backend: port BERT to GGUF

gpt4all-backend/bert.cpp

@@ -4,6 +4,7 @@
 #include "ggml.h"
 
 #include <cassert>
+#include <cinttypes>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
@@ -34,7 +35,6 @@ struct bert_hparams
     int32_t n_intermediate = 1536;
     int32_t n_head = 12;
     int32_t n_layer = 6;
-    int32_t f16 = 1;
 };
 
 struct bert_layer
@@ -88,7 +88,6 @@ struct bert_model
     std::vector<bert_layer> layers;
 
     struct ggml_context *ctx;
-    std::map<std::string, struct ggml_tensor *> tensors;
 };
 
 // Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
@@ -482,7 +481,6 @@ void bert_eval(
 //
 
 void bert_free(bert_ctx * ctx) {
-    ggml_free(ctx->model.ctx);
     delete ctx;
 }
 
@@ -492,63 +490,130 @@ struct bert_ctx * bert_load_from_file(const char *fname)
     printf("%s: loading model from '%s' - please wait ...\n", __func__, fname);
 #endif
 
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin)
-    {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname);
-        return nullptr;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *)&magic, sizeof(magic));
-        if (magic != 0x62657274)
-        {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname);
-            return nullptr;
-        }
-    }
-
     bert_ctx * new_bert = new bert_ctx;
     bert_model & model = new_bert->model;
     bert_vocab & vocab = new_bert->vocab;
 
+    struct gguf_init_params params = {
+        /*.no_alloc = */ false,
+        /*.ctx      = */ &model.ctx,
+    };
+    gguf_context *ggufctx = gguf_init_from_file(fname, params);
+    if (!ggufctx) {
+        fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
+        return nullptr;
+    }
+
+    printf("%s: gguf version     = %d\n", __func__, gguf_get_version(ggufctx));
+    printf("%s: gguf alignment   = %zu\n", __func__, gguf_get_alignment(ggufctx));
+    printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
+
+    // print some standard metadata
+    {
+        int keyidx;
+
+        keyidx = gguf_find_key(ggufctx, "general.name");
+        if (keyidx != -1) { printf("%s: model name           = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.description");
+        if (keyidx != -1) { printf("%s: model description    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.author");
+        if (keyidx != -1) { printf("%s: model author         = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.license");
+        if (keyidx != -1) { printf("%s: model license        = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.architecture");
+        if (keyidx != -1) { printf("%s: model architecture   = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.file_type");
+        if (keyidx != -1) { printf("%s: model file type      = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
+        if (keyidx != -1) { printf("%s: model data layout    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+        keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
+        if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+    }
+
+    // check required metadata
+    {
+        // check model architecture kv
+        int keyidx = gguf_find_key(ggufctx, "general.architecture");
+        if (keyidx == -1) {
+            fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
+            return nullptr;
+        }
+        if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
+            fprintf(stderr, "%s: model architecture not supported!\n", __func__);
+            return nullptr;
+        }
+    }
+
     // load hparams
     {
         auto &hparams = model.hparams;
 
-        fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+        bool ok = false;
-        fin.read((char *)&hparams.n_max_tokens, sizeof(hparams.n_max_tokens));
+        int keyidx;
-        fin.read((char *)&hparams.n_embd, sizeof(hparams.n_embd));
+
-        fin.read((char *)&hparams.n_intermediate, sizeof(hparams.n_intermediate));
+        do {
-        fin.read((char *)&hparams.n_head, sizeof(hparams.n_head));
+            keyidx = gguf_find_key(ggufctx, "bert.context_length");
-        fin.read((char *)&hparams.n_layer, sizeof(hparams.n_layer));
+            if (keyidx == -1) { break; }
-        fin.read((char *)&hparams.f16, sizeof(hparams.f16));
+            hparams.n_max_tokens = gguf_get_val_u32(ggufctx, keyidx);
+
+            keyidx = gguf_find_key(ggufctx, "bert.embedding_length");
+            if (keyidx == -1) { break; }
+            hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
+
+            keyidx = gguf_find_key(ggufctx, "bert.feed_forward_length");
+            if (keyidx == -1) { break; }
+            hparams.n_intermediate = gguf_get_val_u32(ggufctx, keyidx);
+
+            keyidx = gguf_find_key(ggufctx, "bert.attention.head_count");
+            if (keyidx == -1) { break; }
+            hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
+
+            keyidx = gguf_find_key(ggufctx, "bert.block_count");
+            if (keyidx == -1) { break; }
+            hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
+
+            ok = true;
+        } while (false);
+
+        if (!ok) {
+            fprintf(stderr, "%s: required hparam missing!\n", __func__);
+            return nullptr;
+        }
 
