一、项目概述

CANN组织链接: https://atomgit.com/cann
ops-transformer仓库链接: https://atomgit.com/cann/ops-transformer

ops-transformer 是 CANN 专门为 Transformer 类大模型设计的算子库，实现了网络在 NPU 上的高效加速计算。该项目在开源社区拥有超过 650 个 Star，是 CANN 算子库生态中最受关注的项目之一，专门针对 BERT、GPT、LLaMA 等主流大模型进行了深度优化。

1.1 核心定位

随着大语言模型的兴起，Transformer 架构已成为 AI 领域的主流。ops-transformer 专注于提供高性能的 Transformer 相关算子，涵盖自注意力机制、前馈神经网络、层归一化等核心组件，为大模型在 NPU 上的高效运行提供坚实基础。

1.2 技术特点

• Transformer 专用优化: 针对 Transformer 架构特点进行专门优化
• FlashAttention 实现: 高效的注意力机制实现，降低内存占用
• 融合算子支持: 支持 LayerNorm、FFN 等融合算子
• 多精度支持: 支持 FP16、BF16、FP32 等多种精度
• 动态形状支持: 支持变长序列处理

二、核心算子详解

2.1 自注意力算子

/**
 * FlashAttention 算子实现
 * 通过分块计算减少内存访问，提升性能
 */
template <typename T, int HeadDim = 64>
class FlashAttentionKernel {
public:
    /**
     * FlashAttention 前向计算
     *
     * Args:
     *   query: 查询矩阵 (Batch, NumHeads, SeqLen, HeadDim)
     *   key: 键矩阵 (Batch, NumHeads, SeqLen, HeadDim)
     *   value: 值矩阵 (Batch, NumHeads, SeqLen, HeadDim)
     *   output: 输出矩阵 (Batch, NumHeads, SeqLen, HeadDim)
     *   scale: 缩放因子
     */
    static void Forward(const T* query, const T* key, const T* value,
                       T* output, float scale = 1.0f / sqrtf(HeadDim)) {
        constexpr int TileSize = 64;  // 分块大小

        // 外层循环：遍历 Query 的块
        for (int q_block = 0; q_block < SeqLen; q_block += TileSize) {
            int q_end = std::min(q_block + TileSize, SeqLen);

            // 初始化输出和统计量
            InitializeOutput(q_block, q_end);

            // 内层循环：遍历 Key/Value 的块
            for (int kv_block = 0; kv_block < SeqLen; kv_block += TileSize) {
                int kv_end = std::min(kv_block + TileSize, SeqLen);

                // 1. 加载 Key/Value 块
                LoadKVBlock(key, value, kv_block, kv_end);

                // 2. 计算 Q @ K^T
                ComputeQKTranspose(query, q_block, q_end, kv_block, kv_end, scale);

                // 3. 计算 Softmax
                ComputeSoftmax(kv_block, kv_end);

                // 4. 计算 Attention @ V
                ComputeAttentionValue(kv_block, kv_end);
            }

            // 归一化并存储结果
            NormalizeAndStoreOutput(q_block, q_end);
        }
    }

private:
    /**
     * 计算 Q @ K^T 的分块实现
     */
    static void ComputeQKTranspose(const T* query,
                                  int q_start, int q_end,
                                  int kv_start, int kv_end,
                                  float scale) {
        for (int b = 0; b < Batch; ++b) {
            for (int h = 0; h < NumHeads; ++h) {
                for (int i = q_start; i < q_end; ++i) {
                    for (int j = kv_start; j < kv_end; ++j) {
                        // 计算点积
                        float sum = 0.0f;
                        for (int d = 0; d < HeadDim; ++d) {
                            int q_idx = Offset(b, h, i, d);
                            int k_idx = Offset(b, h, j, d);
                            sum += static_cast<float>(query[q_idx]) *
                                   static_cast<float>(key_buffer[d]);
                        }

                        // 缩放并累加
                        int out_idx = i * SeqLen + j;
                        scores_buffer[out_idx] += sum * scale;
                    }
                }
            }
        }
    }

    /**
     * 在线 Softmax 计算（增量式）
     */
    static void ComputeSoftmax(int kv_start, int kv_end) {
        for (int i = 0; i < TileSize; ++i) {
            // 找到当前块的最大值
            float max_val = -INFINITY;
            for (int j = kv_start; j < kv_end; ++j) {
                max_val = std::max(max_val, scores_buffer[i * SeqLen + j]);
            }

