Delete airbench94.py

airbench94.py

@@ -1,483 +0,0 @@
-# Uncompiled variant of airbench94_compiled.py
-# 3.83s runtime on an A100; 0.36 PFLOPs.
-# Evidence: 94.01 average accuracy in n=1000 runs.
-#
-# We recorded the runtime of 3.83 seconds on an NVIDIA A100-SXM4-80GB with the following nvidia-smi:
-# NVIDIA-SMI 515.105.01   Driver Version: 515.105.01   CUDA Version: 11.7
-# torch.__version__ == '2.1.2+cu118'
-
-#############################################
-#            Setup/Hyperparameters          #
-#############################################
-
-import os
-import sys
-import uuid
-from math import ceil
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torchvision
-import torchvision.transforms as T
-
-torch.backends.cudnn.benchmark = True
-
-# We express the main training hyperparameters (batch size, learning rate, momentum, and weight decay)
-# in decoupled form, so that each one can be tuned independently. This accomplishes the following:
-# * Assuming time-constant gradients, the average step size is decoupled from everything but the lr.
-# * The size of the weight decay update is decoupled from everything but the wd.
-# In constrast, normally when we increase the (Nesterov) momentum, this also scales up the step size
-# proportionally to 1 + 1 / (1 - momentum), meaning we cannot change momentum without having to re-tune
-# the learning rate. Similarly, normally when we increase the learning rate this also increases the size
-# of the weight decay, requiring a proportional decrease in the wd to maintain the same decay strength.
-#
-# The practical impact is that hyperparameter tuning is faster, since this parametrization allows each
-# one to be tuned independently. See https://myrtle.ai/learn/how-to-train-your-resnet-5-hyperparameters/.
-
-hyp = {
-    'opt': {
-        'train_epochs': 9.9,
-        'batch_size': 1024,
-        'lr': 11.5,                 # learning rate per 1024 examples
-        'momentum': 0.85,
-        'weight_decay': 0.0153,     # weight decay per 1024 examples (decoupled from learning rate)
-        'bias_scaler': 64.0,        # scales up learning rate (but not weight decay) for BatchNorm biases
-        'label_smoothing': 0.2,
-        'whiten_bias_epochs': 3,    # how many epochs to train the whitening layer bias before freezing
-    },
-    'aug': {
-        'flip': True,
-        'translate': 2,
-    },
-    'net': {
-        'widths': {
-            'block1': 64,
-            'block2': 256,
-            'block3': 256,
-        },
-        'batchnorm_momentum': 0.6,
-        'scaling_factor': 1/9,
-        'tta_level': 2,         # the level of test-time augmentation: 0=none, 1=mirror, 2=mirror+translate
-    },
-}
-
-#############################################
-#                DataLoader                 #
-#############################################
-
-CIFAR_MEAN = torch.tensor((0.4914, 0.4822, 0.4465))
-CIFAR_STD = torch.tensor((0.2470, 0.2435, 0.2616))
-
-def batch_flip_lr(inputs):
-    flip_mask = (torch.rand(len(inputs), device=inputs.device) < 0.5).view(-1, 1, 1, 1)
-    return torch.where(flip_mask, inputs.flip(-1), inputs)
-
-def batch_crop(images, crop_size):
-    r = (images.size(-1) - crop_size)//2
-    shifts = torch.randint(-r, r+1, size=(len(images), 2), device=images.device)
-    images_out = torch.empty((len(images), 3, crop_size, crop_size), device=images.device, dtype=images.dtype)
-    # The two cropping methods in this if-else produce equivalent results, but the second is faster for r > 2.
