ggml : sync cgraph import / export API

ggml.c

@@ -4030,6 +4030,14 @@ void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
     ctx->no_alloc = no_alloc;
 }
 
+void * ggml_get_mem_buffer(struct ggml_context * ctx) {
+    return ctx->mem_buffer;
+}
+
+size_t ggml_get_mem_size(struct ggml_context * ctx) {
+    return ctx->mem_size;
+}
+
 // IMPORTANT:
 // when creating "opt" tensors, always save and load the scratch buffer
 // this is an error prone process, but it is necessary to support inplace
@@ -4523,6 +4531,23 @@ struct ggml_tensor * ggml_view_tensor(
     return result;
 }
 
+struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) {
+    struct ggml_object * obj = ctx->objects_begin;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
+        if (strcmp(cur->name, name) == 0) {
+            return cur;
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 // ggml_dup
@@ -14532,7 +14557,7 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
     }
 }
 
-struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name) {
+struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {
     for (int i = 0; i < cgraph->n_leafs; i++) {
         struct ggml_tensor * leaf = cgraph->leafs[i];
 
@@ -14552,6 +14577,461 @@ struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const
     return NULL;
 }
 
+static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) {
+    const int64_t * ne = tensor->ne;
+    const size_t  * nb = tensor->nb;
+
+    fprintf(fout, "%-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %16p %16s\n",
+            ggml_type_name(tensor->type),
+            ggml_op_name  (tensor->op),
+            tensor->n_dims,
+            ne[0], ne[1], ne[2], ne[3],
+            nb[0], nb[1], nb[2], nb[3],
+            tensor->data,
+            tensor->name);
+}
+
+static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) {
+    const int64_t * ne = tensor->ne;
+    const size_t  * nb = tensor->nb;
+
+    fprintf(fout, "%-6s %-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %8d %16p %16s\n",
+            arg,
+            ggml_type_name(tensor->type),
+            ggml_op_name  (tensor->op),
+            tensor->n_dims,
+            ne[0], ne[1], ne[2], ne[3],
+            nb[0], nb[1], nb[2], nb[3],
+            tensor->n_tasks,
+            tensor->data,
+            tensor->name);
+}
+
+void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
+    assert(cgraph->work      == NULL);
+    assert(cgraph->work_size == 0);
+
+    uint64_t size_eval = 0;
+
+    // compute size of intermediate results
+    // TODO: does not take into account scratch buffers !!!!
+    for (int i = 0; i < cgraph->n_nodes; ++i) {
+        size_eval += ggml_nbytes(cgraph->nodes[i]);
+    }
+
+    // print
+    {
+        FILE * fout = stdout;
+
+        fprintf(fout, "\n");
+        fprintf(fout, "%-16s %8x\n",   "magic",   GGML_FILE_MAGIC);
+        fprintf(fout, "%-16s %8d\n",   "version", GGML_FILE_VERSION);
+        fprintf(fout, "%-16s %8d\n",   "leafs",   cgraph->n_leafs);
+        fprintf(fout, "%-16s %8d\n",   "nodes",   cgraph->n_nodes);
+        fprintf(fout, "%-16s %8llu\n", "eval",    size_eval);
+
+        // header
+        fprintf(fout, "\n");
+        fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n",
+                "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME");
+
+        for (int i = 0; i < cgraph->n_leafs; ++i) {
+            ggml_graph_export_leaf(cgraph->leafs[i], fout);
+
+            GGML_ASSERT(cgraph->leafs[i]->op   == GGML_OP_NONE);
+            GGML_ASSERT(cgraph->leafs[i]->src0 == NULL);
+            GGML_ASSERT(cgraph->leafs[i]->src1 == NULL);
+        }
+
+        // header
+        fprintf(fout, "\n");
+        fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n",
+                "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME");
+
+        for (int i = 0; i < cgraph->n_nodes; ++i) {
+            ggml_graph_export_node(cgraph->nodes[i], "DST", fout);
+
+            if (cgraph->nodes[i]->src0) {
+                ggml_graph_export_node(cgraph->nodes[i]->src0, "SRC0", fout);
+            }
+
+            if (cgraph->nodes[i]->src1) {
+                ggml_graph_export_node(cgraph->nodes[i]->src1, "SRC1", fout);
+            }
+
+            for (int j = 0; j < GGML_MAX_OPT; ++j) {
+                if (cgraph->nodes[i]->opt[j]) {
+                    ggml_graph_export_node(cgraph->nodes[i]->opt[j], "OPT", fout);
+                }
+            }
+
