ggml : extend ggml_get_rows, ggml_repeat, ggml_concat (ggml/639)

* add more int ops

* ggml_compute_forward_dup_bytes

* add tests

* PR comments

* tests : minor indentations

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

ggml.c

@@ -4766,8 +4766,11 @@ struct ggml_tensor * ggml_get_rows(
     }
 
     // TODO: implement non F32 return
-    //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
+    enum ggml_type type = GGML_TYPE_F32;
-    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
+    if (a->type == GGML_TYPE_I32) {
+        type = a->type;
+    }
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
 
     result->op   = GGML_OP_GET_ROWS;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -6938,14 +6941,165 @@ static void ggml_compute_forward_dup_f32(
     }
 }
 
+// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
+static void ggml_compute_forward_dup_bytes(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+    GGML_ASSERT(src0->type == dst->type);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
+        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        return;
+    }
+
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
+    const size_t type_size = ggml_type_size(src0->type);
+    const int ith = params->ith; // thread index
+    const int nth = params->nth; // number of threads
+
+
+    // parallelize by rows
+    const int nr = ne01;
+    // number of rows per thread
+    const int dr = (nr + nth - 1) / nth;
+    // row range for this thread
+    const int ir0 = dr * ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (src0->type == dst->type &&
+        ne00 == ne0 &&
+        nb00 == type_size && nb0 == type_size) {
+        // copy by rows
+        const size_t rs = ne00 * type_size;
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                    memcpy(
+                        ((char *)  dst->data + i01*nb1  + i02*nb2  + i03*nb3),
+                        ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
+                        rs);
+                }
+            }
+        }
+        return;
+    }
+
+    if (ggml_is_contiguous(dst)) {
+        size_t id = 0;
+        char * dst_ptr = (char *) dst->data;
+        const size_t rs = ne00 * type_size;
+
+        if (nb00 == type_size) {
+            // src0 is contigous on first dimension, copy by rows
+            for (int64_t i03 = 0; i03 < ne03; i03++) {
+                for (int64_t i02 = 0; i02 < ne02; i02++) {
+                    id += rs * ir0;
+                    for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                        const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                        memcpy(dst_ptr + id, src0_ptr, rs);
+                        id += rs;
+                    }
+                    id += rs * (ne01 - ir1);
+                }
+            }
+        } else {
+            //printf("%s: this is not optimal - fix me\n", __func__);
+
+            for (int64_t i03 = 0; i03 < ne03; i03++) {
+                for (int64_t i02 = 0; i02 < ne02; i02++) {
+                    id += rs * ir0;
+                    for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                        for (int64_t i00 = 0; i00 < ne00; i00++) {
+                            const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03;
+                            memcpy(dst_ptr + id, src0_ptr, type_size);
+
+                            id += type_size;
+                        }
+                    }
+                    id += rs * (ne01 - ir1);
+                }
+            }
+        }
+
+        return;
+    }
+
+    // dst counters
+
+    int64_t i10 = 0;
+    int64_t i11 = 0;
+    int64_t i12 = 0;
+    int64_t i13 = 0;
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            i10 += ne00 * ir0;
+            while (i10 >= ne0) {
+                i10 -= ne0;
+                if (++i11 == ne1) {
+                    i11 = 0;
+                    if (++i12 == ne2) {
+                        i12 = 0;
+                        if (++i13 == ne3) {
+                            i13 = 0;
+                        }
+                    }
+                }
+            }
+            for (int64_t i01 = ir0; i01 < ir1; i01++) {
+                for (int64_t i00 = 0; i00 < ne00; i00++) {
+                    const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                          char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+
+                    memcpy(dst_ptr, src0_ptr, type_size);
+
+                    if (++i10 == ne0) {
+                        i10 = 0;
+                        if (++i11 == ne1) {
+                            i11 = 0;
+                            if (++i12 == ne2) {
+                                i12 = 0;
+                                if (++i13 == ne3) {
+                                    i13 = 0;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            i10 += ne00 * (ne01 - ir1);
+            while (i10 >= ne0) {
+                i10 -= ne0;
+                if (++i11 == ne1) {
+                    i11 = 0;
+                    if (++i12 == ne2) {
+                        i12 = 0;
+                        if (++i13 == ne3) {
+                            i13 = 0;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
 static void ggml_compute_forward_dup(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
+    if (src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_bytes(params, src0, dst);
         return;
     }
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
@@ -8404,10 +8558,12 @@ static void ggml_compute_forward_repeat(
         struct ggml_tensor * dst) {
     switch (src0->type) {
         case GGML_TYPE_F16:
+        case GGML_TYPE_I16:
             {
                 ggml_compute_forward_repeat_f16(params, src0, dst);
             } break;
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_repeat_f32(params, src0, dst);
             } break;
@@ -8550,6 +8706,7 @@ static void ggml_compute_forward_concat(
     struct ggml_tensor* dst) {
     switch (src0->type) {
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_concat_f32(params, src0, src1, dst);
             } break;
@@ -10674,6 +10831,7 @@ static void ggml_compute_forward_get_rows(
                 ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
             } break;
         case GGML_TYPE_F32:
+        case GGML_TYPE_I32:
             {
                 ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
             } break;

tests/test-backend-ops.cpp

@@ -58,6 +58,9 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
         int64_t hist[16];
         ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
+    } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
+        // This is going to create some weird integers though.
+        ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
     } else {
         GGML_ASSERT(false);
     }
@@ -87,8 +90,13 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                         tv.push_back(*(float *) &buf[i]);
                     } else if (t->type == GGML_TYPE_I32) {
                         tv.push_back((float)*(int32_t *) &buf[i]);
+                    } else if (t->type == GGML_TYPE_I16) {
+                        tv.push_back((float)*(int16_t *) &buf[i]);
+                    } else if (t->type == GGML_TYPE_I8) {
+                        tv.push_back((float)*(int8_t *) &buf[i]);
                     } else if (quantized) {
-                        tt.to_float(&buf[i], vq.data(), bs);
+                        std::vector<float> vq(ggml_blck_size(t->type));
+                        tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
                         GGML_ASSERT(false);
@@ -661,17 +669,26 @@ struct test_repeat : public test_case {
 struct test_dup : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> permute;
+    bool _use_permute;
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        std::string v = VARS_TO_STR2(type, ne);
+        if (_use_permute) v += "," + VAR_TO_STR(permute);
+        return v;
     }
 
     test_dup(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1})
+            std::array<int64_t, 4> ne = {10, 10, 10, 1},
-        : type(type), ne(ne) {}
+            std::array<int64_t, 4> permute = {0, 0, 0, 0})
+        : type(type), ne(ne), permute(permute),
+            _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
+        if (_use_permute) {
+            src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
+        }
         ggml_tensor * out = ggml_dup(ctx, src);
         return out;
     }
@@ -1450,14 +1467,26 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             }
         }
     }
+    for (int b : {1, 7}) {
+        for (bool v : {false, true}) {
+            test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
+        }
+    }
 
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
+    test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
+    test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
 
-    test_cases.emplace_back(new test_dup());
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
 
     for (ggml_type type : all_types) {
        test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
@@ -1565,7 +1594,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     test_cases.emplace_back(new test_alibi());
     test_cases.emplace_back(new test_im2col());
-    test_cases.emplace_back(new test_concat());
+    test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
+    test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
 
     for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));