-    if r <= 2:
-        for sy in range(-r, r+1):
-            for sx in range(-r, r+1):
-                mask = (shifts[:, 0] == sy) & (shifts[:, 1] == sx)
-                images_out[mask] = images[mask, :, r+sy:r+sy+crop_size, r+sx:r+sx+crop_size]
-    else:
-        images_tmp = torch.empty((len(images), 3, crop_size, crop_size+2*r), device=images.device, dtype=images.dtype)
-        for s in range(-r, r+1):
-            mask = (shifts[:, 0] == s)
-            images_tmp[mask] = images[mask, :, r+s:r+s+crop_size, :]
-        for s in range(-r, r+1):
-            mask = (shifts[:, 1] == s)
-            images_out[mask] = images_tmp[mask, :, :, r+s:r+s+crop_size]
-    return images_out
-
-class CifarLoader:
-
-    def __init__(self, path, train=True, batch_size=500, aug=None, drop_last=None, shuffle=None, gpu=0):
-        data_path = os.path.join(path, 'train.pt' if train else 'test.pt')
-        if not os.path.exists(data_path):
-            dset = torchvision.datasets.CIFAR10(path, download=True, train=train)
-            images = torch.tensor(dset.data)
-            labels = torch.tensor(dset.targets)
-            torch.save({'images': images, 'labels': labels, 'classes': dset.classes}, data_path)
-
-        data = torch.load(data_path, map_location=torch.device(gpu))
-        self.images, self.labels, self.classes = data['images'], data['labels'], data['classes']
-        # It's faster to load+process uint8 data than to load preprocessed fp16 data
-        self.images = (self.images.half() / 255).permute(0, 3, 1, 2).to(memory_format=torch.channels_last)
-
-        self.normalize = T.Normalize(CIFAR_MEAN, CIFAR_STD)
-        self.proc_images = {} # Saved results of image processing to be done on the first epoch
-        self.epoch = 0
-
-        self.aug = aug or {}
-        for k in self.aug.keys():
-            assert k in ['flip', 'translate'], 'Unrecognized key: %s' % k
-
-        self.batch_size = batch_size
-        self.drop_last = train if drop_last is None else drop_last
-        self.shuffle = train if shuffle is None else shuffle
-
-    def __len__(self):
-        return len(self.images)//self.batch_size if self.drop_last else ceil(len(self.images)/self.batch_size)
-
-    def __iter__(self):
-
-        if self.epoch == 0:
-            images = self.proc_images['norm'] = self.normalize(self.images)
-            # Pre-flip images in order to do every-other epoch flipping scheme
-            if self.aug.get('flip', False):
-                images = self.proc_images['flip'] = batch_flip_lr(images)
-            # Pre-pad images to save time when doing random translation
-            pad = self.aug.get('translate', 0)
-            if pad > 0:
-                self.proc_images['pad'] = F.pad(images, (pad,)*4, 'reflect')
-
-        if self.aug.get('translate', 0) > 0:
-            images = batch_crop(self.proc_images['pad'], self.images.shape[-2])
-        elif self.aug.get('flip', False):
-            images = self.proc_images['flip']
-        else:
-            images = self.proc_images['norm']
-        # Flip all images together every other epoch. This increases diversity relative to random flipping
-        if self.aug.get('flip', False):
-            if self.epoch % 2 == 1:
-                images = images.flip(-1)
-
-        self.epoch += 1
-
-        indices = (torch.randperm if self.shuffle else torch.arange)(len(images), device=images.device)
-        for i in range(len(self)):
-            idxs = indices[i*self.batch_size:(i+1)*self.batch_size]
-            yield (images[idxs], self.labels[idxs])
-
-#############################################
-#            Network Components             #
-#############################################
-
-class Flatten(nn.Module):
-    def forward(self, x):
-        return x.view(x.size(0), -1)
-
-class Mul(nn.Module):
-    def __init__(self, scale):
-        super().__init__()
-        self.scale = scale
-    def forward(self, x):
-        return x * self.scale
-
-class BatchNorm(nn.BatchNorm2d):
-    def __init__(self, num_features, momentum, eps=1e-12,
-                 weight=False, bias=True):
-        super().__init__(num_features, eps=eps, momentum=1-momentum)
-        self.weight.requires_grad = weight
-        self.bias.requires_grad = bias
-        # Note that PyTorch already initializes the weights to one and bias to zero
-
-class Conv(nn.Conv2d):
-    def __init__(self, in_channels, out_channels, kernel_size=3, padding='same', bias=False):
-        super().__init__(in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
-
-    def reset_parameters(self):
-        super().reset_parameters()
-        if self.bias is not None:
-            self.bias.data.zero_()
-        w = self.weight.data
