| | |
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| | | import torch |
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| | | |
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| | | import numpy as np |
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| | | import logging |
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| | | import math |
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| | | import random |
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| | | import torch.distributed as dist |
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| | | from torch.utils.data import DistributedSampler |
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| | | from torch.utils.data import BatchSampler, Sampler |
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| | | import torch.distributed as dist |
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| | | |
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| | | from funasr.register import tables |
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| | | |
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| | | |
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| | | @tables.register("batch_sampler_classes", "BatchSampler") |
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| | | @tables.register("batch_sampler_classes", "CustomDistributedBatchSampler") |
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| | | @tables.register("batch_sampler_classes", "CustomDistributedDynamicBatchSampler") |
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| | | @tables.register("batch_sampler_classes", "DynamicBatchLocalShuffleSampler") |
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| | | class BatchSampler(torch.utils.data.BatchSampler): |
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| | | |
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| | | def __init__(self, dataset, |
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| | | batch_type: str = "example", |
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| | | batch_size: int = 100, |
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| | | buffer_size: int = 30, |
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| | | drop_last: bool = False, |
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| | | shuffle: bool = True, |
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| | | **kwargs): |
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| | | |
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| | | self.drop_last = drop_last |
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| | | self.pre_idx = -1 |
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| | | self.dataset = dataset |
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| | | self.total_samples = len(dataset) |
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| | | self.batch_type = batch_type |
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| | | self.batch_size = batch_size |
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| | | self.buffer_size = buffer_size |
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| | | self.max_token_length = kwargs.get("max_token_length", 5000) |
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| | | self.shuffle_idx = np.arange(self.total_samples) |
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| | | self.shuffle = shuffle |
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| | | |
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| | | def __len__(self): |
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| | | return self.total_samples |
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| | | |
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| | | def set_epoch(self, epoch): |
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| | | np.random.seed(epoch) |
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| | | |
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| | | def __iter__(self): |
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| | | |
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| | | if self.shuffle: |
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| | | np.random.shuffle(self.shuffle_idx) |
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| | | |
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| | | batch = [] |
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| | | max_token = 0 |
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| | | num_sample = 0 |
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| | | |
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| | | iter_num = (self.total_samples - 1) // self.buffer_size + 1 |
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| | | # print("iter_num: ", iter_num) |
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| | | for iter in range(self.pre_idx + 1, iter_num): |
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| | | datalen_with_index = [] |
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| | | for i in range(self.buffer_size): |
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| | | idx = iter * self.buffer_size + i |
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| | | if idx >= self.total_samples: |
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| | | continue |
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| | | |
