import torch
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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 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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from funasr.register import tables
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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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@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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else:
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batch_sampler = CustomDistributedDynamicBatchSampler(dataset, **kwargs)
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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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return dataloader_args
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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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try:
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rank = dist.get_rank()
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num_replicas = dist.get_world_size()
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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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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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# 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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assert len(indices) == self.total_size
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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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# 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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# 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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# 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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return iter(batches)
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def __len__(self):
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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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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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):
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try:
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rank = dist.get_rank()
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num_replicas = dist.get_world_size()
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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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self.sort_size = sort_size
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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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# 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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assert len(indices) == self.total_size
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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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# 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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# Buffer sorting logic
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sorted_batches = []
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buffer = []
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for idx in indices:
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buffer.append(idx)
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if len(buffer) >= self.sort_size:
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# Sort the buffer based on some criteria, e.g., dataset sample length
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buffer.sort(key=lambda x: self.dataset.get_source_len(x))
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sorted_batches.extend(self._create_batches_from_buffer(buffer))
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buffer = []
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# Handle the remaining items in the buffer
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if buffer:
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buffer.sort(key=lambda x: self.dataset.get_source_len(x))
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sorted_batches.extend(self._create_batches_from_buffer(buffer))
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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
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batched_buffer = [buffer[i:i + self.batch_size] for i in range(0, len(buffer), self.batch_size)]
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if self.drop_last and len(batched_buffer[-1]) != self.batch_size:
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batched_buffer = batched_buffer[:-1]
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return batched_buffer
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def __len__(self):
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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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class CustomDistributedDynamicBatchSampler(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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try:
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rank = dist.get_rank()
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num_replicas = dist.get_world_size()
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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(self.dataset)
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# self.num_samples = int(math.ceil(self.total_size / self.num_replicas))
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self.epoch = 0
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def __iter__(self):
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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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indices = indices[self.rank:self.total_size:self.num_replicas]
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batches = []
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batch = []
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max_len_in_batch = 0
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current_batch_length = 0
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for idx in indices:
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sample_length = self.dataset.get_source_len(idx)
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potential_batch_length = (max_len_in_batch if sample_length < max_len_in_batch else sample_length) * (
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len(batch) + 1)
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if potential_batch_length <= self.batch_size:
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batch.append(idx)
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if sample_length > max_len_in_batch:
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max_len_in_batch = sample_length
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current_batch_length = max_len_in_batch * len(batch)
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else:
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batches.append(batch)
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batch = [idx]
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max_len_in_batch = sample_length
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current_batch_length = max_len_in_batch
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# Add the last batch if it's not empty and we're not dropping it
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if batch and (not self.drop_last or len(batch) * max_len_in_batch == self.batch_size):
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batches.append(batch)
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return iter(batches)
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def __len__(self):
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return 1
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def set_epoch(self, epoch):
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self.epoch = epoch
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class DistributedSamplerWarp(BatchSampler):
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def __init__(self, dataset, batch_size, num_replicas=None, rank=None, shuffle=True, drop_last=False):
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if num_replicas is None:
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if not torch.distributed.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = torch.distributed.get_world_size()
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if rank is None:
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if not torch.distributed.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = torch.distributed.get_rank()
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self.dataset = dataset
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self.batch_size = batch_size
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self.num_replicas = num_replicas
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self.rank = rank
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self.shuffle = shuffle
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self.drop_last = drop_last
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# Create an instance of the DistributedSampler
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self.sampler = DistributedSampler(
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self.dataset,
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num_replicas=self.num_replicas,
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rank=self.rank,
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shuffle=self.shuffle
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)
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# Call BatchSampler's constructor
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super().__init__(self.sampler, batch_size, drop_last)
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def __iter__(self):
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# If we shuffle, we need to call the set_epoch method
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if self.shuffle:
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self.sampler.set_epoch(self.epoch)
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# Generate batch indices using the parent class
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return super().__iter__()
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def set_epoch(self, epoch):
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self.epoch = epoch
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