            // 更新全局最大值
            float old_max = running_max[i];
            running_max[i] = std::max(old_max, max_val);

            // 计算指数和（考虑数值稳定性）
            float sum = 0.0f;
            for (int j = kv_start; j < kv_end; ++j) {
                float exp_val = expf(scores_buffer[i * SeqLen + j] - running_max[i]);
                scores_buffer[i * SeqLen + j] = exp_val;
                sum += exp_val;
            }

            // 更新归一化因子
            running_sum[i] = running_sum[i] * expf(old_max - running_max[i]) + sum;
        }
    }

    // Local buffers
    static constexpr int Batch = 1;
    static constexpr int NumHeads = 32;
    static constexpr int SeqLen = 2048;
    static constexpr int HeadDim = 64;

    alignas(64) T key_buffer[TileSize * HeadDim];
    alignas(64) T value_buffer[TileSize * HeadDim];
    alignas(64) float scores_buffer[TileSize * SeqLen];
    alignas(64) float running_max[TileSize];
    alignas(64) float running_sum[TileSize];
    alignas(64) T output_buffer[TileSize * HeadDim];

    static int Offset(int b, int h, int s, int d) {
        return ((b * NumHeads + h) * SeqLen + s) * HeadDim + d;
    }
};

/**
 * 多头注意力融合算子
 * 融合 QKV 投影 + Attention + 输出投影
 */
template <typename T, int HiddenDim, int NumHeads, int HeadDim>
class FusedMultiHeadAttention {
public:
    /**
     * 融合 MHA 前向计算
     *
     * Args:
     *   input: 输入 (Batch, SeqLen, HiddenDim)
     *   qkv_weight: QKV 权重 (3 * HiddenDim, HiddenDim)
     *   qkv_bias: QKV 偏置 (3 * HiddenDim)
     *   output_weight: 输出权重 (HiddenDim, HiddenDim)
     *   output_bias: 输出偏置 (HiddenDim)
     *   output: 输出 (Batch, SeqLen, HiddenDim)
     */
    static void Forward(const T* input,
                       const T* qkv_weight,
                       const T* qkv_bias,
                       const T* output_weight,
                       const T* output_bias,
                       T* output) {
        // 1. QKV 投影（融合）
        T query[Batch * SeqLen * HiddenDim];
        T key[Batch * SeqLen * HiddenDim];
        T value[Batch * SeqLen * HiddenDim];

        FusedQKVProjection(input, qkv_weight, qkv_bias, query, key, value);

        // 2. Reshape 为多头格式
        ReshapeToHeads(query, key, value);

        // 3. Flash Attention
        T attn_output[Batch * NumHeads * SeqLen * HeadDim];
        FlashAttentionKernel<T, HeadDim>::Forward(query, key, value, attn_output);

        // 4. 输出投影（融合）
        FusedOutputProjection(attn_output, output_weight, output_bias, output);
    }

private:
    /**
     * 融合的 QKV 投影
     */
    static void FusedQKVProjection(const T* input,
                                  const T* weight,
                                  const T* bias,
                                  T* query, T* key, T* value) {
        // 单次矩阵乘法计算 QKV
        T qkv_output[Batch * SeqLen * 3 * HiddenDim];
        MatMul(input, weight, bias, qkv_output);

        // 分离 Q, K, V
        for (int i = 0; i < Batch * SeqLen * HiddenDim; ++i) {
            query[i] = qkv_output[i];
            key[i] = qkv_output[i + Batch * SeqLen * HiddenDim];
            value[i] = qkv_output[i + 2 * Batch * SeqLen * HiddenDim];
        }
    }
};

2.2 前馈网络算子

/**
 * 前馈网络（FFN）算子
 * FFN(x) = GELU(x @ W1) @ W2
 */
template <typename T, int HiddenDim, int FFDim>
class FeedForwardKernel {
public:
    /**
     * FFN 前向计算（融合版本）
     */
    static void Forward(const T* input,
                       const T* weight1,
                       const T* bias1,
                       const T* weight2,
                       const T* bias2,
                       T* output) {
        constexpr int TileSize = 64;

        for (int batch_offset = 0; batch_offset < Batch * SeqLen; batch_offset += TileSize) {
            int end = std::min(batch_offset + TileSize, Batch * SeqLen);

            // 第一个线性层 + GELU
            T hidden[TileSize * FFDim];
            FusedLinearGELU(input + batch_offset * HiddenDim,
                           weight1, bias1,
                           hidden, end - batch_offset);

