From adc88bd9e76644badbbe006913addfa7cbe5d89c Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期四, 23 十一月 2023 20:40:15 +0800
Subject: [PATCH] Merge remote-tracking branch 'refs/remotes/origin/main' update contextual forward
---
funasr/datasets/data_sampler.py | 74 +++++++++++++++++++++++++++++++++++++
1 files changed, 74 insertions(+), 0 deletions(-)
diff --git a/funasr/datasets/data_sampler.py b/funasr/datasets/data_sampler.py
new file mode 100644
index 0000000..6b3407c
--- /dev/null
+++ b/funasr/datasets/data_sampler.py
@@ -0,0 +1,74 @@
+import torch
+
+import numpy as np
+
+class BatchSampler(torch.utils.data.BatchSampler):
+
+ def __init__(self, dataset, batch_size_type: str="example", batch_size: int=14, sort_size: int=30, drop_last: bool=False, shuffle: bool=True, **kwargs):
+
+ self.drop_last = drop_last
+ self.pre_idx = -1
+ self.dataset = dataset
+ self.total_samples = len(dataset)
+ # self.batch_size_type = args.batch_size_type
+ # self.batch_size = args.batch_size
+ # self.sort_size = args.sort_size
+ # self.max_length_token = args.max_length_token
+ self.batch_size_type = batch_size_type
+ self.batch_size = batch_size
+ self.sort_size = sort_size
+ self.max_length_token = kwargs.get("max_length_token", 5000)
+ self.shuffle_idx = np.arange(self.total_samples)
+ self.shuffle = shuffle
+
+
+ def __len__(self):
+ return self.total_samples
+
+ def __iter__(self):
+ print("in sampler")
+
+ if self.shuffle:
+ np.random.shuffle(self.shuffle_idx)
+
+ batch = []
+ max_token = 0
+ num_sample = 0
+
+ iter_num = (self.total_samples-1) // self.sort_size + 1
+ print("iter_num: ", iter_num)
+ for iter in range(self.pre_idx + 1, iter_num):
+ datalen_with_index = []
+ for i in range(self.sort_size):
+ idx = iter * self.sort_size + i
+ if idx >= self.total_samples:
+ continue
+
+ idx_map = self.shuffle_idx[idx]
+ # prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
+ sample_len_cur = self.dataset.indexed_dataset[idx_map]["source_len"] + \
+ self.dataset.indexed_dataset[idx_map]["target_len"]
+
+ datalen_with_index.append([idx, sample_len_cur])
+
+ datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
+ for item in datalen_with_index_sort:
+ idx, sample_len_cur_raw = item
+ if sample_len_cur_raw > self.max_length_token:
+ continue
+
+ max_token_cur = max(max_token, sample_len_cur_raw)
+ max_token_padding = 1 + num_sample
+ if self.batch_size_type == 'token':
+ max_token_padding *= max_token_cur
+ if max_token_padding <= self.batch_size:
+ batch.append(idx)
+ max_token = max_token_cur
+ num_sample += 1
+ else:
+ yield batch
+ batch = [idx]
+ max_token = sample_len_cur_raw
+ num_sample = 1
+
+
\ No newline at end of file
--
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