add tf optimizer

x2paddle/convert.py

@@ -128,13 +128,19 @@ def tf2paddle(model_path,
     else:
         from x2paddle.op_mapper.static.tf2paddle.tf_op_mapper import TFOpMapper
         
-    from x2paddle.optimizer.tensorflow.bias import BiasOpt
-    from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
-    from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
+    
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path, define_input_shape=define_input_shape)
     mapper = TFOpMapper(model)
     mapper.paddle_graph.build()
+    if paddle_type == "dygraph":
+        from x2paddle.optimizer.optimizer import GraphOptimizer
+        graph_opt = GraphOptimizer(source_frame="tf", paddle_type=paddle_type)
+        graph_opt.optimize(mapper.paddle_graph)
+    else:
+        from x2paddle.optimizer.tensorflow.bias import BiasOpt
+        from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
+        from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
         bias_opt = BiasOpt()
         transpose_opt = TransposeOpt()
         batch_norm_opt = BatchNormOpt()

x2paddle/optimizer/elimination/dygraph/__init__.py

+#   Copyright (c) 2020  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.
+
+from .transpose_elimination import Dygraph_TransposeElimination
+from .transpose_eliminate_pass import Dygraph_TransposeEliminatePass
\ No newline at end of file

x2paddle/optimizer/elimination/dygraph/transpose_eliminate_pass.py

+#   Copyright (c) 2020  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.
+
+from x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.elimination.dygraph import Dygraph_TransposeElimination
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class Dygraph_TransposeEliminatePass(Pass):
+    name = "transpose_eliminate_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = Dygraph_TransposeElimination()
+        fuser.operate(graph)
+
+
+# 用于注册
+transpose_eliminate_pass = Dygraph_TransposeEliminatePass()
\ No newline at end of file

