BERT WIP

README.md

@@ -736,21 +736,9 @@ Several quantization methods are supported. They differ in the resulting model d
 |   13B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
 
 - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
-- recent k-quants improvements and new i-quants
+- recent k-quants improvements
   - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
   - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
-  - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
-  - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
-  - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
-  - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
-  - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
-  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
-  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
-  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
-  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
-  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
-  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
-  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
 
 ### Perplexity (measuring model quality)
 

convert-hf-to-gguf.py

@@ -205,6 +205,8 @@ class Model:
             return OrionModel
         if model_architecture == "InternLM2ForCausalLM":
             return InternLM2Model
+        if model_architecture == "BertModel":
+            return BertModel
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -258,6 +260,8 @@ class Model:
             return gguf.MODEL_ARCH.ORION
         if arch == "InternLM2ForCausalLM":
             return gguf.MODEL_ARCH.INTERNLM2
+        if arch == "BertModel":
+            return gguf.MODEL_ARCH.BERT
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -1521,6 +1525,55 @@ in chat mode so that the conversation can end normally.")
                 self.post_write_tensors(tensor_map, name, data_torch)
 
 
+class BertModel(Model):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"]
+
+    def set_gguf_parameters(self):
+        # TODO(cebtenzzre): merge with parent class
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def write_tensors(self):
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        tensors = dict(self.get_tensors())
+        for name, data_torch in tensors.items():
+            # we are only using BERT for embeddings so we don't need the pooling layer
+            if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
+                continue  # we don't need these
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            data = data_torch.squeeze().numpy()
+            n_dims = len(data.shape)
+            new_dtype: type[np.floating[Any]]
+
+            if self.ftype == 1 and name.endswith(".weight") and n_dims == 2:
+                # if f16 desired, convert any float32 2-dim weight tensors to float16
+                new_dtype = np.float16
+            else:
+                # if f32 desired, convert any float16 to float32
+                new_dtype = np.float32
+
+            print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}")
+
+            if data.dtype != new_dtype:
+                data = data.astype(new_dtype)
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
 ###### CONVERSION LOGIC ######
 
 

convert.py

@@ -334,9 +334,9 @@ class Params:
 class BpeVocab:
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
         self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        if isinstance(self.bpe_tokenizer.get('model'), dict):
+        try:
             self.vocab = self.bpe_tokenizer["model"]["vocab"]
-        else:
+        except KeyError:
             self.vocab = self.bpe_tokenizer
         added_tokens: dict[str, int]
         if fname_added_tokens is not None:

