chore: import upstream snapshot with attribution

python/paddle/quantization/imperative/fuse_utils.py

@@ -0,0 +1,221 @@
+#   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 copy
+
+import paddle
+from paddle import nn
+
+from . import utils
+
+
+class Identity(nn.Layer):
+    '''a layer to replace bn or relu layers'''
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+    def forward(self, input):
+        return input
+
+
+def fuse_conv_bn(model):
+    is_train = False
+    if model.training:
+        model.eval()
+        is_train = True
+    fuse_list = []
+    tmp_pair = [None, None]
+    for name, layer in model.named_sublayers():
+        if isinstance(layer, nn.Conv2D):
+            tmp_pair[0] = name
+        if isinstance(layer, nn.BatchNorm2D):
+            tmp_pair[1] = name
+
+        if tmp_pair[0] and tmp_pair[1] and len(tmp_pair) == 2:
+            fuse_list.append(tmp_pair)
+            tmp_pair = [None, None]
+    model = fuse_layers(model, fuse_list)
+    if is_train:
+        model.train()
+
+
+def fuse_layers(model, layers_to_fuse, inplace=False):
+    '''
+    fuse layers in layers_to_fuse
+
+    Args:
+        model(paddle.nn.Layer): The model to be fused.
+        layers_to_fuse(list): The layers' names to be fused. For
+            example,"fuse_list = [["conv1", "bn1"], ["conv2", "bn2"]]".
+            A TypeError would be raised if "fuse" was set as
+            True but "fuse_list" was None.
+                              Default: None.
+        inplace(bool): Whether apply fusing to the input model.
+                       Default: False.
+
+    Return
+        fused_model(paddle.nn.Layer): The fused model.
+    '''
+    if inplace is False:
+        model = copy.deepcopy(model)
+    for layers in layers_to_fuse:
+        _fuse_layers(model, layers)
+    return model
+
+
+def _fuse_layers(model, layers_list):
+    '''fuse all the layers in layers_list'''
+    layer_list = []
+    for layer_name in layers_list:
+        parent_layer, sub_name = utils.find_parent_layer_and_sub_name(
+            model, layer_name
+        )
+        layer_list.append(getattr(parent_layer, sub_name))
+    new_layers = _fuse_func(layer_list)
+    for i, item in enumerate(layers_list):
+        parent_layer, sub_name = utils.find_parent_layer_and_sub_name(
+            model, item
+        )
+        setattr(parent_layer, sub_name, new_layers[i])
+
+
+def _fuse_func(layer_list):
+    '''choose the fuse method and fuse layers'''
+    types = tuple(type(m) for m in layer_list)
+    fusion_method = types_to_fusion_method.get(types, None)
+    new_layers = [None] * len(layer_list)
+    fused_layer = fusion_method(*layer_list)
+    for handle_id, pre_hook_fn in layer_list[0]._forward_pre_hooks.items():
+        fused_layer.register_forward_pre_hook(pre_hook_fn)
+        del layer_list[0]._forward_pre_hooks[handle_id]
+    for handle_id, hook_fn in layer_list[-1]._forward_post_hooks.items():
+        fused_layer.register_forward_post_hook(hook_fn)
+        del layer_list[-1]._forward_post_hooks[handle_id]
+    new_layers[0] = fused_layer
+    for i in range(1, len(layer_list)):
+        identity = Identity()
+        identity.training = layer_list[0].training
+        new_layers[i] = identity
+    return new_layers
+
+
+def _fuse_conv_bn(conv, bn):
+    '''fuse conv and bn for train or eval'''
+    assert conv.training == bn.training, (
+        "Conv and BN both must be in the same mode (train or eval)."
+    )
+    if conv.training:
+        assert bn._num_features == conv._out_channels, (
+            'Output channel of Conv2d must match num_features of BatchNorm2d'
+        )
+        raise NotImplementedError
+    else:
+        return _fuse_conv_bn_eval(conv, bn)
+
+
+def _fuse_conv_bn_eval(conv, bn):
+    '''fuse conv and bn for eval'''
+    assert not (conv.training or bn.training), "Fusion only for eval!"
+    fused_conv = copy.deepcopy(conv)
+
+    fused_weight, fused_bias = _fuse_conv_bn_weights(
+        fused_conv.weight,
+        fused_conv.bias,
+        bn._mean,
+        bn._variance,
+        bn._epsilon,
+        bn.weight,
+        bn.bias,
+    )
+    fused_conv.weight.set_value(fused_weight)
+    if fused_conv.bias is None:
+        fused_conv.bias = paddle.create_parameter(
+            shape=[fused_conv._out_channels], is_bias=True, dtype=bn.bias.dtype
+        )
+    fused_conv.bias.set_value(fused_bias)
+    return fused_conv
+
+
+def _fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
+    '''fuse weights and bias of conv and bn'''
+    if conv_b is None:
+        conv_b = paddle.zeros_like(bn_rm)
+    if bn_w is None:
+        bn_w = paddle.ones_like(bn_rm)
+    if bn_b is None:
+        bn_b = paddle.zeros_like(bn_rm)
+    bn_var_rsqrt = paddle.rsqrt(bn_rv + bn_eps)
+    conv_w = conv_w * (bn_w * bn_var_rsqrt).reshape(
+        [-1] + [1] * (len(conv_w.shape) - 1)
+    )
+    conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
+    return conv_w, conv_b
+
+
+def _fuse_linear_bn(linear, bn):
+    '''fuse linear and bn'''
+    assert linear.training == bn.training, (
+        "Linear and BN both must be in the same mode (train or eval)."
+    )
+    if linear.training:
+        assert bn._num_features == linear.weight.shape[1], (
+            'Output channel of Linear must match num_features of BatchNorm'
+        )
+        raise NotImplementedError
+    else:
+        return _fuse_linear_bn_eval(linear, bn)
+
+
+def _fuse_linear_bn_eval(linear, bn):
+    '''fuse linear and bn for eval'''
+    assert not (linear.training or bn.training), "Fusion only for eval!"
+    fused_linear = copy.deepcopy(linear)
+
+    fused_weight, fused_bias = _fuse_linear_bn_weights(
+        fused_linear.weight,
+        fused_linear.bias,
+        bn._mean,
+        bn._variance,
+        bn._epsilon,
+        bn.weight,
+        bn.bias,
+    )
+    fused_linear.weight.set_value(fused_weight)
+    if fused_linear.bias is None:
+        fused_linear.bias = paddle.create_parameter(
+            shape=[fused_linear.weight.shape[1]],
+            is_bias=True,
+            dtype=bn.bias.dtype,
+        )
+    fused_linear.bias.set_value(fused_bias)
+    return fused_linear
+
+
+def _fuse_linear_bn_weights(
+    linear_w, linear_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b
+):
+    '''fuse weights and bias of linear and bn'''
+    if linear_b is None:
+        linear_b = paddle.zeros_like(bn_rm)
+    bn_scale = bn_w * paddle.rsqrt(bn_rv + bn_eps)
+    fused_w = linear_w * bn_scale.unsqueeze(-1)
+    fused_b = (linear_b - bn_rm) * bn_scale + bn_b
+    return fused_w, fused_b
+
+
+types_to_fusion_method = {
+    (nn.Conv2D, nn.BatchNorm2D): _fuse_conv_bn,
+    (nn.Linear, nn.BatchNorm1D): _fuse_linear_bn,
+}