[Feature] Add runner in eval_backend_diff

graph_net/paddle/test_compiler.py

@@ -43,7 +43,7 @@ def init_env(args):
         paddle.set_flags({"FLAGS_cudnn_exhaustive_search": 1})
 
 
-def get_hardward_name(args):
+def get_hardware_name(args):
     hardware = "unknown"
     if test_compiler_util.is_gpu_device(args.device):
         hardware = paddle.device.cuda.get_device_name(0)
@@ -149,7 +149,7 @@ def measure_performance(model_call, args, compiler, profile=False):
     min_trials = int(100 / np.mean(warmup_e2e_times[1:]))
     trials = max(args.trials, min_trials)
 
-    hardware_name = get_hardward_name(args)
+    hardware_name = get_hardware_name(args)
     print(
         f"[Profiling] Using device: {args.device} {hardware_name}, warm up {args.warmup}, trials {trials}",
         file=sys.stderr,
@@ -327,7 +327,7 @@ def test_single_model(args):
     model.eval()
 
     test_compiler_util.print_basic_config(
-        args, get_hardward_name(args), get_compile_framework_version(args)
+        args, get_hardware_name(args), get_compile_framework_version(args)
     )
 
     # Run on eager mode

graph_net/paddle/test_reference_device.py

@@ -49,7 +49,7 @@ def test_single_model(args):
 
             test_compiler_util.print_basic_config(
                 args,
-                test_compiler.get_hardward_name(args),
+                test_compiler.get_hardware_name(args),
                 test_compiler.get_compile_framework_version(args),
             )
 

graph_net/paddle/test_target_device.py

@@ -89,7 +89,7 @@ def test_single_model(args):
 
     test_compiler_util.print_basic_config(
         args,
-        test_compiler.get_hardward_name(args),
+        test_compiler.get_hardware_name(args),
         test_compiler.get_compile_framework_version(args),
     )
 

graph_net/torch/test_target_device.py

@@ -67,14 +67,12 @@ def test_single_model(args):
     ref_dump = utils.get_output_path(args.reference_dir, args.model_path)
     ref_out = torch.load(str(ref_dump))
     ref_log = utils.get_log_path(args.reference_dir, args.model_path)
-    ref_time_stats = eval_backend_diff.parse_time_stats_from_reference_log(ref_log)
+    ref_time_stats = test_compiler_util.parse_performance_stats(str(ref_log))
 
     target_dump = utils.get_output_path(target_dir, args.model_path)
     target_out = torch.load(str(target_dump))
     target_log = utils.get_log_path(target_dir, args.model_path)
-    target_time_stats = eval_backend_diff.parse_time_stats_from_reference_log(
-        target_log
-    )
+    target_time_stats = test_compiler_util.parse_performance_stats(str(target_log))
 
     eval_backend_diff.compare_correctness(ref_out, target_out, eval_args)
     test_compiler_util.print_times_and_speedup(args, ref_time_stats, target_time_stats)

graph_net_bench/test_compiler_util.py

@@ -7,6 +7,7 @@
 import shutil
 import base64
 import numpy as np
+from typing import Dict, Any
 from dataclasses import dataclass
 from contextlib import contextmanager
 
@@ -381,3 +382,64 @@ def convert_to_dict(config_str):
     config = json.loads(config_str)
     assert isinstance(config, dict), f"config should be a dict. {config_str=}"
     return config
+
+
+def convert_to_base64(config_dict):
+    """Convert a dict to base64 encoded JSON string."""
+    if config_dict is None:
+        return ""
+    config_str = json.dumps(config_dict)
+    return base64.b64encode(config_str.encode("utf-8")).decode("utf-8")
+
+
+def parse_performance_stats(log_path: str) -> Dict[str, Any]:
+    """Parse performance statistics from log file.
+
+    Args:
+        log_path: Path to the log file
+
+    Returns:
+        Dictionary containing time statistics
+
+    Raises:
+        FileNotFoundError: If log_path does not exist
+        ValueError: If performance data cannot be parsed
+    """
+    if not os.path.isfile(log_path):
+        raise FileNotFoundError(f"Log file not found: {log_path}")
+
+    with open(log_path, "r", encoding="utf-8") as f:
+        lines = f.readlines()
+
+    # Search backwards for performance data
+    for line in reversed(lines):
+        if "[Performance][eager]" in line:
+            start = line.find("{")
+            end = line.rfind("}")
+            if start != -1 and end != -1:
+                try:
+                    time_stats = json.loads(line[start : end + 1])
+                    return time_stats
+                except json.JSONDecodeError as e:
+                    raise ValueError(f"Failed to parse performance stats: {e}")
+
+    raise ValueError("No performance statistics found in log file")
+
+
+def extract_log_content(log_path: str) -> str:
+    """Extract and return the entire content of a log file.
+
+    Args:
+        log_path: Path to the log file
+
+    Returns:
+        String containing the log content
+
+    Raises:
+        FileNotFoundError: If log_path does not exist
+    """
+    if not os.path.isfile(log_path):
+        raise FileNotFoundError(f"Log file not found: {log_path}")
+
+    with open(log_path, "r", encoding="utf-8") as f:
+        return f.read()