examples/server/README.md

@@ -276,15 +276,13 @@ Notice that each `probs` is an array of length `n_probs`.
 {
   "assistant_name": "",
   "user_name": "",
-  "default_generation_settings": { ... },
-  "total_slots": 1
+  "default_generation_settings": { ... }
 }
 ```
 
 - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
 - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
-- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
 
 - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
 

examples/server/completion.js.hpp

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-  0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
-  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20,
-  0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67,
-  0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
-  0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
+  0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
 };
-unsigned int completion_js_len = 5782;
+unsigned int completion_js_len = 5346;

examples/server/public/completion.js

@@ -195,8 +195,7 @@ export const llamaComplete = async (params, controller, callback) => {
 // Get the model info from the server. This is useful for getting the context window and so on.
 export const llamaModelInfo = async () => {
   if (!generation_settings) {
-    const props = await fetch("/props").then(r => r.json());
-    generation_settings = props.default_generation_settings;
+    generation_settings = await fetch("/model.json").then(r => r.json());
   }
   return generation_settings;
 }

examples/server/server.cpp

@@ -432,6 +432,7 @@ struct llama_server_context
         }
 
         default_generation_settings_for_props = get_formated_generation(slots.front());
+        default_generation_settings_for_props["num_slots"] = params.n_parallel;
         default_generation_settings_for_props["seed"] = -1;
 
         batch = llama_batch_init(n_ctx, 0, params.n_parallel);
@@ -989,6 +990,11 @@ struct llama_server_context
         queue_results.send(res);
     }
 
+    json get_model_props()
+    {
+        return get_formated_generation(slots[0]);
+    }
+
     json get_formated_generation(llama_client_slot &slot)
     {
         const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@@ -2638,8 +2644,7 @@ int main(int argc, char **argv)
                 json data = {
                     { "user_name",      llama.name_user.c_str() },
                     { "assistant_name", llama.name_assistant.c_str() },
-                    { "default_generation_settings", llama.default_generation_settings_for_props },
-                    { "total_slots",    llama.params.n_parallel }
+                    { "default_generation_settings", llama.default_generation_settings_for_props }
                 };
                 res.set_content(data.dump(), "application/json; charset=utf-8");
             });
@@ -2890,6 +2895,12 @@ int main(int argc, char **argv)
                 }
             });
 
+    svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
+            {
+                const json data = llama.get_model_props();
+                return res.set_content(data.dump(), "application/json; charset=utf-8");
+            });
+
     svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
                 { return res.set_content("", "application/json; charset=utf-8"); });
 

ggml-cuda.cu

@@ -6831,7 +6831,7 @@ static void mul_mat_vec_q_cuda(
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) {
 
     GGML_ASSERT(ncols_x % qk == 0);
-    GGML_ASSERT(ncols_y <= 4);
+    GGML_ASSERT(ncols_y <= 8);
 
     const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
@@ -6853,22 +6853,22 @@ static void mul_mat_vec_q_cuda(
             mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
                 <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
             break;
-        // case 5:
-        //     mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
-        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
-        //     break;
-        // case 6:
-        //     mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
-        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
-        //     break;
-        // case 7:
-        //     mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
-        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
-        //     break;
-        // case 8:
-        //     mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
-        //         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
-        //     break;
+        case 5:
+            mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
+            break;
+        case 6:
+            mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
+            break;
+        case 7:
+            mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
+            break;
+        case 8:
+            mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
+            break;
         default:
             GGML_ASSERT(false);
             // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot>
@@ -9909,7 +9909,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
             }
         } else {
-            if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) {
+            if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
             } else if (use_mul_mat_q) {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);

gguf-py/gguf/constants.py

@@ -60,22 +60,23 @@ class Keys:
         SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"
 
     class Tokenizer:
-        MODEL         = "tokenizer.ggml.model"
-        LIST          = "tokenizer.ggml.tokens"
