feat: support swapping the order of base and raw models when using cli

README.md

@@ -13,7 +13,7 @@ PaDiff 是基于 PaddlePaddle 与 PyTorch 的模型精度对齐工具。传入 P
 
 - 手动修改 Paddle 和 PyTorch 脚本，并在**分别**运行（不需要同时）这两个脚本，根据脚本 PaDiff 会分别监控运行过程并 dump 数据，最后手动调用 API 接口对比数据，得到对齐结果。两次运行可以在**两个环境**中，但每个环境中都需要同时存在 `paddlepaddle` 和 `torch` 包
 
-- 参考[旧版本特性（v0.2版本）](#旧版本特性（v0.2版本）) 使用 auto_diff 接口进行对齐，这种方法需要将 Paddle 和 PyTorch 放在同一个文件中，同时将 `paddlepaddle` 和 `torch` 包安装在同一环境中，不仅两个模型的运行过程耦合，代码修改量也比较大，因此**不推荐**此用法
+- 参考[旧版本特性（v0.2版本）](#readme-v02) 使用 auto_diff 接口进行对齐，这种方法需要将 Paddle 和 PyTorch 放在同一个文件中，同时将 `paddlepaddle` 和 `torch` 包安装在同一环境中，不仅两个模型的运行过程耦合，代码修改量也比较大，因此**不推荐**此用法
 
 
 ## 安装
@@ -79,6 +79,7 @@ if __name__ == "__main__":
 
 为了保持控制台信息简洁，可以设置环境变量 `PADIFF_SILENT=1`，此模式下仅保存 log 文件，不在控制台输出 log 信息
 
+<a id="readme-v02"></a>
 ## 旧版本特性（v0.2版本）
 
 ### 使用 auto_diff 接口和其它方法

docs/CLIConfig.md

@@ -17,6 +17,7 @@
 | `pd_model_name` | string | 否   | "model"        | PaddlePaddle 模型实例的变量名    |
 | `pt_optim_name` | string | 否   | null           | PyTorch 优化器实例的变量名       |
 | `pd_optim_name` | string | 否   | null           | PaddlePaddle 优化器实例的变量名  |
+| `base_framework`| string | 否   | "torch"        | 设置作为 base 的框架            |
 | `log_dir`       | string | 否   | "./padiff_log" | 日志和报告的输出目录             |
 