+            fprintf(fout, "\n");
+        }
+
+        fprintf(fout, "\n");
+    }
+
+    // write binary data
+    {
+        FILE * fout = fopen(fname, "wb");
+
+        if (!fout) {
+            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            return;
+        }
+
+        // header
+        {
+            const uint32_t magic   = GGML_FILE_MAGIC;
+            const uint32_t version = GGML_FILE_VERSION;
+            const uint32_t n_leafs = cgraph->n_leafs;
+            const uint32_t nodes   = cgraph->n_nodes;
+
+            fwrite(&magic,     sizeof(uint32_t), 1, fout);
+            fwrite(&version,   sizeof(uint32_t), 1, fout);
+            fwrite(&n_leafs,   sizeof(uint32_t), 1, fout);
+            fwrite(&nodes,     sizeof(uint32_t), 1, fout);
+            fwrite(&size_eval, sizeof(uint64_t), 1, fout);
+        }
+
+        // leafs
+        {
+            for (int i = 0; i < cgraph->n_leafs; ++i) {
+                const struct ggml_tensor * tensor = cgraph->leafs[i];
+
+                const uint32_t type   = tensor->type;
+                const uint32_t op     = tensor->op;
+                const uint32_t n_dims = tensor->n_dims;
+
+                fwrite(&type,   sizeof(uint32_t), 1, fout);
+                fwrite(&op,     sizeof(uint32_t), 1, fout);
+                fwrite(&n_dims, sizeof(uint32_t), 1, fout);
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    const uint64_t ne = tensor->ne[j];
+                    const uint64_t nb = tensor->nb[j];
+
+                    fwrite(&ne, sizeof(uint64_t), 1, fout);
+                    fwrite(&nb, sizeof(uint64_t), 1, fout);
+                }
+
+                // store the pointer address
+                {
+                    const uint64_t ptr = (uint64_t) tensor->data;
+
+                    fwrite(&ptr, sizeof(uint64_t), 1, fout);
+                }
+
+                fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+
+                // dump the data
+                // TODO: pad this to 32 byte boundary
+                {
+                    const size_t size = ggml_nbytes(tensor);
+
+                    fwrite(tensor->data, sizeof(char), size, fout);
+                }
+            }
+        }
+
+        // nodes
+        {
+            for (int i = 0; i < cgraph->n_nodes; ++i) {
+                const struct ggml_tensor * tensor = cgraph->nodes[i];
+
+                const uint32_t type   = tensor->type;
+                const uint32_t op     = tensor->op;
+                const uint32_t n_dims = tensor->n_dims;
+
+                fwrite(&type,   sizeof(uint32_t), 1, fout);
+                fwrite(&op,     sizeof(uint32_t), 1, fout);
+                fwrite(&n_dims, sizeof(uint32_t), 1, fout);
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    const uint64_t ne = tensor->ne[j];
+                    const uint64_t nb = tensor->nb[j];
+
+                    fwrite(&ne, sizeof(uint64_t), 1, fout);
+                    fwrite(&nb, sizeof(uint64_t), 1, fout);
+                }
+
+                // store the pointer address
+                {
+                    const uint64_t ptr = (uint64_t) tensor->data;
+
+                    fwrite(&ptr, sizeof(uint64_t), 1, fout);
+                }
+
+                fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+
+                // output the op arguments
+                {
+                    struct ggml_tensor * args[2 + GGML_MAX_OPT] = { NULL };
+
+                    args[0] = tensor->src0;
+                    args[1] = tensor->src1;
+
+                    for (int j = 0; j < GGML_MAX_OPT; ++j) {
+                        args[2 + j] = tensor->opt[j];
+                    }
+
+                    for (int j = 0; j < 2 + GGML_MAX_OPT; ++j) {
+                        if (args[j]) {
+                            int32_t idx = -1;
+
+                            // check if leaf
+                            {
+                                for (int k = 0; k < cgraph->n_leafs; ++k) {
+                                    if (args[j] == cgraph->leafs[k]) {
+                                        idx = k;
+                                        break;
+                                    }
+                                }
+                            }
+
+                            // check if node
+                            if (idx == -1) {
+                                for (int k = 0; k < cgraph->n_nodes; ++k) {
+                                    if (args[j] == cgraph->nodes[k]) {
