[WIP] Add original_names for some paddle_samples and enable to recover the tensor metas of re-extracted samples with the original samples..

graph_net/paddle/random_util.py

@@ -0,0 +1,61 @@
+import os
+import pickle
+import numpy as np
+import random
+import re
+import paddle
+
+from graph_net.paddle import samples_util
+
+
+def set_seed(random_seed):
+    paddle.seed(random_seed)
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+
+
+def _extract_model_name_for_original_sample(model_path):
+    fields = model_path.rstrip("/").split(os.sep)
+    pattern = r"^subgraph(_\d+)?$"
+    model_name = (
+        f"{fields[-2]}_{fields[-1]}" if re.match(pattern, fields[-1]) else fields[-1]
+    )
+    return model_name
+
+
+def _extract_model_name_for_decomposed_subgraph(model_path):
+    # Parse model name and subgraph index
+    model_name_with_subgraph_idx = model_path.rstrip("/").split(os.sep)[-1]
+    model_name = "_".join(model_name_with_subgraph_idx.split("_")[:-1])
+    return model_name
+
+
+def _generate_random_state_filename(model_path):
+    samples_dir = samples_util.get_default_samples_directory()
+    if os.path.abspath(model_path).startswith(samples_dir):
+        model_name = _extract_model_name_for_original_sample(model_path)
+    else:
+        model_name = _extract_model_name_for_decomposed_subgraph(model_path)
+    return f"{model_name}.random_states.pkl"
+
+
+def save_random_states(model_path, output_dir, random_state_dict):
+    filepath = os.path.join(output_dir, _generate_random_state_filename(model_path))
+    print(f"Write to {filepath}.", flush=True)
+    try:
+        with open(filepath, "wb") as f:
+            pickle.dump(random_state_dict, f)
+    except Exception:
+        print(f"Fail to open {filepath}.")
+
+
+def load_random_states(model_path, output_dir):
+    filepath = os.path.join(output_dir, _generate_random_state_filename(model_path))
+    print(f"Read from {filepath}.", flush=True)
+    random_states = None
+    try:
+        with open(filepath, "rb") as f:
+            random_states = pickle.load(f)
+    except Exception:
+        print(f"Fail to open {filepath}.")
+    return random_states

graph_net/paddle/run_model.py

@@ -3,19 +3,12 @@
 import base64
 import argparse
 import numpy as np
-import random
 
 os.environ["FLAGS_logging_pir_py_code_dir"] = "/tmp/dump"
 
 import paddle
 from graph_net import imp_util
-from graph_net.paddle import utils
-
-
-def set_seed(random_seed):
-    paddle.seed(random_seed)
-    random.seed(random_seed)
-    np.random.seed(random_seed)
+from graph_net.paddle import utils, random_util
 
 
 def load_class_from_file(file_path: str, class_name: str):
@@ -31,17 +24,20 @@ def get_input_dict(model_path):
     params = inputs_params["weight_info"]
     inputs = inputs_params["input_info"]
 
+    random_state_dict = {}
     input_dict = {}
     for name, meta in params.items():
         original_name = (
             meta["original_name"] if meta.get("original_name", None) else name
         )
+        random_state_dict[name] = np.random.get_state()
         input_dict[name] = paddle.nn.parameter.Parameter(
             utils.replay_tensor(meta), name=original_name
         )
     for name, meta in inputs.items():
+        random_state_dict[name] = np.random.get_state()
         input_dict[name] = utils.replay_tensor(meta)
-    return input_dict
+    return input_dict, random_state_dict
 
 
 def _convert_to_dict(config_str):
@@ -66,9 +62,6 @@ 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"
@@ -77,7 +70,12 @@ def main(args):
     model = model_class()
     print(f"{model_path=}")
 
-    input_dict = get_input_dict(args.model_path)
+    initalize_seed = 123
+    random_util.set_seed(random_seed=initalize_seed)
+
+    input_dict, random_state_dict = get_input_dict(args.model_path)
+    output_dir = "/work/GraphNet/graph_net/test/outputs/pass_0"
+    random_util.save_random_states(model_path, output_dir, random_state_dict)
     model = _get_decorator(args)(model)
     model(**input_dict)
 
