        int rope_dim;
        bool use_gqa;
    };

    TransformerDecoderLayer(const Config& config)
        : config_(config) {

        // 初始化自注意力
        if (config.use_gqa && config.num_kv_heads > 0) {
            typename GroupedQueryAttention<T>::Config attn_config{
                config.hidden_size,
                config.num_heads,
                config.num_kv_heads,
                config.head_dim(),
                true  // causal mask
            };
            self_attn_ = std::make_unique<GroupedQueryAttention<T>>(attn_config);
        } else {
            typename MultiHeadAttention<T>::Config attn_config{
                config.hidden_size,
                config.num_heads,
                config.head_dim(),
                config.dropout_prob,
                true  // causal mask
            };
            self_attn_ = std::make_unique<MultiHeadAttention<T>>(attn_config);
        }

        // 初始化RoPE
        if (config.use_rope) {
            typename RotaryPositionEmbedding<T>::Config rope_config{
                config.rope_dim,
                8192,  // max_position
                10000.0f  // base
            };
            rope_ = std::make_unique<RotaryPositionEmbedding<T>>(rope_config);
        }

        // 初始化归一化层
        typename RMSNorm<T>::Config norm_config{
            config.hidden_size,
            config.epsilon,
            false  // no bias
        };
        input_norm_ = std::make_unique<RMSNorm<T>>(norm_config);
        post_norm_ = std::make_unique<RMSNorm<T>>(norm_config);

        // 初始化FFN
        ffn_weights_.gate = Tensor<T>({config.hidden_size, config.intermediate_size});
        ffn_weights_.up = Tensor<T>({config.hidden_size, config.intermediate_size});
        ffn_weights_.down = Tensor<T>({config.intermediate_size, config.hidden_size});
    }

    Tensor<T> forward(const Tensor<T>& input,
                     int start_pos = 0,
                     const Tensor<T>& attention_mask = Tensor<T>()) {
        // 1. 自注意力子层（Pre-Norm）
        auto residual = input;
        auto normalized = input_norm_->forward(input);

        // 应用RoPE（如果使用）
        if (config_.use_rope && rope_) {
            // 这里需要先投影出QKV，然后对QK应用RoPE
            // 简化起见，假设self_attn内部处理RoPE
        }

        auto attn_output = self_attn_->forward(normalized, attention_mask);
        auto hidden = residual + attn_output;

        // 2. 前馈网络子层（Pre-Norm）
        residual = hidden;
        normalized = post_norm_->forward(hidden);

        auto ffn_output = ffn_forward(normalized);
        hidden = residual + ffn_output;

        return hidden;
    }

private:
    Config config_;

    // 自注意力
    std::unique_ptr<BaseAttention<T>> self_attn_;

    // 位置编码
    std::unique_ptr<RotaryPositionEmbedding<T>> rope_;

    // 归一化层
    std::unique_ptr<RMSNorm<T>> input_norm_;
    std::unique_ptr<RMSNorm<T>> post_norm_;

    // FFN权重
    struct FFNWeights {
        Tensor<T> gate;
        Tensor<T> up;
        Tensor<T> down;
    } ffn_weights_;

    // 前馈网络前向计算
    Tensor<T> ffn_forward(const Tensor<T>& input) {
        if (config_.use_swiglu) {
            // SwiGLU变体
            auto gate_output = matmul(input, ffn_weights_.gate);
            auto up_output = matmul(input, ffn_weights_.up);

            auto activated = swiglu(gate_output, up_output);
            return matmul(activated, ffn_weights_.down);
        } else {
            // 标准FFN
            auto intermediate = matmul(input, ffn_weights_.gate);
            auto activated = gelu(intermediate);
            return matmul(activated, ffn_weights_.down);
        }
    }

    // SwiGLU激活
    Tensor<T> swiglu(const Tensor<T>& gate, const Tensor<T>& up) {
        Tensor<T> result(gate.shape());
        for (size_t i = 0; i < gate.size(); ++i) {
            float gate_val = gate.data()[i];
            float up_val = up.data()[i];
            float swish = gate_val / (1.0f + std::exp(-gate_val));
            result.data()[i] = swish * up_val;
        }
        return result;
    }

    // GELU激活
    Tensor<T> gelu(const Tensor<T>& x) {
        Tensor<T> result(x.shape());
        for (size_t i = 0; i < x.size(); ++i) {
            float val = x.data()[i];
            // GELU近似: x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
            float tanh_arg = 0.7978845608f * (val + 0.044715f * val * val * val);
            result.data()[i] = val * (1.0f + std::tanh(tanh_arg)) / 2.0f;
        }
        return result;
    }
};

// 抽象注意力基类
template<typename T>
class BaseAttention {
public:
    virtual ~BaseAttention() = default;
    virtual Tensor<T> forward(const Tensor<T>& input,
                            const Tensor<T>& mask = Tensor<T>()) = 0;
};

四、Python调用接口

ops-transformer提供了友好的Python接口：

python 复制代码 
import ops_transformer
import torch

# ===== 多头自注意力 =====
class MultiHeadAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout=0.0):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads

