fixes

.gitmodules

@@ -1,9 +1,6 @@
 [submodule "flownet2"]
 	path = flownet2
 	url = https://github.com/NVIDIA/flownet2-pytorch.git
-[submodule "codes/models/switched_conv"]
-	path = codes/models/switched_conv
-	url = https://github.com/neonbjb/SwitchedConvolutions.git
 [submodule "codes/models/flownet2"]
 	path = codes/models/flownet2
 	url = https://github.com/neonbjb/flownet2-pytorch.git

codes/models/classifiers/cifar_resnet_branched.py

@@ -1,280 +0,0 @@
-"""resnet in pytorch
-
-
-
-[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
-
-    Deep Residual Learning for Image Recognition
-    https://arxiv.org/abs/1512.03385v1
-"""
-
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-
-from models.switched_conv.switched_conv_hard_routing import SwitchNorm, RouteTop1
-from trainer.networks import register_model
-
-
-class BasicBlock(nn.Module):
-    """Basic Block for resnet 18 and resnet 34
-
-    """
-
-    #BasicBlock and BottleNeck block
-    #have different output size
-    #we use class attribute expansion
-    #to distinct
-    expansion = 1
-
-    def __init__(self, in_channels, out_channels, stride=1):
-        super().__init__()
-
-        #residual function
-        self.residual_function = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
-            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
-        )
-
-        #shortcut
-        self.shortcut = nn.Sequential()
-
-        #the shortcut output dimension is not the same with residual function
-        #use 1*1 convolution to match the dimension
-        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
-            )
-
-    def forward(self, x):
-        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
-
-class BottleNeck(nn.Module):
-    """Residual block for resnet over 50 layers
-
-    """
-    expansion = 4
-    def __init__(self, in_channels, out_channels, stride=1):
-        super().__init__()
-        self.residual_function = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
-            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
-        )
-
-        self.shortcut = nn.Sequential()
-
-        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
-                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
-            )
-
-    def forward(self, x):
-        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
-
-
-class ResNetTail(nn.Module):
-    def __init__(self, block, num_block, num_classes=100):
-        super().__init__()
-
-        self.in_channels = 64
-        self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
-        self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
-        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
-        self.fc = nn.Linear(256 * block.expansion, num_classes)
-
-    def _make_layer(self, block, out_channels, num_blocks, stride):
-        strides = [stride] + [1] * (num_blocks - 1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_channels, out_channels, stride))
-            self.in_channels = out_channels * block.expansion
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        output = self.conv4_x(x)
-        output = self.conv5_x(output)
-        output = self.avg_pool(output)
-        output = output.view(output.size(0), -1)
-        output = self.fc(output)
-
-        return output
-
-
-class DropoutNorm(SwitchNorm):
-    def __init__(self, group_size, dropout_rate, accumulator_size=256, eps=1e-6):
-        super().__init__(group_size, accumulator_size)
-        self.accumulator_desired_size = accumulator_size
-        self.group_size = group_size
-        self.dropout_rate = dropout_rate
-        self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
-        self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
-        self.register_buffer("accumulator", torch.zeros(accumulator_size, group_size))
-        self.eps = eps
-
-    def add_norm_to_buffer(self, x):
-        flatten_dims = [0] + [k+2 for k in range(len(x.shape)-2)]
-        flat = x.mean(dim=flatten_dims)
-
-        self.accumulator[self.accumulator_index] = flat.detach().clone()
-        self.accumulator_index += 1
-        if self.accumulator_index >= self.accumulator_desired_size:
-            self.accumulator_index *= 0
-            if self.accumulator_filled <= 0:
-                self.accumulator_filled += 1
-
-    # Input into forward is a switching tensor of shape (batch,groups,<misc>)
-    def forward(self, x: torch.Tensor):
-        assert len(x.shape) >= 2
-
-        if not self.training:
-            return x
-
-        # Only accumulate the "winning" switch slots.
-        mask = torch.nn.functional.one_hot(x.argmax(dim=1), num_classes=x.shape[1])
-        if len(x.shape) > 2:
-            mask = mask.permute(0, 3, 1, 2)  # TODO: Make this more extensible.
-        xtop = torch.ones_like(x)
-        xtop[mask != 1] = 0
-
-        # Push the accumulator to the right device on the first iteration.
-        if self.accumulator.device != xtop.device:
-            self.accumulator = self.accumulator.to(xtop.device)
-        self.add_norm_to_buffer(xtop)
-
-        # Reduce across all distributed entities, if needed
-        if dist.is_available() and dist.is_initialized():
-            dist.all_reduce(self.accumulator, op=dist.ReduceOp.SUM)
-            self.accumulator /= dist.get_world_size()
-
-        # Compute the dropout probabilities. This module is a no-op before the accumulator is initialized.
-        if self.accumulator_filled > 0:
-            with torch.no_grad():
-                probs = torch.mean(self.accumulator, dim=0) * self.dropout_rate
-                bs, br = x.shape[:2]
-                drop = torch.rand((bs, br), device=x.device) > probs.unsqueeze(0)
-                # Ensure that there is always at least one switch left un-dropped out
-                fix_blank = (drop.sum(dim=1, keepdim=True) == 0).repeat(1, br)
-                drop = drop.logical_or(fix_blank)
-            x_dropped = drop * x + ~drop * -1e20
-            x = x_dropped
-
-        return x
-
-
-class HardRoutingGate(nn.Module):
-    def __init__(self, breadth, fade_steps=10000, dropout_rate=.8):
-        super().__init__()
-        self.norm = DropoutNorm(breadth, dropout_rate, accumulator_size=128)
-        self.fade_steps = fade_steps
-        self.register_buffer("last_step", torch.zeros(1, dtype=torch.long, device='cpu'))
-
-    def forward(self, x):
-        if self.last_step < self.fade_steps:
-            x = torch.randn_like(x) * (self.fade_steps - self.last_step) / self.fade_steps + \
-                x * self.last_step / self.fade_steps
-            self.last_step = self.last_step + 1
-        soft = nn.functional.softmax(self.norm(x), dim=1)
-        return RouteTop1.apply(soft)
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, num_block, num_classes=100, num_tails=8):
-        super().__init__()
-        self.in_channels = 32
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
-            nn.BatchNorm2d(32),
-            nn.ReLU(inplace=True))
-
-        self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
-        self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
-        self.tails = nn.ModuleList([ResNetTail(block, num_block, 256) for _ in range(num_tails)])
-        self.selector = ResNetTail(block, num_block, num_tails)
-        self.selector_gate = nn.Linear(256, 1)
-        self.gate = HardRoutingGate(num_tails, dropout_rate=2)
-        self.final_linear = nn.Linear(256, num_classes)
-
-    def _make_layer(self, block, out_channels, num_blocks, stride):
-        strides = [stride] + [1] * (num_blocks - 1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_channels, out_channels, stride))
-            self.in_channels = out_channels * block.expansion
-        return nn.Sequential(*layers)
-
-    def get_debug_values(self, step, __):
-        logs = {'histogram_switch_usage': self.latest_masks}
-        return logs
-
-    def forward(self, x, coarse_label, return_selector=False):
-        output = self.conv1(x)
-        output = self.conv2_x(output)
-        output = self.conv3_x(output)
-
-        keys = []
-        for t in self.tails:
-            keys.append(t(output))
-        keys = torch.stack(keys, dim=1)
-
-        query = self.selector(output).unsqueeze(2)
-        selector = self.selector_gate(query * keys).squeeze(-1)
-        selector = self.gate(selector)
-        self.latest_masks = (selector.max(dim=1, keepdim=True)[0].repeat(1,8) == selector).float().argmax(dim=1)
-        values = self.final_linear(selector.unsqueeze(-1) * keys)
-
-        if return_selector:
-            return values.sum(dim=1), selector
-        else:
-            return values.sum(dim=1)
-
-        #bs = output.shape[0]
-        #return (tailouts[coarse_label] * torch.eye(n=bs, device=x.device).view(bs,bs,1)).sum(dim=1)
-
-@register_model
-def register_cifar_resnet18_branched(opt_net, opt):
-    """ return a ResNet 18 object
-    """
-    return ResNet(BasicBlock, [2, 2, 2, 2])
-
-def resnet34():
-    """ return a ResNet 34 object
-    """
-    return ResNet(BasicBlock, [3, 4, 6, 3])
-
-def resnet50():
-    """ return a ResNet 50 object
-    """
-    return ResNet(BottleNeck, [3, 4, 6, 3])
-
-def resnet101():
-    """ return a ResNet 101 object
-    """
-    return ResNet(BottleNeck, [3, 4, 23, 3])
-
-def resnet152():
-    """ return a ResNet 152 object
-    """
-    return ResNet(BottleNeck, [3, 8, 36, 3])
-
-
-if __name__ == '__main__':
-    model = ResNet(BasicBlock, [2,2,2,2])
-    for j in range(10):
-        v = model(torch.randn(256,3,32,32), None)
-        print(model.get_debug_values(0, None))
-    print(v.shape)
-    l = nn.MSELoss()(v, torch.randn_like(v))
-    l.backward()
-

