Remove nested directory: BitTransformerLM/tests/rigorous_training_regime.py

BitTransformerLM/tests/rigorous_training_regime.py

@@ -1,93 +0,0 @@
-import io
-import time
-import contextlib
-from pathlib import Path
-import sys
-import torch
-
-ROOT = Path(__file__).resolve().parents[1]
-if str(ROOT) not in sys.path:
-    sys.path.insert(0, str(ROOT))
-
-from progressive_scaleup import progressive_scale_up_text
-from unified_workflow import run_workflow
-from bit_transformer.bit_io import text_to_bits
-from bit_transformer.safety import hil_safe_inference
-
-
-def capture_run(func, *args, **kwargs):
-    buf = io.StringIO()
-    start = time.time()
-    with contextlib.redirect_stdout(buf):
-        result = func(*args, **kwargs)
-    duration = time.time() - start
-    return result, buf.getvalue(), duration
-
-
-def main() -> None:
-    summary: list[str] = []
-
-    _, log, dur = capture_run(
-        progressive_scale_up_text,
-        improve_thresh=0.01,
-        steps=10,
-        width_mult=2.0,
-        max_len=64,
-        dataset_size=512,
-        forward_kwargs={"causal": True},
-    )
-    summary.append("### Progressive Scale-Up (causal=True)\n")
-    summary.append(log.strip())
-    summary.append(f"Duration: {dur:.2f}s\n")
-
-    _, log, dur = capture_run(
-        progressive_scale_up_text,
-        improve_thresh=0.01,
-        steps=10,
-        width_mult=2.0,
-        max_len=64,
-        dataset_size=512,
-        forward_kwargs={"causal": False},
-    )
-    summary.append("### Progressive Scale-Up (causal=False)\n")
-    summary.append(log.strip())
-    summary.append(f"Duration: {dur:.2f}s\n")
-
-    (model, _), log, dur = capture_run(
-        run_workflow,
-        steps=2,
-        max_len=32,
-        dataset_size=32,
-        plateau_steps=1,
-        epochs_per_step=1,
-        extra_steps=1,
-        diffusion=False,
-    )
-    bits = text_to_bits("hi")
-    tensor = torch.tensor(bits, dtype=torch.long).unsqueeze(0)
-    out_bits, _ = hil_safe_inference(model, tensor, c_floor=0.0, s_floor=0.0)
-    summary.append("### Unified Workflow (causal=True)\n")
-    summary.append(log.strip())
-    summary.append(f"Inference on 'hi': {out_bits.squeeze(0).tolist()}\n")
-    summary.append(f"Duration: {dur:.2f}s\n")
-
-    (_, _), log, dur = capture_run(
-        run_workflow,
-        steps=2,
-        max_len=32,
-        dataset_size=32,
-        plateau_steps=1,
-        epochs_per_step=1,
-        extra_steps=1,
-        diffusion=True,
-    )
-    summary.append("### Unified Workflow (causal=False / Diffusion)\n")
-    summary.append(log.strip())
-    summary.append(f"Duration: {dur:.2f}s\n")
-
-    report = "\n".join(summary)
-    print(report)
-
-
-if __name__ == "__main__":
-    main()