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"""Evaluate one versioned single-task checkpoint on the released clean test."""
import argparse
import contextlib
import gc
import json
import os
import sys
import time
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
REPO_ROOT = os.path.abspath(os.path.join(SCRIPT_DIR, ".."))
SRC_ROOT = os.path.join(REPO_ROOT, "src")
if SRC_ROOT not in sys.path:
sys.path.insert(0, SRC_ROOT)
if SCRIPT_DIR not in sys.path:
sys.path.insert(0, SCRIPT_DIR)
import torch
import torch.nn.functional as F
from clean_eval_utils import (
TASKS,
confusion_metrics,
discard_unused_full_code_features,
load_compressed_graph,
prepare_clean_records,
)
from single_task_model import SingleTaskGAT
def graph_feature_dims(graph):
code_data = graph.nodes["code"].data
code_features = code_data.get("featmean", code_data.get("feat"))
if code_features is None:
raise RuntimeError("Clean graph is missing code featmean/feat.")
data_feat_dim = 1
if "data" in graph.ntypes and graph.num_nodes("data") > 0:
data_features = graph.nodes["data"].data.get("feat")
if data_features is not None:
data_feat_dim = int(data_features.shape[1])
return int(code_features.shape[1]), data_feat_dim
def load_checkpoint(path, task, probe_graph, device):
artifact = torch.load(path, map_location="cpu")
if not isinstance(artifact, dict) or artifact.get("artifact_format") != "icflownet_model_v1":
raise RuntimeError("Expected an icflownet_model_v1 single-task checkpoint.")
model_config = dict(artifact.get("model_config") or {})
model_config.pop("architecture_version", None)
stored_task = model_config.get("task_type")
if stored_task != task:
raise RuntimeError(
"Checkpoint task {!r} does not match requested task {!r}.".format(
stored_task, task
)
)
code_feat_dim, data_feat_dim = graph_feature_dims(probe_graph)
model = SingleTaskGAT(
code_feat_dim=code_feat_dim,
data_feat_dim=data_feat_dim,
**model_config,
)
model.load_state_dict(artifact["model_state_dict"], strict=True)
model.decision_threshold = 0.5
model.to(device)
model.eval()
return model, model_config, artifact.get("artifact_format")
def evaluate_record(
model,
graph,
rows,
candidate_meta,
device,
amp_enabled,
):
"""Evaluate one clean-test graph and return aggregate edge metrics."""
graph = graph.to(device)
pairs = torch.tensor(
[(row["src"], row["dst"]) for row in rows],
dtype=torch.long,
device=device,
)
labels = torch.tensor(
[row["label"] for row in rows], dtype=torch.float32, device=device
)
autocast_context = (
torch.autocast(device_type=device.type, dtype=torch.float16)
if amp_enabled
else contextlib.nullcontext()
)
with torch.no_grad(), autocast_context:
logits = model(graph, pairs, candidate_meta=candidate_meta)
logits = logits.reshape(-1)
scores = torch.sigmoid(logits)
predictions = scores >= 0.5
positives = labels == 1
negatives = ~positives
local_tn = int(((~predictions) & negatives).sum().item())
local_fp = int((predictions & negatives).sum().item())
local_fn = int(((~predictions) & positives).sum().item())
local_tp = int((predictions & positives).sum().item())
local_loss = float(
F.binary_cross_entropy_with_logits(logits, labels, reduction="sum").item()
)
return local_tn, local_fp, local_fn, local_tp, local_loss
def evaluate(args):
if args.task not in TASKS:
raise ValueError("Unsupported task: {}".format(args.task))
if args.gpu >= 0 and not torch.cuda.is_available():
raise RuntimeError("CUDA was requested but is unavailable.")
