298 lines
14 KiB
Python
298 lines
14 KiB
Python
import math
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import torch
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import torch.nn as nn
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import switched_conv_cuda_naive
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from lambda_networks import LambdaLayer
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from torch.nn import init, Conv2d, MSELoss
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import torch.nn.functional as F
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from tqdm import tqdm
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import torch.distributed as dist
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from trainer.losses import ConfigurableLoss
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def SwitchedConvRoutingNormal(input, selector, weight, bias, stride=1):
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convs = []
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b, s, h, w = selector.shape
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for sel in range(s):
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convs.append(F.conv2d(input, weight[:, :, sel, :, :], bias, stride=stride, padding=weight.shape[-1] // 2))
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output = torch.stack(convs, dim=1) * selector.unsqueeze(dim=2)
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return output.sum(dim=1)
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class SwitchedConvHardRoutingFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, selector, weight, bias, stride=1):
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# Build hard attention mask from selector input
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b, s, h, w = selector.shape
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mask = selector.argmax(dim=1).int()
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output = switched_conv_cuda_naive.forward(input, mask, weight, bias, stride)
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ctx.stride = stride
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ctx.breadth = s
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ctx.save_for_backward(*[input, output.detach().clone(), mask, weight, bias])
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return output
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@staticmethod
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def backward(ctx, gradIn):
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#import pydevd
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#pydevd.settrace(suspend=False, trace_only_current_thread=True)
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input, output, mask, weight, bias = ctx.saved_tensors
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gradIn = gradIn
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# Selector grad is simply the element-wise product of grad with the output of the layer, summed across the channel dimension
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# and repeated along the breadth of the switch. (Think of the forward operation using the selector as a simple matrix of 1s
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# and zeros that is multiplied by the output.)
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grad_sel = (gradIn * output).sum(dim=1, keepdim=True).repeat(1,ctx.breadth,1,1)
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grad, grad_w, grad_b = switched_conv_cuda_naive.backward(input, gradIn.contiguous(), mask, weight, bias, ctx.stride)
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return grad, grad_sel, grad_w, grad_b, None
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class RouteTop1(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input):
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mask = torch.nn.functional.one_hot(input.argmax(dim=1), num_classes=input.shape[1]).permute(0,3,1,2)
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out = torch.ones_like(input)
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out[mask != 1] = 0
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ctx.save_for_backward(mask, input.clone())
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return out
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@staticmethod
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def backward(ctx, grad):
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# Enable breakpoints in this function: (Comment out if not debugging)
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#import pydevd
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#pydevd.settrace(suspend=False, trace_only_current_thread=True)
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mask, input = ctx.saved_tensors
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input[mask != 1] = 1
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grad_input = grad.clone()
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grad_input[mask != 1] = 0
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grad_input_n = grad_input / input # Above, we made everything either a zero or a one. Unscale the ones by dividing by the unmasked inputs.
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return grad_input_n
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"""
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SwitchNorm is meant to be applied against the Softmax output of an switching function across a large set of
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switch computations. It is meant to promote an equal distribution of switch weights by decreasing the magnitude
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of switch weights that are over-used and increasing the magnitude of under-used weights.
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The return value has the exact same format as a normal Softmax output and can be used directly into the input of an
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switch equation.
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Since the whole point of convolutional switch is to enable training extra-wide networks to operate on a large number
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of image categories, it makes almost no sense to perform this type of norm against a single mini-batch of images: some
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of the switches will not be used in such a small context - and that's good! This is solved by accumulating. Every
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forward pass computes a norm across the current minibatch. That norm is added into a rotating buffer of size
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<accumulator_size>. The actual normalization occurs across the entire rotating buffer.
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You should set accumulator size according to two factors:
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- Your batch size. Smaller batch size should mean greater accumulator size.
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- Your image diversity. More diverse images have less need for the accumulator.
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- How wide your switch/switching group size is. More groups mean you're going to want more accumulation.
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Note: This norm makes the (potentially flawed) assumption that each forward() pass has unique data. For maximum
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effectiveness, avoid doing this - or make alterations to work around it.
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Note: This norm does nothing for the first <accumulator_size> iterations.
