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llama : fix pre-tokenization of non-special added tokens #8228

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merged 14 commits into from
Jul 14, 2024
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llama : fix pre-tokenization of non-special added tokens #8228

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db2ffd5
llama : fix mpt and olmo pre-tokenizer
compilade Jun 30, 2024
ac0f33c
Merge branch 'master' into compilade/fix-mpt-pretok
compilade Jul 7, 2024
d5d30b2
llama : pre-tokenize non-special user-defined tokens first
compilade Jul 7, 2024
6b961e3
Merge branch 'master' into compilade/fix-mpt-pretok
compilade Jul 7, 2024
56df1fc
llama : fix detection of control-like user-defined tokens
compilade Jul 7, 2024
6e351e0
convert_hf : identify which user-defined tokens are control tokens
compilade Jul 7, 2024
f9d42c5
convert_hf : identify more added control tokens for SPM tokenziers
compilade Jul 8, 2024
31a1b0e
llama : fix Viking pre-tokenizer regex
compilade Jul 8, 2024
d6fe269
llama : fix command-r detokenization
compilade Jul 8, 2024
d4df785
convert_hf : reduce usages of the UNKNOWN token type
compilade Jul 9, 2024
98edea6
llama : add UNKNOWN tokens in the special tokens cache
compilade Jul 9, 2024
afa6119
Merge branch 'master' into compilade/fix-mpt-pretok
compilade Jul 10, 2024
1caa20f
convert_hf : reduce usages of UNKNOWN for InternLM2
compilade Jul 10, 2024
59ce853
test-tokenizer-random : reduce potential confilcts with #8379
compilade Jul 13, 2024
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112 changes: 71 additions & 41 deletions convert_hf_to_gguf.py
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Expand Up @@ -373,6 +373,29 @@ def from_model_architecture(cls, arch: str) -> type[Model]:
except KeyError:
raise NotImplementedError(f'Architecture {arch!r} not supported!') from None

def does_token_look_special(self, token: str | bytes) -> bool:
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"Method 'does_token_look_special' may be 'static'"

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I prefer not to make it a @staticmethod, to allow overriding it in the subclasses of Model if needed.

if isinstance(token, (bytes, bytearray)):
token_text = token.decode(encoding="utf-8")
elif isinstance(token, memoryview):
token_text = token.tobytes().decode(encoding="utf-8")
else:
token_text = token

# Some models mark some added tokens which ought to be control tokens as not special.
# (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
seems_special = token_text in (
"<pad>", # deepseek-coder
"<mask>", "<2mass>", "[@BOS@]", # gemma{,-2}
)

seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder

# TODO: should these be marked as UNUSED instead? (maybe not)
seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">")) # gemma{,-2}
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should things like this be defined in the conversion script under the specific model to avoid accidental false hits? if, for some weird reason, a model comes around with a non-special token that starts with <|, would be annoying to avoid that

maybe does_token_look_special should take in 2 lists: 1 list of strings of known special tokens, and a list of tuples of starts/ends with tokens

So for gemma2, we'd call it with:

special_tokens = ["<mask>", "<2mass>", "[@BOS@]"]
special_tags = [("<unused", "|>")]

self.does_token_look_special(token, special_tokens, special_tags)

and then here we'd have:

seems_special = token_text in special_tokens
for start_tag, end_tag in special_tags:
  seems_special = seems_special or (token_text.startswith(start_tag) and token_text.endswith(end_tag))

return seems_special

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oh i realize I misread the structure of the code, hmmm.. still not impossible but would have to be passed at a higher level

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@compilade compilade Jul 8, 2024 •
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if, for some weird reason, a model comes around with a non-special token that starts with <|, would be annoying to avoid that

This only affects added tokens either from added_tokens in tokenizer.json or from added_tokens_decoder in tokenizer_config.json, so it does not affect normal tokens starting with <| in any way. Not all tokens of the vocab are checked with this, only the ones part of added_tokens (which are treated specially by HF tokenizers too anyway). And added tokens starting with <| and ending with |> are arguably always control tokens; this was added pretty much because some model makers wrongly marked those as non-special (notably, <|User|>, <|Assistant|> and <|EOT|> in deepseek-coder are supposedly non-special. Same with <|START_OF_TURN_TOKEN|> and <|END_OF_TURN_TOKEN|> for command-r).

I did not yet notice any conflict in the added_tokens to justify making model-specific checks instead of always checking for all known "special-but-arent-marked-special" tokens.

