add clip_grad_value_ api (#54603)

* add clip_grad_value_ api

* add test for ClipGradByValue

* typo fix

* refine and modify clip_grad_norm_

* no_grad

* clip_

* remove g=p.grad

* bug: AssertionError: When Variable is used as the condition of if/while , Variable can only contain one element.

python/paddle/nn/utils/__init__.py

@@ -20,6 +20,7 @@ from .transform_parameters import (
     _stride_column,
 )  # noqa: F401
 from .clip_grad_norm_ import clip_grad_norm_  # noqa: F401
+from .clip_grad_value_ import clip_grad_value_  # noqa: F401
 
 __all__ = [  # noqa
     'weight_norm',
@@ -28,4 +29,5 @@ __all__ = [  # noqa
     'parameters_to_vector',
     'vector_to_parameters',
     'clip_grad_norm_',
+    'clip_grad_value_',
 ]

python/paddle/nn/utils/clip_grad_norm_.py

@@ -14,9 +14,10 @@
 
 import paddle
 
-__all__ = ['clip_grad_norm_']
+__all__ = []
 
 
+@paddle.autograd.no_grad()
 def clip_grad_norm_(
     parameters,
     max_norm,
@@ -98,10 +99,9 @@ def clip_grad_norm_(
     clip_coef = max_norm / (total_norm + 1e-6)
     # Note: when the coef is clamped to 1, it is redundant to multiply the clamped coef, but this
     # avoids the `if clip_coef < 1:` condition.
-    clip_coef_clamped = paddle.clip(clip_coef, max=1.0)
-    with paddle.no_grad():
+    clip_coef_clamped = clip_coef.clip_(max=1.0)
+
     for _, p in enumerate(parameters):
-            g = p.grad
-            if g is not None:
-                p.grad = paddle.multiply(x=g, y=clip_coef_clamped)
+        if p.grad is not None:
+            p.grad = paddle.multiply(x=p.grad, y=clip_coef_clamped)
     return total_norm

python/paddle/nn/utils/clip_grad_value_.py

+#   Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+
+__all__ = []
+
+
+@paddle.autograd.no_grad()
+def clip_grad_value_(
+    parameters,
+    clip_value,
+):
+    r"""Clips gradient of an iterable of parameters at specified value.
+    The gradient will be modified in place.
+    This API can only run in dynamic graph mode, not static graph mode.
+    Args:
+        parameters (Iterable[paddle.Tensor] or paddle.Tensor): Tensors or a single Tensor
+            that will be normalized gradients
+        clip_value (float or int): maximum allowed value of the gradients.
+            The gradients are clipped in the range
+            :math:`\left[\text{-clip\_value}, \text{clip\_value}\right]`
+    Example:
+        .. code-block:: python
+
+            import paddle
+            x = paddle.uniform([10, 10], min=-10.0, max=10.0, dtype='float32')
+            clip_value = float(5.0)
+            linear = paddle.nn.Linear(in_features=10, out_features=10)
+            out = linear(x)
+            loss = paddle.mean(out)
+            loss.backward()
+            paddle.nn.utils.clip_grad_value_(linear.parameters(), clip_value)
+            sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
+            sdg.step()
+    """
+    if not paddle.in_dynamic_mode():
+        raise RuntimeError('this API can only run in dynamic mode.')
+
+    if isinstance(parameters, paddle.Tensor):
+        parameters = [parameters]
+
+    clip_value = float(clip_value)
+
+    for _, p in enumerate(parameters):
+        if p.grad is not None:
+            p.grad.clip_(min=-clip_value, max=clip_value)

test/legacy_test/test_clip_grad_value_.py

+#   Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.nn.utils.clip_grad_value_ import clip_grad_value_
+
+
+class TestClipGradValue(unittest.TestCase):
+    def test_basic(self):
+        run_test_equal_np(
+            self,
+            shape=[16, 16],
+            dtype=np.float32,
+            clip_value=1,
+        )
+        run_test_equal_np(
+            self,
+            shape=(100,),
+            dtype=np.float32,
+            clip_value=0.1,
+        )
+        run_test_equal_np(
+            self, shape=[4, 8, 16], dtype=np.float32, clip_value=0
+        )
+        run_test_equal_ClipGradByValue(
+            self,
+            shape=[16, 16],
+            dtype=np.float32,
+            clip_value=1,
+        )
+        run_test_equal_ClipGradByValue(
+            self,
+            shape=(100,),
+            dtype=np.float32,
+            clip_value=0.1,
+        )
+        run_test_equal_ClipGradByValue(
+            self, shape=[4, 8, 16], dtype=np.float32, clip_value=0
+        )
+
+    def test_errors(self):
+        def TestValueError():
+            input_pd = paddle.to_tensor(
+                np.random.random([1, 2]).astype(np.float32)
+            )
+            input_pd.grad = paddle.to_tensor(
+                np.random.random([1, 2]).astype(np.float32)
+            )
+            clip_grad_value_(input_pd, clip_value=-1)
+
+        self.assertRaises(ValueError, TestValueError)
+
+        def TestRuntimeErrorStaticMode():
+            paddle.enable_static()
+            input_pd = paddle.to_tensor(
+                np.random.random([1, 2]).astype(np.float32)
+            )
+            input_pd.grad = paddle.to_tensor(
+                np.random.random([1, 2]).astype(np.float32)
+            )
+            clip_grad_value_(input_pd, clip_value=1)
+            paddle.disable_static()
+
+        self.assertRaises(RuntimeError, TestRuntimeErrorStaticMode)
+
+
+def run_test_equal_np(
+    self,
+    shape,
+    dtype,
+    clip_value,
+):
+    input = np.random.random(shape).astype(dtype)
+    grad = np.random.random(shape).astype(dtype)
+    input_pd = paddle.to_tensor(input)
+    input_pd.grad = paddle.to_tensor(grad)
+
+    output = np.clip(grad, a_min=-clip_value, a_max=clip_value)
+    clip_grad_value_(
+        input_pd,
+        clip_value=clip_value,
+    )
+
+    np.testing.assert_allclose(
+        input_pd.grad.numpy(),
+        output,
+        rtol=1e-05,
+        atol=1e-05,
+        equal_nan=False,
+    )
+
+
+def run_test_equal_ClipGradByValue(
+    self,
+    shape,
+    dtype,
+    clip_value,
+):
+    input = np.random.random(shape).astype(dtype)
+    grad = np.random.random(shape).astype(dtype)
+    input_pd = paddle.to_tensor(input)
+    input_pd.grad = paddle.to_tensor(grad)
+
+    clip = paddle.nn.ClipGradByValue(max=clip_value, min=-clip_value)
+    output = clip([(input_pd, input_pd.grad)])[0][1]
+    clip_grad_value_(
+        input_pd,
+        clip_value=clip_value,
+    )
+
+    np.testing.assert_allclose(
+        input_pd.grad,
+        output,
+        rtol=1e-05,
+        atol=1e-05,
+        equal_nan=False,
+    )
+
+
+if __name__ == '__main__':
+    unittest.main()