mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
llama : fix pre-tokenization of non-special added tokens (#8228)
* llama : fix mpt and olmo pre-tokenizer * llama : pre-tokenize non-special user-defined tokens first * llama : fix detection of control-like user-defined tokens * convert_hf : identify which user-defined tokens are control tokens Only used in _set_vocab_gpt2() for now. * convert_hf : identify more added control tokens for SPM tokenziers This makes Gemma and Gemma-2 tokenize pretty much EVERYTHING correctly, including HTML tags and consecutive spaces, but it unfortunately requires model re-conversion. There seems to be a weird behavior of the HF tokenizer for Gemma, which prefers to use the 16-space token over more lengthy space tokens, while using the SentencePiece tokenizer does not do this. (the implementation in llama.cpp has the same behavior as SentencePiece) * llama : fix wrong pre-tokenization of byte tokens * llama : fix Viking pre-tokenizer regex The order was previously wrong, which caused errors in some tests. * llama : fix command-r detokenization * convert_hf : reduce usages of the UNKNOWN token type * llama : add UNKNOWN tokens in the special tokens cache * convert_hf : reduce usages of UNKNOWN for InternLM2 This makes the changes from #8321 more consistent with the other changes made here. * test-tokenizer-random : reduce potential confilcts with #8379 * test-tokenizer-random : add a failing edge case for falcon
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@ -373,6 +373,29 @@ class Model:
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except KeyError:
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raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
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def does_token_look_special(self, token: str | bytes) -> bool:
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if isinstance(token, (bytes, bytearray)):
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token_text = token.decode(encoding="utf-8")
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elif isinstance(token, memoryview):
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token_text = token.tobytes().decode(encoding="utf-8")
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else:
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token_text = token
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# Some models mark some added tokens which ought to be control tokens as not special.
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# (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
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seems_special = token_text in (
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"<pad>", # deepseek-coder
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"<mask>", "<2mass>", "[@BOS@]", # gemma{,-2}
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)
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seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
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seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder
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# TODO: should these be marked as UNUSED instead? (maybe not)
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seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">")) # gemma{,-2}
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return seems_special
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# used for GPT-2 BPE and WordPiece vocabs
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def get_vocab_base(self) -> tuple[list[str], list[int], str]:
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tokens: list[str] = []
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@ -391,16 +414,18 @@ class Model:
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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toktypes.append(gguf.TokenType.UNUSED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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token: str = reverse_vocab[i]
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if token in added_vocab:
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if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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toktypes.append(gguf.TokenType.NORMAL)
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tokens.append(token)
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return tokens, toktypes, tokpre
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@ -559,7 +584,7 @@ class Model:
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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toktypes.append(gguf.TokenType.UNUSED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.CONTROL)
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@ -609,7 +634,7 @@ class Model:
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tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
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scores: list[float] = [-10000.0] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
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for token_id in range(tokenizer.vocab_size()):
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piece = tokenizer.IdToPiece(token_id)
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@ -644,6 +669,25 @@ class Model:
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scores[token_id] = -1000.0
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toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
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tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
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if tokenizer_config_file.is_file():
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with open(tokenizer_config_file, "r", encoding="utf-8") as f:
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tokenizer_config_json = json.load(f)
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added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
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for token_id, token_data in added_tokens_decoder.items():
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token_id = int(token_id)
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token: str = token_data["content"]
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if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
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assert tokens[token_id] == token.encode("utf-8")
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if token_data.get("special") or self.does_token_look_special(token):
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toktypes[token_id] = SentencePieceTokenTypes.CONTROL
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else:
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token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
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toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
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scores[token_id] = -1000.0
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tokens[token_id] = token.encode("utf-8")
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if vocab_size > len(tokens):
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pad_count = vocab_size - len(tokens)
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logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
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@ -1266,7 +1310,7 @@ class StableLMModel(Model):
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if (self.dir_model / "tokenizer.json").is_file():
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self._set_vocab_gpt2()
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else:
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# StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
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# StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
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self._set_vocab_qwen()
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def set_gguf_parameters(self):
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@ -1578,7 +1622,6 @@ class DbrxModel(Model):
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self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
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self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
