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fix: make h2 patch repair failures diagnosable

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This commit is contained in:
thanhnv
2026-07-18 12:04:32 +07:00
parent 61a8253679
commit 659ed09839
7 changed files with 120 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/casan-harness/scripts/bash/goal-orchestrator.py
+30 -4
View File
@@ -299,9 +299,9 @@ def validate_write_output(text: str) -> str:
def patch_repair_attempts() -> int:
"""Return the bounded number of chances to repair an invalid model diff."""
try:
configured = int(os.environ.get("CASAN_GOAL_PATCH_REPAIR_ATTEMPTS", "2"))
configured = int(os.environ.get("CASAN_GOAL_PATCH_REPAIR_ATTEMPTS", "3"))
except ValueError:
configured = 2
configured = 3
return min(max(configured, 0), 3)
@@ -356,20 +356,45 @@ def repair_write_output(model: str, original_prompt: str, invalid_output: str, c
)
candidates = patch_repair_models(model)[:patch_repair_attempts()]
last_metadata, last_reason = {}, "goal_patch_missing"
for candidate in candidates:
attempts = []
for attempt_number, candidate in enumerate(candidates, start=1):
ok, output, metadata, reason = call_model(candidate, repair_prompt, candidate.startswith(("openai:", "anthropic:", "openai-compatible:")), max_output_tokens=patch_output_tokens())
metadata = dict(metadata)
metadata["repair_model"] = candidate
metadata["repair_attempt"] = attempt_number
last_metadata = metadata
if not ok:
last_reason = f"goal_patch_repair_failed:{reason}"
attempts.append({
"attempt": attempt_number,
"provider": provider_for_model(candidate),
"model": candidate,
"status": "failed",
"reason": last_reason,
})
continue
try:
validate_write_output(output)
except ValueError as error:
last_reason = str(error)
attempts.append({
"attempt": attempt_number,
"provider": provider_for_model(candidate),
"model": candidate,
"status": "failed",
"reason": last_reason,
})
continue
attempts.append({
"attempt": attempt_number,
"provider": provider_for_model(candidate),
"model": candidate,
"status": "pass",
"reason": "ok",
})
metadata["repair_attempts"] = attempts
return True, output, metadata, "ok"
last_metadata["repair_attempts"] = attempts
return False, "", last_metadata, f"goal_patch_repair_invalid:{last_reason}"
@@ -785,8 +810,9 @@ def run(job_path: str) -> int:
reason = f"goal_patch_repair_invalid:{repair_error}"
if reason:
actual_repair_model = str(repaired_meta.get("repair_model") or repair_model)
update_job(job_path, local_usage=local_meta, patch_repair_attempts=repaired_meta.get("repair_attempts", []))
stage(job_path, "local-worker", "error", reason, provider_for_model(actual_repair_model), actual_repair_model)
emit(goal_id, "H2-tool", "error", "Worker violated patch output contract after repair", {"reason": reason, "repair_provider": provider_for_model(actual_repair_model), "repair_model": actual_repair_model})
emit(goal_id, "H2-tool", "error", "Worker violated patch output contract after repair", {"reason": reason, "repair_provider": provider_for_model(actual_repair_model), "repair_model": actual_repair_model, "repair_attempts": repaired_meta.get("repair_attempts", [])})
raise ValueError(reason)
stage(job_path, "local-worker", "pass", "Primary solution prepared", job.get("local_provider", ""), local_model)
update_job(job_path, local_draft=safe_local, local_usage=local_meta)
packages/casan-harness/scripts/bash/model-call.py
+36 -5
View File
@@ -67,6 +67,37 @@ def generation_max_tokens(role):
return min(max(configured, 64), 8192)
def decode_provider_json(response, provider):
"""Decode a provider response without mislabelling an empty 2xx body.
Gateways occasionally terminate an upstream stream after accepting a
request and return an empty (or HTML) 2xx response. Treating that as a
generic JSONDecodeError makes it look like the network failed and obscures
the provider that needs attention. The diagnostic deliberately contains
only response shape metadata: never response content or credentials.
"""
raw = response.read()
content_type = ""
headers = getattr(response, "headers", None)
if headers is not None:
try:
content_type = str(headers.get("Content-Type", "")).split(";", 1)[0].lower()
except (AttributeError, TypeError):
content_type = ""
if not raw:
fail(f"provider_response_empty {provider} content_type={content_type or 'unknown'}")
try:
payload = json.loads(raw.decode("utf-8"))
except (UnicodeDecodeError, json.JSONDecodeError) as exc:
fail(
f"provider_response_invalid {provider} {type(exc).__name__} "
f"bytes={len(raw)} content_type={content_type or 'unknown'}"
)
if not isinstance(payload, dict):
fail(f"provider_response_invalid {provider} payload_type={type(payload).__name__}")
return payload
def enforce_call_budget():
"""Fail closed when a run exceeds CASAN_MODEL_MAX_CALLS. The count is tracked in
CASAN_MODEL_CALL_COUNTER_FILE so it spans the many per-step model-call processes
@@ -192,7 +223,7 @@ def call_ollama(model_name, prompt, role):
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=REQUEST_TIMEOUT) as resp:
payload = json.loads(resp.read().decode())
payload = decode_provider_json(resp, "ollama")
except Exception as exc: # backend/model failure -> honest non-zero, no fake success
fail(f"backend_unreachable {type(exc).__name__}: {str(exc)[:120]}")
latency_ms = int((time.time() - t0) * 1000)
@@ -226,7 +257,7 @@ def call_openai(model_name, prompt, role):
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=REQUEST_TIMEOUT) as resp:
payload = json.loads(resp.read().decode())
payload = decode_provider_json(resp, "openai-chat")
except Exception as exc: # honest non-zero, no fake success
fail(f"backend_unreachable {type(exc).__name__}: {str(exc)[:120]}")
latency_ms = int((time.time() - t0) * 1000)
@@ -278,7 +309,7 @@ def call_openai_responses(model_name, prompt, role):
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=REQUEST_TIMEOUT) as resp:
payload = json.loads(resp.read().decode())
payload = decode_provider_json(resp, "openai-responses")
except Exception as exc:
fail(f"backend_unreachable {type(exc).__name__}: {str(exc)[:120]}")
latency_ms = int((time.time() - t0) * 1000)
@@ -345,7 +376,7 @@ def call_openai_compatible(model_name, prompt, role):
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=REQUEST_TIMEOUT) as resp:
payload = json.loads(resp.read().decode())
payload = decode_provider_json(resp, "openai-compatible")
except Exception as exc:
fail(f"backend_unreachable {type(exc).__name__}: {str(exc)[:120]}")
latency_ms = int((time.time() - t0) * 1000)
@@ -413,7 +444,7 @@ def call_anthropic(model_name, prompt, role):
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=REQUEST_TIMEOUT) as resp:
payload = json.loads(resp.read().decode())
payload = decode_provider_json(resp, "anthropic")
except Exception as exc: # honest non-zero, no fake success
fail(f"backend_unreachable {type(exc).__name__}: {str(exc)[:120]}")
latency_ms = int((time.time() - t0) * 1000)