-        TOKEN_TYPE    = "tokenizer.ggml.token_type"
-        SCORES        = "tokenizer.ggml.scores"
-        MERGES        = "tokenizer.ggml.merges"
-        BOS_ID        = "tokenizer.ggml.bos_token_id"
-        EOS_ID        = "tokenizer.ggml.eos_token_id"
-        UNK_ID        = "tokenizer.ggml.unknown_token_id"
-        SEP_ID        = "tokenizer.ggml.seperator_token_id"
-        PAD_ID        = "tokenizer.ggml.padding_token_id"
-        ADD_BOS       = "tokenizer.ggml.add_bos_token"
-        ADD_EOS       = "tokenizer.ggml.add_eos_token"
-        ADD_PREFIX    = "tokenizer.ggml.add_space_prefix"
-        HF_JSON       = "tokenizer.huggingface.json"
-        RWKV          = "tokenizer.rwkv.world"
-        CHAT_TEMPLATE = "tokenizer.chat_template"
+        MODEL            = "tokenizer.ggml.model"
+        LIST             = "tokenizer.ggml.tokens"
+        TOKEN_TYPE       = "tokenizer.ggml.token_type"
+        TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
+        SCORES           = "tokenizer.ggml.scores"
+        MERGES           = "tokenizer.ggml.merges"
+        BOS_ID           = "tokenizer.ggml.bos_token_id"
+        EOS_ID           = "tokenizer.ggml.eos_token_id"
+        UNK_ID           = "tokenizer.ggml.unknown_token_id"
+        SEP_ID           = "tokenizer.ggml.seperator_token_id"
+        PAD_ID           = "tokenizer.ggml.padding_token_id"
+        ADD_BOS          = "tokenizer.ggml.add_bos_token"
+        ADD_EOS          = "tokenizer.ggml.add_eos_token"
+        ADD_PREFIX       = "tokenizer.ggml.add_space_prefix"
+        HF_JSON          = "tokenizer.huggingface.json"
+        RWKV             = "tokenizer.rwkv.world"
+        CHAT_TEMPLATE    = "tokenizer.chat_template"
 
 
 #
@@ -121,6 +122,7 @@ class MODEL_TENSOR(IntEnum):
     ATTN_OUT        = auto()
     ATTN_NORM       = auto()
     ATTN_NORM_2     = auto()
+    ATTN_OUT_NORM   = auto()
     ATTN_ROT_EMBD   = auto()
     FFN_GATE_INP    = auto()
     FFN_NORM        = auto()
@@ -133,6 +135,7 @@ class MODEL_TENSOR(IntEnum):
     FFN_UP_EXP      = auto()
     ATTN_Q_NORM     = auto()
     ATTN_K_NORM     = auto()
+    LAYER_OUT_NORM  = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -176,6 +179,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
     MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.ATTN_OUT_NORM:   "blk.{bid}.attn_output_norm",
     MODEL_TENSOR.FFN_GATE_INP:    "blk.{bid}.ffn_gate_inp",
     MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
     MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
@@ -185,6 +189,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate.{xid}",
     MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down.{xid}",
     MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up.{xid}",
+    MODEL_TENSOR.LAYER_OUT_NORM:  "blk.{bid}.layer_output_norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -260,17 +265,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     ],
     MODEL_ARCH.BERT: [
         MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
         MODEL_TENSOR.TOKEN_TYPES,
         MODEL_TENSOR.POS_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_OUT_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,
         MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
     ],
     MODEL_ARCH.MPT: [
         MODEL_TENSOR.TOKEN_EMBD,

gguf-py/gguf/gguf_writer.py

@@ -387,6 +387,9 @@ class GGUFWriter:
     def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
         self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
 
+    def add_token_type_count(self, value: int) -> None:
+        self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value)
+
     def add_token_scores(self, scores: Sequence[float]) -> None:
         self.add_array(Keys.Tokenizer.SCORES, scores)
 

gguf-py/gguf/tensor_mapping.py

@@ -30,6 +30,7 @@ class TensorNameMap:
         # Normalization of token embeddings
         MODEL_TENSOR.TOKEN_EMBD_NORM: (
             "word_embeddings_layernorm",  # bloom
+            "embeddings.LayerNorm",       # bert
         ),
 
         # Position embeddings
@@ -54,7 +55,6 @@ class TensorNameMap:
             "transformer.ln_f",                        # gpt2 gpt-j falcon
             "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
-            "embeddings.LayerNorm",                    # bert
             "transformer.norm_f",                      # mpt
             "ln_f",                                    # refact bloom qwen gpt2
             "language_model.encoder.final_layernorm",  # persimmon
@@ -79,7 +79,6 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_mlp",                           # falcon40b
             "model.layers.{bid}.input_layernorm",                   # llama-hf
             "layers.{bid}.attention_norm",                          # llama-pth
-            "encoder.layer.{bid}.attention.output.LayerNorm",       # bert
             "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
             "h.{bid}.ln_1",                                         # gpt2
@@ -155,6 +154,11 @@ class TensorNameMap:
             "model.layers.{bid}.attention.wo",                           # internlm2
         ),
 
+        # Attention output norm
+        MODEL_TENSOR.ATTN_OUT_NORM: (
+            "encoder.layer.{bid}.attention.output.LayerNorm",  # bert
+        ),
+
         # Rotary embeddings
         MODEL_TENSOR.ATTN_ROT_EMBD: (
             "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
@@ -171,7 +175,6 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf
             "layers.{bid}.ffn_norm",                                         # llama-pth
-            "encoder.layer.{bid}.output.LayerNorm",                          # bert
