use class Foo: instead of class Foo(): (#1797)

* use class Foo: instead of class Foo():

* add ruff linter, copy settings from .flake8 to ruff.toml

.github/workflows/test.yml

@@ -31,6 +31,8 @@ jobs:
       run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string='  ' **/*.py
     - name: Lint with flake8
       run: python -m flake8 . --statistics -j4
+    - name: Lint with ruff
+      run: ruff .
     - name: Lint tinygrad with pylint
       run: python -m pylint tinygrad/
     - name: Run mypy

examples/so_vits_svc.py

@@ -210,7 +210,7 @@ class MultiHeadAttention:
     ret =  self.out_proj(wv).transpose(0,1)  # BxTxC -> TxBxC
     return ret
 
-class ConvFeatureExtractionModel():
+class ConvFeatureExtractionModel:
   def __init__(self, conv_layers, dropout=.0, mode="default", conv_bias=False):
     assert mode in {"default", "group_norm_masked", "layer_norm"}
     def block(n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False):
@@ -352,7 +352,7 @@ class Upsample:
     new_shape = (*x.shape[:-1], x.shape[-1] * self.scale)
     return x.unsqueeze(-1).repeat(repeats).reshape(new_shape)
 
-class SineGen():
+class SineGen:
   def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voice_threshold=0, flag_for_pulse=False):
     self.sine_amp, self.noise_std, self.harmonic_num, self.sampling_rate, self.voiced_threshold, self.flag_for_pulse = sine_amp, noise_std, harmonic_num, samp_rate, voice_threshold, flag_for_pulse
     self.dim = self.harmonic_num + 1

examples/yolov8.py

@@ -244,7 +244,7 @@ class Upsample:
     tmp = x.reshape([b, c, -1] + [1] * _lens) * Tensor.ones(*[1, 1, 1] + [self.scale_factor] * _lens)
     return tmp.reshape(list(x.shape) + [self.scale_factor] * _lens).permute([0, 1] + list(chain.from_iterable([[y+2, y+2+_lens] for y in range(_lens)]))).reshape([b, c] + [x * self.scale_factor for x in x.shape[2:]])
 
-class Conv_Block():
+class Conv_Block:
   def __init__(self, c1, c2, kernel_size=1, stride=1, groups=1, dilation=1, padding=None):
     self.conv = Conv2d(c1,c2, kernel_size, stride, padding=autopad(kernel_size, padding, dilation), bias=False, groups=groups, dilation=dilation)
     self.bn = BatchNorm2d(c2, eps=0.001)

extra/gemm/amx.py

@@ -11,7 +11,7 @@ from llvmlite import ir  # type: ignore
 # https://github.com/corsix/amx/blob/main/Instructions.md
 # 12 lines for AMX support
 from functools import partialmethod
-class AMX():
+class AMX:
   @staticmethod
   def nop_op_imm5(op, imm5, builder): builder.asm(ir.FunctionType(ir.VoidType(), []), f".word (0x201000 + ({op} << 5) + {imm5}); amx op {op} imm {imm5}", "", tuple(), True)
   @staticmethod

ruff.toml

@@ -0,0 +1,43 @@
+tab-size = 2
+
+select = [
+  "F",
+  "W6",
+  "E71",
+  "E72",
+  "E112",
+  "E113",
+  # "E124",
+  "E203",
+  "E272",
+  # "E303",
+  # "E304",
+  # "E502",
+  "E702",
+  "E703",
+  "E731",
+  "W191",
+  "UP039",  # unnecessary-class-parentheses
+]
+
+exclude = [
+  "disassemblers/",
+  "docs/",
+  "examples/",
+  "extra/",
+  "models/",
+  "openpilot/",
+]
+
+[per-file-ignores]
+"test/*" = [
+  "F401",
+  "F403",
+  "F405",
+  "F541",
+  "E722",
+  "E731",
+  "F811",
+  "F821",
+  "F841",
+]

setup.py

@@ -35,6 +35,7 @@ setup(name='tinygrad',
             "pylint",
             "mypy",
             "pre-commit",
+            "ruff",
         ],
         'testing': [
             "torch",

test/external/external_test_allocator_on_models.py

@@ -11,7 +11,7 @@ from tinygrad.ops import Device
 from examples.llama import Transformer
 
 ALLOCATED_DEV_BUFS = 0
-class FakeDeviceBuffer():
+class FakeDeviceBuffer:
   def __init__(self, sz, dt, device):
     self.id = 1
     self.size = sz

test/external/external_test_opt.py

@@ -17,7 +17,7 @@ from tinygrad.ops import GlobalCounters, MovementOps, ReduceOps
 from tinygrad.lazy import PUSH_PERMUTES
 from tinygrad.jit import CacheCollector
 
-class CLCache():
+class CLCache:
   def __init__(self, allowed=None, strict=False, preclear=True): self.allowed, self.strict, self.preclear = allowed, strict, preclear
   def __enter__(self):
     if self.preclear:

test/external/external_test_optim.py

@@ -11,7 +11,7 @@ x_init = np.random.randn(1,4).astype(np.float32)
 W_init = np.random.randn(4,4).astype(np.float32)
 m_init = np.random.randn(1,4).astype(np.float32)
 
-class TinyNet():
+class TinyNet:
   def __init__(self):
     self.x = Tensor(x_init.copy(), requires_grad=True)
     self.W = Tensor(W_init.copy(), requires_grad=True)
@@ -23,7 +23,7 @@ class TinyNet():
     out = out.mul(self.m).add(self.m).sum()
     return out
 
-class TinyNetTF():
+class TinyNetTF:
   def __init__(self):
     self.x = tf.Variable(x_init.copy(), trainable=True)
     self.W = tf.Variable(W_init.copy(), trainable=True)

test/test_allocators.py

@@ -12,7 +12,7 @@ def check_gc():
     from extra.introspection import print_objects
     assert print_objects() == 0
 
-class FakeDeviceBuffer():
+class FakeDeviceBuffer:
   def __init__(self, sz, dt, device):
     self.id = 1
     self.size = sz

test/test_cache_collector.py

@@ -5,7 +5,7 @@ from tinygrad.helpers import dtypes
 from tinygrad.jit import CacheCollector
 from weakref import ref
 
-class FakeDeviceBuffer():
+class FakeDeviceBuffer:
   def __init__(self, sz, dt, device):
     self.size = sz
     self.dtype = dt

test/test_optim.py

@@ -12,7 +12,7 @@ x_init = np.random.randn(1,4).astype(np.float32)
 W_init = np.random.randn(4,4).astype(np.float32)
 m_init = np.random.randn(1,4).astype(np.float32)
 
-class TinyNet():
+class TinyNet:
   def __init__(self, tensor):
     self.x = tensor(x_init.copy(), requires_grad=True)
     self.W = tensor(W_init.copy(), requires_grad=True)