branch: master
hlb_cifar10.py
19032 bytesRaw
#!/usr/bin/env python3
# tinygrad implementation of https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
# https://myrtle.ai/learn/how-to-train-your-resnet-8-bag-of-tricks/
# https://siboehm.com/articles/22/CUDA-MMM
import random, time
import numpy as np
from typing import Optional
from extra.lr_scheduler import OneCycleLR
from tinygrad import nn, dtypes, Tensor, Device, GlobalCounters, TinyJit
from tinygrad.nn.state import get_state_dict, get_parameters
from tinygrad.nn import optim
from tinygrad.helpers import Context, BEAM, WINO, getenv, colored, prod
cifar_mean = [0.4913997551666284, 0.48215855929893703, 0.4465309133731618]
cifar_std = [0.24703225141799082, 0.24348516474564, 0.26158783926049628]
BS, STEPS = getenv("BS", 512), getenv("STEPS", 1000)
EVAL_BS = getenv("EVAL_BS", BS)
GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
assert BS % len(GPUS) == 0, f"{BS=} is not a multiple of {len(GPUS)=}, uneven multi GPU is slow"
assert EVAL_BS % len(GPUS) == 0, f"{EVAL_BS=} is not a multiple of {len(GPUS)=}, uneven multi GPU is slow"
class UnsyncedBatchNorm:
def __init__(self, sz:int, eps=1e-5, affine=True, track_running_stats=True, momentum=0.1, num_devices=len(GPUS)):
self.eps, self.track_running_stats, self.momentum = eps, track_running_stats, momentum
self.num_devices = num_devices
if affine: self.weight, self.bias = Tensor.ones(sz, dtype=dtypes.float32), Tensor.zeros(sz, dtype=dtypes.float32)
else: self.weight, self.bias = None, None
self.running_mean = Tensor.zeros(num_devices, sz, dtype=dtypes.float32, requires_grad=False)
self.running_var = Tensor.ones(num_devices, sz, dtype=dtypes.float32, requires_grad=False)
self.num_batches_tracked = Tensor.zeros(1, dtype=dtypes.int, requires_grad=False)
def __call__(self, x:Tensor):
xr = x.reshape(self.num_devices, -1, *x.shape[1:]).cast(dtypes.float32)
batch_mean, batch_invstd = self.calc_stats(xr)
ret = xr.batchnorm(
self.weight.reshape(1, -1).expand((self.num_devices, -1)),
self.bias.reshape(1, -1).expand((self.num_devices, -1)),
batch_mean, batch_invstd, axis=(0, 2))
return ret.reshape(x.shape).cast(x.dtype)
def calc_stats(self, x:Tensor):
if Tensor.training:
# This requires two full memory accesses to x
# https://github.com/pytorch/pytorch/blob/c618dc13d2aa23625cb0d7ada694137532a4fa33/aten/src/ATen/native/cuda/Normalization.cuh
# There's "online" algorithms that fix this, like https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm
batch_mean = x.mean(axis=(1,3,4))
y = (x - batch_mean.detach().reshape(shape=[batch_mean.shape[0], 1, -1, 1, 1])) # d(var)/d(mean) = 0
batch_var = (y*y).mean(axis=(1,3,4))
batch_invstd = batch_var.add(self.eps).pow(-0.5)
# NOTE: wow, this is done all throughout training in most PyTorch models
if self.track_running_stats:
self.running_mean.assign((1-self.momentum) * self.running_mean + self.momentum * batch_mean.detach().cast(self.running_mean.dtype))
batch_var_adjust = prod(y.shape[1:])/(prod(y.shape[1:])-y.shape[2])
self.running_var.assign((1-self.momentum) * self.running_var + self.momentum * batch_var_adjust * batch_var.detach().cast(self.running_var.dtype))
self.num_batches_tracked += 1
else:
batch_mean = self.running_mean
# NOTE: this can be precomputed for static inference. we expand it here so it fuses
batch_invstd = self.running_var.reshape(self.running_var.shape[0], 1, -1, 1, 1).expand(x.shape).add(self.eps).rsqrt()
