ci : add flake8 to github actions (python linting) (#4129)
Disabled rules:
* E203 Whitespace before ':' - disabled because we often use 'C' Style where values are aligned
* E211 Whitespace before '(' (E211) - disabled because we often use 'C' Style where values are aligned
* E221 Multiple spaces before operator - disabled because we often use 'C' Style where values are aligned
* E225 Missing whitespace around operator - disabled because it's broken so often it seems like a standard
* E231 Missing whitespace after ',', ';', or ':' - disabled because we often use 'C' Style where values are aligned
* E241 Multiple spaces after ',' - disabled because we often use 'C' Style where values are aligned
* E251 Unexpected spaces around keyword / parameter equals - disabled because it's broken so often it seems like a standard
* E261 At least two spaces before inline comment - disabled because it's broken so often it seems like a standard
* E266 Too many leading '#' for block comment - sometimes used as "section" separator
* E501 Line too long - disabled because it's broken so often it seems like a standard
* E701 Multiple statements on one line (colon) - broken only in convert.py when defining abstract methods (we can use# noqa instead)
* E704 Multiple statements on one line - broken only in convert.py when defining abstract methods (we can use# noqa instead)
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8 changed files with 153 additions and 97 deletions
60
convert.py
Executable file → Normal file
60
convert.py
Executable file → Normal file
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@ -46,6 +46,7 @@ DEFAULT_CONCURRENCY = 8
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# data types
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#
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@dataclass(frozen=True)
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class DataType:
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name: str
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@ -55,15 +56,18 @@ class DataType:
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def elements_to_bytes(self, n_elements: int) -> int:
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return n_elements * self.dtype.itemsize
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@dataclass(frozen=True)
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class UnquantizedDataType(DataType):
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pass
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DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
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DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
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DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
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DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
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@dataclass(frozen=True)
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class QuantizedDataType(DataType):
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block_size: int
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@ -77,6 +81,7 @@ class QuantizedDataType(DataType):
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assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
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return self.quantized_dtype.itemsize * (n_elements // self.block_size)
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@dataclass(frozen=True)
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class Q8_0QuantizedDataType(QuantizedDataType):
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# Mini Q8_0 quantization in Python!
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@ -86,6 +91,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
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n_blocks = arr.size // self.block_size
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blocks = arr.reshape((n_blocks, self.block_size))
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# Much faster implementation of block quantization contributed by @Cebtenzzre
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def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
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d = abs(blocks).max(axis = 1) / np.float32(127)
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with np.errstate(divide = 'ignore'):
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@ -94,10 +100,11 @@ class Q8_0QuantizedDataType(QuantizedDataType):
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yield from zip(d, qs)
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return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
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DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
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dtype = np.dtype(np.float32), valid_conversions = [],
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ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
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quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
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dtype = np.dtype(np.float32), valid_conversions = [],
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ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
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quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
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# Quantized types skipped here because they may also map to np.float32
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NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
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@ -116,6 +123,8 @@ SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
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# TODO: match this with `llama_ftype`
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# TODO: rename to LLAMAFileType
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# TODO: move to `gguf.py`
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class GGMLFileType(enum.IntEnum):
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AllF32 = 0
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MostlyF16 = 1 # except 1d tensors
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@ -128,6 +137,7 @@ class GGMLFileType(enum.IntEnum):
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# 1D tensors are always F32.
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return dt if len(tensor.shape) > 1 else DT_F32
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GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
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GGMLFileType.AllF32 : DT_F32,
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GGMLFileType.MostlyF16 : DT_F16,
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@ -138,6 +148,7 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
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# hparams loading
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#
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@dataclass
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class Params:
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n_vocab: int
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@ -167,11 +178,11 @@ class Params:
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# try transformer naming first
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if "model.layers.0.self_attn.q_proj.weight" in model:
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n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
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n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
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elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming
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n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
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n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
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else:
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n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
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n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
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if n_layer < 1:
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raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
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@ -308,7 +319,7 @@ class BpeVocab:
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(item['content'], item['id'])
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for item in tokenizer_json.get('added_tokens', [])
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# Added tokens here can be duplicates of the main vocabulary.
