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e6aed17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | #!/usr/bin/env python3
# Copyright 2026 Xiaomi Corp. (authors: Han Zhu)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data collators for OmniVoice training.
Two strategies are available:
- ``PackingDataCollator``: Concatenates samples into a single long sequence
(sequence packing). Used with flex_attention. Batch shape is ``[1, C, L]``.
- ``PaddingDataCollator``: Pads samples to the same length and stacks them.
Used with SDPA/eager attention. Batch shape is ``[B, C, max_len]``.
"""
from typing import Any, Dict, List
import torch
class PaddingDataCollator:
"""Pads a list of processed samples to the same length and stacks them.
Produces a standard ``[B, C, max_len]`` batch suitable for SDPA/eager
attention, where B is the number of samples in the batch, C is the number
of audio codebook layers, and max_len is the longest sequence in the batch.
A 4D boolean attention mask of shape ``[B, 1, max_len, max_len]`` is included.
Each query position can attend to all non-padding key positions (bidirectional),
matching the masked-diffusion training objective. When passed as a 4D tensor,
HuggingFace models use it directly without adding an additional causal mask.
No ``document_ids`` are emitted — each sample occupies its own batch row.
"""
def __init__(self, processor, batch_tokens: int):
self.batch_tokens = batch_tokens
self.processor = processor
def __call__(self, processed_samples: List[Dict[str, Any]]) -> Dict[str, Any]:
pad_id = self.processor.text_tokenizer.pad_token_id
max_len = max(s["length"] for s in processed_samples)
B = len(processed_samples)
padded_input_ids = []
padded_labels = []
padded_audio_mask = []
padded_position_ids = []
# valid[b, j] = True if position j is a real (non-padding) token for sample b
valid = torch.zeros(B, max_len, dtype=torch.bool)
for i, s in enumerate(processed_samples):
length = s["length"]
pad = max_len - length
padded_input_ids.append(
torch.nn.functional.pad(s["input_ids"], (0, pad), value=pad_id)
) # [C, max_len]
padded_labels.append(
torch.nn.functional.pad(s["labels"], (0, pad), value=-100)
) # [C, max_len]
padded_audio_mask.append(
torch.nn.functional.pad(s["audio_mask"], (0, pad), value=False)
) # [max_len]
padded_position_ids.append(
torch.nn.functional.pad(
torch.arange(length, dtype=torch.long), (0, pad), value=0
)
) # [max_len]
valid[i, :length] = True
# Stack into [B, C, max_len] / [B, max_len]
input_ids = torch.stack(padded_input_ids, dim=0) # [B, C, max_len]
labels = torch.stack(padded_labels, dim=0) # [B, C, max_len]
audio_mask = torch.stack(padded_audio_mask, dim=0) # [B, max_len]
position_ids = torch.stack(padded_position_ids, dim=0) # [B, max_len]
# 4D bidirectional attention mask: mask[b, 0, i, j] = valid[b, j]
# All query positions attend to all non-padding key positions.
attention_mask = valid[:, None, None, :].expand(B, 1, max_len, max_len).contiguous()
return {
"input_ids": input_ids, # [B, C, max_len]
"labels": labels, # [B, C, max_len]
"audio_mask": audio_mask, # [B, max_len]
"position_ids": position_ids, # [B, max_len]
"attention_mask": attention_mask, # [B, 1, max_len, max_len]
}
class PackingDataCollator:
def __init__(self, processor, batch_tokens: int):
self.batch_tokens = batch_tokens
self.processor = processor
def __call__(self, processed_samples: List[Dict[str, Any]]) -> Dict[str, Any]:
target_length = self.batch_tokens
input_ids = torch.cat(
[s["input_ids"] for s in processed_samples], dim=1
) # [C, Total_Len], C is the number of codebook layers of the audio tokenizer
labels = torch.cat(
[s["labels"] for s in processed_samples], dim=1
) # [C, Total_Len]
audio_mask = torch.cat(
[s["audio_mask"] for s in processed_samples], dim=0
) # [Total_Len]
position_ids = torch.cat(
[torch.arange(s["length"], dtype=torch.long) for s in processed_samples],
dim=0,
) # [Total_Len]
pad_length = target_length - input_ids.shape[1]
input_ids = torch.nn.functional.pad(
input_ids,
pad=(0, pad_length),
value=self.processor.text_tokenizer.pad_token_id,
)
labels = torch.nn.functional.pad(labels, pad=(0, pad_length), value=-100)
audio_mask = torch.nn.functional.pad(
audio_mask, pad=(0, pad_length), value=False
)
position_ids = torch.nn.functional.pad(
position_ids, pad=(0, pad_length), value=0
)
return_list = {
"input_ids": input_ids.unsqueeze(0), # [1, C, L]
"labels": labels.unsqueeze(0), # [1, C, L]
"audio_mask": audio_mask.unsqueeze(0), # [1, L]
"position_ids": position_ids.unsqueeze(0), # [1, L]
}
document_ids_list = []
for i, s in enumerate(processed_samples):
seq_len = s["length"]
document_ids_list.append(torch.full((seq_len,), i, dtype=torch.int32))
document_ids = torch.cat(document_ids_list, dim=0)
document_ids = torch.nn.functional.pad(
document_ids, pad=(0, pad_length), value=-1
)
return_list["document_ids"] = document_ids.unsqueeze(0) # [1, L]
return return_list
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