x2paddle/optimizer/elimination/dygraph/transpose_elimination.py

+#   Copyright (c) 2020  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 numpy as np
+from x2paddle.optimizer.pattern_matcher import FuseBase
+from x2paddle.core.program import PaddleGraph, PaddleLayer
+from x2paddle.core.util import *
+
+
+class Dygraph_TransposeElimination(FuseBase):
+    def __init__(self):
+        super(Dygraph_TransposeElimination, self).__init__(graph_type="dygraph")
+        self.direct_layers = [
+            'paddle.nn.ReLU', 'paddle.nn.ReLU6', 'paddle.abs',
+            'paddle.nn.Sigmoid', 'paddle.exp', 'paddle.rsqrt',
+            'paddle.nn.Swish', 'paddle.nn.Tanh',
+            'paddle.nn.Softplus', 'paddle.nn.LeakyReLU',
+            'paddle.nn.Softmax', 'paddle.erf', 'paddle.square'
+        ]
+        self.elementwise_layers = [
+            'paddle.add', 'fluid.layers.elementwise_sub',
+            'paddle.multiply', 'paddle.divide'
+        ]
+        #        self.reduce_layers = []
+        self.reduce_layers = [
+            'paddle.mean', 'paddle.all',
+            'paddle.max', 'paddle.any',
+            'paddle.sum', 'paddle.prod'
+        ]
+
+    def get_transpose_num(self, graph):
+        count = 0
+        for layer_id, layer in graph.layers.items():
+            if layer.kernel == "paddle.transpose":
+                count += 1
+        return count
+    
+    def operate(self, graph):
+        total_layer_num = len(graph.layers)
+        scanned_layers = set()
+        optimized_transpose_layers = list()
+        optimized_reduce_layers = list()
+        optimized_concat_layers = list()
+        optimized_elementwise_layers = list()
+
+        def strip_transpose(_graph):
+            layers = copy.deepcopy(_graph.layers)
+            for layer_id, layer in layers.items():
+                if layer_id in scanned_layers:
+                    continue
+                scanned_layers.add(layer_id)
+                percent = round(len(scanned_layers) / total_layer_num * 100, 2)
+                print("\rOptimize Transpose Layers...{}%".format(
+                    percent))
+
+                if layer.kernel != "paddle.transpose":
+                    continue
+                if layer.attrs["perm"] != [0, 2, 3, 1]:
+                    continue
+                transpose_layers = list()
+                propagate_layers = list()
+                reduce_layers = list()
+                concat_layers = list()
+                # 此elementwise_layers专用于存储shape(4) + shape(1)的形式layer
+                elementwise_layers = list()
+                can_be_optimized = True
+                for out in _graph.edges_out.get(layer_id, []):
+                    if _graph.layers[out].kernel == "paddle.transpose":
+                        if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
+                            can_be_optimized = False
+                            break
+                        transpose_layers.append(out)
+                    elif _graph.layers[out].kernel in self.elementwise_layers:
+                        propagate_layers.append(out)
+                    elif _graph.layers[out].kernel in self.direct_layers:
+                        ouput_index = 1 if _graph.layers[out].kernel.startswith("paddle.nn.") else 0
+                        if _graph.layers[out].outputs[ouput_index] in _graph.outputs:
+                            can_be_optimized = False
+                            break
+                        propagate_layers.append(out)
+                    elif _graph.layers[out].kernel in self.reduce_layers:
+                        if _graph.layers[out].outputs[0] in _graph.outputs:
+                            can_be_optimized = False
+                            break
+                        if not _graph.layers[out].attrs.get('keepdim', False):
+                            can_be_optimized = False
+                            break
+                        propagate_layers.append(out)
+                        reduce_layers.append(out)
+                    elif _graph.layers[out].kernel == "paddle.concat":
+                        if _graph.layers[out].outputs[0] in _graph.outputs:
+                            can_be_optimized = False
+                            break
+                        propagate_layers.append(out)
+                        concat_layers.append(out)
+                    else:
+                        can_be_optimized = False
+                        break
+
+                visited_layers = set()
+                while len(propagate_layers) > 0 and can_be_optimized:
+                    current_id = propagate_layers.pop(0)
+                    visited_layers.add(current_id)
+                    for out in _graph.edges_out.get(current_id, []):
+                        if _graph.layers[
+                                out].kernel == "paddle.transpose":
+                            if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
+                                can_be_optimized = False
+                                break
+                            transpose_layers.append(out)
+                        elif _graph.layers[
+                                out].kernel in self.elementwise_layers:
+                            if _graph.layers[out].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if out not in visited_layers:
+                                propagate_layers.append(out)
+                        elif _graph.layers[out].kernel in self.direct_layers:
+                            ouput_index = 1 if _graph.layers[out].kernel.startswith("paddle.nn.") else 0
+                            if _graph.layers[out].outputs[ouput_index] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if out not in visited_layers:
+                                propagate_layers.append(out)
+                        elif _graph.layers[out].kernel in self.reduce_layers:
+                            if _graph.layers[out].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if not _graph.layers[out].attrs.get('keepdim',
+                                                                False):
+                                can_be_optimized = False
+                                break
+                            if out not in visited_layers:
+                                propagate_layers.append(out)
+                                reduce_layers.append(out)
+                        elif _graph.layers[out].kernel == "paddle.concat":