return batch_mean, batch_invstd
class BatchNorm(nn.BatchNorm2d if getenv("SYNCBN") else UnsyncedBatchNorm):
def __init__(self, num_features):
super().__init__(num_features, track_running_stats=False, eps=1e-12, momentum=0.85, affine=True)
self.weight.requires_grad = False
self.bias.requires_grad = True
class ConvGroup:
def __init__(self, channels_in, channels_out):
self.conv1 = nn.Conv2d(channels_in, channels_out, kernel_size=3, padding=1, bias=False)
self.conv2 = nn.Conv2d(channels_out, channels_out, kernel_size=3, padding=1, bias=False)
self.norm1 = BatchNorm(channels_out)
self.norm2 = BatchNorm(channels_out)
def __call__(self, x):
x = self.conv1(x)
x = x.max_pool2d(2)
x = x.float()
x = self.norm1(x)
x = x.cast(dtypes.default_float)
x = x.quick_gelu()
residual = x
x = self.conv2(x)
x = x.float()
x = self.norm2(x)
x = x.cast(dtypes.default_float)
x = x.quick_gelu()
return x + residual
class SpeedyResNet:
def __init__(self, W):
self.whitening = W
self.net = [
nn.Conv2d(12, 32, kernel_size=1, bias=False),
lambda x: x.quick_gelu(),
ConvGroup(32, 64),
ConvGroup(64, 256),
ConvGroup(256, 512),
lambda x: x.max((2,3)),
nn.Linear(512, 10, bias=False),
lambda x: x / 9.,
]
def __call__(self, x, training=True):
# pad to 32x32 because whitening conv creates 31x31 images that are awfully slow to compute with
# TODO: remove the pad but instead let the kernel optimize itself
forward = lambda x: x.conv2d(self.whitening).pad((1,0,0,1)).sequential(self.net)
return forward(x) if training else (forward(x) + forward(x[..., ::-1])) / 2.
# hyper-parameters were exactly the same as the original repo
bias_scaler = 58
hyp = {
'seed' : 209,
'opt': {
'bias_lr': 1.76 * bias_scaler/512,
'non_bias_lr': 1.76 / 512,
'bias_decay': 1.08 * 6.45e-4 * BS/bias_scaler,
'non_bias_decay': 1.08 * 6.45e-4 * BS,
'final_lr_ratio': 0.025,
'initial_div_factor': 1e6,
'label_smoothing': 0.20,
'momentum': 0.85,
'percent_start': 0.23,
'loss_scale_scaler': 1./128 # (range: ~1/512 - 16+, 1/128 w/ FP16)
},
'net': {
'kernel_size': 2, # kernel size for the whitening layer
'cutmix_size': 3,
'cutmix_steps': 499,
'pad_amount': 2
},
'ema': {
'steps': 399,
'decay_base': .95,
'decay_pow': 1.6,
'every_n_steps': 5,
},
}
def train_cifar():
def set_seed(seed):
Tensor.manual_seed(seed)
random.seed(seed)
# ========== Model ==========
def whitening(X, kernel_size=hyp['net']['kernel_size']):
def _cov(X):
return (X.T @ X) / (X.shape[0] - 1)
def _patches(data, patch_size=(kernel_size,kernel_size)):
h, w = patch_size
c = data.shape[1]
axis = (2, 3)
return np.lib.stride_tricks.sliding_window_view(data, window_shape=(h,w), axis=axis).transpose((0,3,2,1,4,5)).reshape((-1,c,h,w))
def _eigens(patches):
n,c,h,w = patches.shape
Σ = _cov(patches.reshape(n, c*h*w))
Λ, V = np.linalg.eigh(Σ, UPLO='U')
return np.flip(Λ, 0), np.flip(V.T.reshape(c*h*w, c, h, w), 0)
# NOTE: np.linalg.eigh only supports float32 so the whitening layer weights need to be converted to float16 manually
Λ, V = _eigens(_patches(X.float().numpy()))
W = V/np.sqrt(Λ+1e-2)[:,None,None,None]
return Tensor(W.astype(np.float32), requires_grad=False).cast(dtypes.default_float)
# ========== Loss ==========
def cross_entropy(x:Tensor, y:Tensor, reduction:str='mean', label_smoothing:float=0.0) -> Tensor:
divisor = y.shape[1]
assert isinstance(divisor, int), "only supported int divisor"
y = (1 - label_smoothing)*y + label_smoothing / divisor
