add ema for sd (#3755)

Author: firestonelib

ppdiffusers/ppdiffusers/models/ema.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2022 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
+from paddle import nn
+
+
+class LitEma(nn.Layer):
+    """
+    Exponential Moving Average (EMA) of model updates
+
+    Parameters:
+        model: The model architecture for apply EMA.
+        decay: The exponential decay. Default 0.9999.
+        use_num_updates: Whether to use number of updates when computing
+            averages.
+    """
+
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay',
+                             paddle.to_tensor(decay, dtype=paddle.float32))
+        self.register_buffer(
+            'num_updates',
+            paddle.to_tensor(0, dtype=paddle.int64)
+            if use_num_upates else paddle.to_tensor(-1, dtype=paddle.int64))
+
+        for name, p in model.named_parameters():
+            if not p.stop_gradient:
+                #remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.', '')
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach())
+
+        self.collected_params = []
+
+    def forward(self, model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay,
+                        (1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with paddle.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if not m_param[key].stop_gradient:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname].scale_(decay)
+                    shadow_params[sname].add_(m_param[key] * one_minus_decay)
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if not m_param[key].stop_gradient:
+                m_param[key].copy_(shadow_params[self.m_name2s_name[key]], True)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `paddle.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `paddle.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.copy_(c_param, True)