+                            if _graph.layers[out].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if out not in visited_layers:
+                                propagate_layers.append(out)
+                                concat_layers.append(out)
+                        else:
+                            can_be_optimized = False
+                            break
+                    for ipt in _graph.edges_in.get(current_id, []):
+                        if _graph.layers[
+                                current_id].kernel in self.elementwise_layers:
+                            try:
+                                x_shape = _graph.layers[
+                                    current_id].input_shapes['x']
+                                y_shape = _graph.layers[
+                                    current_id].input_shapes['y']
+                                if _graph.layers[ipt].outputs[
+                                        0] == _graph.layers[current_id].inputs[
+                                            'x']:
+                                    if len(x_shape) <= 1:
+                                        elementwise_layers.append(current_id)
+                                        continue
+                                elif _graph.layers[ipt].outputs[
+                                        0] == _graph.layers[current_id].inputs[
+                                            'y']:
+                                    if len(y_shape) <= 1:
+                                        elementwise_layers.append(current_id)
+                                        continue
+                                else:
+                                    raise Exception(
+                                        "Unexcepted situation happend while optimizing transpose"
+                                    )
+                            except Exception as e:
+                                can_be_optimized = False
+                                break
+                        if _graph.layers[
+                                ipt].kernel == "paddle.transpose":
+                            if _graph.layers[ipt].attrs["perm"] != [0, 2, 3, 1]:
+                                can_be_optimized = False
+                                break
+                            if ipt not in visited_layers:
+                                transpose_layers.append(ipt)
+                        elif _graph.layers[
+                                ipt].kernel in self.elementwise_layers:
+                            if _graph.layers[ipt].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if ipt not in visited_layers:
+                                propagate_layers.append(ipt)
+                        elif _graph.layers[ipt].kernel in self.direct_layers:
+                            ouput_index = 1 if _graph.layers[ipt].kernel.startswith("paddle.nn.") else 0
+                            if _graph.layers[ipt].outputs[ouput_index] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if ipt not in visited_layers:
+                                propagate_layers.append(ipt)
+                        elif _graph.layers[ipt].kernel in self.reduce_layers:
+                            if _graph.layers[ipt].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if not _graph.layers[ipt].attrs.get('keepdim',
+                                                                False):
+                                can_be_optimized = False
+                                break
+                            if ipt not in visited_layers:
+                                propagate_layers.append(ipt)
+                                reduce_layers.append(ipt)
+                        elif _graph.layers[ipt].kernel == "paddle.concat":
+                            if _graph.layers[ipt].outputs[0] in _graph.outputs:
+                                can_be_optimized = False
+                                break
+                            if ipt not in visited_layers:
+                                propagate_layers.append(ipt)
+                                concat_layers.append(ipt)
+                        else:
+                            can_be_optimized = False
+                            break
+                    if not can_be_optimized:
+                        break
+                if not can_be_optimized:
+                    continue
+
+                transpose_layers.append(layer_id)
+                transpose_layers = list(set(transpose_layers))
+                for l in transpose_layers:
+                    if graph.layers[l].outputs[0] in graph.outputs:
+                        can_be_optimized = False
+                        break
+                if not can_be_optimized:
+                    continue
+
+                for l in transpose_layers:
+                    self.delete_layer_with_associated(_graph, l)
+
+                optimized_transpose_layers.extend(transpose_layers)
+                optimized_reduce_layers.extend(reduce_layers)
+                optimized_concat_layers.extend(concat_layers)
+                optimized_elementwise_layers.extend(elementwise_layers)
+                return True
+            return False
+
+        before_transpose_num = self.get_transpose_num(graph)
+        opt_graph = copy.deepcopy(graph)
+        total_layer_num = len(opt_graph.layers)
+
+        while strip_transpose(opt_graph):
+            pass
+
+        for layer_id in list(set(optimized_transpose_layers)):
+            self.delete_layer_with_associated(graph, layer_id)
+        for layer_id in list(set(optimized_reduce_layers)):
+            dim = graph.layers[layer_id].attrs.get('dim', None)
+            if dim is not None:
+                for i in range(len(dim)):
+                    dim[i] = [0, 2, 3, 1][dim[i]]
+                graph.layers[layer_id].attrs['dim'] = dim
+        for layer_id in list(set(optimized_concat_layers)):
+            axis = graph.layers[layer_id].attrs.get('axis', 0)
+            graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
+        for layer_id in list(set(optimized_elementwise_layers)):
+            axis = graph.layers[layer_id].attrs.get('axis', -1)
+            graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
+
+        current_transpose_num = self.get_transpose_num(graph)
+        print(
+            "\nTranspose layers optimized, before: transpose_num={}, after: transpose_num={}".
+            format(before_transpose_num, current_transpose_num))