        # 调用ops-transformer的优化算子
        self.attn_op = ops_transformer.MultiHeadAttentionOp(
            hidden_size=hidden_size,
            num_heads=num_heads,
            dropout_prob=dropout
        )

    def forward(self, x, attention_mask=None):
        # x: [batch, seq_len, hidden_size]
        return self.attn_op.forward(x, attention_mask)

# ===== Flash Attention =====
class FlashAttentionLayer(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super().__init__()
        self.flash_attn = ops_transformer.FlashAttentionOp(
            hidden_size=hidden_size,
            num_heads=num_heads
        )

    def forward(self, q, k, v):
        # 输入需要已经是投影后的QKV
        # q, k, v: [batch, num_heads, seq_len, head_dim]
        return self.flash_attn.forward(q, k, v)

# ===== 旋转位置编码 =====
class RotaryPositionEmbedding(nn.Module):
    def __init__(self, dim, max_position=8192, base=10000):
        super().__init__()
        self.rope = ops_transformer.RotaryEmbeddingOp(
            dim=dim,
            max_position_embeddings=max_position,
            base=base
        )

    def forward(self, q, k, start_pos=0):
        # 应用RoPE到query和key
        self.rope.apply(q, k, start_pos)
        return q, k

# ===== RMS归一化 =====
class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.eps = eps
        self.norm_op = ops_transformer.RMSNormOp(epsilon=eps)

    def forward(self, x):
        return self.norm_op.forward(x, self.weight)

# ===== SwiGLU激活 =====
class SwiGLUFFN(nn.Module):
    def __init__(self, hidden_size, intermediate_size):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)

        self.swiglu_op = ops_transformer.SwiGLUOp()

    def forward(self, x):
        gate = self.gate_proj(x)
        up = self.up_proj(x)
        activated = self.swiglu_op.forward(gate, up)
        return self.down_proj(activated)

# ===== 完整Transformer层 =====
class TransformerDecoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads

        # 自注意力
        self.self_attn = MultiHeadAttention(
            hidden_size=config.hidden_size,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout
        )

        # RoPE（如果使用）
        if config.use_rope:
            self.rotary_emb = RotaryPositionEmbedding(
                dim=config.hidden_size // config.num_attention_heads,
                max_position=config.max_position_embeddings,
                base=config.rope_theta
            )
        else:
            self.rotary_emb = None

        # 归一化
        self.input_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # FFN
        self.mlp = SwiGLUFFN(
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size
        )

    def forward(self, x, attention_mask=None, start_pos=0):
        # 自注意力子层（Pre-Norm）
        residual = x
        x = self.input_norm(x)

        attn_out = self.self_attn(x, attention_mask)
        x = residual + attn_out

        # FFN子层（Pre-Norm）
        residual = x
        x = self.post_norm(x)

        ffn_out = self.mlp(x)
        x = residual + ffn_out

        return x

# ===== 完整Transformer模型 =====
class TransformerModel(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        # 嵌入层
        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)

        # Transformer层
        self.layers = nn.ModuleList([
            TransformerDecoderLayer(config)
            for _ in range(config.num_hidden_layers)
        ])

        # 最终归一化
        self.final_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # 输出头
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def forward(self, input_ids, attention_mask=None, start_pos=0):
        # 嵌入
        hidden_states = self.embeddings(input_ids)

        # 通过各层
        for layer in self.layers:
            hidden_states = layer(hidden_states, attention_mask, start_pos)

        # 最终归一化和投影
        hidden_states = self.final_norm(hidden_states)
        logits = self.lm_head(hidden_states)

        return logits

# ===== 使用示例 =====
def main():
    # 配置
    class Config:
        vocab_size = 32000
        hidden_size = 4096
        num_hidden_layers = 32
        num_attention_heads = 32
        intermediate_size = 11008
        max_position_embeddings = 2048
        rms_norm_eps = 1e-6
        attention_dropout = 0.0
        use_rope = True
        rope_theta = 10000.0

    config = Config()

    # 创建模型
    model = TransformerModel(config)

    # 前向传播
    batch_size = 2
    seq_len = 128
    input_ids = torch.randint(0, config.vocab_size, (batch_size, seq_len))

    with torch.no_grad():
        logits = model(input_ids)

    print(f"Input shape: {input_ids.shape}")
    print(f"Output logits shape: {logits.shape}")

if __name__ == "__main__":
    main()

五、应用场景与性能优化

5.1 大语言模型推理

python 复制代码 
import ops_transformer
import torch

class LLMInferenceEngine:
    def __init__(self, model_path, config):
        self.config = config
        self.model = self.load_model(model_path)

        # KV缓存优化
        self.use_kv_cache = True
        self.kv_cache = self.init_kv_cache()

        # 使用ops-transformer的PagedAttention
        self.paged_attn = ops_transformer.PagedAttentionOp(
            num_heads=config.num_attention_heads,
            head_dim=config.head_dim,
            block_size=16
        )

    def generate(self, prompt_ids, max_new_tokens=128):
        # 编码阶段
        prompt_output = self.model.forward_prompt(prompt_ids, self.kv_cache)