ret = -x.log_softmax(axis=1).mul(y).sum(axis=1)
if reduction=='none': return ret
if reduction=='sum': return ret.sum()
if reduction=='mean': return ret.mean()
raise NotImplementedError(reduction)
# ========== Preprocessing ==========
# NOTE: this only works for RGB in format of NxCxHxW and pads the HxW
def pad_reflect(X, size=2) -> Tensor:
X = X[...,:,1:size+1].flip(-1).cat(X, X[...,:,-(size+1):-1].flip(-1), dim=-1)
X = X[...,1:size+1,:].flip(-2).cat(X, X[...,-(size+1):-1,:].flip(-2), dim=-2)
return X
# return a binary mask in the format of BS x C x H x W where H x W contains a random square mask
def make_square_mask(shape, mask_size) -> Tensor:
BS, _, H, W = shape
low_x = Tensor.randint(BS, low=0, high=W-mask_size).reshape(BS,1,1,1)
low_y = Tensor.randint(BS, low=0, high=H-mask_size).reshape(BS,1,1,1)
idx_x = Tensor.arange(W, dtype=dtypes.int32).reshape((1,1,1,W))
idx_y = Tensor.arange(H, dtype=dtypes.int32).reshape((1,1,H,1))
return (idx_x >= low_x) * (idx_x < (low_x + mask_size)) * (idx_y >= low_y) * (idx_y < (low_y + mask_size))
def random_crop(X:Tensor, crop_size=32):
mask = make_square_mask(X.shape, crop_size)
mask = mask.expand((-1,3,-1,-1))
X_cropped = Tensor(X.numpy()[mask.numpy()])
return X_cropped.reshape((-1, 3, crop_size, crop_size))
def cutmix(X:Tensor, Y:Tensor, mask_size=3):
# fill the square with randomly selected images from the same batch
mask = make_square_mask(X.shape, mask_size)
order = list(range(0, X.shape[0]))
random.shuffle(order)
X_patch = Tensor(X.numpy()[order], device=X.device, dtype=X.dtype)
Y_patch = Tensor(Y.numpy()[order], device=Y.device, dtype=Y.dtype)
X_cutmix = mask.where(X_patch, X)
mix_portion = float(mask_size**2)/(X.shape[-2]*X.shape[-1])
Y_cutmix = mix_portion * Y_patch + (1. - mix_portion) * Y
return X_cutmix, Y_cutmix
# the operations that remain inside batch fetcher is the ones that involves random operations
def fetch_batches(X_in:Tensor, Y_in:Tensor, BS:int, is_train:bool):
step, epoch = 0, 0
while True:
st = time.monotonic()
X, Y = X_in, Y_in
if is_train:
# TODO: these are not jitted
if getenv("RANDOM_CROP", 1):
X = random_crop(X, crop_size=32)
if getenv("RANDOM_FLIP", 1):
X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
if getenv("CUTMIX", 1):
if step >= hyp['net']['cutmix_steps']:
X, Y = cutmix(X, Y, mask_size=hyp['net']['cutmix_size'])
order = list(range(0, X.shape[0]))
random.shuffle(order)
X, Y = X.numpy()[order], Y.numpy()[order]
else:
X, Y = X.numpy(), Y.numpy()
et = time.monotonic()
print(f"shuffling {'training' if is_train else 'test'} dataset in {(et-st)*1e3:.2f} ms ({epoch=})")
for i in range(0, X.shape[0], BS):
# pad the last batch # TODO: not correct for test
batch_end = min(i+BS, Y.shape[0])
x = Tensor(X[batch_end-BS:batch_end], device=X_in.device, dtype=X_in.dtype)
y = Tensor(Y[batch_end-BS:batch_end], device=Y_in.device, dtype=Y_in.dtype)
step += 1
yield x, y
epoch += 1
if not is_train: break
transform = [
lambda x: x.float() / 255.0,
lambda x: x.reshape((-1,3,32,32)) - Tensor(cifar_mean, device=x.device, dtype=x.dtype).reshape((1,3,1,1)),
lambda x: x / Tensor(cifar_std, device=x.device, dtype=x.dtype).reshape((1,3,1,1)),
]
class modelEMA():
def __init__(self, w, net):
# self.model_ema = copy.deepcopy(net) # won't work for opencl due to unpickeable pyopencl._cl.Buffer
self.net_ema = SpeedyResNet(w)
for net_ema_param, net_param in zip(get_state_dict(self.net_ema).values(), get_state_dict(net).values()):