Also, this is a method of Model, so it technically can be overridden by subclasses should there ever be a model with conflicting added_tokens.


return seems_special

# used for GPT-2 BPE and WordPiece vocabs
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
tokens: list[str] = []
Expand All @@ -391,16 +414,18 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]:
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
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UNUSED solves a lot of vocab and added token problems.
Also reducing the number of added tokens improves speed.

else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
token: str = reverse_vocab[i]
if token in added_vocab:
if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
toktypes.append(gguf.TokenType.CONTROL)
else:
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
toktypes.append(gguf.TokenType.NORMAL)
tokens.append(token)

return tokens, toktypes, tokpre

Expand Down Expand Up @@ -559,7 +584,7 @@ def _set_vocab_qwen(self):
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
Comment on lines 586 to +587
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Padding tokens are set as UNUSED to reflect how it was already done in _set_vocab_sentencepiece, and also to avoid wrongly (pre-)tokenizing strings which happen to correspond to a padding token. (since USER_DEFINED tokens are now always pre-tokenized specially)

elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
Expand Down Expand Up @@ -609,7 +634,7 @@ def _create_vocab_sentencepiece(self):

tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
Expand Down Expand Up @@ -644,6 +669,25 @@ def _create_vocab_sentencepiece(self):
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
for token_id, token_data in added_tokens_decoder.items():
token_id = int(token_id)
token: str = token_data["content"]
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token.encode("utf-8")
if token_data.get("special") or self.does_token_look_special(token):
toktypes[token_id] = SentencePieceTokenTypes.CONTROL
else:
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

scores[token_id] = -1000.0
tokens[token_id] = token.encode("utf-8")

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}]")
Expand Down Expand Up @@ -1267,7 +1311,7 @@ def set_vocab(self):
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
else:
# StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
# StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
self._set_vocab_qwen()

def set_gguf_parameters(self):
Expand Down Expand Up @@ -1579,7 +1623,6 @@ def set_gguf_parameters(self):
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])

self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
self.gguf_writer.add_file_type(self.ftype)

self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
Expand Down Expand Up @@ -1873,7 +1916,7 @@ def set_vocab(self):

tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

for token_id in range(tokenizer.vocab_size()):

Expand Down Expand Up @@ -1918,7 +1961,7 @@ def set_vocab(self):
for token_id, foken_data in added_tokens_decoder.items():
token_id = int(token_id)
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token
tokens[token_id] = token
scores[token_id] = -1000.0
Expand All @@ -1934,7 +1977,7 @@ def set_vocab(self):
for foken_data in added_tokens:
token_id = int(foken_data["id"])
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token
tokens[token_id] = token
scores[token_id] = -1000.0
Expand Down Expand Up @@ -2146,7 +2189,7 @@ def set_vocab(self):
toktype = SentencePieceTokenTypes.BYTE
# take care of ununsed raw token
if piece.startswith('[UNUSED'):
toktype = SentencePieceTokenTypes.UNKNOWN
toktype = SentencePieceTokenTypes.UNUSED

tokens.append(text)
scores.append(score)
Expand Down Expand Up @@ -2176,7 +2219,7 @@ def set_vocab(self):
if token == chat_eos_token:
chat_eos_token_id = token_id
token = token.encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert(tokens[token_id] == token)
tokens[token_id] = token
scores[token_id] = -1000.0
Expand All @@ -2195,7 +2238,7 @@ def set_vocab(self):
if token == chat_eos_token:
chat_eos_token_id = token_id
token = token.encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert(tokens[token_id] == token)
tokens[token_id] = token
scores[token_id] = -1000.0
Expand Down Expand Up @@ -2435,19 +2478,7 @@ class Gemma2Model(Model):
model_arch = gguf.MODEL_ARCH.GEMMA2

def set_vocab(self):
tokens, scores, toktypes = self._create_vocab_sentencepiece()
# hack: This is required so that we can properly use start/end-of-turn for chat template
for i in range(108):
# including <unusedX>, <start_of_turn>, <end_of_turn>
toktypes[i] = SentencePieceTokenTypes.CONTROL
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self._set_vocab_sentencepiece()
Comment on lines -2438 to +2481
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The "hack" from #8244 is no longer required because the control tokens are now identified with tokenizer_config.json.


self.gguf_writer.add_add_space_prefix(False)

Expand Down Expand Up @@ -2771,7 +2802,7 @@ def set_vocab(self):

tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

for token_id in range(tokenizer.vocab_size()):

Expand Down Expand Up @@ -3026,7 +3057,7 @@ def set_vocab(self):

tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
Expand Down Expand Up @@ -3244,15 +3275,14 @@ def set_vocab_chatglm3(self):
if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
score = tokenizer.tokenizer.sp_model.get_score(token_id)

if len(piece) == 0:
text = f"[PAD{token_id}]".encode("utf-8")