        # 解码阶段（使用KV缓存）
        output_ids = prompt_output.clone()

        for _ in range(max_new_tokens):
            # 自回归生成
            logits = self.model.forward_token(
                output_ids[:, -1:],  # 只取最后一个token
                self.kv_cache
            )

            next_token = torch.argmax(logits[:, -1, :], dim=-1, keepdim=True)
            output_ids = torch.cat([output_ids, next_token], dim=1)

            # 检查结束符
            if next_token.item() == self.config.eos_token_id:
                break

        return output_ids

    @torch.no_grad()
    def init_kv_cache(self):
        """初始化KV缓存"""
        cache = {}
        for layer_idx in range(self.config.num_hidden_layers):
            cache[layer_idx] = {
                'key': torch.zeros(
                    self.config.max_batch_size,
                    self.config.num_attention_heads,
                    self.config.max_position_embeddings,
                    self.config.head_dim
                ),
                'value': torch.zeros(
                    self.config.max_batch_size,
                    self.config.num_attention_heads,
                    self.config.max_position_embeddings,
                    self.config.head_dim
                )
            }
        return cache

5.2 混合精度推理

python 复制代码 
class MixedPrecisionTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()

        # 主权重使用FP16
        self.layers = nn.ModuleList([
            TransformerDecoderLayer(config).half()
            for _ in range(config.num_hidden_layers)
        ])

        # 关键算子使用BF16
        self.attention_op = ops_transformer.MixedPrecisionAttentionOp(
            compute_dtype=torch.bfloat16,
            store_dtype=torch.float16
        )

    def forward(self, x):
        # 自动混合精度
        with torch.cuda.amp.autocast(
            dtype=torch.bfloat16,
            enabled=True
        ):
            for layer in self.layers:
                x = layer(x)
        return x

5.3 算子融合优化

cpp 复制代码 
// 融合QKV投影和RoPE应用
template<typename T>
class FusedQKVWithRoPE {
public:
    Tensor<T> forward(const Tensor<T>& input,
                     const Tensor<T>& qkv_weight,
                     const RotaryPositionEmbedding<T>& rope,
                     int start_pos) {
        // 一次性完成QKV投影和RoPE旋转
        // 减少中间结果的内存读写

        int batch = input.shape(0);
        int seq = input.shape(1);
        int hidden = input.shape(2);

        // QKV投影
        auto qkv = matmul(input, qkv_weight);

        // 在片上内存中直接应用RoPE
        apply_rope_inplace(qkv, rope, start_pos);

        return qkv;
    }

private:
    void apply_rope_inplace(Tensor<T>& qkv,
                           const RotaryPositionEmbedding<T>& rope,
                           int start_pos) {
        // 直接在qkv tensor上应用RoPE，避免额外分配
        // ...
    }
};

// 融合RMSNorm和残差连接
template<typename T>
class FusedRMSNormResidual {
public:
    Tensor<T> forward(const Tensor<T>& input,
                     const Tensor<T>& residual,
                     const RMSNorm<T>& norm) {
        // output = residual + norm(input)
        // 在一个kernel中完成

        int size = input.size();
        Tensor<T> output(input.shape());

        for (int i = 0; i < size; ++i) {
            T normed = norm.normalize_single(input.data()[i]);
            output.data()[i] = residual.data()[i] + normed;
        }

        return output;
    }
};

六、性能对比与优化建议

6.1 不同注意力机制的性能对比

注意力类型 显存占用 计算速度 精度 适用场景
标准MHA 高 中 完全 小模型、离线处理
Flash Attention 中 快 完全 通用场景
GQA (1:4) 中低 快 接近 长序列生成
MQA 低 最快 略降 实时推理

6.2 优化建议

  1. 选择合适的注意力机制：

    • 推理优先：使用GQA或MQA
    • 训练优先：使用Flash Attention
    • 长序列：使用分块注意力

  2. 启用KV缓存：

    • 自回归生成必须启用
    • 注意缓存内存管理

  3. 使用混合精度：

    • BF16用于计算（避免溢出）
    • FP16用于存储（节省内存）

  4. 算子融合：

    • QKV+RoPE融合
    • Norm+Residual融合
    • FFN两阶段融合

  5. 批处理优化：

    • 动态批处理
    • PagedAttention管理

七、总结

ops-transformer作为CANN生态系统中专门针对Transformer模型优化的算子库，提供了：

  1. 完整的算子覆盖：从基础的自注意力到高级的GQA、Flash Attention
  2. 卓越的性能表现：通过硬件优化和算法改进实现高效计算
  3. 灵活的配置选项：支持多种Transformer变体和精度组合
  4. 便捷的使用方式：提供C++和Python双重接口

通过合理使用ops-transformer，开发者可以显著提升Transformer模型的推理和训练性能，为自然语言处理、计算机视觉等领域的大模型应用提供强有力的支撑。

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