             "language_model.encoder.layers.{bid}.post_attention_layernorm",  # persimmon
             "model.layers.{bid}.ln2",                                        # yi
             "h.{bid}.ln_2",                                                  # gpt2
@@ -266,6 +269,10 @@ class TensorNameMap:
         MODEL_TENSOR.ROPE_FREQS: (
             "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
         ),
+
+        MODEL_TENSOR.LAYER_OUT_NORM: (
+            "encoder.layer.{bid}.output.LayerNorm",  # bert
+        )
     }
 
     mapping: dict[str, tuple[MODEL_TENSOR, str]]

llama.cpp

@@ -196,6 +196,7 @@ enum llm_arch {
     LLM_ARCH_STARCODER,
     LLM_ARCH_PERSIMMON,
     LLM_ARCH_REFACT,
+    LLM_ARCH_BERT,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
     LLM_ARCH_QWEN,
@@ -219,6 +220,7 @@ static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_STARCODER,       "starcoder" },
     { LLM_ARCH_PERSIMMON,       "persimmon" },
     { LLM_ARCH_REFACT,          "refact"    },
+    { LLM_ARCH_BERT,            "bert"      },
     { LLM_ARCH_BLOOM,           "bloom"     },
     { LLM_ARCH_STABLELM,        "stablelm"  },
     { LLM_ARCH_QWEN,            "qwen"      },
@@ -271,6 +273,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_MODEL,
     LLM_KV_TOKENIZER_LIST,
     LLM_KV_TOKENIZER_TOKEN_TYPE,
+    LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
     LLM_KV_TOKENIZER_SCORES,
     LLM_KV_TOKENIZER_MERGES,
     LLM_KV_TOKENIZER_BOS_ID,
@@ -326,6 +329,7 @@ static std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
     { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
     { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,    "tokenizer.ggml.token_type_count"   },
     { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
     { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
     { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
@@ -534,6 +538,24 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_OUT_NORM,  "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_BLOOM,
         {
@@ -1388,6 +1410,9 @@ static llama_state g_state;
 // available llama models
 enum e_model {
     MODEL_UNKNOWN,
+    MODEL_22M,
+    MODEL_33M,
+    MODEL_109M,
     MODEL_0_5B,
     MODEL_1B,
     MODEL_3B,
@@ -1428,6 +1453,7 @@ struct llama_hparams {
     uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
+    uint32_t n_vocab_type = 0; // for BERT-style token types
 
     float f_norm_eps;
     float f_norm_rms_eps;
@@ -1532,6 +1558,8 @@ struct llama_layer {
     struct ggml_tensor * bqkv;
 
     // normalization
+    struct ggml_tensor * attn_out_norm;
+    struct ggml_tensor * attn_out_norm_b;
     struct ggml_tensor * ffn_norm;
     struct ggml_tensor * ffn_norm_b;
 
@@ -1550,6 +1578,10 @@ struct llama_layer {
     struct ggml_tensor * ffn_down_b; // b2
     struct ggml_tensor * ffn_up_b;   // b3
     struct ggml_tensor * ffn_act;
+
+    // normalization
+    struct ggml_tensor * layer_out_norm;
+    struct ggml_tensor * layer_out_norm_b;
 };
 
 struct llama_kv_cell {
@@ -1667,6 +1699,7 @@ struct llama_model {
     llama_vocab   vocab;
 
     struct ggml_tensor * tok_embd;
+    struct ggml_tensor * type_embd;
     struct ggml_tensor * pos_embd;
     struct ggml_tensor * tok_norm;
     struct ggml_tensor * tok_norm_b;
@@ -1791,8 +1824,10 @@ struct llama_context {
     struct ggml_tensor * inp_tokens;    // I32 [n_batch]
     struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
     struct ggml_tensor * inp_pos;       // I32 [n_batch]
+    struct ggml_tensor * inp_type;      // I32 [n_batch]
     struct ggml_tensor * inp_KQ_mask;   // F32 [n_ctx, n_batch]
     struct ggml_tensor * inp_K_shift;   // I32 [n_ctx]
+    struct ggml_tensor * inp_sum;       // F32 [1, n_batch]
 
 #ifdef GGML_USE_MPI
     ggml_mpi_context * ctx_mpi = NULL;
@@ -2933,6 +2968,21 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_TOKEN_TYPE_COUNT,        hparams.n_vocab_type);
+
+                switch (hparams.n_embd) {
+                    case 384: // MiniLM
+                        switch (hparams.n_layer) {
+                            case 6:  model.type = e_model::MODEL_22M; break;
+                            case 12: model.type = e_model::MODEL_33M; break;
+                        } break;
+                    case 768: // BERT-Base
+                        model.type = e_model::MODEL_109M; break;
+                }
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3719,6 +3769,45 @@ static bool llm_load_tensors(
                         layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64});
                     }
                 } break;
+            case LLM_ARCH_BERT:
+                {
+                    model.tok_embd   = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
+                    model.type_embd  = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES,     "weight"), {n_vocab_type, n_embd});
+                    model.pos_embd   = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,        "weight"), {n_embd, hparams.n_ctx_train});
+                    model.tok_norm   = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
+                    model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd});
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
+
+                        layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd});
+
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa});
+
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa});
+
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+                    }
+                } break;
             case LLM_ARCH_BLOOM:
                 {
                     model.tok_embd   = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