x2paddle/optimizer/fusion/dygraph/__init__.py

@@ -20,6 +20,8 @@ from .bn_scale_fuser import Dygraph_BNScaleFuser
 from .bn_scale_fuse_pass import Dygraph_BNScaleFusePass
 from .constant_fuser import Dygraph_ConstantFuser
 from .constant_fuse_pass import Dygraph_ConstantFusePass
+from .conv2d_add_fuser import Dygraph_Conv2D_AddFuser
+from .conv2d_add_fuse_pass import Dygraph_Conv2D_AddFusePass
 from .dropout_fuser import Dygraph_DropoutFuser
 from .dropout_fuse_pass import Dygraph_DropoutFusePass
 from .fc_fuser import Dygraph_FcFuser
@@ -28,3 +30,5 @@ from .interpolate_bilinear_fuser import Dygraph_InterpolateBilinearFuser
 from .interpolate_bilinear_fuse_pass import Dygraph_InterpolateBilinearFusePass
 from .reshape_fuser import Dygraph_ReshapeFuser
 from .reshape_fuse_pass import Dygraph_ReshapeFusePass
+from .tf_batchnorm_fuser import Dygraph_TF_BatchNormFuser
+from .tf_batchnorm_fuse_pass import Dygraph_TF_BatchNormFusePass