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"""
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class SwitchNorm(nn.Module):
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def __init__(self, group_size, accumulator_size=128):
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super().__init__()
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self.accumulator_desired_size = accumulator_size
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self.group_size = group_size
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self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
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self.register_buffer("accumulator", torch.zeros(accumulator_size, group_size))
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def add_norm_to_buffer(self, x):
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flat = x.sum(dim=[0, 2, 3])
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norm = flat / torch.mean(flat)
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self.accumulator[self.accumulator_index] = norm.detach().clone()
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self.accumulator_index += 1
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if self.accumulator_index >= self.accumulator_desired_size:
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self.accumulator_index *= 0
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if self.accumulator_filled <= 0:
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self.accumulator_filled += 1
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# Input into forward is a switching tensor of shape (batch,groups,width,height)
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def forward(self, x: torch.Tensor, update_attention_norm=True):
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assert len(x.shape) == 4
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# Push the accumulator to the right device on the first iteration.
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if self.accumulator.device != x.device:
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self.accumulator = self.accumulator.to(x.device)
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# In eval, don't change the norm buffer.
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if self.training and update_attention_norm:
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self.add_norm_to_buffer(x)
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# Reduce across all distributed entities, if needed
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if dist.is_available() and dist.is_initialized():
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dist.all_reduce(self.accumulator, op=dist.ReduceOp.SUM)
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self.accumulator /= dist.get_world_size()
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# Compute the norm factor.
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if self.accumulator_filled > 0:
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norm = torch.mean(self.accumulator, dim=0)
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else:
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norm = torch.ones(self.group_size, device=self.accumulator.device)
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x = x / norm.view(1,-1,1,1)
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# Need to re-normalize x so that the groups dimension sum to 1, just like when it was fed in.
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return x / x.sum(dim=1, keepdim=True)
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class HardRoutingGate(nn.Module):
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def __init__(self, breadth, hard_en=True):
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super().__init__()
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self.norm = SwitchNorm(breadth, accumulator_size=256)
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self.hard_en = hard_en
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def forward(self, x):
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soft = self.norm(nn.functional.softmax(x, dim=1))
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if self.hard_en:
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return RouteTop1.apply(soft)
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return soft
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class SwitchedConvHardRouting(nn.Module):
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def __init__(self,
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in_c,
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out_c,
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kernel_sz,
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breadth,
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stride=1,
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bias=True,
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dropout_rate=0.0,
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include_coupler: bool = False, # A 'coupler' is a latent converter which can make any bxcxhxw tensor a compatible switchedconv selector by performing a linear 1x1 conv, softmax and interpolate.
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coupler_mode: str = 'standard',
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coupler_dim_in: int = 0,
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hard_en=True): # A test switch that, when used in 'emulation mode' (where all convs are calculated using torch functions) computes soft-attention instead of hard-attention.
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super().__init__()
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self.in_channels = in_c
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self.out_channels = out_c
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self.kernel_size = kernel_sz
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self.stride = stride
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self.has_bias = bias
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self.breadth = breadth
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self.dropout_rate = dropout_rate
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if include_coupler:
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if coupler_mode == 'standard':
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self.coupler = Conv2d(coupler_dim_in, breadth, kernel_size=1, stride=self.stride)
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elif coupler_mode == 'lambda':
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self.coupler = nn.Sequential(nn.Conv2d(coupler_dim_in, coupler_dim_in, 1),
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nn.GroupNorm(num_groups=2, num_channels=coupler_dim_in),
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nn.ReLU(),
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LambdaLayer(dim=coupler_dim_in, dim_out=breadth, r=23, dim_k=16, heads=2, dim_u=1),
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nn.GroupNorm(num_groups=1, num_channels=breadth),
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nn.ReLU(),
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Conv2d(breadth, breadth, 1, stride=self.stride))
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elif coupler_mode == 'lambda2':
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self.coupler = nn.Sequential(nn.Conv2d(coupler_dim_in, coupler_dim_in, 1),
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nn.GroupNorm(num_groups=2, num_channels=coupler_dim_in),
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nn.ReLU(),
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LambdaLayer(dim=coupler_dim_in, dim_out=coupler_dim_in, r=23, dim_k=16, heads=2, dim_u=1),
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nn.GroupNorm(num_groups=2, num_channels=coupler_dim_in),
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nn.ReLU(),
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LambdaLayer(dim=coupler_dim_in, dim_out=breadth, r=23, dim_k=16, heads=2, dim_u=1),
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nn.GroupNorm(num_groups=1, num_channels=breadth),
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nn.ReLU(),
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Conv2d(breadth, breadth, 1, stride=self.stride))
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else:
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self.coupler = None
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self.gate = HardRoutingGate(breadth, hard_en=hard_en)
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self.weight = nn.Parameter(torch.empty(out_c, in_c, breadth, kernel_sz, kernel_sz))
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if bias:
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self.bias = nn.Parameter(torch.empty(out_c))
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else:
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self.bias = torch.zeros(out_c)
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self.reset_parameters()
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def reset_parameters(self) -> None:
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight[:,:,0,:,:])
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bound = 1 / math.sqrt(fan_in)
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init.uniform_(self.bias, -bound, bound)
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def load_weights_from_conv(self, cnv):
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sd = cnv.state_dict()
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sd['weight'] = sd['weight'].unsqueeze(2).repeat(1,1,self.breadth,1,1)
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self.load_state_dict(sd)
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def forward(self, input, selector=None):
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if self.bias.device != input.device:
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self.bias = self.bias.to(input.device) # Because this bias can be a tensor that is not moved with the rest of the module.