net_ema_param.requires_grad = False
net_ema_param.assign(net_param.numpy())
@TinyJit
def update(self, net, decay):
# TODO with Tensor.no_grad()
Tensor.no_grad = True
for net_ema_param, (param_name, net_param) in zip(get_state_dict(self.net_ema).values(), get_state_dict(net).items()):
# batchnorm currently is not being tracked
if not ("num_batches_tracked" in param_name) and not ("running" in param_name):
net_ema_param.assign(net_ema_param.detach()*decay + net_param.detach()*(1.-decay)).realize()
Tensor.no_grad = False
set_seed(getenv('SEED', hyp['seed']))
X_train, Y_train, X_test, Y_test = nn.datasets.cifar()
# one-hot encode labels
Y_train, Y_test = Y_train.one_hot(10), Y_test.one_hot(10)
# preprocess data
X_train, X_test = X_train.sequential(transform), X_test.sequential(transform)
# precompute whitening patches
W = whitening(X_train)
# initialize model weights
model = SpeedyResNet(W)
# padding is not timed in the original repo since it can be done all at once
X_train = pad_reflect(X_train, size=hyp['net']['pad_amount'])
# Convert data and labels to the default dtype
X_train, Y_train = X_train.cast(dtypes.default_float), Y_train.cast(dtypes.default_float)
X_test, Y_test = X_test.cast(dtypes.default_float), Y_test.cast(dtypes.default_float)
if len(GPUS) > 1:
for k, x in get_state_dict(model).items():
if not getenv('SYNCBN') and ('running_mean' in k or 'running_var' in k):
x.shard_(GPUS, axis=0)
else:
x.to_(GPUS)
# parse the training params into bias and non-bias
params_dict = get_state_dict(model)
params_bias = []
params_non_bias = []
for params in params_dict:
if params_dict[params].requires_grad is not False:
if 'bias' in params:
params_bias.append(params_dict[params])
else:
params_non_bias.append(params_dict[params])
opt_bias = optim.SGD(params_bias, lr=0.01, momentum=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['bias_decay'])
opt_non_bias = optim.SGD(params_non_bias, lr=0.01, momentum=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['non_bias_decay'])
# NOTE taken from the hlb_CIFAR repository, might need to be tuned
initial_div_factor = hyp['opt']['initial_div_factor']
final_lr_ratio = hyp['opt']['final_lr_ratio']
pct_start = hyp['opt']['percent_start']
lr_sched_bias = OneCycleLR(opt_bias, max_lr=hyp['opt']['bias_lr'], pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=STEPS)
lr_sched_non_bias = OneCycleLR(opt_non_bias, max_lr=hyp['opt']['non_bias_lr'], pct_start=pct_start, div_factor=initial_div_factor, final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=STEPS)
def train_step(model, optimizer, lr_scheduler, X, Y):
out = model(X)
loss_batchsize_scaler = 512/BS
loss = cross_entropy(out, Y, reduction='none', label_smoothing=hyp['opt']['label_smoothing']).mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
if not getenv("DISABLE_BACKWARD"):
# index 0 for bias and 1 for non-bias
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler[0].step()
lr_scheduler[1].step()
return loss.realize()
train_step_jitted = TinyJit(train_step)
def eval_step(model, X, Y):
out = model(X, training=False)
loss = cross_entropy(out, Y, reduction='mean')
correct = out.argmax(axis=1) == Y.argmax(axis=1)
return correct.realize(), loss.realize()
eval_step_jitted = TinyJit(eval_step)
eval_step_ema_jitted = TinyJit(eval_step)
