e412370877
* 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
275 lines
10 KiB
Python
275 lines
10 KiB
Python
"""Tests for diffusion trainer integration."""
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# pylint: disable=redefined-outer-name,protected-access
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from unittest.mock import Mock
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import pytest
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import torch
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from axolotl.integrations.diffusion import DiffusionTrainer
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from axolotl.integrations.diffusion.utils import create_bidirectional_attention_mask
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from axolotl.utils.dict import DictDefault
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@pytest.fixture
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def mock_tokenizer():
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"""Create a mock tokenizer."""
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tokenizer = Mock()
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tokenizer.bos_token_id = 1
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tokenizer.eos_token_id = 2
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tokenizer.pad_token_id = 0
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return tokenizer
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@pytest.fixture
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def diffusion_config():
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"""Create a diffusion config."""
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return DictDefault(
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{
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"diffusion": {
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"mask_token_id": 32000,
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"eps": 1e-3,
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"importance_weighting": False,
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},
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"sample_packing": False,
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}
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)
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@pytest.fixture
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def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
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"""Create a diffusion trainer instance for testing methods directly."""
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# Create a minimal trainer instance just for testing methods
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trainer = object.__new__(DiffusionTrainer) # Bypass __init__
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trainer.axolotl_cfg = diffusion_config
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trainer._special_token_ids = {0, 1, 2} # pad, bos, eos
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trainer.processing_class = mock_tokenizer
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trainer.store_metrics = Mock() # Mock metrics storage
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return trainer
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class TestDiffusionTrainer:
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"""Test the DiffusionTrainer class."""
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def test_forward_process_basic(self, diffusion_trainer_instance):
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"""Test basic forward process without labels."""
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input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
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noisy_batch, masked_indices, p_mask = (
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diffusion_trainer_instance._forward_process(input_ids, eps=0.1)
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)
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# Check shapes
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assert noisy_batch.shape == input_ids.shape
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assert masked_indices.shape == input_ids.shape
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assert p_mask.shape == input_ids.shape
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# Check that special tokens are not masked
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special_token_positions = (input_ids == 1) | (input_ids == 2) | (input_ids == 0)
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assert not masked_indices[special_token_positions].any()
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# Check that mask token is applied
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mask_token_id = diffusion_trainer_instance.axolotl_cfg.diffusion.mask_token_id
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masked_positions = masked_indices
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if masked_positions.any():
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assert (noisy_batch[masked_positions] == mask_token_id).all()
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def test_forward_process_with_labels(self, diffusion_trainer_instance):
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"""Test forward process with SFT labels."""
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input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
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labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
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noisy_batch, masked_indices, p_mask = (
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diffusion_trainer_instance._forward_process(
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input_ids, labels=labels, eps=0.1
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)
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)
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# Check shapes
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assert noisy_batch.shape == input_ids.shape
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assert masked_indices.shape == input_ids.shape
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assert p_mask.shape == input_ids.shape
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# Check that only answer tokens can be masked (where labels != -100)
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non_answer_mask = labels == -100
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# No masking should occur on non-answer tokens
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assert not masked_indices[non_answer_mask].any()
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# p_mask should be the same for all positions (sampled timestep),
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# but masking is only applied to answer tokens
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assert p_mask.shape == input_ids.shape
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# Verify that masked_indices respects the answer mask
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assert not masked_indices[non_answer_mask].any()
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def test_forward_process_with_attention_mask(self, diffusion_trainer_instance):
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"""Test forward process with attention mask."""
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input_ids = torch.tensor([[1, 10, 20, 0]], dtype=torch.long)
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attention_mask = torch.tensor([[1, 1, 1, 0]], dtype=torch.long)
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_, masked_indices, p_mask = diffusion_trainer_instance._forward_process(
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input_ids, attention_mask=attention_mask, eps=0.1
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)
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# Check that padding tokens are not masked
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padding_positions = attention_mask == 0
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assert not masked_indices[padding_positions].any()
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assert (p_mask[padding_positions] == 0).all()
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def test_bidirectional_attention_mask_no_packing(self, diffusion_trainer_instance):
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"""Test bidirectional attention mask without sample packing."""
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input_ids = torch.tensor([[1, 10, 20, 2]], dtype=torch.long)
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mask = create_bidirectional_attention_mask(input_ids)
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# Should be all-to-all attention
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expected_shape = (1, 1, 4, 4)
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assert mask.shape == expected_shape
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assert mask.all()
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def test_bidirectional_attention_mask_with_packing(
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self, diffusion_trainer_instance
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):
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"""Test bidirectional attention mask with sample packing."""