+                                        idx = GGML_MAX_NODES + k;
+                                        break;
+                                    }
+                                }
+                            }
+
+                            if (idx == -1) {
+                                fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i);
+                                return;
+                            }
+
+                            fwrite(&idx, sizeof(int32_t), 1, fout);
+                        } else {
+                            const int32_t nul = -1;
+
+                            fwrite(&nul, sizeof(int32_t), 1, fout);
+                        }
+                    }
+                }
+            }
+        }
+
+        fclose(fout);
+    }
+}
+
+struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) {
+    assert(*ctx_data == NULL);
+    assert(*ctx_eval == NULL);
+
+    struct ggml_cgraph result = { 0 };
+
+    struct ggml_tensor * data = NULL;
+
+    // read file into data
+    {
+        FILE * fin = fopen(fname, "rb");
+
+        if (!fin) {
+            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            return result;
+        }
+
+        size_t fsize = 0;
+
+        fseek(fin, 0, SEEK_END);
+        fsize = ftell(fin);
+        fseek(fin, 0, SEEK_SET);
+
+        // create the data context
+        {
+            const size_t overhead = 1*ggml_tensor_overhead();
+
+            struct ggml_init_params params = {
+                .mem_size   = fsize + overhead,
+                .mem_buffer = NULL,
+                .no_alloc   = false,
+            };
+
+            *ctx_data = ggml_init(params);
+
+            if (!*ctx_data) {
+                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
+                return result;
+            }
+        }
+
+        data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize);
+
+        fread(data->data, sizeof(char), fsize, fin);
+
+        fclose(fin);
+    }
+
+    // populate result
+    {
+        char * ptr = (char *) data->data;
+
+        const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic);
+
+        if (magic != GGML_FILE_MAGIC) {
+            fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic);
+            return result;
+        }
+
+        const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version);
+
+        if (version != GGML_FILE_VERSION) {
+            fprintf(stderr, "%s: invalid version number\n", __func__);
+            return result;
+        }
+
+        const uint32_t n_leafs   = *(const uint32_t *) ptr; ptr += sizeof(n_leafs);
+        const uint32_t n_nodes   = *(const uint32_t *) ptr; ptr += sizeof(n_nodes);
+        const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval);
+
+        result.n_leafs = n_leafs;
+        result.n_nodes = n_nodes;
+
+        // create the data context
+        {
+            const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead();
+
+            struct ggml_init_params params = {
+                .mem_size   = size_eval + overhead,
+                .mem_buffer = NULL,
+                .no_alloc   = true,
+            };
+
+            *ctx_eval = ggml_init(params);
+
+            if (!*ctx_eval) {
+                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
+                return result;
+            }
+        }
+
+        // leafs
+        {
+            uint32_t type;
+            uint32_t op;
+            uint32_t n_dims;
+
+            for (uint32_t i = 0; i < n_leafs; ++i) {
+                type   = *(const uint32_t *) ptr; ptr += sizeof(type);
+                op     = *(const uint32_t *) ptr; ptr += sizeof(op);
+                n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
+
+                int64_t ne[GGML_MAX_DIMS];
+                size_t  nb[GGML_MAX_DIMS];
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    uint64_t ne_cur;
+                    uint64_t nb_cur;
+
+                    ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur);
+                    nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur);
+
+                    ne[j] = ne_cur;
+                    nb[j] = nb_cur;
+                }
+
+                struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
+
+                tensor->op = (enum ggml_op) op;
+
+                uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur);
+