if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
if piece in special_tokens:
# show special tokens in prompt
toktype = SentencePieceTokenTypes.USER_DEFINED
toktype = SentencePieceTokenTypes.CONTROL
elif len(piece) == 0:
text = f"[PAD{token_id}]".encode("utf-8")
toktype = SentencePieceTokenTypes.UNUSED
else:
toktype = SentencePieceTokenTypes.UNKNOWN
toktype = SentencePieceTokenTypes.USER_DEFINED
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
Expand Down Expand Up @@ -3341,7 +3371,7 @@ def set_vocab(self):
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:
Expand Down
29 changes: 14 additions & 15 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5419,6 +5419,7 @@ static void llm_load_vocab(
} else if (
tokenizer_pre == "command-r") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
vocab.tokenizer_clean_spaces = false;
} else if (
tokenizer_pre == "qwen2") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
Expand Down Expand Up @@ -5652,7 +5653,7 @@ static void llm_load_vocab(
// build special tokens cache
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
vocab.cache_special_tokens.push_back(id);
}
}
Expand Down Expand Up @@ -15418,17 +15419,6 @@ struct llm_tokenizer_bpe {
"[0-9][0-9][0-9]",
};
break;
case LLAMA_VOCAB_PRE_TYPE_MPT:
// TODO: MPT pre-tokenization regexes are unknown
// the following are close, but not exact. run the following:
// ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
regex_exprs = {
"\\s?\\p{L}+",
"\\s?\\p{P}+",
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
break;
case LLAMA_VOCAB_PRE_TYPE_STARCODER:
case LLAMA_VOCAB_PRE_TYPE_REFACT:
case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
Expand All @@ -15438,6 +15428,7 @@ struct llm_tokenizer_bpe {
};
break;
case LLAMA_VOCAB_PRE_TYPE_GPT2:
case LLAMA_VOCAB_PRE_TYPE_MPT:
case LLAMA_VOCAB_PRE_TYPE_OLMO:
case LLAMA_VOCAB_PRE_TYPE_JAIS:
regex_exprs = {
Expand All @@ -15464,8 +15455,8 @@ struct llm_tokenizer_bpe {
break;
case LLAMA_VOCAB_PRE_TYPE_VIKING:
regex_exprs = {
"\\p{N}",
" ?[^(\\s|.,!?…。，、।۔،)]+",
"\\p{N}",
};
break;
default:
Expand Down Expand Up @@ -16185,12 +16176,20 @@ struct fragment_buffer_variant {

// #define PRETOKENIZERDEBUG

static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
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// for each special token
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
const auto & data = vocab.id_to_token[special_id];
const auto & special_token = data.text;

if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
// Ignore control and unknown tokens when parse_special == false
continue;
// User-defined tokens are still pre-tokenized before everything else
// ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
// This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
}

// for each text fragment
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
while (it != buffer.end()) {
Expand Down Expand Up @@ -16303,7 +16302,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &

if (!raw_text.empty()) {
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
tokenizer_st_partition(vocab, fragment_buffer, parse_special);
}

switch (vocab.type) {
Expand Down
4 changes: 2 additions & 2 deletions tests/test-tokenizer-0.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ int main(int argc, char **argv) {
const bool add_special = false;

for (const auto & test_kv : k_tests) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, true);
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);

printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
Expand Down Expand Up @@ -253,7 +253,7 @@ int main(int argc, char **argv) {
{
const auto t_start = ggml_time_us();

res = llama_tokenize(ctx, text, add_special, true);
res = llama_tokenize(ctx, text, add_special, false);

const auto t_end = ggml_time_us();

Expand Down
10 changes: 5 additions & 5 deletions tests/test-tokenizer-random.py
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Expand Up @@ -20,7 +20,7 @@
from typing_extensions import Buffer

import cffi
from transformers import AutoTokenizer
from transformers import AutoTokenizer, PreTrainedTokenizer


logger = logging.getLogger("test-tokenizer-random")
Expand Down Expand Up @@ -129,7 +129,7 @@ def decode(self, ids: list[int]) -> str:
class TokenizerGroundtruth (Tokenizer):

def __init__(self, dir_tokenizer: str):
self.model = AutoTokenizer.from_pretrained(dir_tokenizer)
self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
# guess BOS and EOS
ids = self.encode("a")
assert 1 <= len(ids) <= 3
Expand All @@ -143,7 +143,7 @@ def __init__(self, dir_tokenizer: str):
self.vocab = list(sorted(self.vocab))
# tokens and lists
self.special_tokens = list(self.model.all_special_tokens)
self.added_tokens = list(self.model.added_tokens_encoder)
self.added_tokens = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
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"PEP 8: E221 multiple spaces before operator" (just FYI - looks good to me)
"PEP 8: E501 line too long (122 > 120 characters)"

self.bos_token = self.model.bos_token
self.eos_token = self.model.eos_token

Expand Down Expand Up @@ -458,8 +458,8 @@ def check_detokenizer(text: str, text1: str, text2: str) -> bool:
i = find_first_mismatch(ids1, ids2)
ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
logger.error(" Expected: " + str(ids1))
logger.error(" Result: " + str(ids2))
logger.error(" Expected: " + str(ids1) + f" {[tokenizer1.decode([id]) for id in ids1]}")
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logger.error(" Result: " + str(ids2) + f" {[tokenizer2.decode([id]) for id in ids2]}")
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"Shadows built-in name 'id'"

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@compilade compilade Jul 13, 2024 •
edited
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I've reverted these two lines in 59ce853, even though they were useful for debugging, because this is a test script anyway, and because these lines also will unnecessarily conflict with #8379

encode_errors += 1
logger.error(f" {encode_errors=}")
if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2):
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