 ## PaDiffGuard 部分
@@ -116,6 +117,16 @@ trainer.train()
 # 由于 trainer.train() 中通常已经包含了完整的前反向过程，因此不需要传递此参数
 ```
 
+#### base 框架 (base_framework)
+
+- 设置对齐方向，默认值 'troch'，即 paddle 代码向 torch 代码对齐
+- 默认值: "torch"，此时在逐层对齐时，会在运行 torch 代码时保存各层输出，并在运行 paddle 代码时读取并替换
+- 当 base 模型的输出个数少于 raw 模型时，会找不到输出而报错，此时可以修改此参数，交换二者的顺序
+
+```
+base_framework: "torch"
+```
+
 #### 日志目录 (log_dir)
 
 - 指定生成报告和日志的目录

docs/config_example.yaml

@@ -8,6 +8,7 @@ CLI:
   pd_model_name: "pd_model"                           # PaddlePaddle 模型变量名
   pt_optim_name: "pt_optimizer"                       # (可选) PyTorch 优化器变量名
   pd_optim_name: "pd_optimizer"                       # (可选) PaddlePaddle 优化器变量名
+  base_framework: "torch"                             # (可选) 设置作为 base 的框架
   log_dir: "./padiff_log"                             # (可选) 日志目录
 
 # --- COMPARE 部分 ---

padiff/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-__version__ = "0.3.0"
+__version__ = "0.4.0"
 
 
 # for api -> Layer

padiff/abstracts/hooks/hook.py

@@ -32,6 +32,9 @@
 from .base import current_report, find_base_report_node, single_step_state
 
 
+_seen_warnings = set()
+
+
 @contextlib.contextmanager
 def register_hooker(model):
     marker = model.marker
@@ -266,27 +269,63 @@ def replace_forward_output(node, current_name=None):
 
     def inner(input_):
         nonlocal cur_idx
-        if isinstance(input_, (paddle.Tensor, torch.Tensor)):
-            if cur_idx >= len(numpy_file_list):
-                raise RuntimeError(
-                    f"\n   ⚠️ Single-step alignment FAILED: the {cur_idx + 1}st output is requested, "
-                    f"but only {len(numpy_file_list)} pre-saved numpy files are available."
+        if not isinstance(input_, (paddle.Tensor, torch.Tensor)):
+            return input_
+
+        if cur_idx >= len(numpy_file_list):
+            warning_key = ("single-step: output_count_mismatch", current_name)
+            if warning_key not in _seen_warnings:
+                logger.warning(
+                    f"\n   ⚠️ Single-step alignment SKIPPED: the {cur_idx + 1}st output is requested, "
+                    f"but only {len(numpy_file_list)} pre-saved from base model, skip the current output."
+                    "\n   ⚠️ This warning will not repeat for this layer."
                     f"\n   📌 Layer Name: {current_name}(raw)"
                     "\n   💡 Possible Causes and Solutions:"
-                    "\n     - The number of outputs from the current layer in the raw model does not match "
+                    "\n     - The number of outputs from the current layer in the raw model is bigger than"
                     "that of its corresponding layer in the base model."
                     "\n     - Verify that both models have identical architectures for this layer."
                     "\n     - If the corresponding relationship of the current layer is correct, "
                     "please disable single step mode, or add the layer to blacklist to skip the check of this layer."
+                    "\n     - Or when you are sure that the extra output does not need to be compared, "
+                    "you can swap the execution order of the base model and the raw model."
                 )
-            value = np.load(numpy_file_list[cur_idx]["path"])
-            cur_idx += 1
-            if isinstance(input_, paddle.Tensor):
-                return paddle.to_tensor(value, dtype=input_.dtype)
-            else:
-                return torch.as_tensor(value, dtype=input_.dtype, device=input_.device)
-        else:
+                _seen_warnings.add(warning_key)
+            return input_
+
+        value = np.load(numpy_file_list[cur_idx]["path"])
+        cur_idx += 1
+        base_shape = tuple(value.shape)
+        raw_shape = tuple(input_.shape)
+
+        if base_shape == raw_shape:
+            pass
+
+        elif np.prod(base_shape) != np.prod(raw_shape):
+            warning_key = ("single-step: shape_mismatch", current_name)
+            if warning_key not in _seen_warnings:
+                logger.warning(
+                    f"\n   ⚠️ Single-step alignment SKIPPED: shape mismatch."
+                    "\n   ⚠️ This warning will not repeat for this layer."
+                    f"\n   📌 Layer Name: {current_name}(raw)"
+                    f"\n   📌 Shape: {base_shape}(base) vs {raw_shape}(raw)"
+                )
+                _seen_warnings.add(warning_key)
             return input_
+        else:
+            value = value.reshape(input_.shape)
+            debug_key = ("single-step: reshape_used", current_name)
+            if debug_key not in _seen_warnings:
+                logger.debug(
+                    f"\n   ⚠️ Try to reshape loaded value to input's shape of layer {current_name}(raw). "
+                    "This may lead to numerical errors even if reshape succeeds."
+                    "\n   ⚠️ This warning will not repeat for this layer."
+                )
+                _seen_warnings.add(debug_key)
+
+        if isinstance(input_, paddle.Tensor):
+            return paddle.to_tensor(value, dtype=input_.dtype)
+        else:
+            return torch.as_tensor(value, dtype=input_.dtype, device=input_.device)
 
     return inner
 
@@ -296,14 +335,17 @@ def single_step_check(report, net_id, step_idx, current_name, node_type, bwd_ite
