Remove axpy dense op (#177)

ggml.c

@@ -14310,146 +14310,6 @@ static void ggml_axpy_avx_f16(const int n, const ggml_fp16_t * restrict vx, cons
 #endif
     (void)vy;
 }
-atomic_flag g_axpy_dense_lock = ATOMIC_FLAG_INIT;
-static void ggml_compute_forward_mul_mat_axpy_dense(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    // const int ith = params->ith;
-    const int nth = params->nth;
-
-    const enum ggml_type type = src0->type;
-
-    // const bool src1_cont = ggml_is_contiguous(src1);
-
-    // ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
-    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
-    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
-
-    // GGML_ASSERT(ne0 == ne01);
-    // GGML_ASSERT(ne1 == ne11);
-    // GGML_ASSERT(ne2 == ne12);
-    // GGML_ASSERT(ne3 == ne13);
-
-    // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == ggml_type_size(type));
-    GGML_ASSERT(nb10 == sizeof(float));
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    // broadcast factors
-    // const int64_t r2 = ne12/ne02;
-    // const int64_t r3 = ne13/ne03;
-
-    // nb01 >= nb00 - src0 is not transposed
-    //   compute by src0 rows
-
-    if (params->type == GGML_TASK_INIT) {
-        ggml_set_zero(dst);
-        if (src1->type != vec_dot_type) {
-            char * wdata = params->wdata;
-            const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
-
-            for (int64_t i13 = 0; i13 < ne13; ++i13) {
-                for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
-                        wdata += row_size;
-                    }
-                }
-            }
-        }
-        atomic_store(params->aic, 0);
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    ggml_fp16_t* wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
-    // const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
-
-    struct ggml_tensor *src2 = dst->src[2];
-
-    // parallelize by src0 rows
-    const int64_t dr = (src2->ne[0] + 8*nth - 1)/(8*nth);
-    const int nr = ggml_nrows(src0);
-
-    // const int64_t ir10 = dr*ith;
-    // const int64_t ir11 = MIN(ir10 + dr, src2->ne[0]);
-
-    // src1 rows
-    // const int64_t nr1 = ne11*ne12*ne13;
-    // float *idx = src2->data;
-    // int *gid = (int *)(dst->src[3]->data);
-    // printf("down %d up %d ne00 %d\n", ir10, ir11, ne00);
-
-#if defined(_MSC_VER)
-    float* vec = (float *)_malloca(ne00 * 4 * sizeof(float));
-#else
-    float vec[ne00*4];
-#endif
-    void *vy = vec;
-    memset(vy, 0, ne00*4);
-    char* src0_row = (char *) src0->data;
-    while(true) {
-        const int ir0 = atomic_fetch_add(params->aic, dr);
-        for (int64_t ir1 = ir0; ir1 < ir0+dr; ir1++) {
-            if (ir1 >= nr) break;
-            ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, wdata[ir1]);
-        }
-        if (ir0 + dr >= nr)
-            break;
-    }
-
-    // 获取锁
-    while (atomic_flag_test_and_set(&g_axpy_dense_lock)) {
-        // 如果锁已经被占用，则等待
-    }
-
-    float *res = (float *)(dst->data);
-    float *tmp = (float *)vy;
-    int i;
-
-
-    // 计算剩余的元素个数
-    int remainder = ne00 % 8;
-
-#if defined(__AVX2__)
-    // 使用AVX指令进行向量化计算
-    for (i = 0; i < ne00 - remainder; i += 8) {
-        __m256 res_vec = _mm256_loadu_ps(res + i);  // 加载res中的8个浮点数
-        __m256 tmp_vec = _mm256_loadu_ps(tmp + i);  // 加载tmp中的8个浮点数
-        __m256 result = _mm256_add_ps(res_vec, tmp_vec);  // 执行加法运算
-        _mm256_storeu_ps(res + i, result);  // 存储结果到res中
-    }
-
-    // 处理剩余的元素
-    for (i = ne00 - remainder; i < ne00; i++) {
-        res[i] += tmp[i];
-    }
-#else
-    for (i = 0; i < dst->ne[0]; i++) {
-        res[i] += tmp[i];
-    }
-#endif
-    atomic_flag_clear(&g_axpy_dense_lock);
-#if defined(_MSC_VER)
-    _freea(vec);
-#endif
-}
 
 static void ggml_compute_forward_mul_mat_axpy(
         const struct ggml_compute_params * params,
@@ -15052,21 +14912,17 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_AXPY:
             {
-                // printf("here? %d\n", tensor->src[0]->type);
+                GGML_ASSERT(tensor->src[2] != NULL && "sparse index is required for AXPY");
                 struct ggml_tensor *src3 = tensor->src[3];
-                if (tensor->src[2] == NULL) {
-                    ggml_compute_forward_mul_mat_axpy_dense(params, tensor->src[0], tensor->src[1], tensor);
-                }
-                else if (src3 != NULL){
+                if (src3 != NULL){
                     if (tensor->src[0]->type != GGML_TYPE_Q4_0) {
                         ggml_compute_forward_mul_mat_axpy(params, tensor->src[0], tensor->src[1], tensor);
                     }
                     else {
                         ggml_compute_forward_mul_mat_axpy_q4_0(params, tensor->src[0], tensor->src[1], tensor);
 
                     }
-                }
-                else {
+                } else {
                     ggml_compute_forward_mul_mat_axpy_head(params, tensor->src[0], tensor->src[1], tensor);
                 }
                 // ggml_compute_forward_mul_mat_axpy(params, tensor->src[0], tensor->src[1], tensor);