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| | | idx_map = self.shuffle_idx[idx] |
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| | | # prompt = self.dataset.indexed_dataset[idx_map]["prompt"] |
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| | | sample_len_cur = self.dataset.get_source_len(idx_map) + \ |
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| | | self.dataset.get_target_len(idx_map) |
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| | | |
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| | | datalen_with_index.append([idx, sample_len_cur]) |
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| | | |
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| | | datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1]) |
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| | | for item in datalen_with_index_sort: |
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| | | idx, sample_len_cur_raw = item |
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| | | if sample_len_cur_raw > self.max_token_length: |
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| | | continue |
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| | | |
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| | | max_token_cur = max(max_token, sample_len_cur_raw) |
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| | | max_token_padding = 1 + num_sample |
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| | | if self.batch_type == 'length': |
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| | | max_token_padding *= max_token_cur |
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| | | if max_token_padding <= self.batch_size: |
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| | | batch.append(idx) |
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| | | max_token = max_token_cur |
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| | | num_sample += 1 |
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| | | else: |
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| | | yield batch |
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| | | batch = [idx] |
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| | | max_token = sample_len_cur_raw |
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| | | num_sample = 1 |
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| | | @tables.register("batch_sampler_classes", "RankFullLocalShuffleBatchSampler") |
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| | | @tables.register("batch_sampler_classes", "RankFullLocalShuffleDynamicBatchSampler") |
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| | | def CustomDistributedBatchSampler_fn(dataset, **kwargs): |
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| | | dataloader_args = {} |
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| | | batch_type = kwargs.get("batch_type", "example") |
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| | | if batch_type == "example": |
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| | | batch_sampler = CustomDistributedBatchSampler(dataset, **kwargs) |
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| | | |
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| | | else: |
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| | | if kwargs.get("sort_size", -1) > 0: |
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| | | batch_sampler = CustomDistributedBufferDynamicBatchSampler(dataset, **kwargs) |
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| | | else: |
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| | | batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs) |
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| | | # batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs) |
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| | | |
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| | | dataloader_args["batch_sampler"] = batch_sampler |
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| | | dataloader_args["num_workers"] = kwargs.get("num_workers", 4) |
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| | | dataloader_args["pin_memory"] = kwargs.get("pin_memory", True) |
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| | | |
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| | | return dataloader_args |
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| | | |
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| | | class CustomDistributedBatchSampler(Sampler): |
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| | | def __init__(self, dataset, |
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| | | batch_size, |
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| | | num_replicas=None, |
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| | | rank=None, |
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| | | shuffle=True, |
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| | | drop_last=False, |
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| | | is_training: bool = True, |
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| | | **kwargs, |
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| | | ): |
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| | | |
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| | | try: |
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| | | rank = dist.get_rank() |