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diffusion_trainer_instance.axolotl_cfg.sample_packing = True
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input_ids = torch.tensor([[1, 10, 20, 30, 40, 2]], dtype=torch.long)
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# Sample IDs: first sample (1), second sample (2)
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attention_mask = torch.tensor([[1, 1, 1, 2, 2, 2]], dtype=torch.long)
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mask = create_bidirectional_attention_mask(
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input_ids, attention_mask, sample_packing=True
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)
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# Check that tokens within same sample can attend to each other
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# but not across samples
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assert mask[0, 0, 0, 1].item() # First sample tokens can attend to each other
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assert mask[0, 0, 1, 2].item()
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assert not mask[0, 0, 0, 3].item() # Can't attend across samples
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assert not mask[0, 0, 2, 4].item()
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assert mask[0, 0, 3, 4].item() # Second sample tokens can attend to each other
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def test_compute_loss_basic(self, diffusion_trainer_instance):
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"""Test basic loss computation."""
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# Mock model that returns logits
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mock_model = Mock()
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mock_outputs = Mock()
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vocab_size = 1000
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seq_len = 5
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mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
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mock_model.return_value = mock_outputs
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mock_model.training = True
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input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
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loss, outputs = diffusion_trainer_instance._compute_diffusion_loss(
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mock_model, input_ids
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)
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# Check that loss is computed
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assert isinstance(loss, torch.Tensor)
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assert loss.requires_grad
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assert outputs == mock_outputs
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# Check that metrics were stored
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diffusion_trainer_instance.store_metrics.assert_called_once()
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def test_compute_loss_sft(self, diffusion_trainer_instance):
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"""Test loss computation with SFT labels."""
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# Mock model
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mock_model = Mock()
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mock_outputs = Mock()
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vocab_size = 1000
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seq_len = 5
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mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
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mock_model.return_value = mock_outputs
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mock_model.training = True
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diffusion_trainer_instance.axolotl_cfg.datasets = Mock()
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input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
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labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
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loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
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mock_model, input_ids, labels=labels
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)
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# Check that loss is computed
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assert isinstance(loss, torch.Tensor)
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assert loss.requires_grad
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# Check that SFT metrics were added
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call_args = diffusion_trainer_instance.store_metrics.call_args[0][0]
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assert "answer_ratio" in call_args
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assert "avg_answer_length" in call_args
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def test_compute_loss_no_masked_tokens(self, diffusion_trainer_instance):
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"""Test loss computation when no tokens are masked."""
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# Mock model
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mock_model = Mock()
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mock_outputs = Mock()
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vocab_size = 1000
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seq_len = 3
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mock_outputs.logits = torch.randn(1, seq_len, vocab_size)
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mock_model.return_value = mock_outputs
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mock_model.training = True
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# Only special tokens (which won't be masked)
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input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
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loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
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mock_model, input_ids
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)
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# Loss should be zero when no tokens are masked
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assert loss.item() == 0.0
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assert loss.requires_grad
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def test_cache_special_token_ids(self, mock_tokenizer):
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"""Test caching of special token IDs."""
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trainer = object.__new__(DiffusionTrainer)
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trainer.processing_class = mock_tokenizer
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trainer._cache_special_token_ids()
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assert trainer._special_token_ids == {0, 1, 2}
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def test_cache_special_token_ids_no_tokenizer(self):
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"""Test caching when no tokenizer is available."""
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trainer = object.__new__(DiffusionTrainer)
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trainer.processing_class = None
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trainer._cache_special_token_ids()
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assert trainer._special_token_ids == set()
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def test_main_compute_loss_interface(self, diffusion_trainer_instance):
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"""Test the main compute_loss interface."""
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# Mock model
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mock_model = Mock()
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mock_outputs = Mock()
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mock_outputs.logits = torch.randn(1, 5, 1000)
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mock_model.return_value = mock_outputs
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mock_model.training = True
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inputs = {
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"input_ids": torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long),
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"attention_mask": torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.long),
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"labels": torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long),
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}
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# Test without return_outputs
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loss = diffusion_trainer_instance.compute_loss(mock_model, inputs)
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assert isinstance(loss, torch.Tensor)
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# Test with return_outputs
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loss, outputs = diffusion_trainer_instance.compute_loss(
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mock_model, inputs, return_outputs=True
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)
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assert isinstance(loss, torch.Tensor)
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assert outputs == mock_outputs
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def test_missing_input_ids_raises_error(self, diffusion_trainer_instance):
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"""Test that missing input_ids raises ValueError."""
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mock_model = Mock()
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inputs = {"attention_mask": torch.tensor([[1, 1, 1]])}
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with pytest.raises(ValueError, match="input_ids is required"):
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diffusion_trainer_instance.compute_loss(mock_model, inputs)
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