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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