| import webdataset as wds |
| import os |
| import torch |
|
|
| class ActivationsDataloader: |
| def __init__(self, paths_to_datasets, block_name, batch_size, output_or_diff='diff', num_in_buffer=50): |
| assert output_or_diff in ['diff', 'output'], "Provide 'output' or 'diff'" |
|
|
| self.dataset = wds.WebDataset( |
| [os.path.join(path_to_dataset, f"{block_name}.tar") |
| for path_to_dataset in paths_to_datasets] |
| ).decode("torch") |
| self.iter = iter(self.dataset) |
| self.buffer = None |
| self.pointer = 0 |
| self.num_in_buffer = num_in_buffer |
| self.output_or_diff = output_or_diff |
| self.batch_size = batch_size |
| self.one_size = None |
|
|
| def renew_buffer(self, to_retrieve): |
| to_merge = [] |
| if self.buffer is not None and self.buffer.shape[0] > self.pointer: |
| to_merge = [self.buffer[self.pointer:].clone()] |
| del self.buffer |
| for _ in range(to_retrieve): |
| sample = next(self.iter) |
| latents = sample['output.pth'] if self.output_or_diff == 'output' else sample['diff.pth'] |
| latents = latents.permute((0, 1, 3, 4, 2)) |
| latents = latents.reshape((-1, latents.shape[-1])) |
| to_merge.append(latents.to('cuda')) |
| self.one_size = latents.shape[0] |
| self.buffer = torch.cat(to_merge, dim=0) |
| shuffled_indices = torch.randperm(self.buffer.shape[0]) |
| self.buffer = self.buffer[shuffled_indices] |
| self.pointer = 0 |
|
|
| def iterate(self): |
| while True: |
| if self.buffer == None or self.buffer.shape[0] - self.pointer < self.num_in_buffer * self.one_size * 4 // 5: |
| try: |
| to_retrieve = self.num_in_buffer if self.buffer is None else self.num_in_buffer // 5 |
| self.renew_buffer(to_retrieve) |
| except StopIteration: |
| break |
| |
| batch = self.buffer[self.pointer: self.pointer + self.batch_size] |
| self.pointer += self.batch_size |
|
|
| assert batch.shape[0] == self.batch_size |
| yield batch |
|
|
| |
