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)
2024-12-24 18:34:36 +00:00 · 2023-11-20 11:35:47 +01:00 · 2023-11-20 11:35:47 +01:00 · f23c0359a3
commit f23c0359a3
parent 40a34fe8d0
8 changed files with 153 additions and 97 deletions
--- a/.github/workflows/python-lint.yml
+++ b/.github/workflows/python-lint.yml
@ -0,0 +1,20 @@
+name: flake8 Lint
+
+on: [push, pull_request]
+
+jobs:
+  flake8-lint:
+    runs-on: ubuntu-latest
+    name: Lint
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v3
+      - name: Set up Python environment
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: flake8 Lint
+        uses: py-actions/flake8@v2
+        with:
+            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704"
+            exclude: "examples/*,examples/*/**,*/**/__init__.py"
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@ -827,13 +827,14 @@ class StableLMModel(Model):
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"]*(hparams["hidden_size"] // hparams["num_attention_heads"])))
+        self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"])))
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
        self.gguf_writer.add_layer_norm_eps(1e-5)

 ###### CONVERSION LOGIC ######

+
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
    parser.add_argument(
--- a/convert-llama-ggml-to-gguf.py
+++ b/convert-llama-ggml-to-gguf.py
@ -14,11 +14,13 @@ if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf

+
 class GGMLFormat(IntEnum):
    GGML = 0
    GGMF = 1
    GGJT = 2

+
 class GGMLFType(IntEnum):
    ALL_F32              = 0
    MOSTLY_F16           = 1
@ -38,6 +40,7 @@ class GGMLFType(IntEnum):
    MOSTLY_Q5_K_M        = 17
    MOSTLY_Q6_K          = 18

+
 class Hyperparameters:
    def __init__(self):
        self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
@ -69,6 +72,7 @@ class Hyperparameters:
    def __str__(self):
        return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'

+
 class Vocab:
    def __init__(self, load_scores = True):
        self.items = []
@ -90,6 +94,7 @@ class Vocab:
            self.items.append((item_text, item_score))
        return offset - orig_offset

+
 class Tensor:
    def __init__(self, use_padding = True):
        self.name = None
@ -123,6 +128,7 @@ class Tensor:
        # print(n_dims, name_len, dtype, self.dims, self.name, pad)
        return offset - orig_offset

+
 class GGMLModel:
    def __init__(self):
        self.hyperparameters = None
@ -159,8 +165,8 @@ class GGMLModel:
            if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
                err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.'
        elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2):
-            if ftype in ( GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
-                          GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
+            if ftype in (GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
+                         GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
                err = 'Q4 and Q8 quantizations changed in GGJTv3.'
        if len(err) > 0:
            raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.')
@ -187,6 +193,7 @@ class GGMLModel:
        hp.set_n_ff(self)
        return offset

+
 class GGMLToGGUF:
    def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
        hp = ggml_model.hyperparameters
@ -217,7 +224,7 @@ class GGMLToGGUF:
        gguf_writer = gguf.GGUFWriter(
            self.cfg.output,
            gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
-            use_temp_file = False )
+            use_temp_file = False)
        self.add_params(gguf_writer)
        self.add_vocab(gguf_writer)
        if self.special_vocab is not None:
@ -341,7 +348,8 @@ class GGMLToGGUF:
                mapped_name,
                data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
                raw_shape = tempdims,
-                raw_dtype = tensor.dtype )
+                raw_dtype = tensor.dtype)
+

 def handle_metadata(cfg, hp):
    import convert
@ -365,38 +373,40 @@ def handle_metadata(cfg, hp):
        raise ValueError('Unable to load metadata')
    vocab = convert.load_vocab(
        cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
-        cfg.vocabtype )
+        cfg.vocabtype)
    # FIXME: Respect cfg.vocab_dir?
    svocab = gguf.SpecialVocab(cfg.model_metadata_dir,
-        load_merges = cfg.vocabtype == 'bpe',
-        n_vocab = vocab.vocab_size)
+                               load_merges = cfg.vocabtype == 'bpe',
+                               n_vocab = vocab.vocab_size)
    convert.check_vocab_size(params, vocab)
    return (params, vocab, svocab)