x2paddle/optimizer/fusion/dygraph/conv2d_add_fuse_pass.py

+#   Copyright (c) 2020  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.
+
+from x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.fusion.dygraph import Dygraph_Conv2D_AddFuser
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class Dygraph_Conv2D_AddFusePass(Pass):
+    name = "dygraph_conv2d_add_fuse_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = Dygraph_Conv2D_AddFuser()
+        fuser.operate(graph, match_kind="edge")
+
+
+# 用于注册
+dygraph_conv2d_add_fuse_pass = Dygraph_Conv2D_AddFusePass()
\ No newline at end of file

x2paddle/optimizer/fusion/dygraph/conv2d_add_fuser.py

+#   Copyright (c) 2020  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 numpy as np
+from x2paddle.optimizer.pattern_matcher import FuseBase
+from x2paddle.core.program import PaddleGraph, PaddleLayer
+from x2paddle.core.util import *
+
+
+class Dygraph_Conv2D_AddFuser(FuseBase):
+    def __init__(self):
+        super(Dygraph_Conv2D_AddFuser, self).__init__(graph_type="dygraph")
+        self.patterns = list()
+
+    def build_pattern(self):
+        """ 描述需要替换的conv2d+add图结构。
+        conv2d+add层模式python实现代码示例:
+        模式一：
+        MobilenetV1_Logits_Conv2d_1c_1x1_biases = self.MobilenetV1_Logits_Conv2d_1c_1x1_biases
+        conv2d_transpose_14 = paddle.transpose(x=MobilenetV1_Logits_AvgPool_1a_AvgPool, perm=[0, 3, 1, 2])
+        MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = self.conv27(conv2d_transpose_14)
+        MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = paddle.transpose(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, perm=[0, 2, 3, 1])
+        MobilenetV1_Logits_Conv2d_1c_1x1_BiasAdd = paddle.add(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, y=MobilenetV1_Logits_Conv2d_1c_1x1_biases)
+        模式二：
+        MobilenetV1_Logits_Conv2d_1c_1x1_biases = self.MobilenetV1_Logits_Conv2d_1c_1x1_biases
+        MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = self.conv27(conv2d_transpose_14)
+        MobilenetV1_Logits_Conv2d_1c_1x1_BiasAdd = paddle.add(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, y=MobilenetV1_Logits_Conv2d_1c_1x1_biases)
+        """
+
+        def gen_name(id):
+            return "x" + str(id)
+
+        pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(0)])
+        pattern.add_layer(
+            kernel="paddle.transpose",
+            inputs={"x": "conv-input-0"},
+            outputs=[gen_name(1)],
+            perm=[0, 3, 1, 2])
+        pattern.add_layer(
+            kernel="paddle.nn.Conv2D",
+            inputs={"input": gen_name(1)},
+            outputs=[gen_name(2)])
+        pattern.add_layer(
+            kernel="paddle.transpose",
+            inputs={"x": gen_name(2)},
+            outputs=[gen_name(2)],
+            perm=[0, 2, 3, 1])
+        pattern.add_layer(
+            kernel="paddle.add",
+            inputs={"x": gen_name(2),
+                    "y": gen_name(0)},
+            outputs=[gen_name(3)])
+        pattern.build(inputs={"input-0": "conv-input-0", })
+        self.patterns.append(pattern)
+        
+        pattern = PaddleGraph(graph_type="dygraph")
+        pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(0)])
+        pattern.add_layer(
+            kernel="paddle.nn.Conv2D",
+            inputs={"input": "conv-input-0"},
+            outputs=[gen_name(1)])
+        pattern.add_layer(
+            kernel="paddle.add",
+            inputs={"x": gen_name(1),
+                    "y": gen_name(0)},
+            outputs=[gen_name(2)])
+        pattern.build(inputs={"input-0": "conv-input-0", })
+        self.patterns.append(pattern)
+
+    def insert_new_layer(self, graph, parameters, matches):
+        self.gen_new_layer(matches, graph)
+        matches_copy = copy.deepcopy(matches)
+        for layer_id, layer in matches_copy.items():
+            if layer.kernel not in ["self.create_parameter", "paddle.add"]:
+                matches.pop(layer_id)
+
+    def gen_new_layer(self, matches, graph):
+        is_transpose = False
+        for layer_id, layer in matches.items():
+            if layer.kernel == "self.create_parameter":
+                bias_name = layer.attrs["attr"]
+            if layer.kernel == "paddle.transpose":
+                is_transpose = True
+            if layer.kernel == "paddle.add":
+                output_name = layer.outputs[0]
+            if layer.kernel == "paddle.nn.Conv2D":
+                conv_id = layer_id
+        for layer_id, layer in matches.items():
+            if layer.kernel == "paddle.nn.functional.conv2d_transpose":
+                layer.bias = bias_name
+                if not is_transpose:
+                    layer.outputs[0] = output_name
+            if layer.kernel == "paddle.nn.Conv2D":
+                layer.attrs["bias_attr"] = bias_name
+                if not is_transpose:
+                    layer.outputs[1] = output_name
+            if layer.kernel == "paddle.transpose":
+                if conv_id in graph.edges_in[layer_id]:
+                    layer.outputs[0] = output_name
+            