            // 第二个线性层
            FusedLinearBias(hidden,
                           weight2, bias2,
                           output + batch_offset * HiddenDim,
                           end - batch_offset);
        }
    }

private:
    /**
     * 融合的线性层 + GELU 激活
     */
    static void FusedLinearGELU(const T* input,
                               const T* weight,
                               const T* bias,
                               T* output,
                               int rows) {
        for (int i = 0; i < rows; ++i) {
            for (int j = 0; j < FFDim; ++j) {
                // 矩阵乘法
                float sum = bias[j];
                for (int k = 0; k < HiddenDim; ++k) {
                    sum += static_cast<float>(input[i * HiddenDim + k]) *
                           static_cast<float>(weight[k * FFDim + j]);
                }

                // GELU 激活
                output[i * FFDim + j] = GELU(sum);
            }
        }
    }

    /**
     * GELU 激活函数
     */
    static T GELU(float x) {
        // GELU 近似实现
        const float sqrt_2_over_pi = 0.7978845608f;
        const float coeff = 0.044715f;
        float tanh_arg = sqrt_2_over_pi * (x + coeff * x * x * x);
        return static_cast<T>(0.5f * x * (1.0f + tanhf(tanh_arg)));
    }

    static constexpr int Batch = 1;
    static constexpr int SeqLen = 2048;
};

2.3 层归一化算子

/**
 * RMS Layer Normalization 算子
 * RMSNorm 是 LLaMA 等模型使用的归一化方法
 */
template <typename T, int HiddenDim>
class RMSNormKernel {
public:
    /**
     * RMSNorm 前向计算
     *
     * RMSNorm(x) = x / sqrt(mean(x^2) + eps) * weight
     */
    static void Forward(const T* input,
                       const T* weight,
                       T* output,
                       float eps = 1e-6f) {
        for (int i = 0; i < Batch * SeqLen; ++i) {
            const T* row = input + i * HiddenDim;

            // 计算平方和
            float sum_sq = 0.0f;
            for (int j = 0; j < HiddenDim; ++j) {
                float val = static_cast<float>(row[j]);
                sum_sq += val * val;
            }

            // 计算 RMS
            float rms = sqrtf(sum_sq / HiddenDim + eps);

            // 归一化并乘以权重
            T* out_row = output + i * HiddenDim;
            for (int j = 0; j < HiddenDim; ++j) {
                out_row[j] = static_cast<T>(
                    static_cast<float>(row[j]) / rms * static_cast<float>(weight[j])
                );
            }
        }
    }

    /**
     * 融合的 RMSNorm + Residual
     */
    static void FusedForward(const T* input,
                            const T* residual,
                            const T* weight,
                            T* output,
                            float eps = 1e-6f) {
        for (int i = 0; i < Batch * SeqLen; ++i) {
            const T* row = input + i * HiddenDim;
            const T* res_row = residual + i * HiddenDim;

            // 计算 RMS
            float sum_sq = 0.0f;
            for (int j = 0; j < HiddenDim; ++j) {
                float val = static_cast<float>(row[j]);
                sum_sq += val * val;
            }
            float rms = sqrtf(sum_sq / HiddenDim + eps);

            // 归一化 + 乘以权重 + 残差连接
            T* out_row = output + i * HiddenDim;
            for (int j = 0; j < HiddenDim; ++j) {
                out_row[j] = static_cast<T>(
                    static_cast<float>(row[j]) / rms * static_cast<float>(weight[j]) +
                    static_cast<float>(res_row[j])
                );
            }
        }
    }

private:
    static constexpr int Batch = 1;
    static constexpr int SeqLen = 2048;
};

2.4 旋转位置编码

/**
 * RoPE (Rotary Position Embedding) 算子
 */
template <typename T, int HeadDim, int MaxSeqLen = 2048>
class RoPEKernel {
public:
    /**
     * 应用 RoPE 到 query 和 key
     */
    static void Apply(T* query, T* key, int seq_len, int head_dim) {
        // 预计算频率
        static bool freq_initialized = false;
        static float freq_cos[MaxSeqLen * HeadDim / 2];
        static float freq_sin[MaxSeqLen * HeadDim / 2];

        if (!freq_initialized) {
            ComputeFrequencies(freq_cos, freq_sin);
            freq_initialized = true;
        }

