import unittest import torch from diffusers import BlockRefinementScheduler, LLaDA2Pipeline class _DummyModelOutput: def __init__(self, logits): self.logits = logits class _DummyCausalLM(torch.nn.Module): def __init__(self, vocab_size: int): super().__init__() self.vocab_size = int(vocab_size) self.register_buffer("_device_anchor", torch.empty(0)) @property def dtype(self): return torch.float32 @property def device(self): return self._device_anchor.device def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): batch_size, seq_len = input_ids.shape logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) # Make confidence vary with token position so top-k commits are deterministic. positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.float32).view(1, seq_len, 1) token_ids = (torch.arange(seq_len, device=input_ids.device) % (self.vocab_size - 2)).view(1, seq_len, 1) logits.scatter_(2, token_ids.expand(batch_size, -1, -1), 1.0 + positions.expand(batch_size, -1, -1) * 0.1) return _DummyModelOutput(logits=logits) def _make_pipeline(tokenizer=None): model = _DummyCausalLM(vocab_size=32) scheduler = BlockRefinementScheduler() return LLaDA2Pipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) class LLaDA2PipelineTest(unittest.TestCase): def test_pipeline_runs(self): pipe = _make_pipeline().to("cpu") input_ids = torch.tensor([[5, 6, 7, 8], [1, 2, 3, 4]], dtype=torch.long) out = pipe( input_ids=input_ids, use_chat_template=False, gen_length=24, block_length=8, num_inference_steps=8, temperature=0.0, threshold=2.0, # force top-k commits minimal_topk=1, eos_early_stop=False, mask_token_id=31, eos_token_id=None, output_type="seq", ) self.assertEqual(out.sequences.shape, (2, 24)) self.assertFalse((out.sequences == 31).any().item()) def test_pipeline_return_tuple(self): pipe = _make_pipeline().to("cpu") input_ids = torch.tensor([[5, 6, 7, 8]], dtype=torch.long) sequences, texts = pipe( input_ids=input_ids, use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="seq", return_dict=False, ) self.assertEqual(sequences.shape, (1, 16)) self.assertIsNone(texts) def test_output_type_seq(self): """output_type='seq' should return sequences but no texts.""" pipe = _make_pipeline().to("cpu") out = pipe( input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="seq", ) self.assertIsNotNone(out.sequences) self.assertEqual(out.sequences.shape, (1, 16)) self.assertIsNone(out.texts) def test_output_type_text_without_tokenizer(self): """output_type='text' without a tokenizer should return texts=None.""" pipe = _make_pipeline(tokenizer=None).to("cpu") out = pipe( input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="text", ) self.assertIsNotNone(out.sequences) self.assertIsNone(out.texts) def test_output_type_text_with_tokenizer(self): """output_type='text' with a tokenizer should return decoded texts.""" tok = type( "Tok", (), { "eos_token_id": None, "mask_token_id": 31, "batch_decode": lambda self, seqs, **kw: [f"decoded_{len(s)}" for s in seqs], }, )() pipe = _make_pipeline(tokenizer=tok).to("cpu") out = pipe( input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, output_type="text", ) self.assertIsNotNone(out.sequences) self.assertIsNotNone(out.texts) self.assertEqual(len(out.texts), 1) self.assertTrue(out.texts[0].startswith("decoded_")) def test_output_type_invalid_raises(self): """Invalid output_type should raise ValueError.""" pipe = _make_pipeline().to("cpu") with self.assertRaises(ValueError): pipe( input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, mask_token_id=31, output_type="invalid", ) def test_prepare_input_ids_from_tensor(self): pipe = _make_pipeline() ids = torch.tensor([[1, 2, 3]], dtype=torch.long) result = pipe._prepare_input_ids( prompt=None, messages=None, input_ids=ids, use_chat_template=False, add_generation_prompt=False, chat_template_kwargs=None, ) self.assertTrue(torch.equal(result, ids)) def test_prepare_input_ids_from_1d_tensor(self): pipe = _make_pipeline() ids = torch.tensor([1, 2, 3], dtype=torch.long) result = pipe._prepare_input_ids( prompt=None, messages=None, input_ids=ids, use_chat_template=False, add_generation_prompt=False, chat_template_kwargs=None, ) self.assertEqual(result.shape, (1, 3)) def test_prepare_input_ids_no_tokenizer_raises(self): pipe = _make_pipeline(tokenizer=None) with self.assertRaises(ValueError): pipe._prepare_input_ids( prompt="hello", messages=None, input_ids=None, use_chat_template=False, add_generation_prompt=False, chat_template_kwargs=None, ) def test_prepare_input_ids_both_prompt_and_messages_raises(self): pipe = _make_pipeline() # Manually set tokenizer to a simple object so _prepare_input_ids doesn't short-circuit pipe.tokenizer = type("Tok", (), {"eos_token_id": None, "mask_token_id": None})() with self.assertRaises(ValueError): pipe._prepare_input_ids( prompt="hello", messages=[{"role": "user", "content": "hi"}], input_ids=None, use_chat_template=False, add_generation_prompt=False, chat_template_kwargs=None, ) def test_prepare_input_ids_neither_raises(self): pipe = _make_pipeline() pipe.tokenizer = type("Tok", (), {"eos_token_id": None, "mask_token_id": None})() with self.assertRaises(ValueError): pipe._prepare_input_ids( prompt=None, messages=None, input_ids=None, use_chat_template=False, add_generation_prompt=False, chat_template_kwargs=None, ) if __name__ == "__main__": unittest.main()