codes/models/image_generation/RRDBNet_arch.py

@@ -11,7 +11,6 @@ from torchvision.models.resnet import Bottleneck
 from models.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
 from trainer.networks import register_model
 from utils.util import checkpoint, sequential_checkpoint, opt_get
-from models.switched_conv.switched_conv import SwitchedConv
 
 
 class ResidualDenseBlock(nn.Module):

codes/models/vqvae/gumbel_quantizer.py

@@ -3,7 +3,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import einsum
 
-from models.switched_conv.switched_conv_hard_routing import SwitchNorm
 from utils.weight_scheduler import LinearDecayWeightScheduler
 
 
@@ -48,7 +47,6 @@ class GumbelQuantizer(nn.Module):
         return sampled.permute(0,2,1), 0, codes
 
 if __name__ == '__main__':
-    from models.diffusion.diffusion_dvae import DiscreteDecoder
     j =  torch.randn(8,40,1024)
     m = GumbelQuantizer(1024, 1024, 4096)
     m2 = DiscreteDecoder(1024, (512, 256), 2)

codes/train.py

@@ -318,7 +318,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts9_mel.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts9_mel_flat.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     args = parser.parse_args()
     opt = option.parse(args.opt, is_train=True)

codes/trainer/losses.py

@@ -52,12 +52,6 @@ def create_loss(opt_loss, env):
         return RecurrentLoss(opt_loss, env)
     elif type == 'for_element':
         return ForElementLoss(opt_loss, env)
-    elif type == 'mixture_of_experts':
-        from models.switched_conv.mixture_of_experts import MixtureOfExpertsLoss
-        return MixtureOfExpertsLoss(opt_loss, env)
-    elif type == 'switch_transformer_balance':
-        from models.switched_conv.mixture_of_experts import SwitchTransformersLoadBalancingLoss
-        return SwitchTransformersLoadBalancingLoss(opt_loss, env)
     elif type == 'nv_tacotron2_loss':
         from models.audio.tts.tacotron2 import Tacotron2Loss
         return Tacotron2Loss(opt_loss, env)