-        torch.nn.init.dirac_(w[:w.size(1)])
-
-class ConvGroup(nn.Module):
-    def __init__(self, channels_in, channels_out, batchnorm_momentum):
-        super().__init__()
-        self.conv1 = Conv(channels_in,  channels_out)
-        self.pool = nn.MaxPool2d(2)
-        self.norm1 = BatchNorm(channels_out, batchnorm_momentum)
-        self.conv2 = Conv(channels_out, channels_out)
-        self.norm2 = BatchNorm(channels_out, batchnorm_momentum)
-        self.activ = nn.GELU()
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.pool(x)
-        x = self.norm1(x)
-        x = self.activ(x)
-        x = self.conv2(x)
-        x = self.norm2(x)
-        x = self.activ(x)
-        return x
-
-#############################################
-#            Network Definition             #
-#############################################
-
-def make_net():
-    widths = hyp['net']['widths']
-    batchnorm_momentum = hyp['net']['batchnorm_momentum']
-    whiten_kernel_size = 2
-    whiten_width = 2 * 3 * whiten_kernel_size**2
-    net = nn.Sequential(
-        Conv(3, whiten_width, whiten_kernel_size, padding=0, bias=True),
-        nn.GELU(),
-        ConvGroup(whiten_width,     widths['block1'], batchnorm_momentum),
-        ConvGroup(widths['block1'], widths['block2'], batchnorm_momentum),
-        ConvGroup(widths['block2'], widths['block3'], batchnorm_momentum),
-        nn.MaxPool2d(3),
-        Flatten(),
-        nn.Linear(widths['block3'], 10, bias=False),
-        Mul(hyp['net']['scaling_factor']),
-    )
-    net[0].weight.requires_grad = False
-    net = net.half().cuda()
-    net = net.to(memory_format=torch.channels_last)
-    for mod in net.modules():
-        if isinstance(mod, BatchNorm):
-            mod.float()
-    return net
-
-#############################################
-#       Whitening Conv Initialization       #
-#############################################
-
-def get_patches(x, patch_shape):
-    c, (h, w) = x.shape[1], patch_shape
-    return x.unfold(2,h,1).unfold(3,w,1).transpose(1,3).reshape(-1,c,h,w).float()
-
-def get_whitening_parameters(patches):
-    n,c,h,w = patches.shape
-    patches_flat = patches.view(n, -1)
-    est_patch_covariance = (patches_flat.T @ patches_flat) / n
-    eigenvalues, eigenvectors = torch.linalg.eigh(est_patch_covariance, UPLO='U')
-    return eigenvalues.flip(0).view(-1, 1, 1, 1), eigenvectors.T.reshape(c*h*w,c,h,w).flip(0)
-
-def init_whitening_conv(layer, train_set, eps=5e-4):
-    patches = get_patches(train_set, patch_shape=layer.weight.data.shape[2:])
-    eigenvalues, eigenvectors = get_whitening_parameters(patches)
-    eigenvectors_scaled = eigenvectors / torch.sqrt(eigenvalues + eps)
-    layer.weight.data[:] = torch.cat((eigenvectors_scaled, -eigenvectors_scaled))
-
-############################################
-#                Lookahead                 #
-############################################
-
-class LookaheadState:
-    def __init__(self, net):
-        self.net_ema = {k: v.clone() for k, v in net.state_dict().items()}
-
-    def update(self, net, decay):
-        for ema_param, net_param in zip(self.net_ema.values(), net.state_dict().values()):
-            if net_param.dtype in (torch.half, torch.float):
-                ema_param.lerp_(net_param, 1-decay)
-                net_param.copy_(ema_param)
-
-############################################
-#                 Logging                  #
-############################################
-
-def print_columns(columns_list, is_head=False, is_final_entry=False):
-    print_string = ''
-    for col in columns_list:
-        print_string += '|  %s  ' % col
-    print_string += '|'
-    if is_head:
-        print('-'*len(print_string))
-    print(print_string)
-    if is_head or is_final_entry:
-        print('-'*len(print_string))
-
-logging_columns_list = ['run   ', 'epoch', 'train_loss', 'train_acc', 'val_acc', 'tta_val_acc', 'total_time_seconds']
-def print_training_details(variables, is_final_entry):
-    formatted = []
-    for col in logging_columns_list:
-        var = variables.get(col.strip(), None)
-        if type(var) in (int, str):
-            res = str(var)
-        elif type(var) is float:
-            res = '{:0.4f}'.format(var)
-        else:
-            assert var is None
-            res = ''
-        formatted.append(res.rjust(len(col)))
-    print_columns(formatted, is_final_entry=is_final_entry)
-
-############################################
-#               Evaluation                 #
-############################################
-
-def infer(model, loader, tta_level=0):
-
-    # Test-time augmentation strategy (for tta_level=2):
-    # 1. Flip/mirror the image left-to-right (50% of the time).