     try:
         base_report_node = find_base_report_node(net_id, step_idx)
         if base_report_node["name"] != current_name:
-            warning_msg = (
-                f"\n   ⚠️ Single-step alignment WARNING: {node_type} with net_id={net_id} mismatch!\n"
-                f"   📌 Mismatch {node_type.capitalize()}: {base_report_node['name']}(base) vs {current_name}(raw)\n"
-                "   💡 Suggestion: Models have different architectures or initialization order. "
-                "Please check the model implementation or decrease 'align_depth' to reduce the alignment "
-                "granularity, or add layers that do not require alignment to the blacklist."
-            )
-            logger.warning(warning_msg)
+            warning_key = ("single-step: name_mismatch", current_name)
+            if warning_key not in _seen_warnings:
+                logger.warning(
+                    f"\n   ⚠️ Single-step alignment WARNING: {node_type} with net_id={net_id} mismatch!"
+                    "\n   ⚠️ This warning will not repeat for this layer."
+                    f"\n   📌 Mismatch {node_type.capitalize()}: {base_report_node['name']}(base) vs {current_name}(raw)"
+                    "\n   💡 Suggestion: Models have different architectures or class name or initialization order. "
+                    "Please check the model implementation or decrease 'align_depth' to reduce the alignment "
+                    "granularity, or add layers that do not require alignment to the blacklist."
+                )
+                _seen_warnings.add(warning_key)
         else:
             logger.debug(f"Single Step: {current_name}(net_id={net_id})")
 

padiff/cli.py

@@ -143,7 +143,15 @@ def main():
               --pt_cmd "python /path/to/your/torch_script.py"
               --pd_cmd "python /path/to/your/paddle_script.py"
 
-        3. 日志目录参数 (--log_dir):
+        3. base 框架设置参数 (--base_framework):
+           * 非必需 被包含在 config 文件中，或 通过命令行传入
+           * 设置对齐方向，默认值 'troch'，即 paddle 代码向 torch 代码对齐
+           * 影响逐层对齐时，是读取哪个框架下保存的数据
+
+           示例：
+              --base_framework "torch"
+
+        4. 日志目录参数 (--log_dir):
            * 可选参数
            * 指定生成报告和日志的目录
            * 默认值: ./padiff_log
@@ -159,6 +167,7 @@ def main():
                 pd_model_name: "pd_model"
                 pt_optim_name: "pt_optimizer"   # not required
                 pd_optim_name: "pd_optimizer"   # not required
+                base_framework: "torch"   # not required
                 log_dir: "./padiff_log"   # not required
 
             PaDiffGuard:
@@ -192,6 +201,12 @@ def main():
         type=str,
         help="Override 'pd_cmd' (paddle command) in config, e.g., 'python /paddle_dir/paddle_model.py'",
     )
+    parser.add_argument(
+        "--base_framework",
+        type=str,
+        choices=["torch", "paddle"],
+        help="Which framework's output should be used as the base framework (base). The other will align to it. Options: 'torch' or 'paddle'.",
+    )
     parser.add_argument(
         "--log_dir",
         type=str,
@@ -213,6 +228,8 @@ def main():
         cli_cfg["pd_cmd"] = args.pd_cmd
     if args.log_dir:
         cli_cfg["log_dir"] = args.log_dir
+    if args.base_framework:
+        cli_cfg["base_framework"] = args.base_framework
 
     log_dir = cli_cfg.pop("log_dir", "./padiff_log")
     logger.reset_dir(log_dir)
@@ -229,26 +246,23 @@ def main():
     pt_optim_name = cli_cfg.get("pt_optim_name")
     pd_optim_name = cli_cfg.get("pd_optim_name")
 
+    tasks = {
+        "torch": {"cmd": pt_cmd, "framework": "torch", "model_name": pt_model_name, "optim_name": pt_optim_name},
+        "paddle": {"cmd": pd_cmd, "framework": "paddle", "model_name": pd_model_name, "optim_name": pd_optim_name},
+    }
+    base_fw = cli_cfg.pop("base_framework", "torch")
+    align_fw = "paddle" if base_fw == "torch" else "torch"
+
     logger.info("Code injection and script execution...")
     try:
-        pt_dump_path = run_with_padiff(
-            cmd=pt_cmd,
-            framework="torch",
-            model_name=pt_model_name,
-            optim_name=pt_optim_name,
-            mode="base",
-            alignment_dir=None,
-            guard_cfg=guard_cfg,
-        )
-        pd_dump_path = run_with_padiff(
-            cmd=pd_cmd,
-            framework="paddle",
-            model_name=pd_model_name,
-            optim_name=pd_optim_name,
-            mode="align",
-            alignment_dir=pt_dump_path,
-            guard_cfg=guard_cfg,
-        )
+        logger.info(f"Running {base_fw.upper()} as BASE")
+        base_path = run_with_padiff(mode="base", alignment_dir=None, guard_cfg=guard_cfg, **tasks[base_fw])
+
+        logger.info(f"Running {align_fw.upper()} in ALIGN mode")
+        align_path = run_with_padiff(mode="align", alignment_dir=base_path, guard_cfg=guard_cfg, **tasks[align_fw])
+
+        pt_dump_path = base_path if base_fw == "torch" else align_path
+        pd_dump_path = base_path if base_fw == "paddle" else align_path
     except Exception as e:
         logger.error(f"An error occurred during execution: {type(e).__name__}: {str(e)}")
         import traceback

setup.py

@@ -15,7 +15,7 @@
 
 from setuptools import find_packages, setup
 
-VERSION = "0.3.0"
+VERSION = "0.4.0"
 
 
 def read_requirements_file(filepath):