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| | | num_replicas = dist.get_world_size() |
|---|
| | | except: |
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| | | rank = 0 |
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| | | num_replicas = 1 |
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| | | self.rank = rank |
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| | | self.num_replicas = num_replicas |
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| | | self.dataset = dataset |
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| | | self.batch_size = batch_size |
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| | | self.is_training = is_training |
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| | | self.shuffle = shuffle and is_training |
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| | | self.drop_last = drop_last |
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| | | # self.total_size = len(dataset) |
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| | | if self.drop_last: |
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| | | self.total_size = (len(self.dataset) // (batch_size * num_replicas)) * (batch_size * num_replicas) |
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| | | else: |
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| | | self.total_size = math.ceil(len(self.dataset) / (batch_size * num_replicas)) * (batch_size * num_replicas) |
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| | | self.num_samples = int(self.total_size // self.num_replicas) |
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| | | self.epoch = 0 |
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| | | self.max_token_length = kwargs.get("max_token_length", None) |
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| | | self.length_scale_source = kwargs.get("length_scale_source", 1.0) |
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| | | |
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| | | def __iter__(self): |
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| | | # Generate a list of indices |
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| | | if self.shuffle: |
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| | | g = torch.Generator() |
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| | | g.manual_seed(self.epoch) |
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| | | indices = torch.randperm(len(self.dataset), generator=g).tolist() |
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| | | else: |
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| | | indices = list(range(len(self.dataset))) |
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| | | |
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| | | # Add extra samples to make it evenly divisible |
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| | | padding_size = self.total_size - len(indices) |
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| | | if padding_size <= len(indices): |
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| | | indices += indices[:padding_size] |
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| | | else: |
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| | | indices += (indices * (padding_size // len(indices)) + indices[:padding_size % len(indices)]) |
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| | | |
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| | | assert len(indices) == self.total_size |
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| | | |
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| | | # Subsample |
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| | | indices = indices[self.rank:self.total_size:self.num_replicas] |
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| | | assert len(indices) == self.num_samples |
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| | | |
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| | | # Filter out indices with length greater than the max length, if provided |
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| | | if self.max_token_length is not None: |
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| | | filtered_indices = [] |
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| | | for idx in indices: |
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| | | source_len = self.dataset.get_source_len(idx) / self.length_scale_source |
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| | | if source_len <= self.max_token_length: |
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| | | filtered_indices.append(idx) |
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| | | indices = filtered_indices |
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| | | |
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| | | # Now that we have only the indices for this replica, chunk them into batches |
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| | | batches = [indices[i:i + self.batch_size] for i in range(0, len(indices), self.batch_size)] |
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| | | |
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| | | # Drop the last batch if it's not full and drop_last is True |
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| | | if self.drop_last and len(batches[-1]) != self.batch_size: |
