Files
gemma4-research/scripts/bakeoff/harness_no_think_flag.py
T
Mortdecai c61394923c fix: walk back round-1/2 conclusions — the cause was think=false all along
Seth asked "was this with think=false?" Yes — and that was the only
question that mattered. Everything I concluded in round 1 and round 2
was wrong.

Actual cause, isolated in round 3:
- At identical message state, gemma4:26b with think=false returns
  eval=4 (silent stop); with think unset or think=true, returns
  eval=165 and emits the correct tool call.
- Original round-1 write_file harness + think unset: 26B passes in
  8 iters, 20s. No mitigations needed.
- 31B dense and qwen3-coder:30b tolerate think=false; 26B MoE does not.

Red herrings (kept on-record in the bakeoff doc, not silently erased):
- Round 1: "write_file tool-call argument size" — wrong
- Round 2a: refuted the arg-size theory but for the wrong reason
  (still failed because think=false was still set)
- Round 2b: "cumulative tool-response context size" — truncating
  did make 26B pass, but by coincidence. Shorter context at the
  decision turn dodged the think=false side effect.

Why the existing "always think:false" guidance was misleading:
it was derived from AI_Visualizer (single-turn JSON pipelines) where
thinking tokens do eat num_predict invisibly. In multi-turn
tool-calling agents the channels are separate and the flag has a
different effect — catastrophic on 26B specifically.

Doc updates:
- GOTCHAS: replaced the 26B entry with the actual cause; scoped the
  original "Thinking Mode Eats Context" entry to single-turn pipelines
- SYNTHESIS: split the "Mandatory Ollama Settings" block into
  single-turn vs multi-turn variants; updated anti-patterns and
  quick-start checklist
- CORPUS_cli_coding_agent.md: revised pointer and config template
- docs/reference/bakeoff-2026-04-18.md: added Round 3 section with
  the correction notice at the top of the file and full diagnostic
  methodology

New artifacts: harness_no_think_flag.py, harness_write_no_think.py,
and 4 new log files demonstrating all three models pass when think
is left at default.
2026-04-18 18:14:05 -04:00