-    # 2. Translate the image by one pixel either up-and-left or down-and-right (50% of the time,
-    #    i.e. both happen 25% of the time).
-    #
-    # This creates 6 views per image (left/right times the two translations and no-translation),
-    # which we evaluate and then weight according to the given probabilities.
-
-    def infer_basic(inputs, net):
-        return net(inputs).clone()
-
-    def infer_mirror(inputs, net):
-        return 0.5 * net(inputs) + 0.5 * net(inputs.flip(-1))
-
-    def infer_mirror_translate(inputs, net):
-        logits = infer_mirror(inputs, net)
-        pad = 1
-        padded_inputs = F.pad(inputs, (pad,)*4, 'reflect')
-        inputs_translate_list = [
-            padded_inputs[:, :, 0:32, 0:32],
-            padded_inputs[:, :, 2:34, 2:34],
-        ]
-        logits_translate_list = [infer_mirror(inputs_translate, net)
-                                 for inputs_translate in inputs_translate_list]
-        logits_translate = torch.stack(logits_translate_list).mean(0)
-        return 0.5 * logits + 0.5 * logits_translate
-
-    model.eval()
-    test_images = loader.normalize(loader.images)
-    infer_fn = [infer_basic, infer_mirror, infer_mirror_translate][tta_level]
-    with torch.no_grad():
-        return torch.cat([infer_fn(inputs, model) for inputs in test_images.split(2000)])
-
-def evaluate(model, loader, tta_level=0):
-    logits = infer(model, loader, tta_level)
-    return (logits.argmax(1) == loader.labels).float().mean().item()
-
-############################################
-#                Training                  #
-############################################
-
-def main(run):
-
-    batch_size = hyp['opt']['batch_size']
-    epochs = hyp['opt']['train_epochs']
-    momentum = hyp['opt']['momentum']
-    # Assuming gradients are constant in time, for Nesterov momentum, the below ratio is how much
-    # larger the default steps will be than the underlying per-example gradients. We divide the
-    # learning rate by this ratio in order to ensure steps are the same scale as gradients, regardless
-    # of the choice of momentum.