@@ -98,4 +96,5 @@ def main(args):
         help="decorator configuration string",
     )
     args = parser.parse_args()
+    print(args)
     main(args=args)

graph_net/paddle/test_compiler.py

@@ -5,16 +5,14 @@
 import sys
 import os
 import numpy as np
-import random
 import platform
 import traceback
 import subprocess
 import re
 
-from graph_net.paddle import utils
 from graph_net import path_utils
 from graph_net import test_compiler_util
-
+from graph_net.paddle import utils, random_util
 from graph_net.paddle.backend.graph_compiler_backend import GraphCompilerBackend
 from graph_net.paddle.backend.cinn_backend import CinnBackend
 from graph_net.paddle.backend.nope_backend import NopeBackend
@@ -31,12 +29,6 @@ def get_compiler_backend(args) -> GraphCompilerBackend:
     return registry_backend[args.compiler]
 
 
-def set_seed(random_seed):
-    paddle.seed(random_seed)
-    random.seed(random_seed)
-    np.random.seed(random_seed)
-
-
 def init_env(args):
     if test_compiler_util.is_gpu_device(args.device):
         paddle.set_flags({"FLAGS_cudnn_exhaustive_search": 1})
@@ -96,14 +88,25 @@ def get_model(model_path):
     return model_class()
 
 
-def get_input_dict(model_path):
+def get_input_dict(model_path, random_states_path=None):
     inputs_params = utils.load_converted_from_text(f"{model_path}")
     params = inputs_params["weight_info"]
     inputs = inputs_params["input_info"]
-
     params.update(inputs)
-    state_dict = {k: utils.replay_tensor(v) for k, v in params.items()}
-    return state_dict
+
+    random_states = (
+        random_util.load_random_states(model_path, random_states_path)
+        if random_states_path
+        else None
+    )
+
+    input_dict = {}
+    for name, meta in params.items():
+        if random_states is not None and random_states.get(name, None) is not None:
+            np.random.set_state(random_states[name])
+        tensor = utils.replay_tensor(meta)
+        input_dict[name] = tensor
+    return input_dict
 
 
 def get_input_spec(model_path):
@@ -486,7 +489,7 @@ def main(args):
     assert args.device in ["cuda", "dcu", "xpu", "cpu"]
 
     initalize_seed = 123
-    set_seed(random_seed=initalize_seed)
+    random_util.set_seed(random_seed=initalize_seed)
 
     if path_utils.is_single_model_dir(args.model_path):
         test_single_model(args)

graph_net/paddle/test_reference_device.py

@@ -9,7 +9,7 @@
 
 from graph_net import path_utils
 from graph_net import test_compiler_util
-from graph_net.paddle import test_compiler
+from graph_net.paddle import random_util, test_compiler
 
 
 def get_reference_log_path(reference_dir, model_path):
@@ -38,8 +38,10 @@ def test_single_model(args):
             compiler = test_compiler.get_compiler_backend(args)
             test_compiler.check_and_print_gpu_utilization(compiler)
 
-            input_dict = test_compiler.get_input_dict(model_path)
-            model = test_compiler.get_model(model_path)
+            input_dict = test_compiler.get_input_dict(
+                args.model_path, args.random_states_path
+            )
+            model = test_compiler.get_model(args.model_path)
             model.eval()
 
             test_compiler_util.print_with_log_prompt(
@@ -107,6 +109,7 @@ def test_multi_models(args):
                     f"--trials {args.trials}",
                     f"--log-prompt {args.log_prompt}",
                     f"--seed {args.seed}",
+                    f"--random-states-path {args.random_states_path}",
                     f"--reference-dir {args.reference_dir}",
                 ]
             )
@@ -130,7 +133,7 @@ def main(args):
     assert args.compiler in {"cinn", "nope"}
     assert args.device in ["cuda"]
 