+
 def handle_args():
    parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
    parser.add_argument('--input', '-i', type = Path, required = True,
-        help = 'Input GGMLv3 filename')
+                        help = 'Input GGMLv3 filename')
    parser.add_argument('--output', '-o', type = Path, required = True,
-        help ='Output GGUF filename')
+                        help ='Output GGUF filename')
    parser.add_argument('--name',
-        help = 'Set model name')
+                        help = 'Set model name')
    parser.add_argument('--desc',
-        help = 'Set model description')
+                        help = 'Set model description')
    parser.add_argument('--gqa', type = int, default = 1,
-        help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
+                        help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
    parser.add_argument('--eps', default = '5.0e-06',
-        help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
+                        help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
    parser.add_argument('--context-length', '-c', type=int, default = 2048,
-        help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
+                        help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
    parser.add_argument('--model-metadata-dir', '-m', type = Path,
-        help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
+                        help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
    parser.add_argument("--vocab-dir", type=Path,
-        help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
+                        help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
    parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
-        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
+                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
    return parser.parse_args()

+
 def main():
    cfg = handle_args()
    print(f'* Using config: {cfg}')
@ -406,7 +416,7 @@ def main():
    data = np.memmap(cfg.input, mode = 'r')
    model = GGMLModel()
    print('* Scanning GGML input file')
-    offset = model.load(data, 0)
+    offset = model.load(data, 0)  # noqa
    print(f'* GGML model hyperparameters: {model.hyperparameters}')
    vocab_override = None
    params_override = None
@ -421,12 +431,15 @@ def main():
        print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
        if model.file_format == GGMLFormat.GGML:
            print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
-    converter = GGMLToGGUF(model, data, cfg,
+    converter = GGMLToGGUF(
+        model, data, cfg,
        params_override = params_override,
        vocab_override = vocab_override,
-        special_vocab = special_vocab )
+        special_vocab = special_vocab
+    )
    converter.save()
    print(f'* Successful completion. Output saved to: {cfg.output}')

+
 if __name__ == '__main__':
    main()
--- a/convert-persimmon-to-gguf.py
+++ b/convert-persimmon-to-gguf.py
@ -9,6 +9,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf

+
 def _flatten_dict(dct, tensors, prefix=None):
    assert isinstance(dct, dict)
    for key in dct.keys():
@ -21,6 +22,7 @@ def _flatten_dict(dct, tensors, prefix=None):
            raise ValueError(type(dct[key]))
    return None

+
 def _get_sentencepiece_tokenizer_info(dir_model: Path):
    tokenizer_path = dir_model / 'adept_vocab.model'
    print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
@ -54,6 +56,7 @@ def _get_sentencepiece_tokenizer_info(dir_model: Path):
        pass
    return tokens, scores, toktypes

+
 def main():
    parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
    parser.add_argument("--outfile",             type=Path, help="path to write to; default: based on input")
@ -125,6 +128,5 @@ def main():
    print("")


-
 if __name__ == '__main__':
    main()
--- a/convert.py
+++ b/convert.py
@ -46,6 +46,7 @@ DEFAULT_CONCURRENCY = 8
 # data types
 #

+
@dataclass(frozen=True)
 class DataType:
    name: str
@ -55,15 +56,18 @@ class DataType:
    def elements_to_bytes(self, n_elements: int) -> int:
        return n_elements * self.dtype.itemsize

+
@dataclass(frozen=True)
 class UnquantizedDataType(DataType):
    pass

+
 DT_F16  = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
 DT_F32  = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
 DT_I32  = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
 DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])

+
@dataclass(frozen=True)
 class QuantizedDataType(DataType):
    block_size: int
@ -77,6 +81,7 @@ class QuantizedDataType(DataType):
        assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
        return self.quantized_dtype.itemsize * (n_elements // self.block_size)

+
@dataclass(frozen=True)
 class Q8_0QuantizedDataType(QuantizedDataType):
    # Mini Q8_0 quantization in Python!
@ -86,6 +91,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
        n_blocks = arr.size // self.block_size
        blocks = arr.reshape((n_blocks, self.block_size))
        # Much faster implementation of block quantization contributed by @Cebtenzzre
+
        def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
            d = abs(blocks).max(axis = 1) / np.float32(127)
            with np.errstate(divide = 'ignore'):
@ -94,10 +100,11 @@ class Q8_0QuantizedDataType(QuantizedDataType):
            yield from zip(d, qs)
        return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)

+
 DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
-    dtype = np.dtype(np.float32), valid_conversions = [],
-    ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
-    quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
+                                dtype = np.dtype(np.float32), valid_conversions = [],
+                                ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
+                                quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))