+                memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
+
+                tensor->data = (void *) ptr;
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    tensor->nb[j] = nb[j];
+                }
+
+                result.leafs[i] = tensor;
+
+                ptr += ggml_nbytes(tensor);
+
+                fprintf(stderr, "%s: loaded leaf %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor));
+            }
+        }
+
+        ggml_set_no_alloc(*ctx_eval, false);
+
+        // nodes
+        {
+            uint32_t type;
+            uint32_t op;
+            uint32_t n_dims;
+
+            for (uint32_t i = 0; i < n_nodes; ++i) {
+                type   = *(const uint32_t *) ptr; ptr += sizeof(type);
+                op     = *(const uint32_t *) ptr; ptr += sizeof(op);
+                n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
+
+                int64_t ne[GGML_MAX_DIMS];
+                size_t  nb[GGML_MAX_DIMS];
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    uint64_t ne_cur;
+                    uint64_t nb_cur;
+
+                    ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur);
+                    nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur);
+
+                    ne[j] = ne_cur;
+                    nb[j] = nb_cur;
+                }
+
+                struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
+
+                tensor->op = (enum ggml_op) op;
+
+                uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur);
+
+                memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
+
+                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
+                    tensor->nb[j] = nb[j];
+                }
+
+                // parse args
+                {
+                    struct ggml_tensor ** args[2 + GGML_MAX_OPT] = {
+                        &tensor->src0,
+                        &tensor->src1,
+                    };
+
+                    for (int j = 0; j < GGML_MAX_OPT; ++j) {
+                        args[2 + j] = &tensor->opt[j];
+                    }
+
+                    for (int j = 0; j < 2 + GGML_MAX_OPT; ++j) {
+                        const int32_t arg_idx = *(const int32_t *) ptr; ptr += sizeof(arg_idx);
+
+                        if (arg_idx == -1) {
+                            continue;
+                        }
+
+                        if (arg_idx < GGML_MAX_NODES) {
+                            *args[j] = result.leafs[arg_idx];
+                        } else {
+                            *args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
+                        }
+                    }
+                }
+
+                result.nodes[i] = tensor;
+
+                fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor));
+            }
+        }
+    }
+
+    return result;
+}
+
 void ggml_graph_print(const struct ggml_cgraph * cgraph) {
     int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
 

ggml.h

@@ -451,9 +451,12 @@ extern "C" {
 
     GGML_API size_t  ggml_used_mem(const struct ggml_context * ctx);
 
-    GGML_API size_t  ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
+    GGML_API size_t  ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
     GGML_API void    ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
 
+    GGML_API void *  ggml_get_mem_buffer(struct ggml_context * ctx);
+    GGML_API size_t  ggml_get_mem_size  (struct ggml_context * ctx);
+
     GGML_API struct ggml_tensor * ggml_new_tensor(
             struct ggml_context * ctx,
             enum   ggml_type type,
@@ -492,6 +495,8 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
     GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
 
+    GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
+
     GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
     GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
@@ -978,7 +983,10 @@ extern "C" {
     GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
     GGML_API void ggml_graph_reset  (struct ggml_cgraph * cgraph);
 
-    GGML_API struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name);
+    GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
+
+    GGML_API void               ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
+    GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
 
     // print info and performance information for the graph
     GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);