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self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
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@ -1872,7 +1915,7 @@ class Phi3MiniModel(Model):
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tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
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scores: list[float] = [-10000.0] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
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for token_id in range(tokenizer.vocab_size()):
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@ -1917,7 +1960,7 @@ class Phi3MiniModel(Model):
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for token_id, foken_data in added_tokens_decoder.items():
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token_id = int(token_id)
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token = foken_data["content"].encode("utf-8")
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if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
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if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
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assert tokens[token_id] == token
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tokens[token_id] = token
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scores[token_id] = -1000.0
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@ -1933,7 +1976,7 @@ class Phi3MiniModel(Model):
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for foken_data in added_tokens:
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token_id = int(foken_data["id"])
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token = foken_data["content"].encode("utf-8")
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if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
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if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
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assert tokens[token_id] == token
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tokens[token_id] = token
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scores[token_id] = -1000.0
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@ -2145,7 +2188,7 @@ class InternLM2Model(Model):
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toktype = SentencePieceTokenTypes.BYTE
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# take care of ununsed raw token
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if piece.startswith('[UNUSED'):
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toktype = SentencePieceTokenTypes.UNKNOWN
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toktype = SentencePieceTokenTypes.UNUSED
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tokens.append(text)
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scores.append(score)
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@ -2175,7 +2218,7 @@ class InternLM2Model(Model):
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if token == chat_eos_token:
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chat_eos_token_id = token_id
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token = token.encode("utf-8")
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if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
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if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
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assert(tokens[token_id] == token)
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tokens[token_id] = token
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scores[token_id] = -1000.0
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@ -2194,7 +2237,7 @@ class InternLM2Model(Model):
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if token == chat_eos_token:
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chat_eos_token_id = token_id
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token = token.encode("utf-8")
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if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
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if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
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assert(tokens[token_id] == token)
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tokens[token_id] = token
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scores[token_id] = -1000.0
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@ -2434,19 +2477,7 @@ class Gemma2Model(Model):
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model_arch = gguf.MODEL_ARCH.GEMMA2
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def set_vocab(self):
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tokens, scores, toktypes = self._create_vocab_sentencepiece()
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# hack: This is required so that we can properly use start/end-of-turn for chat template
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for i in range(108):
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# including <unusedX>, <start_of_turn>, <end_of_turn>
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toktypes[i] = SentencePieceTokenTypes.CONTROL
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self.gguf_writer.add_tokenizer_model("llama")
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self.gguf_writer.add_tokenizer_pre("default")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_scores(scores)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
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special_vocab.add_to_gguf(self.gguf_writer)
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self._set_vocab_sentencepiece()
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self.gguf_writer.add_add_space_prefix(False)
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@ -2770,7 +2801,7 @@ class ArcticModel(Model):
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tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
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scores: list[float] = [-10000.0] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
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for token_id in range(tokenizer.vocab_size()):
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@ -3025,7 +3056,7 @@ class T5Model(Model):
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tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
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scores: list[float] = [-10000.0] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
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for token_id in range(tokenizer.vocab_size()):
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piece = tokenizer.IdToPiece(token_id)
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@ -3243,15 +3274,14 @@ class ChatGLMModel(Model):
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if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
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score = tokenizer.tokenizer.sp_model.get_score(token_id)
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if len(piece) == 0:
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text = f"[PAD{token_id}]".encode("utf-8")
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if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
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if piece in special_tokens:
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# show special tokens in prompt
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toktype = SentencePieceTokenTypes.USER_DEFINED
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toktype = SentencePieceTokenTypes.CONTROL
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elif len(piece) == 0:
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text = f"[PAD{token_id}]".encode("utf-8")
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toktype = SentencePieceTokenTypes.UNUSED
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else:
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toktype = SentencePieceTokenTypes.UNKNOWN
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toktype = SentencePieceTokenTypes.USER_DEFINED
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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@ -3340,7 +3370,7 @@ class ChatGLMModel(Model):
