ggml : group mul_mat_id rows by matrix (cpu only) (#4480)

* ggml : group mul_mat_id rows by matrix (cpu only)

* remove mmid parameters from mm forward

* store row groups in wdata and calculate only once in GGML_TASK_INIT

ggml-ci

ggml.c

@@ -9580,16 +9580,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(
 }
 #endif
 
-// off1 = offset in i11 and i1
-// cne1 = ne11 and ne1
-// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
-// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
 static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
-              struct ggml_tensor * dst,
+              struct ggml_tensor * dst) {
-              int64_t off1, int64_t cne1) {
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -9658,8 +9653,8 @@ static void ggml_compute_forward_mul_mat(
                 const int64_t i02 = i12/r2;
 
                 const void  * x = (char *)            src0->data + i02*nb02 + i03*nb03;
-                const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
+                const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
-                      float * d = (float *) ((char *)  dst->data + off1*nb1  + i12*nb2  + i13*nb3);
+                      float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
 
                 if (type != GGML_TYPE_F32) {
                             float * const wdata    = params->wdata;
@@ -9676,7 +9671,7 @@ static void ggml_compute_forward_mul_mat(
                 }
 
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                         cne1, ne01, ne10,
+                          ne1, ne01, ne10,
                          1.0f,    y, ne10,
                                   x, ne00,
                          0.0f,    d, ne01);
@@ -9718,7 +9713,7 @@ static void ggml_compute_forward_mul_mat(
     const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
     const int64_t nr0 = ne01;          // src0 rows
-    const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+    const int64_t nr1 = ne1*ne12*ne13; // src1 rows
 
     //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
 
@@ -9760,9 +9755,9 @@ static void ggml_compute_forward_mul_mat(
     for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
         for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
             for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
-                const int64_t i13 = (ir1/(ne12*cne1));
+                const int64_t i13 = (ir1/(ne12*ne1));
-                const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
+                const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
-                const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
+                const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
 
                 // broadcast src0 into src1
                 const int64_t i03 = i13/r3;
@@ -9802,28 +9797,191 @@ static void ggml_compute_forward_mul_mat(
 
 static void ggml_compute_forward_mul_mat_id(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
+        const struct ggml_tensor * ids,
         const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
-        // during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
-        ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
-        return;
-    }
 
-    const struct ggml_tensor * ids = src0;
+    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 == ggml_type_size(src1->type));
+
+    // 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;
+
+    // row groups
     const int id   = ggml_get_op_params_i32(dst, 0);
     const int n_as = ggml_get_op_params_i32(dst, 1);
 
+    char * wdata_src1_end = (src1->type == vec_dot_type) ?
+            (char *) params->wdata :
+            (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
+
+    int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
+    int64_t * matrix_rows       = matrix_row_counts + n_as;     // [n_as][ne11]
+
+    #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
+
+   if (params->type == GGML_TASK_INIT) {
+        char * wdata = params->wdata;
+        if (src1->type != vec_dot_type) {
+            const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+            assert(params->wsize >= ne11*ne12*ne13*row_size);
+            assert(src1->type == GGML_TYPE_F32);
+
+            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;
+                    }
+                }
+            }
+        }
+
+        // initialize matrix_row_counts
+        GGML_ASSERT(wdata == wdata_src1_end);
+        memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
+
+        // group rows by src0 matrix
         for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
             const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
 
             GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
+            MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
-        const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+            matrix_row_counts[row_id] += 1;
-        ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
         }
+
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // compute each matrix multiplication in sequence
+    for (int cur_a = 0; cur_a < n_as; ++cur_a) {
+        const int64_t cne1 = matrix_row_counts[cur_a];
+
+        if (cne1 == 0) {
+            continue;
+        }
+
+        const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
+
+        const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+        const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+        const int64_t nr0 = ne01;           // src0 rows
+        const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+
+        //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+
+        // distribute the thread work across the inner or outer loop based on which one is larger
+
+        const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
+        const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
+
+        const int64_t ith0 = ith % nth0;
+        const int64_t ith1 = ith / nth0;
+
+        const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
+        const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
+
+        const int64_t ir010 = dr0*ith0;
+        const int64_t ir011 = MIN(ir010 + dr0, nr0);
+
+        const int64_t ir110 = dr1*ith1;
+        const int64_t ir111 = MIN(ir110 + dr1, nr1);
+
+        //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
+
+        // threads with no work simply yield (not sure if it helps)
+        if (ir010 >= ir011 || ir110 >= ir111) {
+            sched_yield();
+            continue;
+        }
+
+        assert(ne12 % ne02 == 0);
+        assert(ne13 % ne03 == 0);
+
+        // block-tiling attempt
+        const int64_t blck_0 = 16;
+        const int64_t blck_1 = 16;
+
+        // attempt to reduce false-sharing (does not seem to make a difference)
+        float tmp[16];
+
+        for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
+            for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
+                for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+                    const int64_t  i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
+                    const int64_t  i12 = (ir1 - i13*ne12*cne1)/cne1;
+                    const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
+                    const int64_t  i11 = MMID_MATRIX_ROW(cur_a, _i11);
+
+                    // broadcast src0 into src1
+                    const int64_t i03 = i13/r3;
+                    const int64_t i02 = i12/r2;
+
+                    const int64_t i1 = i11;
+                    const int64_t i2 = i12;
+                    const int64_t i3 = i13;
+
+                    const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
+
+                    // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
+                    //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
+                    //       the original src1 data pointer, so we should index using the indices directly
+                    // TODO: this is a bit of a hack, we should probably have a better way to handle this
+                    const char * src1_col = (const char *) wdata +
+                        (src1_cont || src1->type != vec_dot_type
+                        ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
+                        : (i11*nb11 + i12*nb12 + i13*nb13));
+
+                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+                    //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                    //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
+                    //}
+
+                    for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+                        vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                    }
+                    memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
+                }
+            }
+        }
+    }
+
+    #undef MMID_MATRIX_ROW
 }
 
 // ggml_compute_forward_out_prod
@@ -14191,7 +14349,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
+                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
@@ -15991,7 +16149,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
-                // FIXME: blas
                 n_tasks = n_threads;
             } break;
         case GGML_OP_OUT_PROD:
@@ -16325,20 +16482,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                 } break;
             case GGML_OP_MUL_MAT_ID:
                 {
-                    const struct ggml_tensor * a = node->src[2];
+                    const struct ggml_tensor * src0 = node->src[2];
-                    const struct ggml_tensor * b = node->src[1];
+                    const struct ggml_tensor * src1 = node->src[1];
-                    const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
+                    const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
+                    if (src1->type != vec_dot_type) {
-                    if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
+                        cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
-                        if (a->type != GGML_TYPE_F32) {
-                            // here we need memory just for single 2D matrix from src0
-                            cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
-                        }
-                    } else
-#endif
-                    if (b->type != vec_dot_type) {
-                        cur = ggml_row_size(vec_dot_type, ggml_nelements(b));
                     }
+                    const int n_as = ggml_get_op_params_i32(node, 1);
+                    cur = GGML_PAD(cur, sizeof(int64_t));        // align
+                    cur += n_as * sizeof(int64_t);               // matrix_row_counts
+                    cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
                 } break;
             case GGML_OP_OUT_PROD:
                 {