        // 对每个头应用旋转
        for (int pos = 0; pos < seq_len; ++pos) {
            for (int head = 0; head < NumHeads; ++head) {
                T* q = query + Offset(0, head, pos, 0);
                T* k = key + Offset(0, head, pos, 0);

                // 旋转位置编码
                for (int d = 0; d < head_dim; d += 2) {
                    float cos = freq_cos[pos * head_dim / 2 + d / 2];
                    float sin = freq_sin[pos * head_dim / 2 + d / 2];

                    // Query 旋转
                    float q0 = static_cast<float>(q[d]);
                    float q1 = static_cast<float>(q[d + 1]);
                    q[d] = static_cast<T>(q0 * cos - q1 * sin);
                    q[d + 1] = static_cast<T>(q0 * sin + q1 * cos);

                    // Key 旋转
                    float k0 = static_cast<float>(k[d]);
                    float k1 = static_cast<float>(k[d + 1]);
                    k[d] = static_cast<T>(k0 * cos - k1 * sin);
                    k[d + 1] = static_cast<T>(k0 * sin + k1 * cos);
                }
            }
        }
    }

private:
    /**
     * 计算 RoPE 频率
     */
    static void ComputeFrequencies(float* cos, float* sin) {
        float theta[HeadDim / 2];
        for (int i = 0; i < HeadDim / 2; ++i) {
            theta[i] = powf(10000.0f, -2.0f * i / HeadDim);
        }

        for (int pos = 0; pos < MaxSeqLen; ++pos) {
            for (int i = 0; i < HeadDim / 2; ++i) {
                float angle = pos * theta[i];
                cos[pos * HeadDim / 2 + i] = cosf(angle);
                sin[pos * HeadDim / 2 + i] = sinf(angle);
            }
        }
    }

    static constexpr int NumHeads = 32;
    static int Offset(int b, int h, int s, int d) {
        return ((b * NumHeads + h) * MaxSeqLen + s) * HeadDim + d;
    }
};

三、完整 Transformer 层实现

/**
 * 完整的 Transformer 层实现
 */
template <typename T, int HiddenDim, int FFDim, int NumHeads, int HeadDim>
class TransformerLayer {
public:
    /**
     * Transformer 层前向传播
     */
    void Forward(const T* input, T* output) {
        // 1. 自注意力 + 残差 + RMSNorm
        T attn_output[Batch * SeqLen * HiddenDim];
        SelfAttentionWithNorm(input, attn_output);

        // 2. 前馈网络 + 残差 + RMSNorm
        FeedForwardWithNorm(attn_output, output);
    }

    /**
     * 设置权重
     */
    void SetWeights(const TransformerWeights& weights) {
        weights_ = weights;
    }

private:
    /**
     * 自注意力 + RMSNorm
     */
    void SelfAttentionWithNorm(const T* input, T* output) {
        constexpr int QKVProjDim = 3 * HiddenDim;

        // 1. RMSNorm (输入归一化)
        T normalized[Batch * SeqLen * HiddenDim];
        RMSNormKernel<T, HiddenDim>::Forward(
            input, weights_.attn_norm_weight, normalized
        );

        // 2. QKV 投影
        T qkv_output[Batch * SeqLen * QKVProjDim];
        T query[Batch * SeqLen * HiddenDim];
        T key[Batch * SeqLen * HiddenDim];
        T value[Batch * SeqLen * HiddenDim];

        FusedQKVProjection(normalized,
                         weights_.qkv_weight,
                         weights_.qkv_bias,
                         query, key, value);

        // 3. 应用 RoPE
        RoPEKernel<T, HeadDim>::Apply(query, key, SeqLen, HeadDim);

        // 4. Flash Attention
        T attn_out[Batch * NumHeads * SeqLen * HeadDim];
        FlashAttentionKernel<T, HeadDim>::Forward(query, key, value, attn_out);

        // 5. 输出投影
        T o_proj_output[Batch * SeqLen * HiddenDim];
        MatMul(attn_out, weights_.output_weight,
               weights_.output_bias, o_proj_output);

        // 6. 残差连接
        AddResidual(o_proj_output, input, output);
    }

    /**
     * 前馈网络 + RMSNorm
     */
    void FeedForwardWithNorm(const T* input, T* output) {
        // 1. RMSNorm
        T normalized[Batch * SeqLen * HiddenDim];
        RMSNormKernel<T, HiddenDim>::Forward(
            input, weights_.ffn_norm_weight, normalized
        );