x2paddle/optimizer/fusion/dygraph/tf_batchnorm_fuse_pass.py

+#   Copyright (c) 2020  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.
+
+from x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.fusion.dygraph import Dygraph_TF_BatchNormFuser
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class Dygraph_TF_BatchNormFusePass(Pass):
+    name = "dygraph_tf_batchnorm_fuse_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = Dygraph_TF_BatchNormFuser()
+        fuser.operate(graph, match_kind="edge")
+
+
+# 用于注册
+dygraph_tf_batchnorm_fuse_pass = Dygraph_TF_BatchNormFusePass()
\ No newline at end of file

x2paddle/optimizer/fusion/dygraph/tf_batchnorm_fuser.py

+#   Copyright (c) 2020  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 numpy as np
+from collections import OrderedDict
+from x2paddle.optimizer.pattern_matcher import FuseBase
+from x2paddle.core.program import PaddleGraph, PaddleLayer
+from x2paddle.core.util import *
+
+
+class Dygraph_TF_BatchNormFuser(FuseBase):
+    def __init__(self):
+        self.bn_index = 0
+        super(Dygraph_TF_BatchNormFuser, self).__init__(graph_type="dygraph")
+
+    def build_pattern(self):
+        """ 描述需要替换的batchnorm图结构。
+        batchnorm层模式python实现代码示例:
+            
+        """
+
+        def gen_name(id):
+            return "x" + str(id)
+
+        self.pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(0)])
+        self.pattern.add_layer(
+            "paddle.full",
+            inputs={},
+            outputs=[gen_name(1)],
+            shape=[1])
+        self.pattern.add_layer(
+            "paddle.add",
+            inputs={"x": gen_name(0), "y": gen_name(1)},
+            outputs=[gen_name(2)])
+        self.pattern.add_layer(
+            "paddle.rsqrt",
+            inputs={"x": gen_name(2)},
+            outputs=[gen_name(3)])
+        self.pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(4)])
+        self.pattern.add_layer(
+            "paddle.multiply",
+            inputs={"x": gen_name(3), "y": gen_name(4)},
+            outputs=[gen_name(5)])
+        self.pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(6)])
+        self.pattern.add_layer(
+            "paddle.multiply",
+            inputs={"x": gen_name(6), "y": gen_name(5)},
+            outputs=[gen_name(7)])
+        self.pattern.add_layer(
+            "self.create_parameter",
+            inputs={},
+            outputs=[gen_name(8)])
+        self.pattern.add_layer(
+            "fluid.layers.elementwise_sub",
+            inputs={"x": gen_name(8), "y": gen_name(7)},
+            outputs=[gen_name(9)])
+        self.pattern.add_layer(
+            "paddle.multiply",
+            inputs={"x": "bn-input-0", "y": gen_name(5)},
+            outputs=[gen_name(10)])
+        self.pattern.add_layer(
+            "paddle.add",
+            inputs={"x": gen_name(10), "y": gen_name(9)},
+            outputs=[gen_name(11)])
+        self.pattern.build(inputs={"input-0": "bn-input-0", })
+        
+
+    def insert_new_layer(self, graph, parameters, matches):
+        new_layers, last_layer_id = self.gen_new_layer(matches, parameters, graph)
+        matches_copy = copy.deepcopy(matches)
+        for layer_id, layer in matches_copy.items():
+            for i in range(3):
+                if layer_id == new_layers[i].id:
+                    matches.pop(new_layers[i].id)
+        prefix_layers = OrderedDict()
+        mid_layers = OrderedDict()
+        suffix_layers = OrderedDict()
+        is_need_id = False
+        for layer_id, layer in graph.layers.items():
+            if is_need_id:
+                suffix_layers[layer_id] = layer
+            else:
+                if layer_id == last_layer_id:
+                    for i in range(3):
+                        mid_layers[new_layers[i].id] = new_layers[i]
+                    is_need_id = True
+                prefix_layers[layer_id] = layer
+        prefix_layers.update(mid_layers)
+        prefix_layers.update(suffix_layers)
+        graph.layers = prefix_layers
+
+    def gen_new_layer(self, matches, parameters, graph):
+        layer_id_list = list(matches.keys())
+        layer_id_list.sort(key = int)
+        for layer_id, layer in matches.items():
+            if layer.kernel == "paddle.full":
+                full_layer = layer
+                out_layer_id = graph.edges_out[layer_id][0]
+                if matches[out_layer_id].kernel == "paddle.add":
+                    var_layer_id = graph.edges_in[out_layer_id][0]
+                    var_layer = matches[var_layer_id]
+            if layer.kernel == "paddle.rsqrt":
+                out_layer_id = graph.edges_out[layer_id][0]
+                if matches[out_layer_id].kernel == "paddle.multiply":
+                    gamma_layer_id = graph.edges_in[out_layer_id][1]
+                    gamma_layer = matches[gamma_layer_id]
+            if layer.kernel == "fluid.layers.elementwise_sub":
+                in_layer_id = graph.edges_in[layer_id][0]
+                beta_layer = matches[in_layer_id]
+                in_layer_id = graph.edges_in[layer_id][1]
+                in_layer_id = graph.edges_in[in_layer_id][0]
+                mean_layer = matches[in_layer_id]
+                out_layer_id = graph.edges_out[layer_id][0]
+                add_layer = matches[out_layer_id]
+            if layer.kernel == "paddle.multiply":
+                in_layer_id = graph.edges_in[layer_id][1]
+                mul_layer = matches[in_layer_id]
+                if mul_layer.kernel == "paddle.multiply":
+                    in_layer_id = graph.edges_in[layer_id][0]
+                    if in_layer_id not in matches:
+                        input_name = layer.inputs["x"]
+        transpose0 = PaddleLayer(
+            id=layer_id_list[-1] + "_1",
+            kernel="paddle.transpose",
+            inputs={"x": input_name},
+            outputs=["{}_transpose_for_bn".format(input_name)],
+            perm=[0, 3, 1, 2])
+        bn_name = "merge_bn{}".format(self.bn_index)
+        self.bn_index += 1
+        params = parameters[gamma_layer.outputs[0]]
+        c = params.shape[0]
+        bn = PaddleLayer(
+            id=layer_id_list[-1] + "_2",
+            kernel="paddle.nn.BatchNorm",
+            inputs={"input": "{}_transpose_for_bn".format(input_name)},
+            outputs=[bn_name, "{}_bn".format(input_name)],
+            num_channels=c,
+            epsilon=full_layer.attrs["fill_value"],
+            param_attr=string(gamma_layer.outputs[0]),
+            bias_attr=string(beta_layer.outputs[0]),
+            moving_mean_name=string(mean_layer.outputs[0]),
+            moving_variance_name=string(var_layer.outputs[0]),
+            is_test=True)
+        transpose1 = PaddleLayer(
+            id=layer_id_list[-1] + "_3",
+            kernel="paddle.transpose",
+            inputs={"x": "{}_bn".format(input_name)},
+            outputs=add_layer.outputs,
+            perm=[0, 2, 3, 1])
+        return [transpose0, bn, transpose1], layer_id_list[-1]
+

x2paddle/optimizer/optimizer.py

@@ -15,6 +15,7 @@
 from x2paddle.optimizer.pass_manager import PassManager
 from x2paddle.optimizer.fusion.dygraph import *
 from x2paddle.optimizer.fusion.static import *
+from x2paddle.optimizer.elimination.dygraph import *
 