device = torch.device("cuda:{}".format(args.gpu) if args.gpu >= 0 else "cpu")
if device.type == "cuda":
torch.cuda.set_device(device)
clean_root = os.path.abspath(args.clean_root)
resource_root = os.path.join(clean_root, "resources")
records, preflight = prepare_clean_records(clean_root, resource_root, args.scope)
all_eligible = [record for record in records if record["task_rows"].get(args.task)]
indexed_eligible = list(enumerate(all_eligible, start=1))
end_record = args.end_record or len(indexed_eligible)
indexed_eligible = indexed_eligible[args.start_record - 1 : end_record]
if args.max_records is not None:
indexed_eligible = indexed_eligible[: args.max_records]
if not indexed_eligible:
raise RuntimeError("No clean-test records contain task {}.".format(args.task))
probe_graph = discard_unused_full_code_features(
load_compressed_graph(indexed_eligible[0][1]["graph_path"])
)
model, model_config, artifact_format = load_checkpoint(
os.path.abspath(args.checkpoint), args.task, probe_graph, device
)
del probe_graph
tn = fp = fn = tp = 0
loss_sum = 0.0
pair_count = 0
details = []
cpu_model = None
cpu_fallback_records = []
started = time.time()
amp_enabled = device.type == "cuda" and args.enable_amp
for selected_index, (record_index, record) in enumerate(indexed_eligible, start=1):
rows = record["task_rows"][args.task]
graph = discard_unused_full_code_features(
load_compressed_graph(record["graph_path"])
)
if (
graph.num_nodes("code") != record["expected_code_nodes"]
or graph.num_nodes("data") != record["expected_data_nodes"]
):
raise RuntimeError(
"Clean graph node count mismatch for {}.".format(record["binary_key"])
)
try:
(
local_tn,
local_fp,
local_fn,
local_tp,
local_loss,
) = evaluate_record(
model,
graph,
rows,
record["candidate_meta"],
device,
amp_enabled,
)
except RuntimeError as error:
if device.type != "cuda" or not args.cpu_fallback:
raise
print(
"[{}] CUDA evaluation failed for {}; retrying this record on CPU: {}".format(
record_index, record["binary_key"], error
),
flush=True,
)
gc.collect()
try:
torch.cuda.empty_cache()
except RuntimeError:
pass
if cpu_model is None:
cpu_device = torch.device("cpu")
cpu_model, _, _ = load_checkpoint(
os.path.abspath(args.checkpoint), args.task, graph, cpu_device
)
(
local_tn,
local_fp,
local_fn,
local_tp,
local_loss,
) = evaluate_record(
cpu_model,
graph,
rows,
record["candidate_meta"],
torch.device("cpu"),
False,
)
cpu_fallback_records.append(
{
"record_index": record_index,
"binary": record["binary_key"],
"graph_id": record["graph_id"],
"cuda_error": str(error),
}
)
tn += local_tn
fp += local_fp
fn += local_fn
tp += local_tp
loss_sum += local_loss
pair_count += len(rows)
if args.save_details:
details.append(
{
"binary": record["binary_key"],
"graph_id": record["graph_id"],
"pairs": len(rows),
"confusion_matrix": [[local_tn, local_fp], [local_fn, local_tp]],
}
)
if args.log_every and (
selected_index % args.log_every == 0
or selected_index == len(indexed_eligible)
):
print(
"[{}/{} selected; source record {}/{}] {} records evaluated ({:.1f}s)".format(
selected_index,
len(indexed_eligible),
record_index,
len(all_eligible),
args.task,
time.time() - started,
),
flush=True,
)
del graph
if device.type == "cuda":
torch.cuda.empty_cache()
metrics = confusion_metrics(tn, fp, fn, tp)
metrics["loss"] = loss_sum / max(pair_count, 1)
result = {
"format": "icflownet_single_cleantest_eval_v1",
"scope": args.scope,
"task": args.task,
"checkpoint": os.path.abspath(args.checkpoint),
"checkpoint_artifact_format": artifact_format,
"model_config": model_config,
"threshold": 0.5,
"threshold_source": "fixed_evaluator",
"precision": "fp16_autocast" if amp_enabled else "fp32",
"amp_enabled": amp_enabled,
"clean_root": clean_root,
"preflight": preflight,
"available_eligible_records": len(all_eligible),
"eligible_records": len(indexed_eligible),
"selected_record_indices": [index for index, _ in indexed_eligible],
"cpu_fallback_records": cpu_fallback_records,
"metrics": metrics,
"elapsed_seconds": time.time() - started,
}
if args.save_details:
result["record_details"] = details
return result
def evaluate_both_scopes(args):
"""Evaluate overall and long-range pairs with one graph encoding per record."""
if args.task not in TASKS:
raise ValueError("Unsupported task: {}".format(args.task))
if args.gpu >= 0 and not torch.cuda.is_available():
raise RuntimeError("CUDA was requested but is unavailable.")
