From bf341eed2bf671a52bda48232f052015504fe554 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 26 三月 2024 00:09:15 +0800
Subject: [PATCH] train
---
funasr/datasets/audio_datasets/samplers.py | 79 +++++++++++++++++++++++++++++++++++++++
1 files changed, 79 insertions(+), 0 deletions(-)
diff --git a/funasr/datasets/audio_datasets/samplers.py b/funasr/datasets/audio_datasets/samplers.py
index 4d78d52..a56a980 100644
--- a/funasr/datasets/audio_datasets/samplers.py
+++ b/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:
--
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