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| | | batches = batches[:-1] |
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| | | |
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| | | return iter(batches) |
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| | | |
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| | | def __len__(self): |
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| | | |
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| | | return self.num_samples // self.batch_size |
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| | | |
|---|
| | | def set_epoch(self, epoch): |
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| | | self.epoch = epoch |
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| | | |
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| | | class CustomDistributedBufferBatchSampler(Sampler): |
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| | | def __init__(self, dataset, |
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| | | batch_size, |
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| | | num_replicas=None, |
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| | | rank=None, |
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| | | shuffle=True, |
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| | | drop_last=False, |
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| | | is_training: bool = True, |
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| | | sort_size: int = 1024, |
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| | | **kwargs, |
|---|
| | | ): |
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| | | |
|---|
| | | try: |
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| | | rank = dist.get_rank() |
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| | | num_replicas = dist.get_world_size() |
|---|
| | | except: |
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| | | rank = 0 |
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| | | num_replicas = 1 |
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| | | self.rank = rank |
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| | | self.num_replicas = num_replicas |
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| | | self.dataset = dataset |
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| | | self.batch_size = batch_size |
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| | | self.is_training = is_training |
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| | | self.shuffle = shuffle and is_training |
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| | | self.drop_last = drop_last |
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| | | # self.total_size = len(dataset) |
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| | | if self.drop_last: |
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| | | self.total_size = (len(self.dataset) // (batch_size * num_replicas)) * (batch_size * num_replicas) |
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| | | else: |
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| | | self.total_size = math.ceil(len(self.dataset) / (batch_size * num_replicas)) * (batch_size * num_replicas) |
|---|
| | | self.num_samples = int(self.total_size // self.num_replicas) |
|---|
| | | self.epoch = 0 |
|---|
| | | self.max_token_length = kwargs.get("max_token_length", None) |
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| | | self.length_scale_source = kwargs.get("length_scale_source", 1.0) |
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| | | self.sort_size = sort_size |
|---|
| | | |
|---|
| | | def __iter__(self): |
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| | | # Generate a list of indices |
|---|
| | | if self.shuffle: |
|---|
| | | g = torch.Generator() |
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| | | g.manual_seed(self.epoch) |
|---|
| | | indices = torch.randperm(len(self.dataset), generator=g).tolist() |
|---|
| | | else: |
|---|
| | | indices = list(range(len(self.dataset))) |
|---|
| | | |
|---|
| | | # Add extra samples to make it evenly divisible |
|---|
| | | padding_size = self.total_size - len(indices) |
|---|
| | | if padding_size <= len(indices): |
|---|
| | | indices += indices[:padding_size] |
|---|
| | | else: |
|---|
| | | indices += (indices * (padding_size // len(indices)) + indices[:padding_size % len(indices)]) |
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| | | |
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| | | assert len(indices) == self.total_size |
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| | | |
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| | | # Subsample |
|---|
| | | indices = indices[self.rank:self.total_size:self.num_replicas] |
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| | | assert len(indices) == self.num_samples |
|---|
| | | |
|---|
| | | # Filter out indices with length greater than the max length, if provided |
|---|
| | | if self.max_token_length is not None: |
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| | | filtered_indices = [] |
|---|
| | | for idx in indices: |
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| | | source_len = self.dataset.get_source_len(idx) / self.length_scale_source |
|---|
| | | if source_len <= self.max_token_length: |
