Merge branch 'ggml-org:master' into finelayer

Author: graehl

README.md

@@ -130,6 +130,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 <details>
 <summary>Bindings</summary>
 
+- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)

common/arg.cpp

@@ -1301,7 +1301,7 @@ common_params_context common_params_parser_init(common_params & params,
     sampler_type_names.pop_back();
 
     params.optimize = ggml_opt_get_default_optimizer_params(NULL);
-    params.optimize.common.alpha = 1e-8;
+    params.optimize.adamw.alpha = 1e-8;
 
     /**
      * filter options by example
@@ -2127,15 +2127,15 @@ common_params_context common_params_parser_init(common_params & params,
             { "-lr", "--learning-rate" }, "ALPHA",
             string_format(
                 "adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~100x (no momentum)",
-                (double) params.optimize.common.alpha),
-            [](common_params & params, const std::string & value) { params.optimize.common.alpha = std::stof(value); })
+                (double) params.optimize.adamw.alpha),
+            [](common_params & params, const std::string & value) { params.optimize.adamw.alpha = std::stof(value); })
             .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
     add_opt(common_arg(
                 { "-wd", "--weight-decay" }, "WD",
                 string_format(
                     "adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).",
-                    (double) params.optimize.common.wd),
-                [](common_params & params, const std::string & value) { params.optimize.common.wd = std::stof(value); })
+                    (double) params.optimize.adamw.wd),
+                [](common_params & params, const std::string & value) { params.optimize.adamw.wd = std::stof(value); })
                 .set_examples({ LLAMA_EXAMPLE_FINETUNE }));
     add_opt(common_arg({ "-epochs", "--epochs" }, "N",
                        string_format("optimizer max # of epochs (default: %d)", params.epochs),

convert_hf_to_gguf.py

@@ -3814,7 +3814,7 @@ def _xlmroberta_set_vocab(self) -> None:
             remove_whitespaces = tokenizer.clean_up_tokenization_spaces
             precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])
 
-            vocab_size = self.hparams.get("vocab_size", tokenizer.vocab_size)
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)
         else:
             sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
             sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
@@ -3827,7 +3827,7 @@ def _xlmroberta_set_vocab(self) -> None:
             tokenizer = SentencePieceProcessor()
             tokenizer.LoadFromFile(str(tokenizer_path))
 
-            vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size())
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
@@ -3857,33 +3857,26 @@ def _xlmroberta_set_vocab(self) -> None:
             unk_token = tokenizer_config_json.get("unk_token")
             unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))
 
-            for token_id in range(vocab_size):
+            for token_id in range(tokenizer.vocab_size):
                 piece = tokenizer._convert_id_to_token(token_id)
-                text = piece.encode("utf-8")
-                score = tokenizer_json["model"]["vocab"][token_id][1]
-
-                toktype = SentencePieceTokenTypes.NORMAL
-                if token_id == unk_token_id:
-                    toktype = SentencePieceTokenTypes.UNKNOWN
-                elif token_id in tokenizer.all_special_ids:
-                    toktype = SentencePieceTokenTypes.CONTROL
-                elif token_id in added_vocab.values():
-                    toktype = SentencePieceTokenTypes.USER_DEFINED
-                # No reliable way to detect this, but jina doesn't have any
-                # elif tokenizer.IsByte(token_id):
-                #     toktype = SentencePieceTokenTypes.BYTE
-
-                tokens[token_id] = text
-                scores[token_id] = score
-                toktypes[token_id] = toktype
-
-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(SentencePieceTokenTypes.UNUSED)
+                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:
+                    text = piece.encode("utf-8")
+                    score = tokenizer_json["model"]["vocab"][token_id][1]
+
+                    toktype = SentencePieceTokenTypes.NORMAL
+                    if token_id == unk_token_id:
+                        toktype = SentencePieceTokenTypes.UNKNOWN
+                    elif token_id in tokenizer.all_special_ids:
+                        toktype = SentencePieceTokenTypes.CONTROL
+                    elif token_id in added_vocab.values():
+                        toktype = SentencePieceTokenTypes.USER_DEFINED
+                    # No reliable way to detect this, but jina doesn't have any
+                    # elif tokenizer.IsByte(token_id):
+                    #     toktype = SentencePieceTokenTypes.BYTE
+
+                    tokens[token_id] = text
+                    scores[token_id] = score
+                    toktypes[token_id] = toktype
 
         if isinstance(tokenizer, SentencePieceProcessor):
             # realign tokens (see HF tokenizer code)
@@ -3896,6 +3889,12 @@ def _xlmroberta_set_vocab(self) -> None:
                 SentencePieceTokenTypes.UNKNOWN,
             ] + toktypes[3:-1]
 