 # Quantized types skipped here because they may also map to np.float32
 NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
@ -116,6 +123,8 @@ SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
 # TODO: match this with `llama_ftype`
 # TODO: rename to LLAMAFileType
 # TODO: move to `gguf.py`
+
+
 class GGMLFileType(enum.IntEnum):
    AllF32     = 0
    MostlyF16  = 1  # except 1d tensors
@ -128,6 +137,7 @@ class GGMLFileType(enum.IntEnum):
        # 1D tensors are always F32.
        return dt if len(tensor.shape) > 1 else DT_F32

+
 GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
    GGMLFileType.AllF32    : DT_F32,
    GGMLFileType.MostlyF16 : DT_F16,
@ -138,6 +148,7 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
 # hparams loading
 #

+
@dataclass
 class Params:
    n_vocab:    int
@ -167,11 +178,11 @@ class Params:

        # try transformer naming first
        if "model.layers.0.self_attn.q_proj.weight" in model:
-            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
-            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
+            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
        else:
-            n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
+            n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)

        if n_layer < 1:
            raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
@ -308,7 +319,7 @@ class BpeVocab:
                    (item['content'], item['id'])
                    for item in tokenizer_json.get('added_tokens', [])
                    # Added tokens here can be duplicates of the main vocabulary.
-                    if item['content'] not in self.bpe_tokenizer )
+                    if item['content'] not in self.bpe_tokenizer)

        vocab_size: int = len(self.bpe_tokenizer)
        expected_ids    = list(range(vocab_size, vocab_size + len(added_tokens)))
@ -326,7 +337,6 @@ class BpeVocab:

    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        tokenizer = self.bpe_tokenizer
-        from transformers.models.gpt2 import tokenization_gpt2
        reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}

        for i, _ in enumerate(tokenizer):
@ -406,6 +416,7 @@ class SentencePieceVocab:
    def __repr__(self) -> str:
        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"

+
 Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'

 #
@ -413,13 +424,14 @@ Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
 # TODO: reuse (probably move to gguf.py?)
 #

+
 def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
-    #print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
+    # print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
    if n_head_kv is not None and n_head != n_head_kv:
        n_head = n_head_kv
    return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
+            .swapaxes(1, 2)
+            .reshape(weights.shape))


 class Tensor(metaclass=ABCMeta):
@ -500,7 +512,7 @@ class LazyTensor:
        ret = self._load()
        # Should be okay if it maps to the same numpy type?
        assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
-                (self.data_type, ret.data_type, self.description)
+            (self.data_type, ret.data_type, self.description)
        return ret

    def astype(self, data_type: DataType) -> LazyTensor:
@ -588,6 +600,7 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe
        return lazy_tensor.load().permute(n_head, n_head_kv)
    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)

+
 def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
    def load() -> Tensor:
        return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
@ -595,6 +608,7 @@ def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_
    s[0] = s[0] // 3
    return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)

+
 def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
    def load() -> Tensor:
        return lazy_tensor.load().part(n_part)
@ -744,6 +758,7 @@ def lazy_load_file(path: Path) -> ModelPlus:
 In = TypeVar('In')
 Out = TypeVar('Out')

+
 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]:
    '''Parallel map, but with backpressure.  If the caller doesn't call `next`
    fast enough, this will stop calling `func` at some point rather than
@ -778,6 +793,7 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
                    break
            yield result

+
 def check_vocab_size(params: Params, vocab: Vocab) -> None:
    if params.n_vocab != vocab.vocab_size:
        assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@ -796,7 +812,7 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:


 class OutputFile:
-    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
+    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)

    def add_meta_arch(self, params: Params) -> None:
@ -876,7 +892,7 @@ class OutputFile:
        self.gguf.close()

    @staticmethod
-    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
+    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
        check_vocab_size(params, vocab)

        of = OutputFile(fname_out, endianess=endianess)
@ -938,8 +954,9 @@ class OutputFile:

        of.close()

+
 def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
-    wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type
+    wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type

    if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
        return GGMLFileType.AllF32
@ -952,10 +969,12 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT

    raise Exception(f"Unexpected combination of types: {name_to_type}")

+
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
    return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
            for (name, tensor) in model.items()}

