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Work on the BPE tokenizer (ggerganov#3252)
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* Work on the BPE tokenizer

Tokenizer tests work for Falcon-7B

* Try to fix build problem

* Fix debug assertion failure

* Fix MSVC Unicode BOM problem

* Cleanup and an improvement

* Fix compiler warning

* Cleanup

* Test doesn't work over the full range of Unicodes

* Update .gitignore and Makefile

* Another Makefile rule

* Testing Aquila

* Moving byte decoding back to `token_to_piece` ...

... because everyone is using it.

* Guarding some unusable code pathes

* Streamlining code and adding some more assertions

Important change: I'm classifying added tokens as control tokens now for BPE.

* Adding a comment

* Adding another assertion

* Fixed vocabulary guarding assertions

* Fix PR for recent change

* Fix PR for recent change

* Fix for compiler warning

* Fix PR for recent change

* Fix PR for recent change

* Fix PR for recent change

* Fix for compiler warning

* Fixes for more compiler warnings

* Remove unused code

* Fix initialization of static maps

* Add scores and token types back, adapt gptneox

* Update llama.cpp

Co-authored-by: Georgi Gerganov <[email protected]>

* Update unicode.h

Co-authored-by: Georgi Gerganov <[email protected]>

* Update unicode.h

Co-authored-by: Georgi Gerganov <[email protected]>

* Ported Starcoder and added some assertions

* Fix coding style

* Apply @jploski 's fix for missing tokens

---------

Co-authored-by: Georgi Gerganov <[email protected]>
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@goerch @ggerganov
goerch and ggerganov authored Oct 3, 2023
1 parent 1c84003 commit ff5a3f0
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Showing 15 changed files with 852 additions and 227 deletions.
3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -91,4 +91,5 @@ tests/test-quantize-perf
tests/test-sampling
tests/test-tokenizer-0-llama
tests/test-tokenizer-0-falcon
tests/test-tokenizer-1
tests/test-tokenizer-1-llama
tests/test-tokenizer-1-bpe
9 changes: 7 additions & 2 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml simple batched save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o

# Binaries only useful for tests
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe

# Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
Expand Down Expand Up @@ -62,9 +62,11 @@ test: $(TEST_TARGETS)
if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \
./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
continue; \
./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \
continue; \
elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
continue; \
else \
echo "Running test $$test_target..."; \
./$$test_target; \
Expand Down Expand Up @@ -670,6 +672,9 @@ tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h gg
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

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1 change: 1 addition & 0 deletions common/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -923,6 +923,7 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
result += piece;
}

// NOTE: the original tokenizer decodes bytes after collecting the pieces.
return result;
}

Expand Down
47 changes: 7 additions & 40 deletions convert-falcon-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,28 +20,6 @@
import gguf


def bytes_to_unicode():
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
return dict(zip(bs, (chr(n) for n in cs)))


def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
Expand Down Expand Up @@ -133,6 +111,8 @@ def parse_args() -> argparse.Namespace:
print("gguf: get tokenizer metadata")

tokens: list[bytearray] = []
scores: list[float] = []
toktypes: list[int] = []

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
Expand All @@ -148,28 +128,15 @@ def parse_args() -> argparse.Namespace:
assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}

for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)

tokens.append(text)
tokens.append(reverse_vocab[i])
scores.append(0.0) # dummy
toktypes.append(gguf.TokenType.NORMAL)

gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)
Expand Down
48 changes: 7 additions & 41 deletions convert-gptneox-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,29 +19,6 @@
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf

# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py


def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
return dict(zip(bs, (chr(n) for n in cs)))


def count_model_parts(dir_model: Path) -> int:
num_parts = 0
Expand Down Expand Up @@ -130,6 +107,8 @@ def parse_args() -> argparse.Namespace:
print("gguf: get tokenizer metadata")

tokens: list[bytearray] = []
scores: list[float] = []
toktypes: list[int] = []

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
Expand All @@ -145,28 +124,15 @@ def parse_args() -> argparse.Namespace:
assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}

for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)

tokens.append(text)
tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
scores.append(0.0) # dummy
toktypes.append(gguf.TokenType.NORMAL)

gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)
Expand Down
47 changes: 7 additions & 40 deletions convert-starcoder-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,28 +20,6 @@
import gguf


def bytes_to_unicode():
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
return dict(zip(bs, (chr(n) for n in cs)))


def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
Expand Down Expand Up @@ -117,6 +95,8 @@ def parse_args() -> argparse.Namespace:
print("gguf: get tokenizer metadata")

tokens: list[bytearray] = []
scores: list[float] = []
toktypes: list[int] = []

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
Expand All @@ -132,28 +112,15 @@ def parse_args() -> argparse.Namespace:
assert max(tokenizer.vocab.values()) < vocab_size

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}

for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)

tokens.append(text)
tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
scores.append(0.0) # dummy
toktypes.append(gguf.TokenType.NORMAL)

gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)
Expand Down
24 changes: 5 additions & 19 deletions convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -338,29 +338,15 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> No
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.bpe_tokenizer
from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
byte_encoder = tokenization_gpt2.bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
score = 0.0
for i, item in enumerate(tokenizer):
text: bytes = item.encode("utf-8")
# FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior?
if i <= 258 and text.startswith(b'<') and text.endswith(b'>'):
if i == 0 and text == b'<unk>':
toktype = gguf.TokenType.UNKNOWN
elif i == 1 or i == 2:
toktype = gguf.TokenType.CONTROL
elif i >= 3 and text.startswith(b'<0x'):
toktype = gguf.TokenType.BYTE
else:
toktype = gguf.TokenType.NORMAL
else:
toktype = gguf.TokenType.NORMAL
yield text, score, toktype
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}

for i, _ in enumerate(tokenizer):
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL

def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL

def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.bpe_tokens()
Expand Down
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