        // 2. FFN (两个线性层 + GELU)
        T ffn_output[Batch * SeqLen * HiddenDim];
        FeedForwardKernel<T, HiddenDim, FFDim>::Forward(
            normalized,
            weights_.ffn_weight1,
            weights_.ffn_bias1,
            weights_.ffn_weight2,
            weights_.ffn_bias2,
            ffn_output
        );

        // 3. 残差连接
        AddResidual(ffn_output, input, output);
    }

    /**
     * 残差连接
     */
    void AddResidual(const T* x, const T* residual, T* output) {
        for (int i = 0; i < Batch * SeqLen * HiddenDim; ++i) {
            output[i] = x[i] + residual[i];
        }
    }

    /**
     * 权重结构
     */
    struct TransformerWeights {
        // 注意力权重
        T attn_norm_weight[HiddenDim];
        T qkv_weight[HiddenDim * 3 * HiddenDim];
        T qkv_bias[3 * HiddenDim];
        T output_weight[HiddenDim * HiddenDim];
        T output_bias[HiddenDim];

        // FFN 权重
        T ffn_norm_weight[HiddenDim];
        T ffn_weight1[HiddenDim * FFDim];
        T ffn_bias1[FFDim];
        T ffn_weight2[FFDim * HiddenDim];
        T ffn_bias2[HiddenDim];
    };

    TransformerWeights weights_;

    static constexpr int Batch = 1;
    static constexpr int SeqLen = 2048;
};

四、使用示例

4.1 BERT 推理加速

/**
 * BERT 模型加速示例
 */
class BERTAccelerator {
public:
    void Initialize(const std::string& model_path) {
        // 1. 加载模型权重
        LoadWeights(model_path);

        // 2. 初始化算子
        InitializeKernels();
    }

    /**
     * BERT 编码器推理
     */
    void Encode(const int* input_ids,
               const int* attention_mask,
               float* output_embeddings) {
        // 1. 嵌入层
        float embeddings[Batch * SeqLen * HiddenDim];
        EmbeddingLookup(input_ids, embeddings);

        // 2. 位置编码
        AddPositionalEmbeddings(embeddings);

        // 3. Transformer 层
        float hidden[Batch * SeqLen * HiddenDim];
        for (int layer = 0; layer < NumLayers; ++layer) {
            transformer_layers_[layer].Forward(embeddings, hidden);
            std::memcpy(embeddings, hidden, sizeof(hidden));
        }

        // 4. 输出
        std::memcpy(output_embeddings, embeddings, sizeof(embeddings));
    }

private:
    void LoadWeights(const std::string& path) {
        // 加载权重文件
        // ...
    }

    void InitializeKernels() {
        // 初始化算子
        // ...
    }

    static constexpr int Batch = 1;
    static constexpr int SeqLen = 512;
    static constexpr int HiddenDim = 768;
    static constexpr int NumLayers = 12;
    static constexpr int FFDim = 3072;
    static constexpr int NumHeads = 12;
    static constexpr int HeadDim = 64;

    std::array<TransformerLayer<float, HiddenDim, FFDim, NumHeads, HeadDim>,
               NumLayers> transformer_layers_;
};

4.2 GPT 风格生成

/**
 * GPT 风格的自回归生成
 */
class GPTGenerator {
public:
    /**
     * 文本生成
     */
    std::vector<int> Generate(const std::vector<int>& prompt_ids,
                             int max_new_tokens,
                             float temperature = 1.0f,
                             int top_k = 50) {
        std::vector<int> output_ids = prompt_ids;

        for (int step = 0; step < max_new_tokens; ++step) {
            // 1. 获取当前序列
            auto current_ids = output_ids;

            // 2. 前向传播
            float logits[VocabSize];
            Forward(current_ids, logits);

            // 3. 采样下一个 token
            int next_token = Sample(logits, temperature, top_k);
            output_ids.push_back(next_token);

            // 4. 检查是否结束
            if (next_token == eos_token_id_) {
                break;
            }
        }

        return output_ids;
    }

private:
    /**
     * 前向传播（带 KV Cache）
     */
    void Forward(const std::vector<int>& input_ids, float* logits) {
        int seq_len = input_ids.size();

        // 1. 嵌入
        float embeddings[Batch * seq_len * HiddenDim];
        TokenEmbedding(input_ids, embeddings);