device = torch.device("cuda:{}".format(args.gpu) if args.gpu >= 0 else "cpu")
if device.type == "cuda":
torch.cuda.set_device(device)
clean_root = os.path.abspath(args.clean_root)
resource_root = os.path.join(clean_root, "resources")
scopes = ("overall", "long_range")
records_by_scope = {}
preflight_by_scope = {}
eligible_by_scope = {}
for scope in scopes:
records, preflight = prepare_clean_records(clean_root, resource_root, scope)
records_by_scope[scope] = {
record["binary_key"]: record for record in records
}
preflight_by_scope[scope] = preflight
eligible_by_scope[scope] = [
record["binary_key"]
for record in records
if record["task_rows"].get(args.task)
]
ordered_keys = []
seen_keys = set()
for record in records_by_scope["overall"].values():
key = record["binary_key"]
if any(
records_by_scope[scope][key]["task_rows"].get(args.task)
for scope in scopes
):
ordered_keys.append(key)
seen_keys.add(key)
for key in records_by_scope["long_range"]:
if key not in seen_keys and records_by_scope["long_range"][key][
"task_rows"
].get(args.task):
ordered_keys.append(key)
end_record = args.end_record or len(ordered_keys)
selected_keys = ordered_keys[args.start_record - 1 : end_record]
if args.max_records is not None:
selected_keys = selected_keys[: args.max_records]
if not selected_keys:
raise RuntimeError("No clean-test records contain task {}.".format(args.task))
probe_record = records_by_scope["overall"].get(
selected_keys[0], records_by_scope["long_range"][selected_keys[0]]
)
probe_graph = discard_unused_full_code_features(
load_compressed_graph(probe_record["graph_path"])
)
model, model_config, artifact_format = load_checkpoint(
os.path.abspath(args.checkpoint), args.task, probe_graph, device
)
del probe_graph
accumulators = {
scope: {
"tn": 0,
"fp": 0,
"fn": 0,
"tp": 0,
"loss_sum": 0.0,
"pair_count": 0,
"record_count": 0,
"details": [],
}
for scope in scopes
}
started = time.time()
amp_enabled = device.type == "cuda" and args.enable_amp
for selected_index, key in enumerate(selected_keys, start=1):
base_record = records_by_scope["overall"].get(
key, records_by_scope["long_range"][key]
)
combined_rows = []
scope_slices = {}
for scope in scopes:
record = records_by_scope[scope].get(key)
rows = record["task_rows"].get(args.task, []) if record else []
if not rows:
continue
start = len(combined_rows)
combined_rows.extend(rows)
scope_slices[scope] = (start, len(combined_rows), rows)
graph = discard_unused_full_code_features(
load_compressed_graph(base_record["graph_path"])
)
if (
graph.num_nodes("code") != base_record["expected_code_nodes"]
or graph.num_nodes("data") != base_record["expected_data_nodes"]
):
raise RuntimeError(
"Clean graph node count mismatch for {}.".format(key)
)
graph = graph.to(device)
pairs = torch.tensor(
[(row["src"], row["dst"]) for row in combined_rows],
dtype=torch.long,
device=device,
)
labels = torch.tensor(
[row["label"] for row in combined_rows],
dtype=torch.float32,
device=device,
)
autocast_context = (
torch.autocast(device_type=device.type, dtype=torch.float16)
if amp_enabled
else contextlib.nullcontext()
)
with torch.no_grad(), autocast_context:
logits = model(
graph, pairs, candidate_meta=base_record["candidate_meta"]
).reshape(-1)
for scope, (start, end, rows) in scope_slices.items():
local_logits = logits[start:end]
local_labels = labels[start:end]
predictions = torch.sigmoid(local_logits) >= 0.5
positives = local_labels == 1
negatives = ~positives
local_tn = int(((~predictions) & negatives).sum().item())
local_fp = int((predictions & negatives).sum().item())
local_fn = int(((~predictions) & positives).sum().item())
local_tp = int((predictions & positives).sum().item())
local_loss = float(
F.binary_cross_entropy_with_logits(
local_logits, local_labels, reduction="sum"
).item()
)
accumulator = accumulators[scope]
accumulator["tn"] += local_tn
accumulator["fp"] += local_fp
accumulator["fn"] += local_fn
accumulator["tp"] += local_tp
accumulator["loss_sum"] += local_loss
accumulator["pair_count"] += len(rows)
accumulator["record_count"] += 1
if args.save_details:
accumulator["details"].append(
{
"binary": key,
"graph_id": base_record["graph_id"],