 #if defined(DEBUG_BERT)
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
         printf("%s: n_max_tokens   = %d\n", __func__, hparams.n_max_tokens);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        printf("%s: n_embd         = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_intermediate  = %d\n", __func__, hparams.n_intermediate);
+        printf("%s: n_intermediate = %d\n", __func__, hparams.n_intermediate);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
+        printf("%s: n_head         = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+        printf("%s: n_layer        = %d\n", __func__, hparams.n_layer);
-        printf("%s: f16     = %d\n", __func__, hparams.f16);
 #endif
     }
 
     // load vocab
     {
-        int32_t n_vocab = model.hparams.n_vocab;
+        auto & hparams = model.hparams;
 
-        std::string word;
+        int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
-        for (int i = 0; i < n_vocab; i++)
+        if (keyidx == -1) {
-        {
+            fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
-            uint32_t len;
+            return nullptr;
-            fin.read((char *)&len, sizeof(len));
+        }
+        if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
+            fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
+            return nullptr;
+        }
 
-            word.resize(len);
+        int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
-            fin.read((char *)word.data(), len);
+        if (tokens_keyidx == -1) {
+            fprintf(stderr, "%s: bert tokenizer vocab not found!\n", __func__);
+            return nullptr;
+        }
+
+        hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
+        printf("%s: bert tokenizer vocab = %d\n", __func__, int(hparams.n_vocab));
+
+        for (int i = 0; i < hparams.n_vocab; i++) {
+            std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
 
             if (word[0] == '#' && word[1] == '#')
             {
@@ -564,290 +629,52 @@ struct bert_ctx * bert_load_from_file(const char *fname)
         }
     }
 
-    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
-    // in order to save memory and also to speed up the computation
-    ggml_type wtype = GGML_TYPE_COUNT;
-    switch (model.hparams.f16)
-    {
-    case 0:
-        wtype = GGML_TYPE_F32;
-        break;
-    case 1:
-        wtype = GGML_TYPE_F16;
-        break;
-    case 2:
-        wtype = GGML_TYPE_Q4_0;
-        break;
-    case 3:
-        wtype = GGML_TYPE_Q4_1;
-        break;
-    default:
-    {
-        fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
-                __func__, fname, model.hparams.f16);
-        bert_free(new_bert);
-        return nullptr;
-    }
-    }
-
     auto &ctx = model.ctx;
 
