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| # adopted from | |
| # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py | |
| # and | |
| # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py | |
| # and | |
| # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py | |
| # | |
| # thanks! | |
| import os | |
| import math | |
| import torch | |
| import numpy as np | |
| import torch.nn as nn | |
| from einops import repeat | |
| ################################################################################# | |
| # Unet Utils # | |
| ################################################################################# | |
| def checkpoint(func, inputs, params, flag): | |
| """ | |
| Evaluate a function without caching intermediate activations, allowing for | |
| reduced memory at the expense of extra compute in the backward pass. | |
| :param func: the function to evaluate. | |
| :param inputs: the argument sequence to pass to `func`. | |
| :param params: a sequence of parameters `func` depends on but does not | |
| explicitly take as arguments. | |
| :param flag: if False, disable gradient checkpointing. | |
| """ | |
| if flag: | |
| args = tuple(inputs) + tuple(params) | |
| return CheckpointFunction.apply(func, len(inputs), *args) | |
| else: | |
| return func(*inputs) | |
| class CheckpointFunction(torch.autograd.Function): | |
| def forward(ctx, run_function, length, *args): | |
| ctx.run_function = run_function | |
| ctx.input_tensors = list(args[:length]) | |
| ctx.input_params = list(args[length:]) | |
| with torch.no_grad(): | |
| output_tensors = ctx.run_function(*ctx.input_tensors) | |
| return output_tensors | |
| def backward(ctx, *output_grads): | |
| ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] | |
| with torch.enable_grad(): | |
| # Fixes a bug where the first op in run_function modifies the | |
| # Tensor storage in place, which is not allowed for detach()'d | |
| # Tensors. | |
| shallow_copies = [x.view_as(x) for x in ctx.input_tensors] | |
| output_tensors = ctx.run_function(*shallow_copies) | |
| input_grads = torch.autograd.grad( | |
| output_tensors, | |
| ctx.input_tensors + ctx.input_params, | |
| output_grads, | |
| allow_unused=True, | |
| ) | |
| del ctx.input_tensors | |
| del ctx.input_params | |
| del output_tensors | |
| return (None, None) + input_grads | |
| def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): | |
| """ | |
| Create sinusoidal timestep embeddings. | |
| :param timesteps: a 1-D Tensor of N indices, one per batch element. | |
| These may be fractional. | |
| :param dim: the dimension of the output. | |
| :param max_period: controls the minimum frequency of the embeddings. | |
| :return: an [N x dim] Tensor of positional embeddings. | |
| """ | |
| if not repeat_only: | |
| half = dim // 2 | |
| freqs = torch.exp( | |
| -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half | |
| ).to(device=timesteps.device) | |
| args = timesteps[:, None].float() * freqs[None] | |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) | |
| if dim % 2: | |
| embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) | |
| else: | |
| embedding = repeat(timesteps, 'b -> b d', d=dim).contiguous() | |
| return embedding | |
| def zero_module(module): | |
| """ | |
| Zero out the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().zero_() | |
| return module | |
| def scale_module(module, scale): | |
| """ | |
| Scale the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().mul_(scale) | |
| return module | |
| def mean_flat(tensor): | |
| """ | |
| Take the mean over all non-batch dimensions. | |
| """ | |
| return tensor.mean(dim=list(range(1, len(tensor.shape)))) | |
| def normalization(channels): | |
| """ | |
| Make a standard normalization layer. | |
| :param channels: number of input channels. | |
| :return: an nn.Module for normalization. | |
| """ | |
| return GroupNorm32(32, channels) | |
| # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. | |
| class SiLU(nn.Module): | |
| def forward(self, x): | |
| return x * torch.sigmoid(x) | |
| class GroupNorm32(nn.GroupNorm): | |
| def forward(self, x): | |
| return super().forward(x.float()).type(x.dtype) | |
| def conv_nd(dims, *args, **kwargs): | |
| """ | |
| Create a 1D, 2D, or 3D convolution module. | |
| """ | |
| if dims == 1: | |
| return nn.Conv1d(*args, **kwargs) | |
| elif dims == 2: | |
| return nn.Conv2d(*args, **kwargs) | |
| elif dims == 3: | |
| return nn.Conv3d(*args, **kwargs) | |
| raise ValueError(f"unsupported dimensions: {dims}") | |
| def linear(*args, **kwargs): | |
| """ | |
| Create a linear module. | |
| """ | |
| return nn.Linear(*args, **kwargs) | |
| def avg_pool_nd(dims, *args, **kwargs): | |
| """ | |
| Create a 1D, 2D, or 3D average pooling module. | |
| """ | |
| if dims == 1: | |
| return nn.AvgPool1d(*args, **kwargs) | |
| elif dims == 2: | |
| return nn.AvgPool2d(*args, **kwargs) | |
| elif dims == 3: | |
| return nn.AvgPool3d(*args, **kwargs) | |
| raise ValueError(f"unsupported dimensions: {dims}") | |
| # class HybridConditioner(nn.Module): | |
| # def __init__(self, c_concat_config, c_crossattn_config): | |
| # super().__init__() | |
| # self.concat_conditioner = instantiate_from_config(c_concat_config) | |
| # self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) | |
| # def forward(self, c_concat, c_crossattn): | |
| # c_concat = self.concat_conditioner(c_concat) | |
| # c_crossattn = self.crossattn_conditioner(c_crossattn) | |
| # return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} | |
| def noise_like(shape, device, repeat=False): | |
| repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) | |
| noise = lambda: torch.randn(shape, device=device) | |
| return repeat_noise() if repeat else noise() | |
| def count_flops_attn(model, _x, y): | |
| """ | |
| A counter for the `thop` package to count the operations in an | |
| attention operation. | |
| Meant to be used like: | |
| macs, params = thop.profile( | |
| model, | |
| inputs=(inputs, timestamps), | |
| custom_ops={QKVAttention: QKVAttention.count_flops}, | |
| ) | |
| """ | |
| b, c, *spatial = y[0].shape | |
| num_spatial = int(np.prod(spatial)) | |
| # We perform two matmuls with the same number of ops. | |
| # The first computes the weight matrix, the second computes | |
| # the combination of the value vectors. | |
| matmul_ops = 2 * b * (num_spatial ** 2) * c | |
| model.total_ops += torch.DoubleTensor([matmul_ops]) | |
| def count_params(model, verbose=False): | |
| total_params = sum(p.numel() for p in model.parameters()) | |
| if verbose: | |
| print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") | |
| return total_params |