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if item['content'] not in self.bpe_tokenizer )
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if item['content'] not in self.bpe_tokenizer)
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vocab_size: int = len(self.bpe_tokenizer)
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expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
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@ -326,7 +337,6 @@ class BpeVocab:
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def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
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tokenizer = self.bpe_tokenizer
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from transformers.models.gpt2 import tokenization_gpt2
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
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for i, _ in enumerate(tokenizer):
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@ -406,6 +416,7 @@ class SentencePieceVocab:
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def __repr__(self) -> str:
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return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
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#
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@ -413,13 +424,14 @@ Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
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# TODO: reuse (probably move to gguf.py?)
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#
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def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
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#print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
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# print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
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if n_head_kv is not None and n_head != n_head_kv:
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n_head = n_head_kv
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape))
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.swapaxes(1, 2)
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.reshape(weights.shape))
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class Tensor(metaclass=ABCMeta):
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@ -500,7 +512,7 @@ class LazyTensor:
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ret = self._load()
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# Should be okay if it maps to the same numpy type?
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assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
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(self.data_type, ret.data_type, self.description)
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(self.data_type, ret.data_type, self.description)
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return ret
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def astype(self, data_type: DataType) -> LazyTensor:
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@ -588,6 +600,7 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe
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return lazy_tensor.load().permute(n_head, n_head_kv)
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return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
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def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
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def load() -> Tensor:
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return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
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@ -595,6 +608,7 @@ def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_
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s[0] = s[0] // 3
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return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
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def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
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def load() -> Tensor:
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return lazy_tensor.load().part(n_part)
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@ -744,6 +758,7 @@ def lazy_load_file(path: Path) -> ModelPlus:
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In = TypeVar('In')
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Out = TypeVar('Out')
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def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: int | None = None, use_processpool_executor: bool = False) -> Iterable[Out]:
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'''Parallel map, but with backpressure. If the caller doesn't call `next`
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fast enough, this will stop calling `func` at some point rather than
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@ -778,6 +793,7 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
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break
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yield result
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def check_vocab_size(params: Params, vocab: Vocab) -> None:
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if params.n_vocab != vocab.vocab_size:
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assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
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@ -796,7 +812,7 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
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class OutputFile:
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def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
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def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
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self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
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def add_meta_arch(self, params: Params) -> None:
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@ -876,7 +892,7 @@ class OutputFile:
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self.gguf.close()
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@staticmethod
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def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
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def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
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check_vocab_size(params, vocab)
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of = OutputFile(fname_out, endianess=endianess)
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@ -938,8 +954,9 @@ class OutputFile:
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of.close()
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def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
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wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type
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wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type
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if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
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return GGMLFileType.AllF32
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@ -952,10 +969,12 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
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raise Exception(f"Unexpected combination of types: {name_to_type}")
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def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
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return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
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for (name, tensor) in model.items()}
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def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
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tmap = gguf.TensorNameMap(ARCH, params.n_layer)
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should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
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@ -968,7 +987,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
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print(f"Permuting layer {i}")
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tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head)
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tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv)
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#tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
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# tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
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elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
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print(f"Unpacking and permuting layer {i}")
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tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head)
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@ -993,6 +1012,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
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return out
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def nth_multifile_path(path: Path, n: int) -> Path | None:
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'''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
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the nth path in the model.
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@ -1174,8 +1194,8 @@ def main(args_in: list[str] | None = None) -> None:
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# FIXME: Try to respect vocab_dir somehow?
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vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
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special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
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load_merges = args.vocabtype == 'bpe',
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n_vocab = vocab.vocab_size)
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load_merges = args.vocabtype == 'bpe',
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n_vocab = vocab.vocab_size)
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outfile = args.outfile
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OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
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print(f"Wrote {outfile}")
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@ -1188,8 +1208,8 @@ def main(args_in: list[str] | None = None) -> None:
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vocab = load_vocab(vocab_dir, args.vocabtype)
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# FIXME: Try to respect vocab_dir somehow?
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special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
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load_merges = args.vocabtype == 'bpe',
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n_vocab = vocab.vocab_size)
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load_merges = args.vocabtype == 'bpe',
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n_vocab = vocab.vocab_size)
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model = model_plus.model
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model = convert_model_names(model, params)
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