-    size_t model_mem_req = 0;
-
-    {
-        const auto &hparams = model.hparams;
-
-        const int n_embd = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_max_tokens = hparams.n_max_tokens;
-        const int n_intermediate = hparams.n_intermediate;
-        const int n_vocab = hparams.n_vocab;
-
-        // Calculate size requirements
-
-        model_mem_req += n_embd * n_vocab * ggml_type_sizef(wtype); // word_embeddings
-        model_mem_req += n_embd * 2 * ggml_type_sizef(wtype); // token_type_embeddings
-        model_mem_req += n_embd * n_max_tokens * ggml_type_sizef(wtype); // position_embeddings
-
-        model_mem_req += 2 * n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_e_*
-
-        model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_*
-
-        model_mem_req += 4 * n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // kqvo weights
-        model_mem_req += 4 * n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // kqvo bias
-
-        model_mem_req += 2 * n_layer * (n_embd * n_intermediate * ggml_type_sizef(wtype)); // ff_*_w
-        model_mem_req += n_layer * (n_intermediate * ggml_type_sizef(GGML_TYPE_F32)); // ff_i_b
-        model_mem_req += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ff_o_b
-
-        model_mem_req += (5 + 16 * n_layer) * ggml_tensor_overhead(); // object overhead
-
 #if defined(DEBUG_BERT)
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, model_mem_req / (1024.0 * 1024.0));
+    printf("%s: ggml ctx size = %6.2f MB\n", __func__, ggml_get_mem_size(ctx) / (1024.0 * 1024.0));
 #endif
-    }
-
-    // create the ggml context
-    {
-        struct ggml_init_params params = {
-            .mem_size = model_mem_req,
-            .mem_buffer = NULL,
-            .no_alloc = false,
-        };
-
-        model.ctx = ggml_init(params);
-        if (!model.ctx)
-        {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
-            bert_free(new_bert);
-            return nullptr;
-        }
-    }
 
     // prepare memory for the weights
     {
-        const auto &hparams = model.hparams;
+        const int n_layer = model.hparams.n_layer;
-
-        const int n_embd = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_intermediate = hparams.n_intermediate;
-        const int n_max_tokens = hparams.n_max_tokens;
-        const int n_vocab = hparams.n_vocab;
-
         model.layers.resize(n_layer);
 
-        model.word_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+        model.word_embeddings       = ggml_get_tensor(ctx, "token_embd.weight");
-        model.token_type_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, 2);
+        model.token_type_embeddings = ggml_get_tensor(ctx, "token_types.weight");
-        model.position_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_max_tokens);
+        model.position_embeddings   = ggml_get_tensor(ctx, "position_embd.weight");
+        model.ln_e_w                = ggml_get_tensor(ctx, "output_norm.weight");
+        model.ln_e_b                = ggml_get_tensor(ctx, "output_norm.bias");
 
-        model.ln_e_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        auto name = [](int i, std::string n) {
-        model.ln_e_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            static std::string key;
-
+            key = "blk." + std::to_string(i) + "." + n;
-        // map by name
+            return key.c_str();
-        model.tensors["embeddings.word_embeddings.weight"] = model.word_embeddings;
+        };
-        model.tensors["embeddings.token_type_embeddings.weight"] = model.token_type_embeddings;
-        model.tensors["embeddings.position_embeddings.weight"] = model.position_embeddings;
-
-        model.tensors["embeddings.LayerNorm.weight"] = model.ln_e_w;
-        model.tensors["embeddings.LayerNorm.bias"] = model.ln_e_b;
 
         for (int i = 0; i < n_layer; ++i)
         {
             auto &layer = model.layers[i];
 