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# If a coupler was specified, run that to convert selector into a softmax distribution.
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if self.coupler:
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if selector is None: # A coupler can convert from any input to a selector, so 'None' is allowed.
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selector = input
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selector = self.coupler(selector)
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assert selector is not None
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# Apply dropout at the batch level per kernel.
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if self.training and self.dropout_rate > 0:
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b, c, h, w = selector.shape
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drop = torch.rand((b, c, 1, 1), device=input.device) > self.dropout_rate
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# Ensure that there is always at least one switch left un-dropped out
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fix_blank = (drop.sum(dim=1, keepdim=True) == 0).repeat(1, c, 1, 1)
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drop = drop.logical_or(fix_blank)
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selector = drop * selector
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selector = self.gate(selector)
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# Debugging variables
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self.last_select = selector.detach().clone()
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self.latest_masks = (selector.max(dim=1, keepdim=True)[0].repeat(1,self.breadth,1,1) == selector).float().argmax(dim=1)
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if False:
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# This is a custom CUDA implementation which should be faster and less memory intensive (once completed).
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return SwitchedConvHardRoutingFunction.apply(input, selector, self.weight, self.bias, self.stride)
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else:
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# This composes the switching functionality using raw Torch, which basically consists of computing each of <breadth> convs separately and combining them.
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return SwitchedConvRoutingNormal(input, selector, self.weight, self.bias, self.stride)
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# Given a state_dict and the module that that sd belongs to, strips out all Conv2d.weight parameters and replaces them
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# with the equivalent SwitchedConv.weight parameters. Does not create coupler params.
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def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_list=[]):
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state_dict = module.state_dict()
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for name, m in module.named_modules():
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if not isinstance(m, nn.Conv2d):
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continue
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ignored = False
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for smod in ignore_list:
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if smod in name:
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ignored = True
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continue
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if ignored:
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continue
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if name == '':
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key = 'weight'
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else:
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key = f'{name}.weight'
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state_dict[key] = state_dict[key].unsqueeze(2).repeat(1,1,switch_breadth,1,1)
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return state_dict
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def test_net():
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for j in tqdm(range(100)):
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base_conv = Conv2d(32, 64, 3, stride=2, padding=1, bias=True).to('cuda')
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mod_conv = SwitchedConvHardRouting(32, 64, 3, breadth=8, stride=2, bias=True, include_coupler=True, coupler_dim_in=32, dropout_rate=.2).to('cuda')
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mod_sd = convert_conv_net_state_dict_to_switched_conv(base_conv, 8)
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mod_conv.load_state_dict(mod_sd, strict=False)
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inp = torch.randn((128,32,128,128), device='cuda')
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out1 = base_conv(inp)
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out2 = mod_conv(inp, None)
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compare = (out2+torch.rand_like(out2)*1e-6).detach()
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MSELoss()(out2, compare).backward()
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assert(torch.max(torch.abs(out1-out2)) < 1e-5)
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if __name__ == '__main__':
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test_net() |