use class Foo: instead of class Foo(): (#1797)
* use class Foo: instead of class Foo(): * add ruff linter, copy settings from .flake8 to ruff.tomlpull/1807/head
parent
fd25792c8b
commit
52a92bf95d
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@ -31,6 +31,8 @@ jobs:
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run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string=' ' **/*.py
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- name: Lint with flake8
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run: python -m flake8 . --statistics -j4
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- name: Lint with ruff
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run: ruff .
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- name: Lint tinygrad with pylint
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run: python -m pylint tinygrad/
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- name: Run mypy
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@ -210,7 +210,7 @@ class MultiHeadAttention:
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ret = self.out_proj(wv).transpose(0,1) # BxTxC -> TxBxC
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return ret
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class ConvFeatureExtractionModel():
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class ConvFeatureExtractionModel:
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def __init__(self, conv_layers, dropout=.0, mode="default", conv_bias=False):
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assert mode in {"default", "group_norm_masked", "layer_norm"}
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def block(n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False):
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@ -352,7 +352,7 @@ class Upsample:
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new_shape = (*x.shape[:-1], x.shape[-1] * self.scale)
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return x.unsqueeze(-1).repeat(repeats).reshape(new_shape)
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class SineGen():
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class SineGen:
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def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voice_threshold=0, flag_for_pulse=False):
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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
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self.dim = self.harmonic_num + 1
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@ -244,7 +244,7 @@ class Upsample:
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tmp = x.reshape([b, c, -1] + [1] * _lens) * Tensor.ones(*[1, 1, 1] + [self.scale_factor] * _lens)
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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:]])
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class Conv_Block():
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class Conv_Block:
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def __init__(self, c1, c2, kernel_size=1, stride=1, groups=1, dilation=1, padding=None):
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self.conv = Conv2d(c1,c2, kernel_size, stride, padding=autopad(kernel_size, padding, dilation), bias=False, groups=groups, dilation=dilation)
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self.bn = BatchNorm2d(c2, eps=0.001)
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@ -11,7 +11,7 @@ from llvmlite import ir # type: ignore
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# https://github.com/corsix/amx/blob/main/Instructions.md
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# 12 lines for AMX support
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from functools import partialmethod
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class AMX():
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class AMX:
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@staticmethod
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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)
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@staticmethod
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@ -0,0 +1,43 @@
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tab-size = 2
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select = [
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"F",
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"W6",
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"E71",
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"E72",
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"E112",
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"E113",
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# "E124",
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"E203",
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"E272",
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# "E303",
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# "E304",
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# "E502",
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"E702",
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"E703",
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"E731",
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"W191",
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"UP039", # unnecessary-class-parentheses
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]
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exclude = [
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"disassemblers/",
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"docs/",
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"examples/",
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"extra/",
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"models/",
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"openpilot/",
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]
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[per-file-ignores]
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"test/*" = [
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"F401",
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"F403",
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"F405",
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"F541",
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"E722",
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"E731",
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"F811",
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"F821",
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"F841",
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]
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1
setup.py
1
setup.py
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@ -35,6 +35,7 @@ setup(name='tinygrad',
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"pylint",
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"mypy",
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"pre-commit",
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"ruff",
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],
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'testing': [
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"torch",
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@ -11,7 +11,7 @@ from tinygrad.ops import Device
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from examples.llama import Transformer
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ALLOCATED_DEV_BUFS = 0
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class FakeDeviceBuffer():
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class FakeDeviceBuffer:
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def __init__(self, sz, dt, device):
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self.id = 1
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self.size = sz
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@ -17,7 +17,7 @@ from tinygrad.ops import GlobalCounters, MovementOps, ReduceOps
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from tinygrad.lazy import PUSH_PERMUTES
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from tinygrad.jit import CacheCollector
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class CLCache():
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class CLCache:
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def __init__(self, allowed=None, strict=False, preclear=True): self.allowed, self.strict, self.preclear = allowed, strict, preclear
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def __enter__(self):
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if self.preclear:
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@ -11,7 +11,7 @@ x_init = np.random.randn(1,4).astype(np.float32)
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W_init = np.random.randn(4,4).astype(np.float32)
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m_init = np.random.randn(1,4).astype(np.float32)
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class TinyNet():
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class TinyNet:
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def __init__(self):
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self.x = Tensor(x_init.copy(), requires_grad=True)
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self.W = Tensor(W_init.copy(), requires_grad=True)
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@ -23,7 +23,7 @@ class TinyNet():
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out = out.mul(self.m).add(self.m).sum()
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return out
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class TinyNetTF():
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class TinyNetTF:
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def __init__(self):
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self.x = tf.Variable(x_init.copy(), trainable=True)
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self.W = tf.Variable(W_init.copy(), trainable=True)
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@ -12,7 +12,7 @@ def check_gc():
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from extra.introspection import print_objects
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assert print_objects() == 0
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class FakeDeviceBuffer():
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class FakeDeviceBuffer:
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def __init__(self, sz, dt, device):
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self.id = 1
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self.size = sz
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@ -5,7 +5,7 @@ from tinygrad.helpers import dtypes
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from tinygrad.jit import CacheCollector
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from weakref import ref
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class FakeDeviceBuffer():
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class FakeDeviceBuffer:
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def __init__(self, sz, dt, device):
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self.size = sz
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self.dtype = dt
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@ -12,7 +12,7 @@ x_init = np.random.randn(1,4).astype(np.float32)
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W_init = np.random.randn(4,4).astype(np.float32)
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m_init = np.random.randn(1,4).astype(np.float32)
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class TinyNet():
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class TinyNet:
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def __init__(self, tensor):
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self.x = tensor(x_init.copy(), requires_grad=True)
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self.W = tensor(W_init.copy(), requires_grad=True)
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