train

游雁

2024-03-26 bf341eed2bf671a52bda48232f052015504fe554

 funasr/datasets/audio_datasets/samplers.py

@@ -23,6 +23,10 @@
        batch_sampler = CustomDistributedBatchSampler(dataset, **kwargs)
        
    else:
        # if kwargs.get("sort_size", -1) > 0:
        #     batch_sampler = CustomDistributedBufferDynamicBatchSampler(dataset, **kwargs)
        # else:
        #     batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
        batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
        
    dataloader_args["batch_sampler"] = batch_sampler
@@ -286,6 +290,81 @@
        self.epoch = epoch


class CustomDistributedBufferDynamicBatchSampler(DistributedSampler):
    def __init__(self, dataset,
                 batch_size,
                 num_replicas=None,
                 rank=None,
                 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
        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.sort_size = sort_size
    
    def __iter__(self):
        if self.shuffle:
            g = torch.Generator()
            g.manual_seed(self.epoch)
            indices = torch.randperm(self.total_size, generator=g).tolist()
        else:
            indices = list(range(self.total_size))
        
        # Distribute indices among replicas
        indices = indices[self.rank:self.total_size:self.num_replicas]

        # Sort indices into buffers
        sorted_buffers = [sorted(indices[i:i + self.sort_size], key=lambda idx: self.dataset.get_source_len(idx)) for i in range(0, len(indices), self.sort_size)]

        batches = []
        for buffer in sorted_buffers:
            batch = []
            max_len_in_batch = 0
            for idx in buffer:
                sample_length = self.dataset.get_source_len(idx)
                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:
                    batches.append(batch)
                    batch = [idx]
                    max_len_in_batch = sample_length
                    
            # 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 DistributedSamplerWarp(BatchSampler):
    def __init__(self, dataset, batch_size, num_replicas=None, rank=None, shuffle=True, drop_last=False):
        if num_replicas is None: