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import pytest |
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import torch |
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from types import SimpleNamespace |
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from cognitive_mapping_probe.llm_iface import LLM |
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@pytest.fixture(scope="session") |
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def mock_llm_config(): |
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"""Stellt eine minimale, Schein-Konfiguration für das LLM bereit.""" |
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return SimpleNamespace( |
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hidden_size=128, |
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num_hidden_layers=2, |
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num_attention_heads=4 |
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) |
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@pytest.fixture |
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def mock_llm(mocker, mock_llm_config): |
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""" |
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Erstellt einen schnellen "Mock-LLM" für Unit-Tests. |
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ERWEITERT: Patcht nun alle relevanten Stellen, an denen das LLM geladen wird, |
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um in allen Testdateien zu funktionieren. |
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""" |
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mock_tokenizer = mocker.MagicMock() |
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mock_tokenizer.eos_token_id = 1 |
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def mock_model_forward(*args, **kwargs): |
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batch_size = 1 |
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if 'input_ids' in kwargs: |
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seq_len = kwargs['input_ids'].shape[1] |
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elif 'past_key_values' in kwargs: |
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seq_len = kwargs['past_key_values'][0][0].shape[-2] + 1 |
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else: |
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seq_len = 1 |
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mock_outputs = { |
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"hidden_states": tuple( |
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[torch.randn(batch_size, seq_len, mock_llm_config.hidden_size) for _ in range(mock_llm_config.num_hidden_layers + 1)] |
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), |
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"past_key_values": tuple( |
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[ |
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(torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16), |
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torch.randn(batch_size, mock_llm_config.num_attention_heads, seq_len, 16)) |
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for _ in range(mock_llm_config.num_hidden_layers) |
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] |
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), |
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"logits": torch.randn(batch_size, seq_len, 32000) |
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} |
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return SimpleNamespace(**mock_outputs) |
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llm_instance = LLM.__new__(LLM) |
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llm_instance.model = mock_model_forward |
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llm_instance.model.config = mock_llm_config |
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llm_instance.model.device = 'cpu' |
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llm_instance.model.dtype = torch.float32 |
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mock_lm_head = mocker.MagicMock(return_value=torch.randn(1, 32000)) |
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llm_instance.model.lm_head = mock_lm_head |
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llm_instance.tokenizer = mock_tokenizer |
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llm_instance.config = mock_llm_config |
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llm_instance.seed = 42 |
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llm_instance.set_all_seeds = mocker.MagicMock() |
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mocker.patch('cognitive_mapping_probe.llm_iface.get_or_load_model', return_value=llm_instance) |
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mocker.patch('cognitive_mapping_probe.orchestrator_seismograph.get_or_load_model', return_value=llm_instance) |
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mocker.patch('cognitive_mapping_probe.resonance_seismograph.LLM', return_value=llm_instance, create=True) |
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return llm_instance |
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