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
31 lines
932 B
Python
31 lines
932 B
Python
"""
|
|
Unit tests for the monkeypatch utils
|
|
"""
|
|
import unittest
|
|
|
|
import torch
|
|
|
|
from axolotl.monkeypatch.utils import get_cu_seqlens, get_cu_seqlens_from_pos_ids
|
|
|
|
|
|
class TestMonkeyPatchUtils(unittest.TestCase):
|
|
"""
|
|
Unit test class for monkeypatch utils
|
|
"""
|
|
|
|
def test_get_cu_seqlens_1d(self):
|
|
attn_mask = torch.tensor([[1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 0, 0]])
|
|
target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
|
|
self.assertTrue(torch.allclose(get_cu_seqlens(attn_mask)[0], target_res))
|
|
|
|
def test_get_cu_seqlens_from_pos_ids_1d(self):
|
|
position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 2, 0, 1, 2, 3, 4, 0, 1, 0, 0]])
|
|
target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
|
|
self.assertTrue(
|
|
torch.allclose(get_cu_seqlens_from_pos_ids(position_ids)[0], target_res)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|