issue/958 - Add read tensor from file feature in dynamic ops test && …

test/infinicore/framework/entities.py

@@ -133,6 +133,10 @@ def __str__(self):
             for key, value in self.kwargs.items():
                 if key == "out" and isinstance(value, int):
                     kwargs_strs.append(f"{key}={self.inputs[value].name}")
+                elif isinstance(value, (list, tuple)):
+                    # Handle tuple/list of TensorSpecs or other values
+                    item_strs = [str(item) for item in value]
+                    kwargs_strs.append(f"{key}=({', '.join(item_strs)})")
                 else:
                     kwargs_strs.append(f"{key}={value}")
 

test/infinicore/framework/results.py

@@ -155,9 +155,6 @@ def print_live_result(self, result):
         if result.error_message:
             print(f"💥 Error: {result.error_message}")
 
-        if result.stdout or result.stderr or self.verbose:
-            print("-" * 40)
-
     def print_summary(self, results, cumulative_timing, ops_dir, total_expected=0):
         print(f"\n{'='*80}\nCUMULATIVE TEST SUMMARY\n{'='*80}")
 
@@ -367,12 +364,17 @@ def _resolve_name(self, obj, default_name):
         return getattr(obj, "name", None) or default_name
 
     def _spec_to_dict(self, s, name=None):
-        return {
+        spec_dict = {
             "name": name if name else getattr(s, "name", "unknown"),
             "shape": list(s.shape) if s.shape else None,
             "dtype": str(s.dtype).split(".")[-1],
             "strides": list(s.strides) if s.strides else None,
         }
+        # Add file_path if it exists (stored in kwargs for TensorSpec)
+        file_path = getattr(s, "kwargs", {}).get("file_path")
+        if file_path:
+            spec_dict["file_path"] = file_path
+        return spec_dict
 
     def _fmt_result(self, res):
         if not (is_dataclass(res) or hasattr(res, "success")):

test/infinicore/framework/tensor.py

@@ -358,4 +358,8 @@ def __str__(self):
             dtype_str = (
                 f", {str(self.dtype).replace('infinicore.', '')}" if self.dtype else ""
             )
-            return f"{name_str}tensor{self.shape}{strides_str}{dtype_str}"
+            # Add [FROM_FILE] marker when loading from file
+            from_file_marker = (
+                " [FROM_FILE]" if self.init_mode == TensorInitializer.FROM_FILE else ""
+            )
+            return f"{name_str}tensor{self.shape}{strides_str}{dtype_str}{from_file_marker}"

test/infinicore/framework/utils/json_utils.py

@@ -120,6 +120,7 @@ def _write_field(f, key, value, indent, sub_indent, close_comma=""):
         items = value
 
     current_len = len(indent) + len(f'"{key}": {open_char}')
+    wrapped = False  # Track if we wrapped to new line
 
     for i, item in enumerate(items):
         if is_dict:
@@ -135,8 +136,12 @@ def _write_field(f, key, value, indent, sub_indent, close_comma=""):
         if current_len + len(item_str) + len(item_comma) > 180:
             f.write(f"\n{sub_indent}")
             current_len = len(sub_indent)
+            wrapped = True
 
         f.write(f"{item_str}{item_comma}")
         current_len += len(item_str) + len(item_comma)
 
+    # Add newline before closing bracket if we wrapped
+    if wrapped:
+        f.write(f"\n{indent}")
     f.write(f"{close_char}{close_comma}\n")

test/infinicore/framework/utils/load_utils.py

@@ -20,6 +20,7 @@
 import infinicore
 from framework import (
     BaseOperatorTest,
+    TensorInitializer,
     TensorSpec,
     TestCase,
     GenericTestRunner,
@@ -42,11 +43,25 @@ def _parse_dtype(dtype_str):
 def _dict_to_spec(spec_dict):
     """Convert JSON dict to TensorSpec object."""
     if not isinstance(spec_dict, dict): return spec_dict
+
+    # Collect optional fields
+    kwargs = {k: spec_dict[k] for k in ('name', 'file_path') if k in spec_dict}
+
+    # Determine init_mode: file_path always uses FROM_FILE, otherwise use specified mode or default RANDOM
+    if 'file_path' in spec_dict:
+        init_mode = TensorInitializer.FROM_FILE
+    else:
+        init_mode = spec_dict.get('init_mode', TensorInitializer.RANDOM)
+        if isinstance(init_mode, str):
+            # Map string to enum, default to RANDOM if unknown
+            init_mode = getattr(TensorInitializer, init_mode.upper(), TensorInitializer.RANDOM)
+
     return TensorSpec(
         shape=tuple(spec_dict['shape']),
         dtype=_parse_dtype(spec_dict['dtype']),
-        name=spec_dict.get('name'),
-        strides=tuple(spec_dict['strides']) if spec_dict.get('strides') else None
+        strides=tuple(spec_dict['strides']) if spec_dict.get('strides') else None,
+        init_mode=init_mode,
+        **kwargs
     )
 
 def parse_test_cases():
@@ -131,6 +146,7 @@ def main():
     main()
 '''
 
+
 class TestGenerator:
 
     def __init__(self, project_root):
@@ -151,8 +167,9 @@ def generate(self, json_list, output_dir):
             # If the op name is provided, generate the return statement.
             # If it's None/null, use 'pass' to avoid syntax errors.
             make_body = lambda name, tag: (
-                f"return {name}(*args, **self._resolve_kwargs(args, kwargs))" 
-                if name else f"pass  # {tag} is null, skipping implementation"
+                f"return {name}(*args, **self._resolve_kwargs(args, kwargs))"
+                if name
+                else f"pass  # {tag} is null, skipping implementation"
             )
 
             torch_body = make_body(torch_op_name, "torch_op")
@@ -162,7 +179,7 @@ def generate(self, json_list, output_dir):
             config_str = pprint.pformat(op_config, indent=4, width=120)
             file_content = _TEST_FILE_TEMPLATE.replace("{op_config_json}", config_str)
             file_content = file_content.replace("{project_root}", self.project_root)
-            
+
             # Injected Method Bodies
             file_content = file_content.replace("{torch_method_body}", torch_body)
             file_content = file_content.replace("{infini_method_body}", infini_body)

test/infinicore/run.py

@@ -280,7 +280,7 @@ def main():
             )
 
         if bench:
-            print(f"Benchmark mode: {args.bench.upper()} timing")
+            print(f"Benchmark mode: {bench.upper()} timing")
 
         # 3. Initialize and Execute
         test_manager = TestManager(