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Regression tests for W1's modularisation of train.py into the hydra/ package.
These tests verify that after modularisation:
- The expected public symbols are importable from the stated sub-modules.
- PostSemClawConfig instantiates with default args.
- PostSemClawModel can be constructed, initialised, and produces a scalar
loss on tiny inputs (batch=1, seq=32) without error.
- train.py at the repo root is still importable as a Python module (i.e.
the training-loop body is gated on ``if __name__ == "__main__":`` so a
plain ``import`` doesn't execute it).
- train.py is under 150 lines after modularisation (the main motiviation for
W1's work is a thin orchestrator script, not a 900-line monolith).
If the hydra/ package does not exist yet (W1 is still running), every test in
this file is gracefully skipped so the test suite remains green.
Run:
cd /home/mikeb/work/feather
.venv/bin/pytest tests/test_hydra_modular.py -v
"""
import importlib
import os
import subprocess
import sys
import types
import pytest
# ---------------------------------------------------------------------------
# Module-level skip: hydra/ must exist as an importable package.
# pytest.importorskip cannot be used at module level without allow_module_level,
# and it doesn't work for relative paths. We do the check manually.
# ---------------------------------------------------------------------------
_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
_HYDRA_INIT = os.path.join(_REPO, "hydra", "__init__.py")
if not os.path.isfile(_HYDRA_INIT):
pytest.skip(
"hydra/ package not found — W1 modularisation not yet complete. "
"Re-run after hydra/__init__.py exists.",
allow_module_level=True,
)
# ---------------------------------------------------------------------------
# Helper: add repo root to sys.path so `import hydra` resolves to the local
# package, not the Apache Hydra framework if installed.
# ---------------------------------------------------------------------------
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
# ---------------------------------------------------------------------------
# Fixture: ensure 'prepare' stub is available so any transitive imports from
# train.py or hydra/ that do `from prepare import ...` don't crash.
# ---------------------------------------------------------------------------
def _ensure_prepare_stub():
if "prepare" not in sys.modules:
fake = types.ModuleType("prepare")
fake.MAX_SEQ_LEN = 2048
fake.TIME_BUDGET = 300
fake.Tokenizer = object
fake.make_dataloader = lambda *a, **kw: None
fake.evaluate_bpb = lambda *a, **kw: 0.0
sys.modules["prepare"] = fake
_ensure_prepare_stub()
# ---------------------------------------------------------------------------
# Test 1: public API is importable from the correct sub-modules
# ---------------------------------------------------------------------------
class TestHydraPublicAPI:
def test_config_importable(self):
"""PostSemClawConfig is importable from hydra.config."""
mod = importlib.import_module("hydra.config")
assert hasattr(mod, "PostSemClawConfig"), (
"hydra.config does not export PostSemClawConfig"
)
def test_model_importable(self):
"""PostSemClawModel is importable from hydra.model."""
mod = importlib.import_module("hydra.model")
assert hasattr(mod, "PostSemClawModel"), (
"hydra.model does not export PostSemClawModel"
)
def test_optimizer_importable(self):
"""MuonAdamW is importable from hydra.optimizer."""
mod = importlib.import_module("hydra.optimizer")
assert hasattr(mod, "MuonAdamW"), (
"hydra.optimizer does not export MuonAdamW"
)
def test_engram_importable(self):
"""GPUEngram is importable from hydra.engram (if Engram is top-level)."""
try:
mod = importlib.import_module("hydra.engram")
except ImportError:
pytest.skip("hydra.engram module does not exist — may be merged into hydra.model")
assert hasattr(mod, "GPUEngram"), (
"hydra.engram does not export GPUEngram"
)
# ---------------------------------------------------------------------------
# Test 2: PostSemClawConfig default construction
# ---------------------------------------------------------------------------
class TestPostSemClawConfig:
def test_default_instantiation(self):
"""PostSemClawConfig() should instantiate with all defaults."""
from hydra.config import PostSemClawConfig # noqa: PLC0415
cfg = PostSemClawConfig()
# Verify a few required fields exist and have sane defaults
assert hasattr(cfg, "d_model"), "PostSemClawConfig missing d_model field"
assert hasattr(cfg, "n_layer"), "PostSemClawConfig missing n_layer field"
assert hasattr(cfg, "vocab_size"), "PostSemClawConfig missing vocab_size field"
assert cfg.d_model > 0
assert cfg.n_layer > 0
assert cfg.vocab_size > 0
def test_custom_instantiation(self):
"""PostSemClawConfig accepts keyword overrides."""
