Fix lints (#262)

Author: mergennachin

build/builder.py

@@ -37,7 +37,7 @@ class BuilderArgs:
     setup_caches: bool = False
     use_tp: bool = False
     is_chat_model: bool = False
-    
+
     def __post_init__(self):
         if not (
             (self.checkpoint_path and self.checkpoint_path.is_file())
@@ -77,15 +77,15 @@ def from_args(cls, args):  # -> BuilderArgs:
                 args.checkpoint_dir,
                 args.dso_path,
                 args.pte_path,
-                args.gguf_path
+                args.gguf_path,
             ]:
                 path = str(path)
-                if path.endswith('/'):
+                if path.endswith("/"):
                     path = path[:-1]
                 path_basename = os.path.basename(path)
                 if "chat" in path_basename:
                     is_chat_model = True
-                    
+
         return cls(
             checkpoint_path=args.checkpoint_path,
             checkpoint_dir=args.checkpoint_dir,
@@ -189,6 +189,7 @@ def _set_gguf_kwargs(builder_args, is_et, context: str):
     if is_et:
         builder_args.gguf_kwargs["load_as_quantized"] = False
 
+
 def _unset_gguf_kwargs(builder_args):
     builder_args.gguf_kwargs = None
 
@@ -264,6 +265,7 @@ def _load_model(builder_args):
 
     if builder_args.use_tp:
         from tp import apply_tp
+
         print("Applying tensor parallel to model ...")
         apply_tp(model)
 

build/gguf_loader.py

@@ -8,27 +8,21 @@
 import copy
 import logging
 import sys
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 import gguf
 
 import torch
-import torch.nn as nn
 
 wd = Path(__file__).parent.resolve()
 sys.path.append(str(wd))
 
-from gguf import GGUFValueType, ReaderTensor
-from quantize import (
-    group_dequantize_tensor_from_qparams,
-    pack_scales_and_zeros,
-    WeightOnlyInt4Linear,
-)
-
-from build.gguf_util import F16, F32, Q4_0, Q6_K, to_float
+from gguf import GGUFValueType
 from model import ModelArgs, Transformer
+from quantize import pack_scales_and_zeros, WeightOnlyInt4Linear
+
+from build.gguf_util import Q4_0, to_float
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -116,9 +110,7 @@ def load_model(gguf_file: str) -> torch.nn.Module:
     metadata = _get_metadata(reader)
 
     arch = metadata["general.architecture"]
-    assert (
-        arch == "llama"
-    ), "Only LLaMa models are supported by this converter."
+    assert arch == "llama", "Only LLaMa models are supported by this converter."
 
     model_args = ModelArgs(
         dim=metadata[f"{arch}.embedding_length"],
@@ -139,7 +131,13 @@ def load_model(gguf_file: str) -> torch.nn.Module:
     return model
 
 
-def load_model_and_state_dict(gguf_file: str, *, load_state_dict: bool = True, load_as_quantized: bool = True, inner_k_tiles = 8) -> torch.nn.Module:
+def load_model_and_state_dict(
+    gguf_file: str,
+    *,
+    load_state_dict: bool = True,
+    load_as_quantized: bool = True,
+    inner_k_tiles=8,
+) -> torch.nn.Module:
     """
     Parses the GGUF file and returns an nn.Module on meta device along with a state_dict
     that can be loaded into it.

build/model.py

@@ -248,6 +248,7 @@ def from_params(cls, params_path: str):
     @classmethod
     def from_gguf(cls, gguf_path: str, **kwargs):
         from build.gguf_loader import load_model_and_state_dict
+
         model, state_dict = load_model_and_state_dict(gguf_path, **kwargs)
         if state_dict != {}:
             model.load_state_dict(state_dict, assign=True)

generate.py

@@ -7,7 +7,6 @@
 import itertools
 
 import logging
-import os
 import sys
 import time
 from dataclasses import dataclass
@@ -109,12 +108,12 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
 
 
 def prefill(
-        model: Transformer,
-        x: torch.Tensor,
-        input_pos: torch.Tensor,
-        *,
-        sequential_prefill = True,
-        **sampling_kwargs
+    model: Transformer,
+    x: torch.Tensor,
+    input_pos: torch.Tensor,
+    *,
+    sequential_prefill=True,
+    **sampling_kwargs,
 ) -> torch.Tensor:
     logging.debug(f"x: {x}, input_pos: {input_pos}")
     width = x.size(1)
@@ -348,7 +347,7 @@ def _main(
     is_speculative = speculative_builder_args.checkpoint_path is not None
 
     if generator_args.chat_mode and not builder_args.is_chat_model:
-        # This is not a log message, it's a dangerous condition message 
+        # This is not a log message, it's a dangerous condition message
         # that we must ensure is displayed
         print(
             """