-            layer.ln_att_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.ln_att_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
-            layer.ln_att_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.ln_att_b = ggml_get_tensor(ctx, name(i, "attn_norm.bias"));
-            layer.ln_out_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.ln_out_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
-            layer.ln_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.ln_out_b = ggml_get_tensor(ctx, name(i, "ffn_norm.bias"));
-
+            layer.q_w      = ggml_get_tensor(ctx, name(i, "attn_q.weight"));
-            layer.q_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.q_b      = ggml_get_tensor(ctx, name(i, "attn_q.bias"));
-            layer.q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.k_w      = ggml_get_tensor(ctx, name(i, "attn_k.weight"));
-            layer.k_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.k_b      = ggml_get_tensor(ctx, name(i, "attn_k.bias"));
-            layer.k_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.v_w      = ggml_get_tensor(ctx, name(i, "attn_v.weight"));
-            layer.v_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.v_b      = ggml_get_tensor(ctx, name(i, "attn_v.bias"));
-            layer.v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.o_w      = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
-            layer.o_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.o_b      = ggml_get_tensor(ctx, name(i, "attn_output.bias"));
-            layer.o_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+            layer.ff_i_w   = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
-
+            layer.ff_i_b   = ggml_get_tensor(ctx, name(i, "ffn_up.bias"));
-            layer.ff_i_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_intermediate);
+            layer.ff_o_w   = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
-            layer.ff_i_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_intermediate);
+            layer.ff_o_b   = ggml_get_tensor(ctx, name(i, "ffn_down.bias"));
-
-            layer.ff_o_w = ggml_new_tensor_2d(ctx, wtype, n_intermediate, n_embd);
-            layer.ff_o_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-            // map by name
-
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.query.weight"] = layer.q_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.query.bias"] = layer.q_b;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.key.weight"] = layer.k_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.key.bias"] = layer.k_b;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.value.weight"] = layer.v_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.self.value.bias"] = layer.v_b;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.LayerNorm.weight"] = layer.ln_att_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.LayerNorm.bias"] = layer.ln_att_b;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.dense.weight"] = layer.o_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".attention.output.dense.bias"] = layer.o_b;
-
-            model.tensors["encoder.layer." + std::to_string(i) + ".intermediate.dense.weight"] = layer.ff_i_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".intermediate.dense.bias"] = layer.ff_i_b;
-
-            model.tensors["encoder.layer." + std::to_string(i) + ".output.LayerNorm.weight"] = layer.ln_out_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".output.LayerNorm.bias"] = layer.ln_out_b;
-            model.tensors["encoder.layer." + std::to_string(i) + ".output.dense.weight"] = layer.ff_o_w;
-            model.tensors["encoder.layer." + std::to_string(i) + ".output.dense.bias"] = layer.ff_o_b;
         }
     }
 
-    // load weights
-    {
-        int n_tensors = 0;
-#if defined(DEBUG_BERT)
-        size_t total_size = 0;
-#endif
-
-#if defined(DEBUG_BERT)
-        printf("%s: ", __func__);
-#endif
-
-        while (true)
-        {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ftype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
-
-            if (fin.eof())
-            {
-                break;
-            }
-
-            int64_t nelements = 1;
-            int64_t ne[2] = {1, 1};
-            for (int i = 0; i < n_dims; ++i)
-            {
-                int32_t ne_cur;
-                fin.read(reinterpret_cast<char *>(&ne_cur), sizeof(ne_cur));
-                ne[i] = ne_cur;
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end())
-            {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                bert_free(new_bert);
-                return nullptr;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_nelements(tensor) != nelements)
-            {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                bert_free(new_bert);
-                return nullptr;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1])
-            {
-                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%ld, %ld], expected [%ld, %ld]\n",
-                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
-                bert_free(new_bert);
-                return nullptr;
-            }
-
-#if defined(DEBUG_BERT)
-            static const char *ftype_str[] = {
-                "f32",
-                "f16",
-                "q4_0",
-                "q4_1",
-            };
-            printf("%24s - [%5ld, %5ld], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
-#endif
-
-            size_t bpe = 0;
-
-            switch (ftype)
-            {
-            case 0:
-                bpe = ggml_type_size(GGML_TYPE_F32);
-                break;
-            case 1:
-                bpe = ggml_type_size(GGML_TYPE_F16);
-                break;
-            case 2:
-                bpe = ggml_type_size(GGML_TYPE_Q4_0);
-                assert(ne[0] % 64 == 0);
-                break;
-            case 3:
-                bpe = ggml_type_size(GGML_TYPE_Q4_1);
-                assert(ne[0] % 64 == 0);
-                break;
-            default:
-            {
-                fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
-                bert_free(new_bert);
-                return nullptr;
-            }
-            };
-
-            if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor))
-            {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %lu\n",
-                        __func__, name.data(), ggml_nbytes(tensor), nelements * bpe);
-                bert_free(new_bert);
-                return nullptr;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
-
-#if defined(DEBUG_BERT)
-            // printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
-            total_size += ggml_nbytes(tensor);
-#endif
-
-            if (++n_tensors % 8 == 0)
-            {
-#if defined(DEBUG_BERT)
-                printf(".");
-                fflush(stdout);
-#endif
-            }
-        }
-
-#if defined(DEBUG_BERT)
-        printf(" done\n");
-        printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
-#endif
-    }
-
-    fin.close();
-
     // Calculate space requirements for setting up context buffers later
     {
         bert_vocab_id tokens[] = {0, 1, 2, 3};
@@ -1019,6 +846,16 @@ const std::vector<LLModel::Token> &Bert::endTokens() const
     return out;
 }
 
+std::string get_arch_name(gguf_context *ctx_gguf) {
+    std::string arch_name;
+    const int kid = gguf_find_key(ctx_gguf, "general.architecture");
+    enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
+    if (ktype != GGUF_TYPE_STRING) {
+        throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
+    }
+    return gguf_get_val_str(ctx_gguf, kid);
+}
+
 #if defined(_WIN32)
 #define DLL_EXPORT __declspec(dllexport)
 #else
@@ -1038,16 +875,21 @@ DLL_EXPORT const char *get_build_variant() {
     return GGML_BUILD_VARIANT;
 }
 
-DLL_EXPORT bool magic_match(const char* fname) {
+DLL_EXPORT bool magic_match(const char * fname) {
-#if 0
+    struct ggml_context * ctx_meta = NULL;
-    uint32_t magic = 0;
+    struct gguf_init_params params = {
-    f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
+        /*.no_alloc = */ true,
-    if (magic != 0x62657274) {
+        /*.ctx      = */ &ctx_meta,
-         return false;
+    };
-    }
+    gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
-    return true;
+    if (!ctx_gguf)
-#endif
+        return false;
-    return false;
+
+    bool isValid = gguf_get_version(ctx_gguf) <= 2;
+    isValid = isValid && get_arch_name(ctx_gguf) == "bert";
+
+    gguf_free(ctx_gguf);
+    return isValid;
 }
 
 DLL_EXPORT LLModel *construct() {

gpt4all-backend/scripts/convert_bert_hf_to_ggml.py

@@ -1,102 +0,0 @@
-import sys
-import struct
-import json
-import torch
-import numpy as np
-
-from transformers import AutoModel, AutoTokenizer
-
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
-    sys.exit(1)
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-fname_out = sys.argv[1] + "/ggml-model.bin"
-
-with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
-    encoder = json.load(f)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
-    hparams = json.load(f)
-
-with open(dir_model + "/vocab.txt", "r", encoding="utf-8") as f:
-    vocab = f.readlines()
-# possible data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-#
-# map from ftype to string
-ftype_str = ["f32", "f16"]
-
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
-        sys.exit(1)
-    fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
-
-
-tokenizer = AutoTokenizer.from_pretrained(dir_model)
-model = AutoModel.from_pretrained(dir_model, low_cpu_mem_usage=True)
-print (model)
-
-print(tokenizer.encode('I believe the meaning of life is'))
-
-list_vars = model.state_dict()
-for name in list_vars.keys():
-    print(name, list_vars[name].shape, list_vars[name].dtype)
-
-fout = open(fname_out, "wb")
-
-print(hparams)
-
-fout.write(struct.pack("i", 0x62657274)) # magic: ggml in hex
-fout.write(struct.pack("i", hparams["vocab_size"]))
-fout.write(struct.pack("i", hparams["max_position_embeddings"]))
-fout.write(struct.pack("i", hparams["hidden_size"]))
-fout.write(struct.pack("i", hparams["intermediate_size"]))
-fout.write(struct.pack("i", hparams["num_attention_heads"]))
-fout.write(struct.pack("i", hparams["num_hidden_layers"]))
-fout.write(struct.pack("i", ftype))
-
-for i in range(hparams["vocab_size"]):
-    text = vocab[i][:-1] # strips newline at the end
-    #print(f"{i}:{text}")
-    data = bytes(text, 'utf-8')
-    fout.write(struct.pack("i", len(data)))
-    fout.write(data)
-
-for name in list_vars.keys():
-    data = list_vars[name].squeeze().numpy()
-    if name in ['embeddings.position_ids', 'pooler.dense.weight', 'pooler.dense.bias']:
-        continue
-    print("Processing variable: " + name + " with shape: ", data.shape)
-
-    n_dims = len(data.shape);
-
-    # ftype == 0 -> float32, ftype == 1 -> float16
-    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
-            print("  Converting to float16")
-            data = data.astype(np.float16)
-            l_type = 1
-    else:
-        l_type = 0
-
-    # header
-    str = name.encode('utf-8')
-    fout.write(struct.pack("iii", n_dims, len(str), l_type))
-    for i in range(n_dims):
-        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
-    fout.write(str);
-
-    # data
-    data.tofile(fout)
-
-fout.close()
-
-print("Done. Output file: " + fname_out)
-print("")