174 lines
7.6 KiB
Python

"""Diagnostic: patch-mode harness with think flag OMITTED (Ollama default).
Exact copy of harness_patch.py except the payload does NOT set "think".
Testing whether Gemma 4 26B's silent-stop at iter 6 is caused by
`think: false` specifically, rather than by tool-response context.
"""
from __future__ import annotations
import json
import os
import shutil
import subprocess
import sys
import time
from pathlib import Path
from urllib import request as urlreq
OLLAMA_HOST = os.environ.get("OLLAMA_HOST", "http://127.0.0.1:11434")
MAX_ITERATIONS = 15
BASH_TIMEOUT_S = 30
REQUEST_TIMEOUT_S = 540
SYSTEM_PROMPT = """You are a terminal coding agent.
## What you do
- Read source and test files to understand the code
- Make targeted edits to fix bugs so the tests pass
- Run pytest to verify your fix
- Stop once all tests pass and reply with a one-sentence summary
## What you do NOT do
- Never modify files under tests/
- Never disable, skip, or delete tests
- Never write outside the working directory
- Never call tools after all tests pass — just reply with the summary and stop
## Available tools
- read_file(path): read a file relative to the working directory
- apply_patch(path, old_text, new_text): replace an exact unique text span in a file
- run_bash(command): run a shell command in the working directory
## Rules
- Start by reading README.md
- Prefer minimal edits. Do not refactor unrelated code.
- Run the full test suite after each edit to verify.
- apply_patch requires old_text to appear EXACTLY ONCE in the file; include enough surrounding context to make it unique.
"""
USER_PROMPT = "Make the failing tests pass. Begin."
TOOLS = [
{"type": "function", "function": {"name": "read_file", "description": "Read a file. Path is relative to the working directory.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}}},
{"type": "function", "function": {"name": "apply_patch", "description": "Replace a unique span of text in a file. old_text must appear exactly once. Include surrounding context if needed to make the match unique.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "old_text": {"type": "string"}, "new_text": {"type": "string"}}, "required": ["path", "old_text", "new_text"]}}},
{"type": "function", "function": {"name": "run_bash", "description": "Run a shell command in the working directory. Returns stdout, stderr, and exit code.", "parameters": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}},
]
def safe_path(workdir, rel):
p = (workdir / rel).resolve()
if not str(p).startswith(str(workdir.resolve())):
raise ValueError(f"path escapes workdir: {rel}")
return p
def tool_read_file(workdir, args):
p = safe_path(workdir, args["path"])
if not p.exists():
return f"ERROR: {args['path']} does not exist"
return p.read_text()
def tool_apply_patch(workdir, args):
p = safe_path(workdir, args["path"])
if not p.exists():
return f"ERROR: {args['path']} does not exist"
old, new = args["old_text"], args["new_text"]
text = p.read_text()
n = text.count(old)
if n == 0:
return f"ERROR: old_text not found in {args['path']}."
if n > 1:
return f"ERROR: old_text appears {n} times in {args['path']}."
p.write_text(text.replace(old, new, 1))
return f"patched {args['path']} (replaced {len(old)} chars with {len(new)} chars)"
def tool_run_bash(workdir, args):
try:
r = subprocess.run(["bash", "-c", args["command"]], cwd=workdir, capture_output=True, text=True, timeout=BASH_TIMEOUT_S)
except subprocess.TimeoutExpired:
return f"ERROR: command timed out after {BASH_TIMEOUT_S}s"
return f"exit={r.returncode}\n--- stdout ---\n{r.stdout[-4000:]}\n--- stderr ---\n{r.stderr[-2000:]}"
TOOL_DISPATCH = {"read_file": tool_read_file, "apply_patch": tool_apply_patch, "run_bash": tool_run_bash}
def ollama_chat(model, messages):
# NOTE: no "think" key — Ollama default behavior
payload = {
"model": model, "messages": messages, "tools": TOOLS,
"stream": False, "keep_alive": "10m",
"options": {"num_ctx": 32768, "num_predict": 4096, "temperature": 0.3},
}
req = urlreq.Request(f"{OLLAMA_HOST}/api/chat", data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"})
with urlreq.urlopen(req, timeout=REQUEST_TIMEOUT_S) as resp:
return json.loads(resp.read())
def pytest_passes(workdir):
r = subprocess.run(["python3", "-m", "pytest", "tests/", "-q"], cwd=workdir, capture_output=True, text=True, timeout=60)
return r.returncode == 0
def run_bakeoff(model, workdir, log_path):
log_path.parent.mkdir(parents=True, exist_ok=True)
messages = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": USER_PROMPT}]
trace = {"model": model, "edit_tool": "apply_patch", "think_setting": "unset (default)", "workdir": str(workdir), "started_at": time.time(), "turns": [], "final": None}
counts = {"read_file": 0, "apply_patch": 0, "run_bash": 0}
halt = None
for i in range(1, MAX_ITERATIONS + 1):
t0 = time.time()
try:
r = ollama_chat(model, messages)
except Exception as e:
halt = f"chat_error: {e}"
trace["turns"].append({"iteration": i, "error": str(e)})
break
msg = r.get("message", {})
content = msg.get("content", "") or ""
tcs = msg.get("tool_calls") or []
thinking = msg.get("thinking")
turn = {"iteration": i, "elapsed_s": round(time.time() - t0, 2), "content": content, "tool_calls": [], "prompt_eval_count": r.get("prompt_eval_count"), "eval_count": r.get("eval_count"), "thinking_field_len": len(thinking) if thinking else 0}
messages.append({"role": "assistant", "content": content, "tool_calls": tcs})
if not tcs:
trace["turns"].append(turn)
halt = "no_tool_calls"
break
for tc in tcs:
fn = tc.get("function", {})
name = fn.get("name")
args = fn.get("arguments") or {}
if isinstance(args, str):
try: args = json.loads(args)
except: args = {"_raw": args}
try: result = TOOL_DISPATCH[name](workdir, args) if name in TOOL_DISPATCH else f"ERROR: unknown {name}"
except Exception as e: result = f"ERROR: {e}"
if name in counts: counts[name] += 1
turn["tool_calls"].append({"name": name, "arguments": args, "result": result[:800]})
messages.append({"role": "tool", "content": result})
trace["turns"].append(turn)
if i == MAX_ITERATIONS:
halt = "iteration_cap"
break
trace["final"] = {"halt_reason": halt, "tests_pass": pytest_passes(workdir), "iterations_used": len(trace["turns"]), "tool_call_counts": counts, "wall_clock_s": round(time.time() - trace["started_at"], 2)}
log_path.write_text(json.dumps(trace, indent=2, default=str))
return trace
def main():
model, workdir_s, log_s = sys.argv[1], sys.argv[2], sys.argv[3]
workdir, log_path = Path(workdir_s).resolve(), Path(log_s).resolve()
seed = Path(__file__).parent / "task_seed"
if workdir.exists(): shutil.rmtree(workdir)
shutil.copytree(seed, workdir)
r = run_bakeoff(model, workdir, log_path)
f = r["final"]
print(f"model={model} pass={f['tests_pass']} iters={f['iterations_used']} reads={f['tool_call_counts']['read_file']} patches={f['tool_call_counts']['apply_patch']} bashes={f['tool_call_counts']['run_bash']} halt={f['halt_reason']} wall={f['wall_clock_s']}s")
if __name__ == "__main__":
main()