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| | | filtered_indices.append(idx) |
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| | | indices = filtered_indices |
|---|
| | | |
|---|
| | | # Buffer sorting logic |
|---|
| | | sorted_batches = [] |
|---|
| | | buffer = [] |
|---|
| | | |
|---|
| | | for idx in indices: |
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| | | buffer.append(idx) |
|---|
| | | if len(buffer) >= self.sort_size: |
|---|
| | | # Sort the buffer based on some criteria, e.g., dataset sample length |
|---|
| | | buffer.sort(key=lambda x: self.dataset.get_source_len(x)) |
|---|
| | | sorted_batches.extend(self._create_batches_from_buffer(buffer)) |
|---|
| | | buffer = [] |
|---|
| | | |
|---|
| | | # Handle the remaining items in the buffer |
|---|
| | | if buffer: |
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| | | buffer.sort(key=lambda x: self.dataset.get_source_len(x)) |
|---|
| | | sorted_batches.extend(self._create_batches_from_buffer(buffer)) |
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| | | |
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| | | return iter(sorted_batches) |
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| | | |
|---|
| | | def _create_batches_from_buffer(self, buffer): |
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| | | # Function to convert the sorted buffer into batches |
|---|
| | | batched_buffer = [buffer[i:i + self.batch_size] for i in range(0, len(buffer), self.batch_size)] |
|---|
| | | if self.drop_last and len(batched_buffer[-1]) != self.batch_size: |
|---|
| | | batched_buffer = batched_buffer[:-1] |
|---|
| | | return batched_buffer |
|---|
| | | |
|---|
| | | def __len__(self): |
|---|
| | | |
|---|
| | | return self.num_samples // self.batch_size |
|---|
| | | |
|---|
| | | def set_epoch(self, epoch): |
|---|
| | | self.epoch = epoch |
|---|
| | | |
|---|
| | | class CustomDistributedDynamicBatchSampler(DistributedSampler): |
|---|
| | | def __init__(self, dataset, |
|---|
| | | batch_size, |
|---|
| | | num_replicas=None, |
|---|
| | | rank=None, |
|---|
| | | shuffle=True, |
|---|
| | | drop_last=False, |
|---|
| | | is_training: bool = True, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | |
|---|
| | | try: |
|---|
| | | rank = dist.get_rank() |
|---|
| | | num_replicas = dist.get_world_size() |
|---|
| | | except: |
|---|
| | | rank = 0 |
|---|
| | | num_replicas = 1 |
|---|
| | | self.rank = rank |
|---|
| | | self.num_replicas = num_replicas |
|---|
| | | self.dataset = dataset |
|---|
| | | self.batch_size = batch_size |
|---|
| | | self.is_training = is_training |
|---|
| | | self.shuffle = shuffle and is_training |
|---|
| | | self.drop_last = drop_last |
|---|
| | | |
|---|
| | | self.total_size = len(self.dataset) |
|---|
| | | # self.num_samples = int(math.ceil(self.total_size / self.num_replicas)) |
|---|
| | | self.epoch = 0 |
|---|
| | | self.max_token_length = kwargs.get("max_token_length", 2048) |
|---|
| | | self.length_scale_source = kwargs.get("length_scale_source", 1.0) |
|---|
| | | |
|---|
| | | def __iter__(self): |
|---|
| | | if self.shuffle: |
|---|
| | | g = torch.Generator() |
|---|
| | | g.manual_seed(self.epoch) |
|---|
| | | indices = torch.randperm(len(self.dataset), generator=g).tolist() |
|---|
| | | else: |
|---|
| | | indices = list(range(len(self.dataset))) |
|---|
| | | |
|---|
| | | indices = indices[self.rank:self.total_size:self.num_replicas] |
|---|
| | | |
|---|
| | | batches = [] |
|---|
| | | batch = [] |
|---|
| | | max_len_in_batch = 0 |
|---|
| | | current_batch_length = 0 |
|---|
| | | |
|---|
| | | for idx in indices: |
|---|
| | | sample_length = self.dataset.get_source_len(idx) |
|---|
| | | if sample_length > self.max_token_length: |
|---|
| | | continue |
|---|
| | | potential_batch_length = (max_len_in_batch if sample_length < max_len_in_batch else sample_length) * ( |
|---|
| | | len(batch) + 1) |
|---|
| | | |
|---|
| | | if potential_batch_length <= self.batch_size: |
|---|
| | | batch.append(idx) |
|---|
| | | if sample_length > max_len_in_batch: |
|---|
| | | max_len_in_batch = sample_length |
|---|
| | | # current_batch_length = max_len_in_batch * len(batch) |
|---|
| | | else: |
|---|
| | | batches.append(batch) |
|---|
| | | batch = [idx] |
|---|
| | | max_len_in_batch = sample_length |
|---|
| | | # current_batch_length = max_len_in_batch |
|---|
| | | |
|---|
| | | # Add the last batch if it's not empty and we're not dropping it |
|---|
| | | if batch and (not self.drop_last or len(batch) * max_len_in_batch == self.batch_size): |
|---|
| | | batches.append(batch) |
|---|
| | | |
|---|
| | | return iter(batches) |
|---|
| | | |
|---|
| | | def __len__(self): |
|---|
| | | |
|---|
| | | return 1 |
|---|
| | | |
|---|
| | | def set_epoch(self, epoch): |
|---|
| | | self.epoch = epoch |
|---|
| | | |
|---|
| | | |
|---|
| | | class CustomDistributedBufferDynamicBatchSampler(DistributedSampler): |
|---|
| | | def __init__(self, dataset, |
|---|
| | | batch_size, |
|---|
| | | batch_type="token", |
|---|
| | | num_replicas=None, |
|---|
| | | rank=None, |
|---|
| | | rank_split=False, |
|---|
| | | shuffle=True, |
|---|
| | | drop_last=False, |
|---|
| | | is_training: bool = True, |
|---|
| | | sort_size: int = 1024, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | |
|---|
| | | try: |
|---|
| | | rank = dist.get_rank() |
|---|
| | | num_replicas = dist.get_world_size() |
