Fix error and set random seed in run_model.

Author: Xreki

graph_net/analysis_util.py

@@ -5,6 +5,9 @@
 from graph_net.config.datatype_tolerance_config import get_precision
 from graph_net.positive_tolerance_interpretation import PositiveToleranceInterpretation
 from graph_net.verify_aggregated_params import determine_tolerances
+from graph_net.positive_tolerance_interpretation_manager import (
+    get_positive_tolerance_interpretation,
+)
 
 
 def detect_sample_status(log_text: str) -> str:
@@ -430,7 +433,10 @@ def check_sample_correctness(sample: dict, tolerance: int) -> tuple[bool, str]:
 
 
 def get_incorrect_models(
-    tolerance: int, log_file_path: str, type: str = "ESt"
+    tolerance: int,
+    log_file_path: str,
+    type: str = "ESt",
+    positive_tolerance_interpretation_type: str = "default",
 ) -> set[str]:
     """
     Filters and returns models with accuracy issues based on given tolerance threshold.
@@ -459,9 +465,15 @@ def get_incorrect_models(
             is_correct_at_t1[idx] = is_correct
             fail_type_at_t1[idx] = fail_type if fail_type is not None else "correct"
 
+        positive_tolerance_interpretation = get_positive_tolerance_interpretation(
+            positive_tolerance_interpretation_type
+        )
+
         for idx, sample in enumerate(samples):
             if not is_correct_at_t1[idx]:
-                current_correctness = fake_perf_degrad(tolerance, fail_type_at_t1[idx])
+                current_correctness = fake_perf_degrad(
+                    tolerance, fail_type_at_t1[idx], positive_tolerance_interpretation
+                )
                 failed_models.add(
                     sample.get("model_path")
                 ) if current_correctness != "correct" else None

graph_net/paddle/run_model.py

@@ -2,6 +2,8 @@
 import json
 import base64
 import argparse
+import numpy as np
+import random
 
 os.environ["FLAGS_logging_pir_py_code_dir"] = "/tmp/dump"
 
@@ -10,6 +12,12 @@
 from graph_net.paddle import utils
 
 
+def set_seed(random_seed):
+    paddle.seed(random_seed)
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+
+
 def load_class_from_file(file_path: str, class_name: str):
     print(f"Load {class_name} from {file_path}")
     module = imp_util.load_module(file_path, "unnamed")
@@ -23,12 +31,17 @@ def get_input_dict(model_path):
     params = inputs_params["weight_info"]
     inputs = inputs_params["input_info"]
 
-    state_dict = {}
-    for k, v in params.items():
-        state_dict[k] = paddle.nn.parameter.Parameter(utils.replay_tensor(v), name=k)
-    for k, v in inputs.items():
-        state_dict[k] = utils.replay_tensor(v)
-    return state_dict
+    input_dict = {}
+    for name, meta in params.items():
+        original_name = (
+            meta["original_name"] if meta.get("original_name", None) else name
+        )
+        input_dict[name] = paddle.nn.parameter.Parameter(
+            utils.replay_tensor(meta), name=original_name
+        )
+    for name, meta in inputs.items():
+        input_dict[name] = utils.replay_tensor(meta)
+    return input_dict
 
 
 def _convert_to_dict(config_str):
@@ -53,6 +66,9 @@ def _get_decorator(args):
 
 
 def main(args):
+    initalize_seed = 123
+    set_seed(random_seed=initalize_seed)
+
     model_path = args.model_path
     model_class = load_class_from_file(
         f"{model_path}/model.py", class_name="GraphModule"

graph_net/subgraph_decompose_and_evaluation_step.py

@@ -126,7 +126,7 @@ class ModelRecord:
     original_path: str
     uniform_split_positions: List[int] = field(default_factory=list)
     subgraph_paths: List[str] = field(default_factory=list)
-    incorrect_subgraph_idxs: List[int] = field(default_factory=list)
+    incorrect_subgraph_idxs: List[int] = None
 
     def get_split_positions(self, decompose_method):
         if decompose_method == "fixed-start":
@@ -466,7 +466,9 @@ def generate_initial_tasks(args):
     )
     decompose_config.update_running_state(
         pass_id=-1,
-        running_state=RunningState(incorrect_models_from_log=initial_incorrect_models),
+        running_state=RunningState(
+            incorrect_models_from_log=list(sorted(initial_incorrect_models))
+        ),
     )
     decompose_config.update_running_state(
         pass_id=0, running_state=RunningState(model_name2record=model_name2record)