new tokenizer-verifier tool to check gguf tokenizer parameters

[anisse]

This program verifies that a given gguf model file can tokenize all potential valid characters. Since llama.cpp currently raises an exception when tokenization is not possible, this tool helps verifying that valid ascii and utf-8 will always be properly tokenized.

[anisse]

Here is what it looks like for a model with a BPE tokenizer that can tokenize anything:

> ./build/bin/tokenizer-verifier  ../models/llama-2-7b-chat.Q2_K.gguf 2>/dev/null
0/127 7-bit ascii characters could not be tokenized
0/1114111 potential unicode codepoints not tokenized

And for another where this fails because of an incomplete (or misconfigured?) tokenizer:

> ./build/bin/tokenizer-verifier  ../models/croissantllmchat-v0.1.Q8_0.gguf 2> /dev/null
0x1 -> Tokenization failed for char ''
0x2 -> Tokenization failed for char ''
0x3 -> Tokenization failed for char ''
0x4 -> Tokenization failed for char ''
0x5 -> Tokenization failed for char ''
0x6 -> Tokenization failed for char ''
0x7 -> Tokenization failed for char ''
0x8 -> Tokenization failed for char '
0xb -> Tokenization failed for char '
                                     '
0xc -> Tokenization failed for char '
                                     '
0xe -> Tokenization failed for char ''
0xf -> Tokenization failed for char ''
0x10 -> Tokenization failed for char ''
0x11 -> Tokenization failed for char ''
0x12 -> Tokenization failed for char ''
0x13 -> Tokenization failed for char ''
0x14 -> Tokenization failed for char ''
0x15 -> Tokenization failed for char ''
0x16 -> Tokenization failed for char ''
0x17 -> Tokenization failed for char ''
0x18 -> Tokenization failed for char '▒'
0x19 -> Tokenization failed for char ''
0x1a -> Tokenization failed for char '▒'
0x1b -> Tokenization failed for char '
x1c -> Tokenization failed for char ''
0x1d -> Tokenization failed for char ''
0x1e -> Tokenization failed for char ''
0x1f -> Tokenization failed for char ''
0x7f -> Tokenization failed for char ''
29/127 7-bit ascii characters could not be tokenized
1113111/1114111 potential unicode codepoints not tokenized

Note that recent changes have made it very slow fol llama3 (or maybe it's my gguf file?).

[mofosyne]

I noticed that other tools in example folder has README.md, I think we need one for this as well, since it's for a specific purpose as a tool.

[mofosyne]

$ ./build/bin/tokenizer-verifier ./models/ggml-vocab-aquila.gguf 
WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_calloc!
llama_model_loader: loaded meta data with 18 key-value pairs and 0 tensors from ./models/ggml-vocab-aquila.gguf (version GGUF V2)
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = D:\Diverses\models
llama_model_loader: - kv   2:                       llama.context_length u32              = 2048
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   4:                          llama.block_count u32              = 32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 11008
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 32
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  10:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  11:                      tokenizer.ggml.tokens arr[str,100008]  = ["<|endoftext|>", "!", "\"", "#", "$"...
llama_model_loader: - kv  12:                      tokenizer.ggml.scores arr[f32,100008]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  13:                  tokenizer.ggml.token_type arr[i32,100008]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  14:                      tokenizer.ggml.merges arr[str,99743]   = ["Ġ Ġ", "ä ¸", "Ġ t", "ï ¼", "...
llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 0
llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 0
llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0
llm_load_vocab: missing pre-tokenizer type, using: 'default'
llm_load_vocab:                                             
llm_load_vocab: ************************************        
llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!        
llm_load_vocab: CONSIDER REGENERATING THE MODEL             
llm_load_vocab: ************************************        
llm_load_vocab:                                             
llm_load_vocab: mismatch in special tokens definition ( 9/100008 vs 8/100008 ).
llm_load_print_meta: format           = GGUF V2
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 100008
llm_load_print_meta: n_merges         = 99743
llm_load_print_meta: n_ctx_train      = 2048
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 4096
llm_load_print_meta: n_embd_v_gqa     = 4096
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-06
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 11008
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 2048
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = all F32 (guessed)
llm_load_print_meta: model params     = 0.00 K
llm_load_print_meta: model size       = 0.00 MiB (-nan BPW) 
llm_load_print_meta: general.name     = D:\Diverses\models
llm_load_print_meta: BOS token        = 0 '<|endoftext|>'
llm_load_print_meta: EOS token        = 0 '<|endoftext|>'
llm_load_print_meta: UNK token        = 0 '<|endoftext|>'
llm_load_print_meta: LF token         = 129 'Ä'
llama_model_load: vocab only - skipping tensors
0/127 7-bit ascii characters could not be tokenized

... hangs around here?

Also doesn't seem to handle missing models well, ends up segfaulting.

$ ./build/bin/tokenizer-verifier ./models/ggml-vocab-aquil3 
llama_model_load: error loading model: llama_model_loader: failed to load model from ./models/ggml-vocab-aquil3

llama_load_model_from_file: failed to load model
Segmentation fault (core dumped)

This PR might be a bit more adjusting.

[anisse]

Thanks a lot for your review @mofosyne, I'll add a README in the next iteration.

... hangs around here?

I don't think it hanged, but it illustrates the issue I talked about earlier:

Note that recent changes have made it very slow fol llama3 (or maybe it's my gguf file?).

Something made the tokenization very slow, but I don't know what. I might bisect it to find the issue.

Also doesn't seem to handle missing models well, ends up segfaulting.

If you look at the code, it loads the models in a very simple and straightforward way, just like the tokenize example; I'll check why it segfaults, but I wouldn't be surprised if it just ends up exposing an actual issue in the llama.cpp API.