 class GraphOptimizer(object):
     def __init__(self, source_frame, paddle_type="dygraph"):
@@ -30,6 +31,12 @@ class GraphOptimizer(object):
                 self.passes = ["dygraph_bn_scale_fuse_pass"]
             else:
                 self.passes = ["static_bn_scale_fuse_pass"]
+        elif source_frame == "tf":
+            self.passes = [
+                "dygraph_conv2d_add_fuse_pass",
+                "dygraph_tf_batchnorm_fuse_pass",
+                "transpose_eliminate_pass"
+            ]
         else:
             # TODO
             pass
@@ -37,6 +44,9 @@ class GraphOptimizer(object):
     def optimize(self, graph):
         for pass_name in self.passes:
             pass_ = PassManager.lookup(pass_name)()
+            if pass_name.endswith("_eliminate_pass"):
+                pass_.apply(graph)
+            else:
                 while True:
                     before_len = len(graph.layers)
                     pass_.apply(graph)

x2paddle/optimizer/pattern_matcher.py

@@ -19,6 +19,7 @@ class PatternMatcher(object):
     def __init__(self, pattern):
         self.pattern = pattern
         # matches的每个match是按照拓扑排序组成layer的dict
+
         self.matches = list()
 
     def operate(self, graph, match_kind="topo"):
@@ -154,7 +155,7 @@ class PatternMatcher(object):
                 if len(block.layers) > 0:
                     self.detect_patterns_by_topo(layer.blocks[j])
 
-    def detect_patterns_by_edge(self, graph, ignore_list_inputs=True):
+    def detect_patterns_by_edge(self, graph):
         """当遇见顺序没有强制规定的pattern时使用该方式
         """
 
@@ -163,8 +164,8 @@ class PatternMatcher(object):
             pattern_ids = list(pattern_id2layers.keys())
             pattern_layer_id = pattern_ids[0]
             subgraph_id2layers = dict()
-            graph_layers = dict(list(graph.layers.items())[start_index:])
-            layer_id = list(graph_layers.keys())[0]
+            layer_id = list(graph.layers.keys())[start_index]
+            graph_layers = graph.layers
 
             def update(layer_id, pattern_layer_id):
                 layer = graph_layers[layer_id]
@@ -172,14 +173,25 @@ class PatternMatcher(object):
                 if layer.kernel != pattern_layer.kernel:
                     return False
                 subgraph_id2layers[layer_id] = layer
+#                 for k, v in subgraph_id2layers.items():
+#                     print(k)
+#                     print(v.kernel)
+#                     print(v.outputs)
+#                 print("=========")
+                
+                if pattern.edges_in.get(pattern_layer_id, 0) != 0:
+                    if len(pattern.edges_in[pattern_layer_id]) != \
+                            len(graph.edges_in[layer_id]):
+                        return False
                     for i, pattern_layer_id_in in enumerate(pattern.edges_in[
                             pattern_layer_id]):
-                    if pattern_layer_id_in == -1 or ignore_list_inputs:
+                        if pattern_layer_id_in == -1:
                             continue
-                    layer_id_in = graph.edges_in[layer_id][i]
-                    subgraph_ids = list(subgraph_id2layers.keys())
-                    if layer_id_in not in subgraph_ids:
-                        return False
+                        if pattern_layer_id_in in pattern_ids:
+                            new_layer_id_in = graph.edges_in[layer_id][i]
+                            if new_layer_id_in in subgraph_id2layers:
+                                continue
+                            update(new_layer_id_in, pattern_layer_id_in)
                 if pattern.edges_out.get(pattern_layer_id, 0) != 0:
                     if len(pattern.edges_out[pattern_layer_id]) != \
                             len(graph.edges_out[layer_id]):
@@ -188,17 +200,8 @@ class PatternMatcher(object):
                             pattern_layer_id]):
                         if pattern_layer_id_out in pattern_ids:
                             new_layer_id_out = graph.edges_out[layer_id][i]
-                            for j, new_new_layer_id_in in enumerate(
-                                    graph.edges_in[new_layer_id_out]):
-                                if new_new_layer_id_in not in subgraph_id2layers:
-                                    if ignore_list_inputs:
-                                        continue
-                                    new_new_pattern_layer_id_in = pattern.edges_in[
-                                        pattern_layer_id_out][j]
-                                    if new_new_pattern_layer_id_in == -1:
+                            if new_layer_id_out in subgraph_id2layers:
                                 continue
-                                    update(new_new_layer_id_in,
-                                           new_new_pattern_layer_id_in)
                             update(new_layer_id_out, pattern_layer_id_out)
 
             while len(subgraph_id2layers) != len(pattern_id2layers):
@@ -258,6 +261,7 @@ def get_subgraph(prefix_layer_id, suffix_layer_id, graph):
 class FuseBase(object):
     def __init__(self, graph_type):
         self.pattern = PaddleGraph(graph_type=graph_type)
+        self.patterns = list()
 