|---|
| | | except: |
|---|
| | | rank = 0 |
|---|
| | | num_replicas = 1 |
|---|
| | | |
|---|
| | | if rank_split: |
|---|
| | | logging.info(f"Warning, rank_split: {rank_split}, batch and shuffle data in local rank") |
|---|
| | | rank = 0 |
|---|
| | | num_replicas = 1 |
|---|
| | | |
|---|
| | | self.rank = rank |
|---|
| | | self.num_replicas = num_replicas |
|---|
| | | self.dataset = dataset |
|---|
| | | self.batch_size = batch_size |
|---|
| | | self.batch_type = batch_type |
|---|
| | | self.is_training = is_training |
|---|
| | | self.shuffle = shuffle and is_training |
|---|
| | | self.drop_last = drop_last |
|---|
| | | |
|---|
| | | self.total_size = len(self.dataset) |
|---|
| | | self.num_samples = int(math.ceil(self.total_size / self.num_replicas)) |
|---|
| | | self.epoch = 0 |
|---|
| | | self.sort_size = sort_size * num_replicas |
|---|
| | | self.max_token_length = kwargs.get("max_token_length", 2048) |
|---|
| | | self.length_scale_source = kwargs.get("length_scale_source", 1.0) |
|---|
| | | super().__init__(dataset, num_replicas=num_replicas, rank=rank, |
|---|
| | | shuffle=shuffle, drop_last=drop_last) |
|---|
| | | |
|---|
| | | def __iter__(self): |
|---|
| | | if self.shuffle: |
|---|
| | | g = torch.Generator() |
|---|
| | | g.manual_seed(self.epoch) |
|---|
| | | random.seed(self.epoch) |
|---|
| | | |
|---|
| | | indices = torch.randperm(len(self.dataset), generator=g).tolist() |
|---|
| | | else: |
|---|
| | | indices = list(range(len(self.dataset))) |
|---|
| | | |
|---|
| | | # Create sorted buffers and form batches |
|---|
| | | buffer_batches = [] |
|---|
| | | for i in range(0, len(indices), self.sort_size): |
|---|
| | | buffer = sorted(indices[i:i + self.sort_size], key=lambda idx: self.dataset.get_source_len(idx)) |
|---|
| | | batch = [] |
|---|
| | | max_len_in_batch = 0 |
|---|
| | | for idx in buffer: |
|---|
| | | original_sample_length = self.dataset.get_source_len(idx) |
|---|
| | | if original_sample_length > self.max_token_length: |
|---|
| | | continue |
|---|
| | | sample_length = 1 if self.batch_type == "example" else original_sample_length |
|---|
| | | potential_batch_length = max(max_len_in_batch, sample_length) * (len(batch) + 1) |
|---|
| | | if potential_batch_length <= self.batch_size: |
|---|
| | | batch.append(idx) |
|---|
| | | max_len_in_batch = max(max_len_in_batch, sample_length) |
|---|
| | | else: |
|---|
| | | buffer_batches.append(batch) |
|---|
| | | batch = [idx] |
|---|
| | | max_len_in_batch = sample_length |
|---|
| | | if batch: |
|---|
| | | buffer_batches.append(batch) |
|---|
| | | |
|---|
| | | # Ensure each rank gets the same number of batches, duplicate data if needed |
|---|
| | | batches_per_rank = math.ceil(len(buffer_batches) / self.num_replicas) |
|---|
| | | total_batches_needed = batches_per_rank * self.num_replicas |
|---|
| | | |
|---|
| | | extra_batches = total_batches_needed - len(buffer_batches) |
|---|
| | | buffer_batches += random.choices(buffer_batches, k=extra_batches) |
|---|
| | | |
|---|
| | | # Evenly distribute batches from buffer_batches to each rank |
|---|
| | | rank_batches = [[] for _ in range(self.num_replicas)] |
|---|
| | | for i, batch in enumerate(buffer_batches): |
|---|
| | | rank_batches[i % self.num_replicas].append(batch) |
|---|
| | | |
|---|
| | | # Assign all batches for the current rank directly |
|---|
| | | final_batches = rank_batches[self.rank] |
|---|
| | | |
|---|
| | | return iter(final_batches) |
|---|
| | | |
|---|
| | | |
|---|
| | | def __len__(self): |
|---|
| | | |
|---|
| | | return 1 |
|---|
| | | |
|---|
| | | def set_epoch(self, epoch): |
|---|
| | | self.epoch = epoch |
|---|
| | | |
|---|
| | | |
|---|
| | | class DistributedSamplerWarp(BatchSampler): |
|---|
| | | def __init__(self, dataset, batch_size, num_replicas=None, rank=None, shuffle=True, drop_last=False): |
|---|
| | | if num_replicas is None: |
|---|
| | | if not torch.distributed.is_available(): |
|---|
| | | raise RuntimeError("Requires distributed package to be available") |
|---|
| | | num_replicas = torch.distributed.get_world_size() |
|---|
| | | if rank is None: |
|---|
| | | if not torch.distributed.is_available(): |
|---|
| | | raise RuntimeError("Requires distributed package to be available") |
|---|
| | | rank = torch.distributed.get_rank() |
|---|
| | | |
|---|
| | | self.dataset = dataset |
|---|
| | | self.batch_size = batch_size |
|---|
| | | self.num_replicas = num_replicas |
|---|
| | | self.rank = rank |
|---|
| | | self.shuffle = shuffle |
|---|
| | | self.drop_last = drop_last |
|---|
| | | |
|---|
| | | # Create an instance of the DistributedSampler |
|---|
| | | self.sampler = DistributedSampler( |
|---|
| | | self.dataset, |
|---|
| | | num_replicas=self.num_replicas, |
|---|
| | | rank=self.rank, |
|---|
| | | shuffle=self.shuffle |
|---|
| | | ) |
|---|
| | | |
|---|
| | | # Call BatchSampler's constructor |
|---|
| | | super().__init__(self.sampler, batch_size, drop_last) |
|---|
| | | |
|---|
| | | def __iter__(self): |
|---|
| | | # If we shuffle, we need to call the set_epoch method |
|---|
| | | if self.shuffle: |
|---|
| | | self.sampler.set_epoch(self.epoch) |
|---|
| | | |
|---|
| | | # Generate batch indices using the parent class |
|---|
| | | return super().__iter__() |
|---|
| | | |
|---|
| | | def set_epoch(self, epoch): |
|---|
| | | self.epoch = epoch |
|---|