+            if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE:
+                # Add mask token missing from sentencepiece.bpe.model
+                tokens[250001] = b'<mask>'
+                scores[250001] = 0.0
+                toktypes[250001] = SentencePieceTokenTypes.CONTROL
+
         self.gguf_writer.add_tokenizer_model("t5")
         self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)

examples/parallel/parallel.cpp

@@ -158,7 +158,7 @@ int main(int argc, char ** argv) {
     common_params params;
 
     params.n_predict = 128;
-    params.n_junk = 0;
+    params.n_junk = 1;
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
         return 1;
@@ -182,7 +182,7 @@ int main(int argc, char ** argv) {
     const bool is_sp_shared = params.is_pp_shared;
 
     // extra text to insert in each client's prompt in order to make it larger
-    const int32_t n_junk = params.n_junk;
+    const int32_t n_junk = std::max(1, params.n_junk);
 
     // init llama.cpp
     llama_backend_init();

examples/training/finetune.cpp

@@ -62,7 +62,7 @@ int main(int argc, char ** argv) {
     struct ggml_opt_optimizer_params & optimizer_params = params.optimize;
 
     LOG_INF("-optimizer %s -lr %.2g -wd %.2g -epochs %d\n", ggml_opt_optimizer_name(optimizer_params.optimizer),
-            (double) optimizer_params.common.alpha, (double) optimizer_params.common.wd, params.epochs);
+            (double) optimizer_params.adamw.alpha, (double) optimizer_params.adamw.wd, params.epochs);
 
     struct llama_opt_params lopt_params {
         /*n_ctx_train     =*/ 0,

ggml/include/ggml-opt.h

@@ -90,13 +90,10 @@ extern "C" {
         // SGD and AdamW optimizer parameters
         struct {
             float alpha;  // learning rate
-            float wd;     // weight decay for SGD or AdamW, use 0.0f to disable
-        } common;
-
-        struct {
             float beta1;  // adamw
             float beta2;  // adamw
             float eps;    // epsilon for numerical stability
+            float wd;     // weight decay for SGD or AdamW, use 0.0f to disable
         } adamw;
 
         // only GGML_OPT_OPTIMIZER_ADAMW allocates m, v per parameter

ggml/include/ggml.h

@@ -2104,9 +2104,6 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_graph_get_grad    (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
     GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
 
-    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);
 

ggml/src/CMakeLists.txt

@@ -196,6 +196,7 @@ add_library(ggml-base
             ../include/ggml-opt.h
             ../include/gguf.h
             ggml.c
+            ggml.cpp
             ggml-alloc.c
             ggml-backend.cpp
             ggml-opt.cpp
@@ -226,6 +227,7 @@ function(ggml_add_backend_library backend)
         set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
         target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
         add_dependencies(ggml ${backend})
+        install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
     else()
         add_library(${backend} ${ARGN})
         target_link_libraries(ggml PUBLIC ${backend})

ggml/src/ggml-blas/CMakeLists.txt

@@ -81,7 +81,7 @@ if (BLAS_FOUND)
     target_link_libraries     (ggml-blas PRIVATE ${BLAS_LIBRARIES})
     target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS})
 else()
-    message(ERROR "BLAS not found, please refer to "
-                  "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
-                  " to set correct GGML_BLAS_VENDOR")
+    message(FATAL_ERROR "BLAS not found, please refer to "
+                        "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
+                        " to set correct GGML_BLAS_VENDOR")
 endif()

ggml/src/ggml-impl.h

@@ -32,6 +32,8 @@
 extern "C" {
 #endif
 
+void ggml_print_backtrace(void);
+
 #ifndef MIN
 #    define MIN(a, b) ((a) < (b) ? (a) : (b))
 #endif