-    kilostep_scale = 1024 * (1 + 1 / (1 - momentum))
-    lr = hyp['opt']['lr'] / kilostep_scale # un-decoupled learning rate for PyTorch SGD
-    wd = hyp['opt']['weight_decay'] * batch_size / kilostep_scale
-    lr_biases = lr * hyp['opt']['bias_scaler']
-
-    loss_fn = nn.CrossEntropyLoss(label_smoothing=hyp['opt']['label_smoothing'], reduction='none')
-    test_loader = CifarLoader('cifar10', train=False, batch_size=2000)
-    train_loader = CifarLoader('cifar10', train=True, batch_size=batch_size, aug=hyp['aug'])
-    if run == 'warmup':
-        # The only purpose of the first run is to warmup, so we can use dummy data
-        train_loader.labels = torch.randint(0, 10, size=(len(train_loader.labels),), device=train_loader.labels.device)
-    total_train_steps = ceil(len(train_loader) * epochs)
-
-    model = make_net()
-    current_steps = 0
-
-    norm_biases = [p for k, p in model.named_parameters() if 'norm' in k and p.requires_grad]
-    other_params = [p for k, p in model.named_parameters() if 'norm' not in k and p.requires_grad]
-    param_configs = [dict(params=norm_biases, lr=lr_biases, weight_decay=wd/lr_biases),
-                     dict(params=other_params, lr=lr, weight_decay=wd/lr)]
-    optimizer = torch.optim.SGD(param_configs, momentum=momentum, nesterov=True)
-
-    def get_lr(step):
-        warmup_steps = int(total_train_steps * 0.23)
-        warmdown_steps = total_train_steps - warmup_steps
-        if step < warmup_steps:
-            frac = step / warmup_steps
-            return 0.2 * (1 - frac) + 1.0 * frac
-        else:
-            frac = (step - warmup_steps) / warmdown_steps
-            return 1.0 * (1 - frac) + 0.07 * frac
-    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, get_lr)
-
-    alpha_schedule = 0.95**5 * (torch.arange(total_train_steps+1) / total_train_steps)**3
-    lookahead_state = LookaheadState(model)
-
-    # For accurately timing GPU code
-    starter = torch.cuda.Event(enable_timing=True)
-    ender = torch.cuda.Event(enable_timing=True)
-    total_time_seconds = 0.0
-
-    # Initialize the whitening layer using training images
-    starter.record()
-    train_images = train_loader.normalize(train_loader.images[:5000])
-    init_whitening_conv(model[0], train_images)
-    ender.record()
-    torch.cuda.synchronize()
-    total_time_seconds += 1e-3 * starter.elapsed_time(ender)
-
-    for epoch in range(ceil(epochs)):
-
-        model[0].bias.requires_grad = (epoch < hyp['opt']['whiten_bias_epochs'])
-
-        ####################
-        #     Training     #
-        ####################
-
-        starter.record()
-
-        model.train()
-        for inputs, labels in train_loader:
-
-            outputs = model(inputs)
-            loss = loss_fn(outputs, labels).sum()
-            optimizer.zero_grad(set_to_none=True)
-            loss.backward()
-            optimizer.step()
-            scheduler.step()
-
-            current_steps += 1
-
-            if current_steps % 5 == 0:
-                lookahead_state.update(model, decay=alpha_schedule[current_steps].item())
-
-            if current_steps >= total_train_steps:
-                if lookahead_state is not None:
-                    lookahead_state.update(model, decay=1.0)
-                break
-
-        ender.record()
-        torch.cuda.synchronize()
-        total_time_seconds += 1e-3 * starter.elapsed_time(ender)
-
-        ####################
-        #    Evaluation    #
-        ####################
-
-        # Save the accuracy and loss from the last training batch of the epoch
-        train_acc = (outputs.detach().argmax(1) == labels).float().mean().item()
-        train_loss = loss.item() / batch_size
-        val_acc = evaluate(model, test_loader, tta_level=0)
-        print_training_details(locals(), is_final_entry=False)
-        run = None # Only print the run number once
-
-    ####################
-    #  TTA Evaluation  #
-    ####################
-
-    starter.record()
-    tta_val_acc = evaluate(model, test_loader, tta_level=hyp['net']['tta_level'])
-    ender.record()
-    torch.cuda.synchronize()
-    total_time_seconds += 1e-3 * starter.elapsed_time(ender)
-
-    epoch = 'eval'
-    print_training_details(locals(), is_final_entry=True)
-
-    return tta_val_acc
-
-if __name__ == "__main__":
-    with open(sys.argv[0]) as f:
-        code = f.read()
-
-    print_columns(logging_columns_list, is_head=True)
-    #main('warmup')
-    accs = torch.tensor([main(run) for run in range(25)])
-    print('Mean: %.4f    Std: %.4f' % (accs.mean(), accs.std()))
-
-    log = {'code': code, 'accs': accs}
-    log_dir = os.path.join('logs', str(uuid.uuid4()))
-    os.makedirs(log_dir, exist_ok=True)
-    log_path = os.path.join(log_dir, 'log.pt')
-    print(os.path.abspath(log_path))
-    torch.save(log, os.path.join(log_dir, 'log.pt'))
-