# 97 steps in 2 seconds = 20ms / step
# step is 1163.42 GOPS = 56 TFLOPS!!!, 41% of max 136
# 4 seconds for tfloat32 ~ 28 TFLOPS, 41% of max 68
# 6.4 seconds for float32 ~ 17 TFLOPS, 50% of max 34.1
# 4.7 seconds for float32 w/o channels last. 24 TFLOPS. we get 50ms then i'll be happy. only 64x off
# https://www.anandtech.com/show/16727/nvidia-announces-geforce-rtx-3080-ti-3070-ti-upgraded-cards-coming-in-june
# 136 TFLOPS is the theoretical max w float16 on 3080 Ti
model_ema: Optional[modelEMA] = None
projected_ema_decay_val = hyp['ema']['decay_base'] ** hyp['ema']['every_n_steps']
i = 0
eval_acc_pct = 0.0
batcher = fetch_batches(X_train, Y_train, BS=BS, is_train=True)
with Tensor.train():
st = time.monotonic()
while i <= STEPS:
if i % getenv("EVAL_STEPS", STEPS) == 0 and i > 1 and not getenv("DISABLE_BACKWARD"):
# Use Tensor.training = False here actually bricks batchnorm, even with track_running_stats=True
corrects = []
corrects_ema = []
losses = []
losses_ema = []
for Xt, Yt in fetch_batches(X_test, Y_test, BS=EVAL_BS, is_train=False):
if len(GPUS) > 1:
Xt.shard_(GPUS, axis=0)
Yt.shard_(GPUS, axis=0)
correct, loss = eval_step_jitted(model, Xt, Yt)
losses.append(loss.numpy().tolist())
corrects.extend(correct.numpy().tolist())
if model_ema:
correct_ema, loss_ema = eval_step_ema_jitted(model_ema.net_ema, Xt, Yt)
losses_ema.append(loss_ema.numpy().tolist())
corrects_ema.extend(correct_ema.numpy().tolist())
# collect accuracy across ranks
correct_sum, correct_len = sum(corrects), len(corrects)
if model_ema: correct_sum_ema, correct_len_ema = sum(corrects_ema), len(corrects_ema)
eval_acc_pct = correct_sum/correct_len*100.0
if model_ema: acc_ema = correct_sum_ema/correct_len_ema*100.0
print(f"eval {correct_sum}/{correct_len} {eval_acc_pct:.2f}%, {(sum(losses)/len(losses)):7.2f} val_loss STEP={i} (in {(time.monotonic()-st)*1e3:.2f} ms)")
if model_ema: print(f"eval ema {correct_sum_ema}/{correct_len_ema} {acc_ema:.2f}%, {(sum(losses_ema)/len(losses_ema)):7.2f} val_loss STEP={i}")
if STEPS == 0 or i == STEPS: break
GlobalCounters.reset()
X, Y = next(batcher)
if len(GPUS) > 1:
X.shard_(GPUS, axis=0)
Y.shard_(GPUS, axis=0)
with Context(BEAM=getenv("LATEBEAM", BEAM.value), WINO=getenv("LATEWINO", WINO.value)):
loss = train_step_jitted(model, optim.OptimizerGroup(opt_bias, opt_non_bias), [lr_sched_bias, lr_sched_non_bias], X, Y)
et = time.monotonic()
loss_cpu = loss.numpy()
# EMA for network weights
if getenv("EMA") and i > hyp['ema']['steps'] and (i+1) % hyp['ema']['every_n_steps'] == 0:
if model_ema is None:
model_ema = modelEMA(W, model)
model_ema.update(model, Tensor([projected_ema_decay_val*(i/STEPS)**hyp['ema']['decay_pow']]))
cl = time.monotonic()
device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
# 53 221.74 ms run, 2.22 ms python, 219.52 ms CL, 803.39 loss, 0.000807 LR, 4.66 GB used, 3042.49 GFLOPS, 674.65 GOPS
print(f"{i:3d} {(cl-st)*1000.0:7.2f} ms run, {(et-st)*1000.0:7.2f} ms python, {(cl-et)*1000.0:7.2f} ms {device_str}, {loss_cpu:7.2f} loss, {opt_non_bias.lr.numpy()[0]:.6f} LR, {GlobalCounters.mem_used/1e9:.2f} GB used, {GlobalCounters.global_ops*1e-9/(cl-st):9.2f} GFLOPS, {GlobalCounters.global_ops*1e-9:9.2f} GOPS")
st = cl
i += 1
# verify eval acc
if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
if eval_acc_pct >= target:
print(colored(f"{eval_acc_pct=} >= {target}", "green"))
else:
raise ValueError(colored(f"{eval_acc_pct=} < {target}", "red"))
if __name__ == "__main__":
train_cifar()