        // 2. Transformer 层（使用 KV Cache）
        float hidden[Batch * seq_len * HiddenDim];
        for (int layer = 0; layer < NumLayers; ++layer) {
            // 使用缓存的 Key/Value
            transformer_layers_[layer].ForwardWithKVCache(
                embeddings,
                kv_cache_[layer].key,
                kv_cache_[layer].value,
                kv_cache_[layer].seq_len,
                hidden
            );

            // 更新 cache
            UpdateKVCache(layer, embeddings, seq_len);

            std::memcpy(embeddings, hidden, sizeof(hidden));
        }

        // 3. LM Head
        float all_logits[Batch * seq_len * VocabSize];
        LMHead(embeddings, all_logits);

        // 4. 取最后一个位置的 logits
        std::memcpy(logits,
                   all_logits + (seq_len - 1) * VocabSize,
                   VocabSize * sizeof(float));
    }

    /**
     * Token 采样
     */
    int Sample(const float* logits, float temperature, int top_k) {
        // 应用温度
        float scaled_logits[VocabSize];
        for (int i = 0; i < VocabSize; ++i) {
            scaled_logits[i] = logits[i] / temperature;
        }

        // Top-K 采样
        std::vector<std::pair<float, int>> probs;
        for (int i = 0; i < VocabSize; ++i) {
            probs.push_back({scaled_logits[i], i});
        }
        std::partial_sort(probs.begin(), probs.begin() + top_k,
                         probs.end(), std::greater<>());

        // Softmax
        float sum = 0.0f;
        for (int i = 0; i < top_k; ++i) {
            probs[i].first = expf(probs[i].first);
            sum += probs[i].first;
        }
        for (int i = 0; i < top_k; ++i) {
            probs[i].first /= sum;
        }

        // 采样
        float r = static_cast<float>(rand()) / RAND_MAX;
        float cumsum = 0.0f;
        for (int i = 0; i < top_k; ++i) {
            cumsum += probs[i].first;
            if (r < cumsum) {
                return probs[i].second;
            }
        }

        return probs[top_k - 1].second;
    }

    static constexpr int Batch = 1;
    static constexpr int HiddenDim = 4096;
    static constexpr int NumLayers = 32;
    static constexpr int NumHeads = 32;
    static constexpr int HeadDim = 128;
    static constexpr int FFDim = 16384;
    static constexpr int VocabSize = 50257;
    static constexpr int MaxSeqLen = 2048;

    int eos_token_id_ = 50256;

    struct KVCache {
        float key[Batch * NumHeads * MaxSeqLen * HeadDim];
        float value[Batch * NumHeads * MaxSeqLen * HeadDim];
        int seq_len = 0;
    };

    std::array<KVCache, NumLayers> kv_cache_;
};

五、性能优化技巧

5.1 性能对比

模型	优化前	优化后	加速比
BERT-Base 推理	45ms	8ms	5.6x
GPT-2 (117M) 推理	180ms	32ms	5.6x
LLaMA-7B 推理	850ms	145ms	5.9x
训练吞吐量	120 TFLOPS	680 TFLOPS	5.7x

5.2 内存优化

优化技术	内存节省	说明
FlashAttention	40%	减少注意力机制的内存占用
KV Cache	60%	生成时缓存 Key/Value
激活重计算	50%	反向传播时重新计算激活
梯度检查点	35%	减少训练时的激活存储

六、总结

ops-transformer 作为 CANN 专为大模型设计的算子库，通过 FlashAttention、融合算子等技术，为 Transformer 模型在 NPU 上的高效运行提供了强大的支持。无论是 BERT、GPT 还是 LLaMA，都能获得显著的性能提升。

6.1 核心优势

1. 专用优化: 针对 Transformer 架构深度优化
2. 高效实现: FlashAttention 等先进算法
3. 融合支持: 多种算子融合模式
4. 生产就绪: 支持主流大模型

6.2 相关链接

• CANN组织: https://atomgit.com/cann
• ops-transformer仓库: https://atomgit.com/cann/ops-transformer
• ops-nn (神经网络算子库): https://atomgit.com/cann/ops-nn
• ascend-transformer-boost (领域加速库): https://atomgit.com/cann/ascend-transformer-boost
• cann-recipes-infer (推理实践): https://atomgit.com/cann/cann-recipes-infer

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本文档基于 CANN 开源项目编写，展示了 ops-transformer 大模型算子库的核心功能和使用方法。更多详细信息请参考官方文档和源代码。