2bb0b78975
* fix attetion mask with packing * set position ids and use block diagonal attn mask * fix expand mask for multiple batch items, make sure we pad position_ids * don't move masks to cpu * use multi pack dataloader w random sampler * add position_ids back * more fixes for dataloader integration * est total tokens, fix field loop * more fixes, position_ids seems broken * more fixes for sample packing * use distributed sampler, avoid accelerate prepare * use accelerator prepare for dataloader * fix for position_ids w packing * Update src/axolotl/utils/dataloader.py * validation for sample packing and doc * more fixes for 4k and optimizations * optimized expand mask fn * better handling of variance in multipack dataloader length and trainer hanging when it runs out of data * fix rounding of len of batches to int * better handling so that all devices have the same dataloader len * fix step calc for packing * pass sample packing efficiency to training args * add a test for the mask expansion for sequence packing * only process eval dataset for packing if not None * don't split batches when packing * weighted CE losses * weighted CEL fixes * limit packing to sequences of max seq len * seq_len_multiple for packing * make sure the chunk size is an int * sample_packing_seq_len_multiplier config * use cumulative seq len with var len flash attn v2 w packing * properly calculate max len * fix flash-attn, xformers, packing, support chatml * fix chatml system prompt for openorca, legacy tokenizer opts * add chatml * add unit tests for cum seq lens, add ability to build cu_seq_lens from positional ids, fix prompt test * fix test and pylint checks * more packing and dataset optimizations and fixes * filter w multiple cpus * more fixes and optimizations * fixes and go back to distributed sampler since batch sampler won't work * fix counts by accounting for num devices * fix steps calculation * previous accelerate is still most performant * add numba to requirements. * use custom distributed checks * fix sampler to prevent overfit w new epochs * let's not cleanup the cached datasets * calculate cum seq lens with pos_ids instead of mask, simplify packing params, fix distributed barrier * speed optimizations and set accelerate fsdp env vars * optimize dataset concatenation? * more optimizations for dataset handling * fix import for annotation * manual pre-commit fixes * another sum optimization and bug fix for calc steps * fix packing estimations * fix formatting * pylint problems * add back flash attention branch for handling unpacked sequences seperately * Address PR feedback * add optional sample packing config params to readme
45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
"""
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Unit tests for the monkey patch for expand mask to handle packed sequences
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"""
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import unittest
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import torch
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from axolotl.monkeypatch.llama_expand_mask import _expand_mask
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class TestExpandMask(unittest.TestCase):
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"""
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Test class for attention mask expansion for packed sequences
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"""
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def test_output(self):
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mask = torch.tensor([[1, 1, 1, 2], [2, 3, 3, 0]])
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dtype = torch.float32
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expected_output = torch.tensor(
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[
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[
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[
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[0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
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[0.0000e00, 0.0000e00, -3.4028e38, -3.4028e38],
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[0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
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[-3.4028e38, -3.4028e38, -3.4028e38, 0.0000e00],
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]
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],
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[
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[
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[0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
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[-3.4028e38, 0.0000e00, -3.4028e38, -3.4028e38],
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[-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
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[-3.4028e38, -3.4028e38, -3.4028e38, -3.4028e38],
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]
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],
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]
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)
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# Check that the output matches the expected output
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self.assertTrue(torch.allclose(_expand_mask(mask, dtype), expected_output))
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if __name__ == "__main__":
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unittest.main()
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