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| """ | |
| Shared pytest fixtures for Sub-env 3 tests. | |
| Exposes ``synthetic_lora_path`` so it can be used by both | |
| ``test_node7_extractor.py`` and ``test_subenv3.py`` without duplication. | |
| """ | |
| from __future__ import annotations | |
| import pytest | |
| import torch | |
| import safetensors.torch | |
| def synthetic_lora_path(tmp_path): | |
| """Write a minimal 3-layer LoRA ``.safetensors`` file and return its path. | |
| Layout (PEFT-style keys): | |
| layer_{i}.lora_A.weight shape (8, 32) β treated as A^T β A: (32, 8) | |
| layer_{i}.lora_B.weight shape (64, 32) | |
| So: rank = 8, in_features = 32, out_features = 64, layers = 3. | |
| """ | |
| torch.manual_seed(0) | |
| tensors = {} | |
| for i in range(3): | |
| # PEFT convention: | |
| # lora_A.weight shape = (rank, in_features) = (8, 32) | |
| # lora_B.weight shape = (out_features, rank) = (64, 8) | |
| # _find_lora_pairs sees A.shape[0] < A.shape[1] β transposes to (32, 8) | |
| # Then QR((32,8)) β Q:(32,8), R:(8,8); B:(64,8) @ R_a:(8,8) β | |
| A_peft = torch.randn(8, 32) # (rank, in_features) | |
| B_peft = torch.randn(64, 8) # (out_features, rank) | |
| tensors[f"layer_{i}.lora_A.weight"] = A_peft.contiguous() | |
| tensors[f"layer_{i}.lora_B.weight"] = B_peft.contiguous() | |
| path = tmp_path / "test_lora.safetensors" | |
| safetensors.torch.save_file(tensors, str(path)) | |
| return path | |