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Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425)

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* Update brute force test: add_special
* Update brute force test: default values for add_bos_token and add_eos_token
* Enable rtrim when pre-inserting BOS

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Revert "server : fix test regexes"
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jaime-m-p 2024-05-21 14:39:48 +02:00 committed by GitHub
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commit d7e852c1bc
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5 changed files with 28 additions and 23 deletions
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4
convert-hf-to-gguf.py
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@ -1749,7 +1749,7 @@ class Phi3MiniModel(Model):
token_id = int(token_id) token_id = int(token_id)
token = foken_data["content"].encode("utf-8") token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
assert(tokens[token_id] == token) assert tokens[token_id] == token
tokens[token_id] = token tokens[token_id] = token
scores[token_id] = -1000.0 scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
@ -1765,7 +1765,7 @@ class Phi3MiniModel(Model):
token_id = int(foken_data["id"]) token_id = int(foken_data["id"])
token = foken_data["content"].encode("utf-8") token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
assert(tokens[token_id] == token) assert tokens[token_id] == token
tokens[token_id] = token tokens[token_id] = token
scores[token_id] = -1000.0 scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

10
examples/server/tests/features/server.feature
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@ -37,8 +37,8 @@ Feature: llama.cpp server
Examples: Prompts Examples: Prompts
| prompt | n_predict | re_content | n_prompt | n_predicted | truncated | | prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
| I believe the meaning of life is | 8 | (read\|going\|pretty)+ | 18 | 8 | not | | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 45 | 64 | not | | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not |
Scenario: Completion prompt truncated Scenario: Completion prompt truncated
Given a prompt: Given a prompt:
@ -67,8 +67,8 @@ Feature: llama.cpp server
Examples: Prompts Examples: Prompts
| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 76 | 8 | disabled | not | | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|fireplace)+ | -1 | 64 | enabled | | | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | |
Scenario Outline: OAI Compatibility w/ response format Scenario Outline: OAI Compatibility w/ response format
@ -84,7 +84,7 @@ Feature: llama.cpp server
| response_format | n_predicted | re_content | | response_format | n_predicted | re_content |
| {"type": "json_object", "schema": {"const": "42"}} | 5 | "42" | | {"type": "json_object", "schema": {"const": "42"}} | 5 | "42" |
| {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] |
| {"type": "json_object"} | 10 | \{ " Saragine. | | {"type": "json_object"} | 10 | \{ " Jacky. |
Scenario: Tokenize / Detokenize Scenario: Tokenize / Detokenize

4
examples/server/tests/features/slotsave.feature
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@ -26,7 +26,7 @@ Feature: llama.cpp server slot management
# Since we have cache, this should only process the last tokens # Since we have cache, this should only process the last tokens
Given a user prompt "What is the capital of Germany?" Given a user prompt "What is the capital of Germany?"
And a completion request with no api error And a completion request with no api error
Then 24 tokens are predicted matching (Thank|special|Lily) Then 24 tokens are predicted matching (Thank|special)
And 7 prompt tokens are processed And 7 prompt tokens are processed
# Loading the original cache into slot 0, # Loading the original cache into slot 0,
# we should only be processing 1 prompt token and get the same output # we should only be processing 1 prompt token and get the same output
@ -41,7 +41,7 @@ Feature: llama.cpp server slot management
Given a user prompt "What is the capital of Germany?" Given a user prompt "What is the capital of Germany?"
And using slot id 1 And using slot id 1
And a completion request with no api error And a completion request with no api error
Then 24 tokens are predicted matching (Thank|special|Lily) Then 24 tokens are predicted matching (Thank|special)
And 1 prompt tokens are processed And 1 prompt tokens are processed
Scenario: Erase Slot Scenario: Erase Slot

11
llama.cpp
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@ -12498,15 +12498,16 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
// tokenizer.encode('', add_special_tokens=True) returns [1] // tokenizer.encode('', add_special_tokens=True) returns [1]
// tokenizer.encode('', add_special_tokens=False) returns [] // tokenizer.encode('', add_special_tokens=False) returns []
if (add_special && vocab.special_add_bos != 0) {
GGML_ASSERT(vocab.special_bos_id != -1);
output.push_back(vocab.special_bos_id);
}
static const bool rtrim = true; //TODO: as param static const bool rtrim = true; //TODO: as param
bool is_prev_special = false; bool is_prev_special = false;
bool special_token_rtrim = false; bool special_token_rtrim = false;
if (add_special && vocab.special_add_bos != 0) {
GGML_ASSERT(vocab.special_bos_id != -1);
output.push_back(vocab.special_bos_id);
is_prev_special = true;
}
for (const auto & fragment : fragment_buffer) { for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
// without adding this leading whitespace, we do not get the same results as the original tokenizer // without adding this leading whitespace, we do not get the same results as the original tokenizer

22
tests/test-tokenizer-random.py
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@ -154,19 +154,22 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
'\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
'Cửa Việt', # llama-3, ignore_merges = true 'Cửa Việt', # llama-3, ignore_merges = true
'<s>a', # Phi-3 fail '<s>a', # Phi-3 fail
'<unk><|endoftext|><s>' # Phi-3 fail '<unk><|endoftext|><s>', # Phi-3 fail
'a\na', # TODO: Bert fail 'a\na', # TODO: Bert fail
] ]
def generator_random_special_tokens(special_tokens:list[str], iterations=100) -> Iterator[str]: def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
special_tokens = set(special_tokens) special_tokens = set(tokenizer.all_special_tokens)
special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]) special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"])
special_tokens = list(sorted(special_tokens)) special_tokens = list(sorted(special_tokens))
rand = random.Random() rand = random.Random()
for m in range(iterations): for m in range(iterations):
rand.seed(m) rand.seed(m)
words = rand.choices(special_tokens, k=500) words = rand.choices(special_tokens, k=500)
if tokenizer.add_bos_token: # skip spam warning of double BOS
while words and words[0] == tokenizer.bos_token:
words.pop(0)
yield "".join(words) yield "".join(words)
@ -290,18 +293,19 @@ def main(argv: list[str] = None):
model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
def func_tokenize2(text: str): tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True)
return tokenizer.encode(text, add_special_tokens=False) tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False)
parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens)
def func_tokenize1(text: str): def func_tokenize1(text: str):
return model.tokenize(text, add_special=False, parse_special=parse_special) return model.tokenize(text, add_special=True, parse_special=True)
def func_tokenize2(text: str):
return tokenizer.encode(text, add_special_tokens=True)
vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer.all_special_tokens, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))