gpt4all-backend/scripts/convert_bert_hf_to_gguf.py

@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+
+import gguf
+import numpy as np
+from transformers import AutoModel, AutoTokenizer
+
+
+if not 2 <= len(sys.argv) < 4:
+    print("Usage: {} dir-model [ftype]\n".format(os.path.basename(__file__)))
+    print("  ftype == 0 -> float32")
+    print("  ftype == 1 -> float16")
+    sys.exit(1)
+
+# output in the same directory as the model
+dir_model = Path(sys.argv[1])
+
+with open(dir_model / "vocab.txt", encoding="utf-8") as f:
+    vocab = f.readlines()
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+ftype = 1
+if len(sys.argv) > 2:
+    ftype = int(sys.argv[2])
+    if ftype < 0 or ftype > 1:
+        print("Invalid ftype: " + str(ftype))
+        sys.exit(1)
+
+fname_out = dir_model / ("ggml-model-" + ftype_str[ftype] + ".gguf")
+
+
+ARCH = gguf.MODEL_ARCH.BERT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+model = AutoModel.from_pretrained(dir_model, low_cpu_mem_usage=True)
+hparams = model.config
+print(model)
+
+block_count = hparams.num_hidden_layers
+gguf_writer.add_name("BERT")
+gguf_writer.add_context_length(hparams.max_position_embeddings)
+gguf_writer.add_embedding_length(hparams.hidden_size)
+gguf_writer.add_feed_forward_length(hparams.intermediate_size)
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(hparams.num_attention_heads)
+gguf_writer.add_file_type(ftype)
+
+print("gguf: get tokenizer metadata")
+
+try:
+    with open(dir_model / "tokenizer.json", encoding="utf-8") as f:
+        tokenizer_json = json.load(f)
+except FileNotFoundError as e:
+    print(f'Error: Missing {e.filename!r}', file=sys.stderr)
+    sys.exit(1)
+
+print("gguf: get wordpiece tokenizer vocab")
+
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+print(tokenizer.encode('I believe the meaning of life is'))
+
+tokens: list[bytearray] = []
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+for i in range(hparams.vocab_size):
+    try:
+        text = reverse_vocab[i]
+    except KeyError:
+        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
+        pad_token = f"[PAD{i}]".encode("utf8")
+        text = bytearray(pad_token)
+
+    tokens.append(text)
+
+gguf_writer.add_tokenizer_model("bert")  # wordpiece
+gguf_writer.add_token_list(tokens)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
+special_vocab.add_to_gguf(gguf_writer)
+
+print("gguf: get tensor metadata")
+
+tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
+
+list_vars = model.state_dict()
+for name in list_vars.keys():
+    print(name, list_vars[name].shape, list_vars[name].dtype)
+
+for name in list_vars.keys():
+    data = list_vars[name].squeeze().numpy()
+    if name in ['embeddings.position_ids', 'pooler.dense.weight', 'pooler.dense.bias']:
+        continue
+    print("Processing variable:", name, "with shape:", data.shape)
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
+        print("  Converting to float16")
+        data = data.astype(np.float16)
+        l_type = 1
+    else:
+        l_type = 0
+
+    # map tensor names
+    new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+    if new_name is None:
+        print("Can not map tensor '" + name + "'")
+        sys.exit()
+
+    gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+print("gguf: write tensors")
+gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print()