-    test_compiler.set_seed(random_seed=args.seed)
+    random_util.set_seed(random_seed=args.seed)
     test_compiler.init_env(args)
 
     ref_dump_dir = Path(args.reference_dir)
@@ -191,6 +194,12 @@ def main(args):
         default=123,
         help="Random seed (default: 123)",
     )
+    parser.add_argument(
+        "--random-states-path",
+        type=str,
+        required=False,
+        help="Path to random-states of model (s)",
+    )
     parser.add_argument(
         "--reference-dir",
         type=str,

graph_net/paddle/test_target_device.py

@@ -7,7 +7,7 @@
 import paddle
 from graph_net import path_utils
 from graph_net import test_compiler_util
-from graph_net.paddle import test_compiler, test_reference_device
+from graph_net.paddle import random_util, test_compiler, test_reference_device
 
 
 def parse_config_from_reference_log(log_path):
@@ -53,7 +53,7 @@ def update_args_and_set_seed(args, model_path):
     vars(args)["compiler"] = config.get("compiler")
     vars(args)["trials"] = int(config.get("trials"))
     vars(args)["warmup"] = int(config.get("warmup"))
-    test_compiler.set_seed(random_seed=int(config.get("seed")))
+    random_util.set_seed(random_seed=int(config.get("seed")))
     return args
 
 
@@ -67,8 +67,8 @@ def test_single_model(args):
     compiler = test_compiler.get_compiler_backend(args)
     test_compiler.check_and_print_gpu_utilization(compiler)
 
-    input_dict = test_compiler.get_input_dict(model_path)
-    model = test_compiler.get_model(model_path)
+    input_dict = test_compiler.get_input_dict(args.model_path, args.random_states_path)
+    model = test_compiler.get_model(args.model_path)
     model.eval()
 
     test_compiler_util.print_basic_config(
@@ -146,6 +146,7 @@ def test_multi_models(args):
                     f"--device {args.device}",
                     f"--log-prompt {args.log_prompt}",
                     f"--reference-dir {args.reference_dir}",
+                    f"--random-states-path {args.random_states_path}",
                 ]
             )
             cmd_ret = os.system(cmd)
@@ -210,5 +211,11 @@ def main(args):
         default=None,
         help="Path to samples list, each line contains a sample path",
     )
+    parser.add_argument(
+        "--random-states-path",
+        type=str,
+        required=False,
+        help="Path to random-states of model (s)",
+    )
     args = parser.parse_args()
     main(args=args)

graph_net/paddle/utils.py

@@ -2,6 +2,7 @@
 import ast
 import math
 import numpy as np
+from scipy.stats import truncnorm
 import paddle
 
 kLiteralTensorSize = 64
@@ -197,17 +198,21 @@ def init_integer_tensor(dtype, shape, min_val, max_val, use_numpy):
 def init_float_tensor(shape, mean, std, min_val, max_val, use_numpy):
     tensor = None
     if use_numpy:
-        if mean is not None and std is not None:
+        if mean is not None and std is not None and std != 0.0:
             # NumPy does not support truncated normal, we simulate it here.
-            array = np.random.normal(0, 1, shape) * std * 0.2 + mean
-            array = np.clip(array, min_val, max_val)
+            a = (min_val - mean) / std
+            b = (max_val - mean) / std
+            array = truncnorm.rvs(a, b, loc=mean, scale=std, size=shape)
         else:
             array = np.random.uniform(low=min_val, high=max_val, size=shape)
         tensor = paddle.to_tensor(array)
     else:
         if mean is not None and std is not None:
-            tensor = paddle.randn(shape, dtype="float32") * std * 0.2 + mean
-            tensor = paddle.clip(tensor, min=min_val, max=max_val)
+            tensor = paddle.empty(shape=shape, dtype="float32")
+            initializer = paddle.nn.initializer.TruncatedNormal(
+                mean=mean, std=std, a=min_val, b=max_val
+            )
+            initializer(tensor)
         else:
             tensor = paddle.uniform(
                 shape=shape, dtype="float32", min=min_val, max=max_val

graph_net/subgraph_decompose_and_evaluation_step.py

@@ -400,6 +400,7 @@ def run_evaluation(
         test_module_arguments["reference-dir"] = os.path.join(
             work_dir, "reference_device_outputs"
         )
+        test_module_arguments["random-states-path"] = work_dir
 
     cmd = [sys.executable, "-m", f"graph_net.{framework}.{test_module_name}"] + [
         item