[Enhance] add documents for clip_grad , and support clip grad by value. (#513)

* [Enhance] add documents for , and support clip grad by value

* refine docstring

* fix as comment

* Fix as comment

* minor refine

* minor refine

* remove error comment for clip grad

* refine docstring

docs/zh_cn/tutorials/optim_wrapper.md

@@ -132,6 +132,18 @@ for idx, (input, target) in enumerate(zip(inputs, targets)):
         optim_wrapper.zero_grad()
 ```
 
+我们同样可以为优化器封装配置梯度裁减策略：
+
+```python
+# 基于 torch.nn.utils.clip_grad_norm_ 对梯度进行裁减
+optim_wrapper = AmpOptimWrapper(
+    optimizer=optimizer, clip_grad=dict(max_norm=1))
+
+# 基于 torch.nn.utils.clip_grad_value_ 对梯度进行裁减
+optim_wrapper = AmpOptimWrapper(
+    optimizer=optimizer, clip_grad=dict(clip_value=0.2))
+```
+
 ### 获取学习率/动量：
 
 优化器封装提供了 `get_lr` 和 `get_momentum` 接口用于获取优化器的一个参数组的学习率

mmengine/optim/optimizer/optimizer_wrapper.py

@@ -5,7 +5,6 @@ from typing import Dict, List, Optional
 
 import torch
 import torch.nn as nn
-from torch.nn.utils import clip_grad
 from torch.optim import Optimizer
 
 from mmengine.logging import MessageHub, print_log
@@ -32,7 +31,27 @@ class OptimWrapper:
             gradients. The parameters will be updated per
             ``accumulative_counts``.
         clip_grad (dict, optional): If ``clip_grad`` is not None, it will be
-            the arguments of ``torch.nn.utils.clip_grad``.
+            the arguments of :func:`torch.nn.utils.clip_grad_norm_` or
+            :func:`torch.nn.utils.clip_grad_value_`. ``clip_grad`` should be a
+            dict, and the keys could be set as follows:
+
+            If the key ``type`` is not set, or ``type`` is "norm",
+            the accepted keys are as follows:
+
+            - max_norm (float or int): Max norm of the gradients.
+            - norm_type (float or int): Type of the used p-norm. Can be
+              ``'inf'`` for infinity norm.
+            - error_if_nonfinite (bool): If True, an error is thrown if
+              the total norm of the gradients from :attr:`parameters` is
+              ``nan``, ``inf``, or ``-inf``. Default: False (will switch
+              to True in the future)
+
+            If the key ``type`` is set to "value", the accepted keys are as
+            follows:
+
+            - clip_value (float or int): maximum allowed value of the
+              gradients. The gradients are clipped in the range
+              ``(-clip_value, +clip_value)``.
 
     Note:
         If ``accumulative_counts`` is larger than 1, perform
@@ -49,11 +68,18 @@ class OptimWrapper:
         ``_inner_count += 1`` is automatically performed.
 
     Examples:
-        >>> # Config sample of OptimWrapper.
+        >>> # Config sample of OptimWrapper and enable clipping gradient by
+        >>> # norm.
         >>> optim_wrapper_cfg = dict(
         >>>     type='OptimWrapper',
         >>>     _accumulative_counts=1,
         >>>     clip_grad=dict(max_norm=0.2))
+        >>> # Config sample of OptimWrapper and enable clipping gradient by
+        >>> # value.
+        >>> optim_wrapper_cfg = dict(
+        >>>     type='OptimWrapper',
+        >>>     _accumulative_counts=1,
+        >>>     clip_grad=dict(type='value', clip_value=0.2))
         >>> # Use OptimWrapper to update model.
         >>> import torch.nn as nn
         >>> import torch
@@ -105,7 +131,22 @@ class OptimWrapper:
             # clip_grad_kwargs should not be non-empty dict.
             assert isinstance(clip_grad, dict) and clip_grad, (
                 'If `clip_grad` is not None, it should be a `dict` '
-                'which is the arguments of `torch.nn.utils.clip_grad`')
+                'which is the arguments of `torch.nn.utils.clip_grad_norm_` '
+                'or clip_grad_value_`.')
+            clip_type = clip_grad.pop('type', 'norm')
+            if clip_type == 'norm':
+                self.clip_func = torch.nn.utils.clip_grad_norm_
+                self.grad_name = 'grad_norm'
+            elif clip_type == 'value':
+                self.clip_func = torch.nn.utils.clip_grad_value_
+                self.grad_name = 'grad_value'
+            else:
+                raise ValueError('type of clip_grad should be "norm" or '
+                                 f'"value" but got {clip_type}')
+            assert clip_grad, ('`clip_grad` should contain other arguments '
+                               'besides `type`. The arguments should match '
+                               'with the `torch.nn.utils.clip_grad_norm_` or '
+                               'clip_grad_value_`')
         self.clip_grad_kwargs = clip_grad
         # Used to update `grad_norm` log message.
         self.message_hub = MessageHub.get_current_instance()
@@ -305,9 +346,11 @@ class OptimWrapper:
         params = list(
             filter(lambda p: p.requires_grad and p.grad is not None, params))
         if len(params) > 0:
-            grad_norm = clip_grad.clip_grad_norm_(params,
+            grad = self.clip_func(params, **self.clip_grad_kwargs)
-                                                  **self.clip_grad_kwargs)
+            # `torch.nn.utils.clip_grad_value_` will return None.
-            self.message_hub.update_scalar('train/grad_norm', float(grad_norm))
+            if grad is not None:
+                self.message_hub.update_scalar(f'train/{self.grad_name}',
+                                               float(grad))
 
     def initialize_count_status(self, model: nn.Module, init_counts: int,
                                 max_counts: int) -> None:

tests/test_optim/test_optimizer/test_optimizer_wrapper.py

@@ -191,6 +191,7 @@ class TestOptimWrapper(MultiProcessTestCase):
     #       in the future).
     @pytest.mark.skipif(True, reason='Solved in the future')
     def test_clip_grads(self):
+        # Test `clip_grad` with `clip_norm_`
         optim_wrapper = OptimWrapper(
             self.optimizer, clip_grad=dict(max_norm=35))
         loss = self.model(torch.Tensor(1, 1, 1, 1))
@@ -198,6 +199,15 @@ class TestOptimWrapper(MultiProcessTestCase):
         optim_wrapper._clip_grad()
         log_scalars = self.message_hub.log_scalars
         self.assertIn('train/grad_norm', log_scalars)
+        self.message_hub._log_scalars.clear()
+
+        # Test `clip_grad` with `clip_value_`
+        optim_wrapper = OptimWrapper(
+            self.optimizer, clip_grad=dict(type='value', clip_value=0.5))
+        loss = self.model(torch.Tensor(1, 1, 1, 1))
+        loss.backward()
+        optim_wrapper._clip_grad()
+        self.assertNotIn('train/grad_norm', log_scalars)
 
     def test_state_dict(self):
         optim_wrapper = OptimWrapper(self.optimizer)