"pairs": len(rows),
"confusion_matrix": [
[local_tn, local_fp],
[local_fn, local_tp],
],
}
)
if args.log_every and (
selected_index % args.log_every == 0
or selected_index == len(selected_keys)
):
print(
"[{}/{} selected] {} records evaluated for both scopes ({:.1f}s)".format(
selected_index,
len(selected_keys),
args.task,
time.time() - started,
),
flush=True,
)
del graph
if device.type == "cuda":
torch.cuda.empty_cache()
elapsed_seconds = time.time() - started
results = {}
for scope in scopes:
accumulator = accumulators[scope]
metrics = confusion_metrics(
accumulator["tn"],
accumulator["fp"],
accumulator["fn"],
accumulator["tp"],
)
metrics["loss"] = accumulator["loss_sum"] / max(
accumulator["pair_count"], 1
)
result = {
"format": "icflownet_single_cleantest_eval_v1",
"scope": scope,
"task": args.task,
"checkpoint": os.path.abspath(args.checkpoint),
"checkpoint_artifact_format": artifact_format,
"model_config": model_config,
"threshold": 0.5,
"threshold_source": "fixed_evaluator",
"precision": "fp16_autocast" if amp_enabled else "fp32",
"amp_enabled": amp_enabled,
"clean_root": clean_root,
"preflight": preflight_by_scope[scope],
"available_eligible_records": len(eligible_by_scope[scope]),
"eligible_records": accumulator["record_count"],
"selected_record_indices": list(
range(1, accumulator["record_count"] + 1)
),
"cpu_fallback_records": [],
"metrics": metrics,
"elapsed_seconds": elapsed_seconds,
"joint_scope_evaluation": True,
}
if args.save_details:
result["record_details"] = accumulator["details"]
results[scope] = result
return results
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--clean_root", required=True)
parser.add_argument("--task", choices=TASKS, required=True)
parser.add_argument(
"--scope", choices=("overall", "long_range", "both"), default="overall"
)
parser.add_argument("--output", required=True)
parser.add_argument("--gpu", type=int, default=0)
parser.add_argument("--max_records", type=int, default=None)
parser.add_argument("--start_record", type=int, default=1)
parser.add_argument("--end_record", type=int, default=None)
parser.add_argument("--log_every", type=int, default=25)
precision_group = parser.add_mutually_exclusive_group()
precision_group.add_argument(
"--enable_amp",
action="store_true",
help="Explicitly opt into FP16 autocast; FP32 is the default.",
)
precision_group.add_argument(
"--disable_amp",
action="store_true",
help="Deprecated compatibility flag; FP32 is already the default.",
)
parser.add_argument(
"--cpu_fallback",
action="store_true",
help="Retry an individual record on CPU if its CUDA evaluation fails.",
)
parser.add_argument("--save_details", action="store_true")
args = parser.parse_args()
if args.enable_amp:
print(
"[WARNING] FP16 autocast was explicitly enabled; artifact results "
"must use the default FP32 mode.",
flush=True,
)
if args.start_record < 1:
parser.error("--start_record must be at least 1")
if args.end_record is not None and args.end_record < args.start_record:
parser.error("--end_record must be greater than or equal to --start_record")
output = os.path.abspath(args.output)
if args.scope == "both":
results = evaluate_both_scopes(args)
os.makedirs(output, exist_ok=True)
summaries = []
for scope, result in results.items():
scope_output = os.path.join(output, scope + ".json")
with open(scope_output, "w") as handle:
json.dump(result, handle, indent=2)
handle.write("\n")
summaries.append(
{
"output": scope_output,
"task": result["task"],
"scope": result["scope"],
"eligible_records": result["eligible_records"],
"metrics": result["metrics"],
"elapsed_seconds": result["elapsed_seconds"],
}
)
print(json.dumps(summaries, indent=2))
else:
result = evaluate(args)
os.makedirs(os.path.dirname(output), exist_ok=True)
with open(output, "w") as handle:
json.dump(result, handle, indent=2)
handle.write("\n")
print(json.dumps({
"output": output,
"task": result["task"],
"scope": result["scope"],
"eligible_records": result["eligible_records"],
"metrics": result["metrics"],
"elapsed_seconds": result["elapsed_seconds"],
}, indent=2))
if __name__ == "__main__":
main()
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