tests : add test-tokenizer-0.sh

.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 125
 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
-exclude = examples/*,examples/*/**,*/**/__init__.py
+exclude = examples/*,examples/*/**,*/**/__init__.py,scripts/gen-unicode-data.py,tests/test-tokenizer-0.py

Makefile

@@ -77,11 +77,10 @@ test: $(TEST_TARGETS)
 			./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \
-			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
 			continue; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \

convert-hf-to-gguf-update.py

@@ -31,6 +31,7 @@
 from enum import IntEnum, auto
 from transformers import AutoTokenizer
 
+logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger("convert-hf-to-gguf-update")
 
 
@@ -62,6 +63,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
     {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
+    {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
 ]
 
 # make directory "models/tokenizers" if it doesn't exist
@@ -158,8 +160,8 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         chktok = tokenizer.encode(chktxt)
         chkhsh = sha256(str(chktok).encode()).hexdigest()
 
-        print(f"chktok: {{chktok}}")
-        print(f"chkhsh: {{chkhsh}}")
+        logger.debug(f"chktok: {{chktok}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
 
         res = None
 
@@ -168,22 +170,22 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         #       don't edit the hashes manually!
 {src_ifs}
         if res is None:
-            print("\\n")
-            print("**************************************************************************************")
-            print("** WARNING: The BPE pre-tokenizer was not recognized!")
-            print("**          There are 2 possible reasons for this:")
-            print("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
-            print("**          - the pre-tokenization config has changed upstream")
-            print("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
-            print("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
-            print("**")
-            print(f"** chkhsh:  {{chkhsh}}")
-            print("**************************************************************************************")
-            print("\\n")
+            logger.warning("\\n")
+            logger.warning("**************************************************************************************")
+            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
+            logger.warning("**          There are 2 possible reasons for this:")
+            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the pre-tokenization config has changed upstream")
+            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
+            logger.warning("**")
+            logger.warning(f"** chkhsh:  {{chkhsh}}")
+            logger.warning("**************************************************************************************")
+            logger.warning("\\n")
             raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
 
-        print(f"tokenizer.ggml.pre: {{repr(res)}}")
-        print(f"chkhsh: {{chkhsh}}")
+        logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
+        logger.debug(f"chkhsh: {{chkhsh}}")
 
         return res
 """
@@ -197,6 +199,8 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 # generate tests for each tokenizer model
 
 tests = [
+    "ied 4 ½ months",
+    "Führer",
     "",
     " ",
     "  ",
@@ -281,6 +285,6 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 for model in models:
     name = model["name"]
 
-    logger.info(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only")
+    print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
 
 logger.info("\n")

convert-hf-to-gguf.py

@@ -308,6 +308,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
             # ref: https://huggingface.co/openai-community/gpt2
             res = "gpt-2"
+        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
+            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
+            res = "refact"
 
         if res is None:
             logger.warning("\n")
@@ -324,7 +327,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
             logger.warning("\n")
             raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
 
-        logger.debug(f"tokenizer.ggml.pre: {res}")
+        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
         logger.debug(f"chkhsh: {chkhsh}")
 
         return res

llama.cpp

@@ -4383,6 +4383,9 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "gpt-2") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
+            } else if (
+                    tokenizer_pre == "refact") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -11952,7 +11955,7 @@ static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id
 static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
     GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
     GGML_ASSERT(llama_is_byte_token(vocab, id));
-    const auto& token_data = vocab.id_to_token.at(id);
+    const auto & token_data = vocab.id_to_token.at(id);
     switch (llama_vocab_get_type(vocab)) {
         case LLAMA_VOCAB_TYPE_SPM: {
             auto buf = token_data.text.substr(3, 2);
@@ -12212,14 +12215,13 @@ struct llm_tokenizer_bpe {
                             "\\s?\\p{L}+",
                             "\\s?\\p{P}+",
                             "[一-龥ࠀ-一가-퟿]+",
-                            "\\p{N}+",
+                            "\\p{N}",
                         });
                         break;
                     case LLAMA_VOCAB_PRE_TYPE_FALCON:
                         word_collection = unicode_regex_split(text, {
                             "[\\p{P}\\$\\+<=>\\^~\\|]+",
                             "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                            "\\p{N}+",
                             "[0-9][0-9][0-9]",
                         });
                         break;
@@ -12235,6 +12237,12 @@ struct llm_tokenizer_bpe {
                         });
                         break;
                     case LLAMA_VOCAB_PRE_TYPE_STARCODER:
+                    case LLAMA_VOCAB_PRE_TYPE_REFACT:
+                        word_collection = unicode_regex_split(text, {
+                            "\\p{N}",
+                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                        });
+                        break;
                     case LLAMA_VOCAB_PRE_TYPE_GPT2:
                         word_collection = unicode_regex_split(text, {
                             "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
@@ -17466,9 +17474,10 @@ int32_t llama_tokenize(
 
 static std::string llama_decode_text(const std::string & text) {
     std::string decoded_text;
-    auto unicode_sequences = unicode_cpts_from_utf8(text);
-    for (auto & unicode_sequence : unicode_sequences) {
-        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence));
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+    for (const auto cpt : cpts) {
+        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt));
     }
 
     return decoded_text;

llama.h

@@ -79,6 +79,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_MPT            = 5,
         LLAMA_VOCAB_PRE_TYPE_STARCODER      = 6,
         LLAMA_VOCAB_PRE_TYPE_GPT2           = 7,
+        LLAMA_VOCAB_PRE_TYPE_REFACT         = 8,
     };
 
     // note: these values should be synchronized with ggml_rope

models/ggml-vocab-bert-bge.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-bert-bge.gguf.out

@@ -1,3 +1,5 @@
+ 29464 2094 1018 1092 2706
+ 11865 17875
 
 
 

models/ggml-vocab-deepseek-coder.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-deepseek-coder.gguf.out

@@ -1,3 +1,5 @@
+ 1050 207 19 207 19192 4217
+ 37 32009 71 6247
 
  207
  243

models/ggml-vocab-deepseek-llm.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-deepseek-llm.gguf.out

@@ -1,3 +1,5 @@
+ 1052 207 19 207 19109 4223
+ 37 100014 71 6245
 
  207
  243

models/ggml-vocab-falcon.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-falcon.gguf.out

@@ -1,3 +1,5 @@
+ 878 204 31 3068 133 2137
+ 28611 132 30042
 
  204
  258

models/ggml-vocab-gpt-2.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-gpt-2.gguf.out

@@ -1,3 +1,5 @@
+ 798 604 25208 1933
+ 37 9116 71 11751
 
  220
  220 220

models/ggml-vocab-llama-bpe.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-llama-bpe.gguf.out

@@ -1,3 +1,5 @@
+ 1142 220 19 220 27154 4038
+ 37 51853 261
 
  220
  256

models/ggml-vocab-llama-spm.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-llama-spm.gguf.out

@@ -1,3 +1,5 @@
+ 474 287 29871 29946 29871 30226 7378
+ 383 4000 261
 
  259
  1678

models/ggml-vocab-mpt.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-mpt.gguf.out

@@ -1,3 +1,5 @@
+ 728 577 24142 2607
+ 39 26288 6554
 
  209
  50276

models/ggml-vocab-phi-3.gguf.inp

@@ -1,3 +1,7 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
 
 __ggml_vocab_test__
 

models/ggml-vocab-phi-3.gguf.out

@@ -1,3 +1,5 @@
+ 474 287 29871 29946 29871 30226 7378
+ 383 4000 261
 
  259
  1678