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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toktypes.append(gguf.TokenType.UNUSED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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@ -5419,6 +5419,7 @@ static void llm_load_vocab(
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} else if (
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tokenizer_pre == "command-r") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
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vocab.tokenizer_clean_spaces = false;
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} else if (
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tokenizer_pre == "qwen2") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
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@ -5652,7 +5653,7 @@ static void llm_load_vocab(
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// build special tokens cache
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{
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for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
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if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
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if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
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vocab.cache_special_tokens.push_back(id);
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}
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}
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@ -15411,17 +15412,6 @@ struct llm_tokenizer_bpe {
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"[0-9][0-9][0-9]",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_MPT:
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// TODO: MPT pre-tokenization regexes are unknown
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// the following are close, but not exact. run the following:
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// ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
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GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
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regex_exprs = {
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"\\s?\\p{L}+",
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"\\s?\\p{P}+",
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"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_STARCODER:
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case LLAMA_VOCAB_PRE_TYPE_REFACT:
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case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
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@ -15431,6 +15421,7 @@ struct llm_tokenizer_bpe {
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_GPT2:
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case LLAMA_VOCAB_PRE_TYPE_MPT:
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case LLAMA_VOCAB_PRE_TYPE_OLMO:
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case LLAMA_VOCAB_PRE_TYPE_JAIS:
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regex_exprs = {
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@ -15457,8 +15448,8 @@ struct llm_tokenizer_bpe {
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break;
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case LLAMA_VOCAB_PRE_TYPE_VIKING:
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regex_exprs = {
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"\\p{N}",
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" ?[^(\\s|.,!?…。，、।۔،)]+",
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"\\p{N}",
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};
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break;
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default:
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@ -16178,12 +16169,20 @@ struct fragment_buffer_variant {
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// #define PRETOKENIZERDEBUG
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static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
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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
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for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
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const auto & data = vocab.id_to_token[special_id];
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const auto & special_token = data.text;
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if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
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// Ignore control and unknown tokens when parse_special == false
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continue;
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// User-defined tokens are still pre-tokenized before everything else
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// ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
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// This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
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}
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// for each text fragment
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std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
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while (it != buffer.end()) {
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@ -16296,7 +16295,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
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if (!raw_text.empty()) {
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fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
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if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
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tokenizer_st_partition(vocab, fragment_buffer, parse_special);
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}
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switch (vocab.type) {
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@ -195,7 +195,7 @@ int main(int argc, char **argv) {
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const bool add_special = false;
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for (const auto & test_kv : k_tests) {
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const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, true);
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const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
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printf("\n");
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printf("src: '%s'\n", test_kv.first.c_str());
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@ -253,7 +253,7 @@ int main(int argc, char **argv) {
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{
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const auto t_start = ggml_time_us();
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res = llama_tokenize(ctx, text, add_special, true);
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res = llama_tokenize(ctx, text, add_special, false);
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const auto t_end = ggml_time_us();
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@ -20,7 +20,7 @@ from typing import Any, Iterator, cast
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from typing_extensions import Buffer
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import cffi
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from transformers import AutoTokenizer
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from transformers import AutoTokenizer, PreTrainedTokenizer
|
||||
|
||||
|
||||
logger = logging.getLogger("test-tokenizer-random")
|
||||
@ -129,7 +129,7 @@ class Tokenizer:
|
||||
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
|
||||
@ -143,7 +143,7 @@ class TokenizerGroundtruth (Tokenizer):
|
||||
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)
|
||||
self.bos_token = self.model.bos_token
|
||||
self.eos_token = self.model.eos_token
|
||||
|
||||
@ -232,6 +232,7 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
|
||||
'a\na', # bert fail
|
||||
'"`', # falcon
|
||||
' \u2e4e', # falcon
|
||||
'\n\x0b ', # falcon
|
||||
'a\xa0\xa0\x00b', # jina-v2-es
|
||||
'one <mask>', # jina-v2-es <mask> lstrip=true
|
||||
'a </s> b', # rstrip phi-3
|
||||
|
||||
Loading…
Reference in New Issue
Block a user