     def operate(self, graph, match_kind="topo"):
         parameters = graph.parameters
@@ -267,16 +271,22 @@ class FuseBase(object):
             first_layer_id = list(match.keys())[0]
             subgraph = get_subgraph("", first_layer_id, graph)
             self.insert_new_layer(subgraph, parameters, match)
-        self.delete_inter_layer(graph)
+        self.delete_layer(graph)
         graph.build()
 
     def perform_pattern_matcher(self, graph, match_kind="topo"):
         """ 执行模式匹配，找到匹配的子图。
         """
+        if len(self.patterns) > 0:
+            self.matches = list()
+            for pattern in self.patterns:
+                pattern_matcher = PatternMatcher(pattern)
+                self.matches.extend(pattern_matcher.operate(graph, match_kind))
+        else:
             pattern_matcher = PatternMatcher(self.pattern)
             self.matches = pattern_matcher.operate(graph, match_kind)
 
-    def delete_inter_layer(self, graph):
+    def delete_layer(self, graph):
         """ 删除不需要的中间layer及其对应参数。
         """
         for match in self.matches:
@@ -291,3 +301,52 @@ class FuseBase(object):
                 if layer_id in subgraph.layers:
                     # layer_id可能是属于子图的，此时删除父layer，即删除整个子图
                     subgraph.layers.pop(layer_id)
+                    
+    def delete_layer_with_associated(self, graph, layer_id):
+        """ 删除不需要的中间layer及其相关连接点。
+        """
+        layer = graph.layers[layer_id]
+        outputs = graph.edges_out.get(layer_id, [])
+        inputs = graph.edges_in.get(layer_id, [])
+
+        assert len(
+            inputs) <= 1, "There should be 0 or 1 input for deleted layer."
+
+        if len(inputs) == 0:
+            for out in outputs:
+                while layer_id in graph.edges_in[out]:
+                    index = graph.edges_in[out].index(layer_id)
+                    del graph.edges_in[out][index]
+
+                input_keys = list(graph.layers[out].inputs.keys())
+                for k in input_keys:
+                    if graph.layers[out].inputs[k] == layer.outputs[0]:
+                        del graph.layers[out].inputs[k]
+
+            del graph.layers[layer_id]
+            if layer_id in graph.edges_in:
+                del graph.edges_in[layer_id]
+            if layer_id in graph.edges_out:
+                del graph.edges_out[layer_id]
+            return
+
+        # 将所有输出layer的输入layer进行替换
+        for out in outputs:
+            for i in range(len(graph.edges_in[out])):
+                if graph.edges_in[out][i] == layer_id:
+                    graph.edges_in[out][i] = inputs[0]
+
+        # 将输出layer赋给输入layer的输出
+        replace_index = graph.edges_out[inputs[0]].index(layer_id)
+        del graph.edges_out[inputs[0]][replace_index]
+        for i, out in enumerate(outputs):
+            graph.edges_out[inputs[0]].insert(replace_index + i, out)
+            for k, v in graph.layers[out].inputs.items():
+                if v == layer.outputs[0]:
+                    graph.layers[out].inputs[k] = list(layer.inputs.values())[0]
+
+        del graph.layers[layer_id]
+        if layer_id in graph.edges_out:
+            del graph.edges_out[layer_id]
+        if layer_id in graph.edges_in:
+            del graph.edges_in[layer_id]

x2paddle/optimizer/tensorflow/batch_norm.py

@@ -178,3 +178,4 @@ class BatchNormOpt:
                 graph.layers[bn.id] = bn
                 graph.layers[transpose1.id] = transpose1
         graph.build()
+        print("=============")

x2paddle/optimizer/tensorflow/bias.py

@@ -6,11 +6,6 @@ class BiasOpt:
         self.conv_layers = [
             'fluid.layers.conv2d', 'fluid.layers.conv2d_transpose'
         ]
-        self.act_layers = [
-            'fluid.layers.relu', 'fluid.layers.relu6', 'fluid.layers.sigmoid',
-            'fluid.layers.exp', 'fluid.layers.tanh', 'fluid.layers.softplus',
-            'fluid.layers.leaky_relu'
-        ]
 
     def run(self, graph):
         print("Optimize: BiasOpt...")