@@ -5561,6 +5650,113 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_bert() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
+
+        inpL = ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.type_embd, lctx.inp_type),
+                inpL);
+        inpL = ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.pos_embd, lctx.inp_pos),
+                inpL);
+
+        inpL = llm_build_norm(ctx0, inpL, hparams,
+                model.tok_norm,
+                model.tok_norm_b,
+                LLM_NORM, cb, -1);
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * cur = inpL;
+
+            // self-attention
+            {
+                // compute Q and K
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head, n_tokens);
+                struct ggml_tensor * K = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head, n_tokens);
+                struct ggml_tensor * V = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
+
+                struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);
+                // KQ = soft_max(KQ / sqrt(head width))
+                KQ = ggml_soft_max(ctx0,
+                        ggml_scale(ctx0, KQ, 1.0f / sqrt((float)n_embd_head)));
+
+                V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
+                struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V, KQ);
+                KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+                cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV), n_embd, N);
+
+                // attention output
+                cur = ggml_add(ctx0,
+                        model.layers[il].bo,
+                        ggml_mul_mat(ctx0, model.layers[il].wo, cur));
+            }
+
+            // re-add the layer input
+            cur = ggml_add(ctx0, cur, inpSA);
+
+            // attention layer norm
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.layers[il].attn_out_norm,
+                    model.layers[il].attn_out_norm_b,
+                    LLM_NORM, cb, il);
+
+            struct ggml_tensor * ffn_inp = cur;
+
+            // feed-forward network
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                    NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+
+            // attentions bypass the intermediate layer
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            // output layer norm
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.layers[il].layer_out_norm,
+                    model.layers[il].layer_out_norm_b,
+                    LLM_NORM, cb, il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        // pooling (sum = [L, 1, B])
+        cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), lctx.inp_sum); // [E, 1, B] 
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_bloom() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -6835,6 +7031,12 @@ static struct ggml_cgraph * llama_build_graph(
             ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
         }
 
+        {
+            // for embedding models, token type is always zero ("sentence A")
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_type->buffer));
+            memset(lctx.inp_type->data, 0, batch.n_tokens * ggml_element_size(lctx.inp_type));
+        }
+
         {
             const int64_t n_kv     = llm.n_kv;
             const int64_t n_tokens = batch.n_tokens;
@@ -6870,6 +7072,15 @@ static struct ggml_cgraph * llama_build_graph(
                 data[i] = lctx.kv_self.cells[i].delta;
             }
         }
+
+        {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_inp_sum->buffer));
+            float * data = (float *) lctx.inp_sum->data;
+
+            for (int i = 0; i < batch.n_tokens; ++i) {
+                data[i] = 1.0f/float(batch.n_tokens);
+            }
+        }
     }
 
     llm.init();
@@ -6899,6 +7110,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_refact();
             } break;
+        case LLM_ARCH_BERT:
+            {
+                result = llm.build_bert();
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 result = llm.build_bloom();
@@ -10574,7 +10789,7 @@ struct llama_context * llama_new_context_with_model(
         // graph inputs
         {
             ggml_init_params init_params = {
-                /* .mem_size   */ ggml_tensor_overhead()*5,
+                /* .mem_size   */ ggml_tensor_overhead()*7,
                 /* .mem_buffer */ nullptr,
                 /* .no_alloc   */ true,
             };
@@ -10583,14 +10798,18 @@ struct llama_context * llama_new_context_with_model(
             ctx->inp_tokens  = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
             ctx->inp_embd    = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch);
             ctx->inp_pos     = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
+            ctx->inp_type    = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch);
             ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch);
             ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx);
+            ctx->inp_sum     = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, 1, cparams.n_batch);
 
             ggml_set_name(ctx->inp_tokens,  "inp_tokens");
             ggml_set_name(ctx->inp_embd,    "inp_embd");
             ggml_set_name(ctx->inp_pos,     "inp_pos");
+            ggml_set_name(ctx->inp_type,    "inp_type");
             ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask");
             ggml_set_name(ctx->inp_K_shift, "inp_K_shift");
+            ggml_set_name(ctx->inp_sum,     "inp_sum");
 
             ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true));