+
 def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
    tmap = gguf.TensorNameMap(ARCH, params.n_layer)
    should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
@ -968,7 +987,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
            print(f"Permuting layer {i}")
            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)
            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)
-           #tmp[f"model.layers.{i}.self_attn.v_proj.weight"] =              model[f"model.layers.{i}.self_attn.v_proj.weight"]
+            # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] =              model[f"model.layers.{i}.self_attn.v_proj.weight"]
        elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
            print(f"Unpacking and permuting layer {i}")
            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)
@ -993,6 +1012,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:

    return out

+
 def nth_multifile_path(path: Path, n: int) -> Path | None:
    '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
    the nth path in the model.
@ -1174,8 +1194,8 @@ def main(args_in: list[str] | None = None) -> None:
        # FIXME: Try to respect vocab_dir somehow?
        vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
        special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
-            load_merges = args.vocabtype == 'bpe',
-            n_vocab = vocab.vocab_size)
+                                          load_merges = args.vocabtype == 'bpe',
+                                          n_vocab = vocab.vocab_size)
        outfile = args.outfile
        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
        print(f"Wrote {outfile}")
@ -1188,8 +1208,8 @@ def main(args_in: list[str] | None = None) -> None:
        vocab = load_vocab(vocab_dir, args.vocabtype)
    # FIXME: Try to respect vocab_dir somehow?
    special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
-        load_merges = args.vocabtype == 'bpe',
-        n_vocab = vocab.vocab_size)
+                                      load_merges = args.vocabtype == 'bpe',
+                                      n_vocab = vocab.vocab_size)

    model   = model_plus.model
    model   = convert_model_names(model, params)
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@ -221,7 +221,7 @@ class GGUFWriter:
        if self.endianess == GGUFEndian.BIG:
            tensor.byteswap(inplace=True)
        if self.use_temp_file and self.temp_file is None:
-            fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
+            fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024)
            fp.seek(0)
            self.temp_file = fp

--- a/tests/test-tokenizer-0-falcon.py
+++ b/tests/test-tokenizer-0-falcon.py
@ -14,34 +14,34 @@ dir_tokenizer = args.dir_tokenizer
 tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)

 tests = [
-        "",
-        " ",
-        "  ",
-        "   ",
-        "\t",
-        "\n",
-        "\t\n",
-        "Hello world",
-        " Hello world",
-        "Hello World",
-        " Hello World",
-        " Hello World!",
-        "Hello, world!",
-        " Hello, world!",
-        " this is 🦙.cpp",
-        "w048 7tuijk dsdfhu",
-        "нещо на Български",
-        "កាន់តែពិសេសអាចខលចេញ",
-        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-        "Hello",
-        " Hello",
-        "  Hello",
-        "   Hello",
-        "    Hello",
-        "    Hello\n    Hello",
-        "\n =",
-        "' era",
-    ]
+    "",
+    " ",
+    "  ",
+    "   ",
+    "\t",
+    "\n",
+    "\t\n",
+    "Hello world",
+    " Hello world",
+    "Hello World",
+    " Hello World",
+    " Hello World!",
+    "Hello, world!",
+    " Hello, world!",
+    " this is 🦙.cpp",
+    "w048 7tuijk dsdfhu",
+    "нещо на Български",
+    "កាន់តែពិសេសអាចខលចេញ",
+    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+    "Hello",
+    " Hello",
+    "  Hello",
+    "   Hello",
+    "    Hello",
+    "    Hello\n    Hello",
+    "\n =",
+    "' era",
+]

 for text in tests:
    print('text: ', text)
--- a/tests/test-tokenizer-0-llama.py
+++ b/tests/test-tokenizer-0-llama.py
@ -14,32 +14,32 @@ dir_tokenizer = args.dir_tokenizer
 tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')

 tests = [
-        "",
-        " ",
-        "  ",
-        "   ",
-        "\t",
-        "\n",
-        "\t\n",
-        "Hello world",
-        " Hello world",
-        "Hello World",
-        " Hello World",
-        " Hello World!",
-        "Hello, world!",
-        " Hello, world!",
-        " this is 🦙.cpp",
-        "w048 7tuijk dsdfhu",
-        "нещо на Български",
-        "កាន់តែពិសេសអាចខលចេញ",
-        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-        "Hello",
-        " Hello",
-        "  Hello",
-        "   Hello",
-        "    Hello",
-        "    Hello\n    Hello",
-    ]
+    "",
+    " ",
+    "  ",
+    "   ",
+    "\t",
+    "\n",
+    "\t\n",
+    "Hello world",
+    " Hello world",
+    "Hello World",
+    " Hello World",
+    " Hello World!",
+    "Hello, world!",
+    " Hello, world!",
+    " this is 🦙.cpp",
+    "w048 7tuijk dsdfhu",
+    "нещо на Български",
+    "កាន់តែពិសេសអាចខលចេញ",
+    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+    "Hello",
+    " Hello",
+    "  Hello",
+    "   Hello",
+    "    Hello",
+    "    Hello\n    Hello",
+]


 for text in tests: