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docs/api/utils.collators.batching.html

@@ -453,10 +453,6 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
   <li><a href="#axolotl.utils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq" id="toc-axolotl.utils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq" class="nav-link" data-scroll-target="#axolotl.utils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq">PretrainingBatchSamplerDataCollatorForSeq2Seq</a></li>
   <li><a href="#axolotl.utils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq" id="toc-axolotl.utils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq" class="nav-link" data-scroll-target="#axolotl.utils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq">V2BatchSamplerDataCollatorForSeq2Seq</a></li>
   </ul></li>
-  <li><a href="#functions" id="toc-functions" class="nav-link" data-scroll-target="#functions">Functions</a>
-  <ul class="collapse">
-  <li><a href="#axolotl.utils.collators.batching.adjust_position_ids_for_slice" id="toc-axolotl.utils.collators.batching.adjust_position_ids_for_slice" class="nav-link" data-scroll-target="#axolotl.utils.collators.batching.adjust_position_ids_for_slice">adjust_position_ids_for_slice</a></li>
-  </ul></li>
   </ul></li>
   </ul>
 </nav>
@@ -696,30 +692,6 @@ includes logic for handling sequence parallelism collation.</p>
 <span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>    sequence_parallel_degree<span class="op">=</span><span class="dv">1</span>,</span>
 <span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>Collator for multipack specific to the using the BatchSampler</p>
-</section>
-</section>
-<section id="functions" class="level2">
-<h2 class="anchored" data-anchor-id="functions">Functions</h2>
-<table class="caption-top table">
-<thead>
-<tr class="header">
-<th>Name</th>
-<th>Description</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td><a href="#axolotl.utils.collators.batching.adjust_position_ids_for_slice">adjust_position_ids_for_slice</a></td>
-<td>Adjust position IDs for a sliced sequence to maintain proper relative positions.</td>
-</tr>
-</tbody>
-</table>
-<section id="axolotl.utils.collators.batching.adjust_position_ids_for_slice" class="level3">
-<h3 class="anchored" data-anchor-id="axolotl.utils.collators.batching.adjust_position_ids_for_slice">adjust_position_ids_for_slice</h3>
-<div class="sourceCode" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>utils.collators.batching.adjust_position_ids_for_slice(position_ids, start_idx)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>Adjust position IDs for a sliced sequence to maintain proper relative positions.
-This handles the case where position IDs might not be contiguous due to sample
-packing.</p>
 
 
 </section>

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-    "text": "utils.collators.batching\nData collators for axolotl to pad labels and position_ids for packed sequences. Also\nincludes logic for handling sequence parallelism collation.\n\n\n\n\n\nName\nDescription\n\n\n\n\nBatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\nDataCollatorForSeq2Seq\nData collator that will dynamically pad the inputs received, as well as the labels and position_ids\n\n\nPretrainingBatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\nV2BatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\n\n\n\nutils.collators.batching.BatchSamplerDataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nCollator for multipack specific to the using the BatchSampler\n\n\n\nutils.collators.batching.DataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nData collator that will dynamically pad the inputs received, as well as the labels and position_ids\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ntokenizer\n[PreTrainedTokenizer] or [PreTrainedTokenizerFast]\nThe tokenizer used for encoding the data.\nrequired\n\n\nmodel\n[PreTrainedModel]\nThe model that is being trained. If set and has the prepare_decoder_input_ids_from_labels, use it to prepare the decoder_input_ids This is useful when using label_smoothing to avoid calculating loss twice.\nNone\n\n\npadding\nbool, str or [~utils.PaddingStrategy], optional, defaults to True\nSelect a strategy to pad the returned sequences (according to the model’s padding side and padding index) among: - True or 'longest' (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided). - 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. - False or 'do_not_pad': No padding (i.e., can output a batch with sequences of different lengths).\nTrue\n\n\nmax_length\nint, optional\nMaximum length of the returned list and optionally padding length (see above).\nNone\n\n\npad_to_multiple_of\nint, optional\nIf set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).\nNone\n\n\nlabel_pad_token_id\nint, optional, defaults to -100\nThe id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).\n-100\n\n\nreturn_tensors\nstr\nThe type of Tensor to return. Allowable values are “np”, “pt” and “tf”.\n'pt'\n\n\nsequence_parallel_degree\nint\nThe degree of sequence parallelism. Default to 1 for no sequence parallelism.\n1\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\napply_sequence_parallelism\nApply sequence parallelism slicing to a batch.\n\n\n\n\n\nutils.collators.batching.DataCollatorForSeq2Seq.apply_sequence_parallelism(\n    batch,\n)\nApply sequence parallelism slicing to a batch.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nbatch\ndict[str, torch.Tensor]\nBatch dictionary from parent collator.\nrequired\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\ntorch.Tensor\nSliced batch dictionary.\n\n\n\n\n\n\n\n\n\nutils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq(\n    self,\n    *args,\n    multipack_attn=True,\n    **kwargs,\n)\nCollator for multipack specific to the using the BatchSampler\n\n\n\nutils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nCollator for multipack specific to the using the BatchSampler\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nadjust_position_ids_for_slice\nAdjust position IDs for a sliced sequence to maintain proper relative positions.\n\n\n\n\n\nutils.collators.batching.adjust_position_ids_for_slice(position_ids, start_idx)\nAdjust position IDs for a sliced sequence to maintain proper relative positions.\nThis handles the case where position IDs might not be contiguous due to sample\npacking."
+    "text": "utils.collators.batching\nData collators for axolotl to pad labels and position_ids for packed sequences. Also\nincludes logic for handling sequence parallelism collation.\n\n\n\n\n\nName\nDescription\n\n\n\n\nBatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\nDataCollatorForSeq2Seq\nData collator that will dynamically pad the inputs received, as well as the labels and position_ids\n\n\nPretrainingBatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\nV2BatchSamplerDataCollatorForSeq2Seq\nCollator for multipack specific to the using the BatchSampler\n\n\n\n\n\nutils.collators.batching.BatchSamplerDataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nCollator for multipack specific to the using the BatchSampler\n\n\n\nutils.collators.batching.DataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nData collator that will dynamically pad the inputs received, as well as the labels and position_ids\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ntokenizer\n[PreTrainedTokenizer] or [PreTrainedTokenizerFast]\nThe tokenizer used for encoding the data.\nrequired\n\n\nmodel\n[PreTrainedModel]\nThe model that is being trained. If set and has the prepare_decoder_input_ids_from_labels, use it to prepare the decoder_input_ids This is useful when using label_smoothing to avoid calculating loss twice.\nNone\n\n\npadding\nbool, str or [~utils.PaddingStrategy], optional, defaults to True\nSelect a strategy to pad the returned sequences (according to the model’s padding side and padding index) among: - True or 'longest' (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided). - 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. - False or 'do_not_pad': No padding (i.e., can output a batch with sequences of different lengths).\nTrue\n\n\nmax_length\nint, optional\nMaximum length of the returned list and optionally padding length (see above).\nNone\n\n\npad_to_multiple_of\nint, optional\nIf set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).\nNone\n\n\nlabel_pad_token_id\nint, optional, defaults to -100\nThe id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).\n-100\n\n\nreturn_tensors\nstr\nThe type of Tensor to return. Allowable values are “np”, “pt” and “tf”.\n'pt'\n\n\nsequence_parallel_degree\nint\nThe degree of sequence parallelism. Default to 1 for no sequence parallelism.\n1\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\napply_sequence_parallelism\nApply sequence parallelism slicing to a batch.\n\n\n\n\n\nutils.collators.batching.DataCollatorForSeq2Seq.apply_sequence_parallelism(\n    batch,\n)\nApply sequence parallelism slicing to a batch.\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nbatch\ndict[str, torch.Tensor]\nBatch dictionary from parent collator.\nrequired\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\ntorch.Tensor\nSliced batch dictionary.\n\n\n\n\n\n\n\n\n\nutils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq(\n    self,\n    *args,\n    multipack_attn=True,\n    **kwargs,\n)\nCollator for multipack specific to the using the BatchSampler\n\n\n\nutils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq(\n    self,\n    tokenizer,\n    model=None,\n    padding=True,\n    max_length=None,\n    pad_to_multiple_of=None,\n    label_pad_token_id=-100,\n    position_pad_token_id=0,\n    return_tensors='pt',\n    sequence_parallel_degree=1,\n)\nCollator for multipack specific to the using the BatchSampler"
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