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roundup_power2_divisions not needed with newer pytorch versions (#3540)

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* roundup_power2_divisions not needed with newer pytorch versions

* remove typo

* update qwen3.5 moe 35b-a3b yaml for 5090

* more bug fixes

* fix tests to match updated trainer

* don't use fa2 for hooks test

* reset plugins on the instance

* retry download

* fix references to renamed axolotl_cfg property on trainer

* Fix ref to trainer cfg
This commit is contained in:
Wing Lian
2026-03-24 15:40:05 -04:00
committed by GitHub
parent 86be9f329e
commit e412370877
14 changed files with 100 additions and 60 deletions
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8
tests/integrations/test_diffusion.py
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@@ -42,7 +42,7 @@ def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
"""Create a diffusion trainer instance for testing methods directly."""
# Create a minimal trainer instance just for testing methods
trainer = object.__new__(DiffusionTrainer) # Bypass __init__
trainer.cfg = diffusion_config
trainer.axolotl_cfg = diffusion_config
trainer._special_token_ids = {0, 1, 2} # pad, bos, eos
trainer.processing_class = mock_tokenizer
trainer.store_metrics = Mock() # Mock metrics storage
@@ -70,7 +70,7 @@ class TestDiffusionTrainer:
assert not masked_indices[special_token_positions].any()
# Check that mask token is applied
mask_token_id = diffusion_trainer_instance.cfg.diffusion.mask_token_id
mask_token_id = diffusion_trainer_instance.axolotl_cfg.diffusion.mask_token_id
masked_positions = masked_indices
if masked_positions.any():
assert (noisy_batch[masked_positions] == mask_token_id).all()
@@ -132,7 +132,7 @@ class TestDiffusionTrainer:
self, diffusion_trainer_instance
):
"""Test bidirectional attention mask with sample packing."""
diffusion_trainer_instance.cfg.sample_packing = True
diffusion_trainer_instance.axolotl_cfg.sample_packing = True
input_ids = torch.tensor([[1, 10, 20, 30, 40, 2]], dtype=torch.long)
# Sample IDs: first sample (1), second sample (2)
attention_mask = torch.tensor([[1, 1, 1, 2, 2, 2]], dtype=torch.long)
@@ -184,7 +184,7 @@ class TestDiffusionTrainer:
mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
mock_model.return_value = mock_outputs
mock_model.training = True
diffusion_trainer_instance.cfg.datasets = Mock()
diffusion_trainer_instance.axolotl_cfg.datasets = Mock()
input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)