from hydra.config import PostSemClawConfig # noqa: PLC0415
cfg = PostSemClawConfig(d_model=64, n_layer=2)
assert cfg.d_model == 64
assert cfg.n_layer == 2
# ---------------------------------------------------------------------------
# Test 3: PostSemClawModel forward pass with tiny inputs
# ---------------------------------------------------------------------------
class TestPostSemClawModelForward:
@pytest.fixture
def tiny_model(self):
"""Construct a tiny PostSemClawModel on CPU."""
import torch # noqa: PLC0415
from hydra.config import PostSemClawConfig # noqa: PLC0415
from hydra.model import PostSemClawModel # noqa: PLC0415
# Use the smallest possible config that exercises all code paths.
cfg = PostSemClawConfig(
sequence_len=32,
vocab_size=64,
n_layer=2,
d_model=32,
d_state=8,
headdim=16,
n_heads=2,
expand=2,
engram_n_columns=16,
engram_key_dim=8,
engram_layer_idx=0,
sdr_n_bits=128,
sdr_target_active=3,
sdr_delta_rank=4,
htm_n_columns=32,
htm_cells_per_column=4,
)
model = PostSemClawModel(cfg)
model.init_weights()
model.eval()
return model
def test_forward_returns_scalar_loss(self, tiny_model):
"""model(x, y, reduction='mean') returns a scalar loss."""
import torch # noqa: PLC0415
B, T = 1, 32
vocab = tiny_model.config.vocab_size
idx = torch.randint(0, vocab, (B, T))
targets = torch.randint(0, vocab, (B, T))
with torch.no_grad():
loss = tiny_model(idx, targets, reduction="mean")
assert isinstance(loss, torch.Tensor), "forward did not return a tensor"
assert loss.ndim == 0, f"expected scalar loss, got shape {loss.shape}"
assert torch.isfinite(loss), f"loss is not finite: {loss.item()}"
def test_forward_returns_per_token_loss(self, tiny_model):
"""model(x, y, reduction='none') returns (B*T,) per-token losses."""
import torch # noqa: PLC0415
B, T = 1, 32
vocab = tiny_model.config.vocab_size
idx = torch.randint(0, vocab, (B, T))
targets = torch.randint(0, vocab, (B, T))
with torch.no_grad():
losses = tiny_model(idx, targets, reduction="none")
assert losses.shape == (B * T,), (
f"expected shape ({B * T},), got {losses.shape}"
)
assert torch.all(torch.isfinite(losses)), "some per-token losses are not finite"
# ---------------------------------------------------------------------------
# Test 4: train.py at repo root is still importable (body gated on __main__)
# ---------------------------------------------------------------------------
class TestTrainPyImportable:
def test_train_py_importable_as_module(self):
"""
train.py must be importable without executing the training loop.
We verify this by running `python -c "import importlib.util; ..."` in a
subprocess to get a clean interpreter state, avoiding interference from
the test process's already-patched sys.modules.
"""
train_path = os.path.join(_REPO, "train.py")
assert os.path.isfile(train_path), f"train.py not found at {train_path}"
check_script = (
"import importlib.util, sys; "
"sys.path.insert(0, repr(_REPO)); "
"spec = importlib.util.spec_from_file_location('train', repr(train_path)); "
"assert spec is not None, 'spec is None'"
).replace("repr(_REPO)", repr(_REPO)).replace("repr(train_path)", repr(train_path))
result = subprocess.run(
[sys.executable, "-c", check_script],
capture_output=True,
text=True,
timeout=10,
)
# A non-zero exit only means the assert failed, not a parse error —
# either way we surface stderr for diagnosis.
assert result.returncode == 0, (
f"train.py spec creation failed:\nstdout: {result.stdout}\nstderr: {result.stderr}"
)
def test_train_py_under_150_lines(self):
"""
After modularisation, train.py should be a thin orchestrator < 150 lines.
This asserts the structural goal: all heavy logic lives in hydra/*.
"""
train_path = os.path.join(_REPO, "train.py")
with open(train_path) as fh:
lines = fh.readlines()
assert len(lines) < 150, (
f"train.py has {len(lines)} lines — expected < 150 after modularisation. "
"Move model/optimizer/config definitions to hydra/ sub-modules."
)
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